ORCID Profile
0000-0002-2939-2090
Current Organisations
Australian National University
,
UNSW Sydney
,
Technische Universität Dresden
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Road Transportation and Freight Services | Transportation and Freight Services | Transport Engineering | Operations Research | Logistics and Supply Chain Management | Commercial Services | Food and Hospitality Services
ANZSRC Socio-Economic Objective (SEO)
Road Passenger Movements (excl. Public Transport) | Road Infrastructure and Networks | Road Freight | Hospitality Services | Road Public Transport | Preference, Behaviour and Welfare | Management of Greenhouse Gas Emissions from Transport Activities | Structure, Delivery and Financing of Community Services |
Related Links
Publications
Large-Scale Dynamic Traffic Assignment: Implementation Issues and Computational Analysis
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2004
Feasibility study of automatically performing the concrete delivery dispatching through machine learning techniques
Publisher: Emerald
Date: 21-09-2015
DOI: 10.1108/ECAM-06-2014-0081
Abstract: – The purpose of this paper is to study the implementation of machine learning (ML) techniques in order to automatically measure the feasibility of performing ready mixed concrete (RMC) dispatching jobs. – Six ML techniques were selected and tested on data that was extracted from a developed simulation model and answered by a human expert. – The results show that the performance of most of selected algorithms were the same and achieved an accuracy of around 80 per cent in terms of accuracy for the examined cases. – This approach can be applied in practice to match experts’ decisions. – In this paper the feasibility of handling complex concrete delivery problems by ML techniques is studied. Currently, most of the concrete mixing process is done by machines. However, RMC dispatching still relies on human resources to complete many tasks. In this paper the authors are addressing to reconstruct experts’ decisions as only practical solution.
Range-Constrained Traffic Assignment with Multi-Modal Recharge for Electric Vehicles
Publisher: Springer Science and Business Media LLC
Date: 16-04-2019
A quantum-inspired genetic algorithm for dynamic continuous network design problem
Publisher: Informa UK Limited
Date: 2009
Improving the Convergence of Simulation-based Dynamic Traffic Assignment Methodologies
Publisher: Springer Science and Business Media LLC
Date: 24-04-2015
A computationally efficient methodology to characterize travel time reliability using the fast Fourier transform
Publisher: Elsevier BV
Date: 12-2010
Examining the macro-level factors affecting vehicle breakdown duration
Publisher: Elsevier BV
Date: 03-2022
A static network level model for the information propagation in vehicular ad hoc networks
Publisher: Elsevier BV
Date: 06-2010
An algorithmic framework for the scheduling of construction projects based on ant colony optimization and expert knowledge
Publisher: IEEE
Date: 10-2014
Hazard-based model for concrete pouring duration using construction site and supply chain parameters
Publisher: Elsevier BV
Date: 11-2016
Quantifying distributions of freeway operational metrics
Publisher: Informa UK Limited
Date: 2011
Integrating uncertainty considerations into multi-objective transportation network design projects accounting for environment disruption
Publisher: Informa UK Limited
Date: 08-08-2017
A Strategic User Equilibrium for Independently Distributed Origin-Destination Demands
Publisher: Wiley
Date: 06-09-2018
DOI: 10.1111/MICE.12292
A three-stage framework for motorway travel time prediction
Publisher: IEEE
Date: 10-2014
Two Methods to Calibrate the Total Travel Demand and Variability for a Regional Traffic Network
Publisher: Wiley
Date: 02-10-2017
DOI: 10.1111/MICE.12278
Ramp Metering Strategy Implementation: A Case Study Review
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-2022
Impact of autonomous vehicles on traffic management
Publisher: SAGE Publications
Date: 2016
DOI: 10.3141/2567-10
Abstract: As the future of autonomous vehicles (AVs) becomes more certain, transport network managers may seek ways to reinvent elements of the traffic network to improve efficiency. One possibility is dynamic lane reversal, in which the network operator makes use of AV communications and behavior to change the direction of flow on a road link at smaller time intervals than would be possible with human drivers. Although there is much research into the mechanical details of AVs, this study motivates the need for future research by focusing on a planning application in which AVs are already present. A novel extension to an established system optimal dynamic traffic assignment model based on the cell transmission model was examined. The model determined the optimal lane configuration at small space–time intervals. Results demonstrate the model on a single link and a grid network and explore the dynamic demand scenarios that are most conducive to increasing system efficiency with dynamic lane reversal.
On the online shortest path problem with limited arc cost dependencies
Publisher: Wiley
Date: 12-2002
DOI: 10.1002/NET.10049
Analyzing and Modeling Network Travel Patterns During the Ukraine Invasion Using Crowd-Sourced Pervasive Traffic Data
Publisher: SAGE Publications
Date: 17-04-2023
DOI: 10.1177/03611981231161622
Abstract: In 2022, Ukraine is suffering an invasion which has resulted in acute impacts playing out over time and geography. This paper examines the impact of the ongoing disruption on traffic behavior using analytics as well as zonal-based network models. The methodology is a data-driven approach that utilizes obtained travel-time conditions within an evolutionary algorithm framework which infers origin–destination demand values in an automated process based on traffic assignment. Because of the automation of the implementation, numerous daily models can be approximated for multiple cities. The novelty of this paper versus the previously published core methodology includes an analysis to ensure the obtained data is appropriate, since some data sources were disabled because of the ongoing disruption. Further novelty includes a direct linkage of the analysis to the timeline of disruptions to examine the interaction in a new way. Finally, specific network metrics are identified which are particularly suited for conceptualizing the impact of conflict disruptions on traffic network conditions. The ultimate aim is to establish processes, concepts, and analysis to advance the broader activity of rapidly quantifying the traffic impacts of conflict scenarios.
Modeling, equilibrium, and demand management for mobility and delivery services in Mobility‐as‐a‐Service ecosystems
Publisher: Wiley
Date: 27-12-2022
DOI: 10.1111/MICE.12958
Abstract: Mobility‐as‐a‐Service (MaaS) is an emerging business model integrating various travel modes into a single mobility service accessible on demand. Besides the on‐demand mobility services, instant delivery services have increased rapidly and particularly boomed during the coronavirus (COVID‐19) pandemic, requiring online orders to be delivered timely. In this study, to deal with the redundant mobility resources and high costs of instant delivery services, we model an MaaS ecosystem that provides mobility and instant delivery services by sharing the same multimodal transport system. We derive a two‐class bundle choice user equilibrium (BUE) for mobility and delivery users in the MaaS ecosystems. We propose a bilateral surcharge–reward scheme (BSRS) to manage the integrated mobility and delivery demand in different incentive scenarios. We further formulate a bilevel programming problem to optimize the proposed BSRS, where the upper level problem aims to minimize the total system equilibrium costs of mobility and delivery users, and the lower level problem is the derived two‐class BUE with BSRS. We analyze the optimal operational strategies of the BSRS and develop a solution algorithm for the proposed bilevel programming problem based on the system performance under BSRS. Numerical studies conducted with real‐world data validate the theoretical analysis, highlight the computational efficiency of the proposed algorithm, and indicate the benefits of the BSRS in managing the integrated mobility and delivery demand and reducing total system equilibrium costs of the MaaS ecosystems.
Inferring contagion patterns in social contact networks using a maximum likelihood approach
Publisher: American Society of Civil Engineers (ASCE)
Date: 08-2014
On ride-sourcing services of electric vehicles considering cruising for charging and parking
Publisher: Elsevier BV
Date: 05-2023
A framework for evaluating the role of electric vehicles in transportation network infrastructure under travel demand variability
Publisher: Elsevier BV
Date: 03-2013
Evacuation Planning Using the Integrated System of Activity-Based Modeling and Dynamic Traffic Assignment
Publisher: SAGE Publications
Date: 2009
DOI: 10.3141/2132-08
Abstract: The occurrence of natural disasters in the coastal regions and numerous potential events in urban regions have drawn considerable attention among transportation stakeholders. Federal, state, and local officials need to be effectively prepared to address the challenges raised by an evacuation. The focus of this research effort is to develop a tool to study the repercussions of evacuation of an entire regional transportation network recognizing the human behavior element. Neglecting these seemingly chaotic traffic flow patterns would lead to inaccurate system assessment and predictions. The influences of evacuees’ locations in the urban region at the moment of emergency alert are studied. In addition, the locations of all members of the household are identified, and household member interactions are explicitly considered. Further, the accurate times the in iduals enter the network to evacuate the study region are studied times can vary according to where the other household members are located at that time and the travel time on the network to reach those locations. To accomplish the goals, the integration framework of activity-based modeling and dynamic traffic assignment is used to study the evacuation traffic flow patterns at the time of evacuation. Specifically, the paper describes the evacuation problem, discusses the utility of deploying the integrated module of activity-based modeling and dynamic traffic assignment for evacuation planning, and outlines the challenges in integrating these two tools.
Using benders decomposition for solving ready mixed concrete dispatching problems
Publisher: International Association for Automation and Robotics in Construction (IAARC)
Date: 08-07-2014
How COVID-19 transformed the landscape of transportation research: an integrative scoping review and roadmap for future research
Publisher: Informa UK Limited
Date: 02-01-2023
Capacity allocation and tolling-rewarding schemes for the morning commute with carpooling
Publisher: Elsevier BV
Date: 09-2022
Optimizing the design of railway tank cars to minimize accident-caused releases
Publisher: Elsevier BV
Date: 05-2007
Equity-Oriented Aircraft Collision Avoidance Model
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2015
A Clustering Algorithm for Bi-Criteria Stop Location Design with Elastic Demand
Publisher: Wiley
Date: 31-08-2016
DOI: 10.1111/MICE.12162
Network-Level Road Pavement Maintenance and Rehabilitation Scheduling for Optimal Performance Improvement and Budget Utilization
Publisher: Wiley
Date: 22-09-2012
A Dantzig-Wolfe Decomposition Based Heuristic Scheme for Bi-level Dynamic Network Design Problem
Publisher: Springer Science and Business Media LLC
Date: 10-12-2008
Frequentist and bayesian approaches for understanding route choice of drivers under stop-and-go traffic
Publisher: SAGE Publications
Date: 07-07-2020
Abstract: Driving in congested traffic is a nuisance that not only results in longer travel times, but also triggers frustration and impatience among drivers. A few studies have modeled the effects of congested traffic in the resulting route choice behavior of car drivers. The studies used frequentist models such as discrete choice models to analyze large s les. However, these studies did not compare the inferences obtained from the frequentist and Bayesian approaches, particularly for datasets which are not sufficiently large. It has been shown by researchers that Bayesian models perform well, especially when the s le size is small. Thus, this paper develops and compares a multinomial logit (frequentist) and a Naïve Bayes (Bayesian) model on a mid-sized dataset of size around 100 participants which was obtained from a driving simulator experiment to understand driver’s route choice under stop-and-go traffic. The results show that the prediction power of the Naïve Bayes model is much higher than the multinomial logit model (MNL). The Naïve Bayes model is also found to perform better than machine learning algorithms like the decision tree model. The findings from this study will be useful to researchers and practitioners as they should test both the approaches and select the appropriate model, particularly in the case of seemingly large datasets.
New horizontal equity measure for ramp meters
Publisher: SAGE Publications
Date: 2016
DOI: 10.3141/2568-14
Abstract: R metering is a control technology used to manage the flow of traffic entering motorways and freeways, with the primary aim of minimizing congestion on the main thoroughfare. This technique has been studied and implemented globally since the 1960s. It has been shown that r meters improve the overall efficiency of the system however, the distribution of the benefits and costs across users has been questioned, and this is one of the main constraints on user acceptance of the r metering system. The typical methodology used in the literature is to assume that the most equitable condition is when all on-r s have the same delay across space or time. This research developed a new definition of horizontal equity for r meters and a proposed method for calculating it. A hypothetical microsimulation model was developed on the basis of a motorway in Sydney, Australia, and used as the platform to demonstrate how the proposed equity definition can be evaluated. To assist in the interpretation, two configurations of a r metering algorithm were simulated and compared. Finally, the typical equality measure used in the literature was calculated for the same scenarios and compared with the proposed equity measure. The results showed that these two measures can favor scenarios. A qualitative discussion of the expected benefits of the proposed equity measure is offered. Those expected benefits are an easy-to-communicate means of justifying the metering rates for user acceptance (rates that are arguably fairer, compared with the typical equality measure) a measure that is complementary to integration with other intelligent transportation system technology such as tolled bypass lanes and ease of incorporation in the long-term traffic management plan.
A predictive spatial model to quantify the risk of air-travel-associated dengue importation into the United States and Europe
Publisher: Hindawi Limited
Date: 2012
DOI: 10.1155/2012/103679
Abstract: The number of travel-acquired dengue infections has been on a constant rise in the United States and Europe over the past decade. An increased volume of international passenger air traffic originating from regions with endemic dengue contributes to the increasing number of dengue cases. This paper reports results from a network-based regression model which uses international passenger travel volumes, travel distances, predictive species distribution models (for the vector species), and infection data to quantify the relative risk of importing travel-acquired dengue infections into the US and Europe from dengue-endemic regions. Given the necessary data, this model can be used to identify optimal locations (origin cities, destination airports, etc.) for dengue surveillance. The model can be extended to other geographical regions and vector-borne diseases, as well as other network-based processes.
Statistically reviewing construction accidents within South Australia during 2002-2013
Publisher: International Association for Automation and Robotics in Construction (IAARC)
Date: 18-06-2015
Incentive-compatible mechanisms for online resource allocation in Mobility-as-a-Service systems
Publisher: Elsevier BV
Date: 04-2023
A Hybrid Lagrangian Relaxation and Tabu Search Method for Interdependent-Choice Network Design Problems
Publisher: IGI Global
Date: 2011
DOI: 10.4018/978-1-61350-086-6.CH013
Abstract: Hybridization offers a promising approach in designing and developing improved metaheuristic methods for a variety of complex combinatorial optimization problems. This chapter presents a hybrid Lagrangian relaxation and tabu search method for a class of discrete network design problems with complex interdependent-choice constraints. This method takes advantage of Lagrangian relaxation for problem decomposition and complexity reduction while its algorithmic logic is designed based on the principles of tabu search. The algorithmic advance and solution performance of the method are illustrated by implementing it for solving a network design problem with lane reversal and crossing elimination strategies, arising from urban evacuation planning.
The System Impact of Travel Demand Variability in the Context of Electric Vehicles
Publisher: IEEE
Date: 2013
Finding Outbreak Trees in Networks with Limited Information
Publisher: Springer Science and Business Media LLC
Date: 17-04-2016
Urban mobility analytics: A deep spatial–temporal product neural network for traveler attributes inference
Publisher: Elsevier BV
Date: 03-2021
Integration of Activity-Based Modeling and Dynamic Traffic Assignment
Publisher: SAGE Publications
Date: 2008
DOI: 10.3141/2076-06
Abstract: The traditional trip-based approach to transportation modeling has been used for the past 30 years. Because of limitations of traditional planning for short-term policy analysis, researchers have explored alternative paradigms for incorporating more behavioral realism in planning methodologies. On the demand side, activity-based approaches have evolved as an alternative to traditional trip-based transportation demand forecasting. On the supply side, dynamic traffic assignment models have been developed as an alternative to static assignment procedures. Much of the research effort in activity-based approaches (the demand side) and dynamic traffic assignment techniques (the supply side) has been undertaken relatively independently. To maximize benefits from these advanced methodologies, it is essential to combine them through a unified framework. The objective of this paper is to develop a conceptual framework and explore practical integration issues for combining the two streams of research. Technical, computational, and practical issues involved in this demand–supply integration problem are discussed. The framework is general, but specific technical details related to the integration are explored by using CEMDAP for activity-based modeling and VISTA for dynamic traffic assignment modeling. Solution convergence properties of the integrated system, specifically examining different criteria for convergence, different methods of accommodating time of day, and the influence of step size on convergence are studied. The integrated system developed is empirically applied to two s le networks selected from the Dallas–Fort Worth system in Texas.
Mobility Irregularity Detection with Smart Transit Card Data
Publisher: Springer International Publishing
Date: 2020
Impact of beaconing policies on traffic density estimation accuracy in traffic information systems
Publisher: IEEE
Date: 06-2014
Intersection Origin–Destination Flow Optimization Problem for Evacuation Network Design
Publisher: SAGE Publications
Date: 2011
DOI: 10.3141/2234-12
Abstract: In a lane-based evacuation network design problem that incorporates lane reversal and crossing elimination strategies, the network can be virtually decomposed to a number of roadway subnetworks and intersection subnetworks. Lane reversal and crossing elimination are implemented on roadway subnetworks and intersection subnetworks, respectively. Although this network decomposition mechanism naturally offers an appealing algorithmic approach for network solutions by relaxing the crossing elimination constraints, one needs to consider—from the solution feasibility perspective—the mutual connectivity requirements imposed by the two capacity–reallocation and connectivity–reallocation network settings simultaneously. This paper considers an intersection origin–destination flow distribution problem arising from the evacuation network design and outputs whether the crossing elimination constraints are satisfied or violated given a lane reversal solution. The main contribution of this work is to provide a condition of network flows sufficient for the existence and validity of the problem and develop an efficient simplex-based method to obtain solutions to the problem. Numerical ex les are provided to illustrate the method's effectiveness and efficiency.
Policy implications of incorporating distance constrained electric vehicles into the traffic network design problem
Publisher: Informa UK Limited
Date: 25-10-2016
Analysis of vehicle breakdown frequency: A case study of new south wales, Australia
Publisher: MDPI AG
Date: 07-10-2020
DOI: 10.3390/SU12198244
Abstract: Traffic incidents such as crashes, vehicle breakdowns, and hazards impact traffic speeds and induce congestion. Recognizing the factors that influence the frequency of these traffic incidents is helpful in proposing countermeasures. There have been several studies on evaluating crash frequencies. However, research on other incident types is sparse. The main objective of this research is to identify critical variables that affect the number of reported vehicle breakdowns. A traffic incident dataset covering 4.5 years (January 2012 to June 2016) in the Australian state of New South Wales (NSW) was arranged in a panel data format, consisting of monthly reported vehicle breakdowns in 28 SA4s (Statistical Area Level 4) in NSW. The impact of different independent variables on the number of breakdowns reported in each month–SA4 observation is captured using a random-effect negative binomial regression model. The results indicate that increases in population density, the number of registered vehicles, the number of public holidays, average temperature, the percentage of heavy vehicles, and percentage of white-collared jobs in an area increase the number of breakdowns. On the other hand, an increase in the percentage of unrestricted driving licenses and families with children, number of school holidays, and average rainfall decrease the breakdown frequency. The insights offered in this study contribute to a complete picture of the relevant factors that can be used by transport authorities, vehicle manufacturers, sellers, roadside assistance companies, and mechanics to better manage the impact of vehicle breakdowns.
Estimation and application of a Bayesian network model for discrete travel choice analysis
Publisher: Informa UK Limited
Date: 04-2010
Multitype Recharge Facility Location for Electric Vehicles
Publisher: Wiley
Date: 06-06-2018
DOI: 10.1111/MICE.12379
Integrated Network Capacity Expansion and Traffic Signal Optimization Problem: Robust Bi-level Dynamic Formulation
Publisher: Springer Science and Business Media LLC
Date: 06-09-2008
Integrated traffic simulation-statistical analysis framework for online prediction of freeway travel time
Publisher: SAGE Publications
Date: 2007
DOI: 10.3141/2039-03
Abstract: This paper introduces a novel approach to the online short-term prediction of point-to-point freeway travel time, combining statistical forecasting techniques with traffic simulation. At every freeway entrance point, a time series analysis model based on traffic detector counts is used to predict traffic demands, whose flow through the freeway segment is simulated by a cell transmission model. This procedure, applied within a rolling-horizon framework, generates online travel time predictions consistent with traffic flow theory. Experimental results obtained from synthetic data strongly suggest that the estimates obtained with this methodology are robust and accurate. For a wide range of congestion conditions and freeway segment lengths, more than half of the predictions errors were found to be smaller than 15%. Moreover, 80% of these errors fell below 40 s when the actual travel times ranged between 3 and 10 min. Further analyses of the model sensitivity to traffic detector coverage revealed that detector separations of approximately 1 mi resulted in the most precise travel time estimates. In addition to its satisfactory performance, the proposed framework is flexible, and it can make use of additional online data and easily incorporate different forecasting and simulation techniques. Therefore, this work provides a powerful tool for online travel time prediction, suitable for a variety of practical implementation conditions and requirements.
Modelling supplier selection and material purchasing for the construction supply chain in a fuzzy scenario-based environment
Publisher: Elsevier BV
Date: 06-2023
Reliability assessment for traffic data
Publisher: Informa UK Limited
Date: 04-2012
An experimental study of the Online Information Paradox: Does en-route information improve road network performance?
Publisher: Public Library of Science (PLoS)
Date: 13-09-2017
Developing a disaggregate travel demand system of models using data mining techniques
Publisher: Elsevier BV
Date: 11-2017
Sensitivity analysis on a dynamic coupling model for V2V communication distance control
Publisher: Elsevier BV
Date: 2021
Dirty Money: An Investigation into the Hygiene Status of Some of the World's Currencies as Obtained from Food Outlets
Publisher: Mary Ann Liebert Inc
Date: 12-2010
Abstract: A total of 1280 banknotes were obtained from food outlets in 10 different countries (Australia, Burkina Faso, China, Ireland, the Netherlands, New Zealand, Nigeria, Mexico, the United Kingdom, and the United States), and their bacterial content was enumerated. The presence of bacteria on banknotes was found to be influenced by the material of the notes, and there was a strong correlation between the number of bacteria per square centimeter and a series of indicators of economic prosperity of the various countries. The strongest correlation was found with the "index of economic freedom," indicating that the lower the index value, the higher the typical bacterial content on the banknotes in circulation. Other factors that appear to influence the number of bacteria on banknotes were the age of the banknotes and the material used to produce the notes (polymer-based vs. cotton-based). The banknotes were also screened for the presence of a range of pathogens. It was found that pathogens could only be isolated after enrichment and their mere presence does not appear to be alarming. In light of our international findings, it is recommended that current guidelines as they apply in most countries with regard to the concurrent hygienic handling of foods and money should be universally adopted. This includes that, in some instances, the handling of food and money have to be physically separated by employing separate in iduals to carry out one task each whereas in other instances, it could be advantageous to handle food only with a gloved hand and money with the other hand. If neither of these precautions can be effectively implemented, it is highly recommended that food service personnel practice proper hand washing procedures after handling money and before handling food.
Transit Route Design Solved with Wireless Data Collection Algorithms
Publisher: SAGE Publications
Date: 2014
DOI: 10.3141/2466-05
Spatial Aggregation Method for Anonymous Surveys: Case Study for Associations Between Urban Environment and Obesity
Publisher: SAGE Publications
Date: 2016
DOI: 10.3141/2598-04
Abstract: Obesity and other chronic diseases are becoming more prevalent in affluent countries such as Australia. Researchers are trying to understand and combat this trend. One related growing stream of research explores the role of the built environment and transport system on an in idual’s weight. However, results from many studies conducted have been contradictory. A primary cause of these contradictions is due to how neighborhood areas are defined, which directly affects how the built environment variables are calculated in geographic information systems. The potential impacts on regression analysis resulting from different data aggregation methods are well documented in spatial studies, geography, and regional planning fields, and the problem is primarily referred to as the modifiable aerial unit problem. In this paper, the focus is on reducing the error caused by the modifiable aerial unit problem by introducing a new data aggregation method. In idual health and lifestyle data are obtained from the survey of households, income, and labor dynamics in Australia, and the relationship between the built environment and obesity is evaluated by using a discrete choice model. The proposed aggregation method is evaluated across three spatial scales and compared against a conventional data aggregation method (i.e., using predefined administrative boundaries such as census tracts). The results reveal a stronger relationship between land use variables and obesity when the proposed aggregation method is implemented. This paper is relevant primarily to researchers because it provides an improved aggregation method to deal with some privacy restrictions of surveys. It is also relevant to practitioners and policy makers by its quantification of the association between specific built environment variables and obesity.
Highway Improvement Project Rankings due to Uncertain Model Inputs: Application of Traditional Transportation and Land Use Models
Publisher: American Society of Civil Engineers (ASCE)
Date: 12-2010
Scheduling Concrete Delivery Problems by a Robust Meta Heuristic Method
Publisher: IEEE
Date: 11-2013
DOI: 10.1109/EMS.2013.64
Assessing the Accuracy of Expert-Based Decisions in Dispatching Ready Mixed Concrete
Publisher: American Society of Civil Engineers (ASCE)
Date: 06-2014
Understanding the Li diffusion mechanism and positive effect of current collector volume expansion in anode free batteries*
Publisher: IOP Publishing
Date: 06-2020
Abstract: In anode free batteries (AFBs), the current collector acts as anode simultaneously and has large volume expansion which is generally considered as a negative effect decreasing the structural stability of a battery. Moreover, despite many studies on the fast lithium diffusion in the current collector materials of AFB such as copper and aluminum, the involved Li diffusion mechanism in these materials remains poorly understood. Through first-principles calculation and stress-assisted diffusion equations, here we study the Li diffusion mechanism in several current collectors and related alloys and clarify the effect of volume expansion on Li diffusion respectively. It is suggested that due to the lower Li migration barriers in aluminum and tin, they should be more suitable to be used as AFB anodes, compared to copper, silver, and lead. The Li diffusion facilitation in copper with a certain number of vacancies is proposed to explain why the use of copper with a thickness ⩽ 100 nm as the protective coating on the anode improves the lifetime of the batteries. We show that the volume expansion has a positive effect on Li diffusion via mechanical–electrochemical coupling. Namely, the volume expansion caused by Li diffusion will further induce stress which in turn affects the diffusion. These findings not only provide in-depth insight into the operating principle of AFBs, but also open a new route toward design of improved anode through utilizing the positive effect of mechanical–electrochemical coupling.
Modeling and evaluating the impact of electricity price on commute network flows of battery electric vehicles
Publisher: Informa UK Limited
Date: 23-02-2022
On the primal and dual formulations of traffic assignment problems with perception stochasticity and demand elasticity
Publisher: Informa UK Limited
Date: 13-05-2022
Inequality and access to services for remote populations: An Australian case study
Publisher: Elsevier BV
Date: 12-2022
A maximum entropy-least squares estimator for elastic origin-destination trip matrix estimation
Publisher: Elsevier BV
Date: 2011
Path-Constrained Traffic Assignment
Publisher: SAGE Publications
Date: 2012
DOI: 10.3141/2283-03
Abstract: This paper presents a mathematical programming model and solution method for the path-constrained traffic assignment problem, in which route choices simultaneously follow the Wardropian equilibrium principle and yield the distance constraint imposed on the path. This problem is motivated by the need for modeling distance-restrained electric vehicles in congested networks, but the resulting model and solution method can be applied to various conditions with similar path-based constraints. The equilibrium conditions of the problem reveal that any path cost in the network is the sum of corresponding link costs and a path-specific out-of-range penalty term. The suggested method, based on the classic Frank–Wolfe algorithm, incorporates an efficient constrained shortest-path algorithm as its subroutine. This algorithm fully exploits the underlying network structure of the problem and is relatively easy to implement. Numerical results from the ex les of problems provided show how the equilibrium conditions are reshaped by the path constraint and how the traffic flow patterns are affected by different constraint tightness levels.
Link Transmission Model-Based Linear Programming Formulation for Network Design
Publisher: SAGE Publications
Date: 04-06-2018
Abstract: This article presents a linear programming formulation to solve the network design problem using the link transmission model (LTM) as the underlying traffic flow model. The original LTM was adapted by incorporating link-sending and receiving flows using linear inequalities. Furthermore, route choice was relaxed, and transfer flow variables were used to model vehicles’ routing decisions within the network. The objective function of the linear program aimed to minimize the total difference between the cumulative vehicle numbers (CVN) at the upstream and at the downstream boundaries of each link subject to flow-conservation and budget constraints. CVN were represented using transfer flows from connected links. The resulting formulation is a linear program that represents a dynamic system optimum traffic flow pattern, embedding the LTM’s network loading procedure. In contrast to the single-destination system optimum dynamic traffic assignment, based on the cell transmission model, the proposed formulation requires considerably fewer decision variables, thus potentially providing a more scalable approach. The proposed formulation was implemented on an ex le network to illustrate the behavior of the model, and was compared with the cell-based formulation. We show that the model describes free-flow and congested traffic flow states accurately in terms of shock-wave propagation, queuing of vehicles, and optimal total system travel time.
Large-Scale Dynamic Traffic Assignment: Practical Lessons from an Application in Sydney, Australia
Publisher: IEEE
Date: 09-2015
Heuristic Scheme for Heterogeneous Vehicle Routing Problem on Trees Based on Generalized Assignment and Bin-Packing Upper Bounds
Publisher: SAGE Publications
Date: 2012
DOI: 10.3141/2283-01
Abstract: The vehicle routing problem (VRP) is a classical problem in logistics that aims to design minimum-cost delivery routes from a centralized depot. A special case of the VRP arises in situations in which the network has a tree structure (TVRP). Such tree networks arise when the cost of road construction and maintenance is much more than the routing cost or when the transportation network consists of a main highway (e.g., Interstate system) and the customer locations are located off the highway. A heuristic for a constrained case of TVRP in which the vehicle fleet is capacitated and heterogeneous is proposed. The heuristic first determines the customers that will be served by each vehicle by use of bin-packing and Lagrangian-based generalized assignment algorithms. The in idual vehicle routes are then determined by use of a depth-first search method. A procedure for further refinement of the heuristic solution quality is also described. The heuristic algorithm was implemented on two real-world networks and on randomly generated networks that varied in size from 20 to 120 nodes. The heuristic solution was found to be between 2% and 10% for almost all of the 200 instances tested and took a fraction of the time taken to find the optimal solution.
Choice Set Formation Behavior: Joint Mode and Route Choice Selection Model
Publisher: SAGE Publications
Date: 2016
DOI: 10.3141/2563-14
Abstract: To improve the level of service of traffic and predict travel demand, it is essential to analyze the behavioral factors that affect transportation mode choice and route choice at the in idual level. Such analysis requires detailed data on the behavior of people in the selection of different modes and routes. This paper presents a unique data collection endeavor, intended to observe the formation of the choice set from which the final alternative is selected. The revealed preference information about the routes people considered and used for their last work or study trip was targeted. Google Maps API (application programming interface), which has the capacity to calculate and return car and public transportation routes, was employed to program the survey and adaptively show the respondents the routes according to the reported origin and destination. A pilot survey was conducted with this survey tool at the University of New South Wales, Sydney, Australia, with a s le of 200 respondents. A preliminary analysis was carried out to analyze the effectiveness of the survey tool and the specification of the choice set. Three modeling structures—multinomial logit (MNL), nested logit, and mixed MNL—were used to estimate the parameters of the preliminary analysis. The results were fairly intuitive as far as the signs of the parameters were concerned, and travel time significantly influenced route choice.
Choice Set Formation Behavior in Selecting Travel Routes: Application of an Interactive Online Survey Platform
Publisher: SAGE Publications
Date: 2017
DOI: 10.3141/2669-01
Abstract: One of the major challenges associated with the analysis of route choice modeling is the formulation of the choice set of alternatives that may allow a relatively accurate prediction of demand for travel routes. The subset of route alternatives in the choice set should be relevant and feasible and include the attributes considered most by travelers when they choose a route. This research investigated the role and significance of route choice set formations with a focus on the perspectives of the modeler and of travelers. Revealed preference data were collected from Sydney, Australia, residents about their choice of route for their most recent commuting trip. The survey tool was programmed to use the Google Maps application programming interfaces to collect the route choice information, including the selected route and the set of routes that were considered. Three discrete choice models were used to investigate the traveler’s inclination toward certain attributes of routes, considering both car and public transit routes with the master choice set. The effect of possible bias generated because of the formation of route choice from the perspective of the modeler was also analyzed and presented with the results. The results show the intuitive signs of various attributes, with travel time being the significant factor for route choice. The difference between the choice sets considered by the traveler and by the modeler also suggests that those considered by the modeler possess enough variation to offer the possibility of better capturing important factors affecting route choice behavior.
A multi-task memory network with knowledge adaptation for multimodal demand forecasting
Publisher: Elsevier BV
Date: 10-2021
Relaxing the multivariate normality assumption in the simulation of transportation system dependencies
Publisher: Informa UK Limited
Date: 04-2010
Two-Phase Model of Ramp Closure for Incident Management
Publisher: SAGE Publications
Date: 2008
DOI: 10.3141/2047-10
Abstract: Temporary on-r closure has been proposed as a strategy to reduce the impact of severe incidents on freeway facilities however, to date no rigorous procedure has been made available to provide guidance on how such a technique should best be used. In particular, one must decide which r s to close and for how long. A two-phase approach is proposed to answer these questions. The first phase is macroscopic in nature and predicts how motorists will reroute in response to any r closure and recommends which r s should be closed. The second phase uses microsimulation to study the vicinity of the incident in greater detail, more fully accounting for dynamic traffic phenomena and attempting to answer the question of how long these r s should be closed. From a computational standpoint, the first phase is designed to run as quickly as possible to allow the r closure policy to be enacted as the second phase begins, since the results of the second phase are not needed until later. This procedure is demonstrated by using a fictitious incident in the El Paso region of Texas.
Environmental justice analysis: Challenges for metropolitan transportation planning
Publisher: SAGE Publications
Date: 2007
DOI: 10.3141/2013-02
Abstract: This research focuses on three major challenges of incorporating environmental justice into metropolitan transportation planning. The data needed are compared with the data currently available on the spatial distributions of race and income, the spatial distributions of trip ends, trip tables, network performance, and cost estimates of improvements. Several conflicting definitions of equity are offered, as are applications for each within the context of environmental justice. The importance of choosing a correct unit of analysis is discussed, with particular emphasis on how the geographic unit of analysis is a poor proxy for the group unit, which is theoretically required, as the analysis's purpose is to compare performance measures across groups. The primary goal of this paper is to explore challenging topics such as these raising questions and concerns. The answers to the questions raised will differ depending on each implementing agency's objectives and resources.
Comparing Dynamic User Equilibrium and Noniterative Stochastic Route Choice in a Simulation-Based Dynamic Traffic Assignment Model: Practical Considerations for Large-Scale Networks
Publisher: Hindawi Limited
Date: 05-05-2021
DOI: 10.1155/2021/6667335
Abstract: Simulation-based dynamic traffic assignment (DTA) models play a vital role in transportation planning and operations. While the widely studied equilibrium-seeking DTA including dynamic user equilibrium (DUE) often provides robust and consistent outcomes, their expensive computational cost for large-scale network applications has been a burden in practice. The noniterative stochastic route choice (SRC) model, as a nonequilibrium seeking DTA model, provides an alternative for specific transportation operations applications that may not require equilibrium results after all (e.g., evacuation and major network disruptions) and thus tend to be computationally less expensive, yet may suffer from inconsistent outcomes. While DUE is a widely accepted approach for many strategic planning applications, SRC has been increasingly used in practice for traffic operations purposes. This paper aims to provide a comparative and quantitative analysis of the two modeling approaches. Specifically, a comparison has been made at two levels: link-level flows and network-level congestion patterns. Results suggest that adaptive driving improves the quality of the SRC solution, but its difference from DUE still remains significant at the link level. Results have practical implications for the application of large-scale simulation-based DTA models for planning and operations purposes.
Robust pricing of transportation networks under uncertain demand
Publisher: SAGE Publications
Date: 2008
DOI: 10.3141/2085-03
Abstract: Traditionally, tolls on transportation networks are determined on the basis of a single value of travel demand, deterministic elastic demand relationships, or informal scenario analysis. However, since the demand on the network cannot be forecast perfectly, pricing may prove to be suboptimal when the realized value of demand deviates significantly from the planned value. Therefore, there is a need for a robust pricing scheme that accounts for demand uncertainty. Optimal pricing is examined through marginal costs in which origin-destination travel demand is a random variable to understand better the direct impact and sensitivity of the uncertainty. Three methods are evaluated for determining robust prices: inflation or deflation of the planning demand, averaging tolls from various planning demands, and genetic algorithms. The performance of these three methods is evaluated by analyzing user equilibrium for various future travel demand scenarios. From the results of the analysis, a more robust pricing scheme that accounts for variations in demand is developed.
Job Assignment Based on Brain Demands and Human Resource Strategies
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-2017
Accounting for Transport Times in Planning Off-Site Shipment of Construction Materials
Publisher: American Society of Civil Engineers (ASCE)
Date: 2016
Linear Programming Models for the User and System Optimal Dynamic Network Design Problem: Formulations, Comparisons and Extensions
Publisher: Springer Science and Business Media LLC
Date: 20-04-2007
Finding Minimum-Cost Dynamic Routing Policies in Stochastic-State Networks with Link Failures
Publisher: SAGE Publications
Date: 2012
DOI: 10.3141/2283-12
Abstract: The focus of this research is to develop minimum-cost dynamic routing policies that can identify connecting paths between nodes in a stochastic-state network. In this context, the stochastic element of the network is the network structure, that is, the set of links that exist under each realization of the network state. It is assumed that information about the true network state can be gathered only endogenously through the routing decisions themselves. As such, the objective becomes finding a dynamic policy that accounts for information gathered en route that minimizes the cost of detection of a viable path between a given origin and destination. An exact solution method, based on a Markovian decision process, is presented, and then a heuristic based on an aggregating function of the network is developed.
Dynamic Traveling Salesman Problem in Stochastic-State Network Setting for Search-and-Rescue Application
Publisher: SAGE Publications
Date: 2012
DOI: 10.3141/2283-13
Abstract: The problem presented in this paper was motivated by the need for a solution to be used in a search-and-rescue application and is formulated as a dynamic traveling salesman problem in a stochastic-state network setting. This problem formulation features a full-recourse decision framework and stochastic demands that are revealed only through direct observation. This problem is defined in a stochastic-state network setting, which allows the modeling of implicitly correlated demand stochasticity. The problem is then formulated as a Markovian decision process, and, finally, a heuristic solution is provided. The heuristic solution is based on a two-stage stochastic program with recourse solved on a set of aggregated networks generated by the use of an aggregating function. Subsets of the feasible solutions obtained at each stage are fixed, and the heuristic is used iteratively to further refine the routing policy.
A simulation-optimisation genetic algorithm approach to product allocation in vending machine systems
Publisher: Elsevier BV
Date: 05-2020
Arrival Time Reliability in Strategic User Equilibrium
Publisher: Springer Science and Business Media LLC
Date: 07-2020
Implications of volatility in day-to-day travel flow and road capacity on traffic network design projects
Publisher: SAGE Publications
Date: 2015
DOI: 10.3141/2498-07
Abstract: This work addresses the traffic network design problem when day-to-day uncertainties in travel demand and link capacity are taken into account. Specifically, this work proposes a network design formulation that uses a strategic behavior approach in which total demand and link capacity are treated as random variables, and a strategic user equilibrium results in fixed equilibrium link proportions. The bilevel model is formulated, system performance metrics are derived, and a solution method is then developed according to a tailored genetic algorithm. Results under varying levels of volatility reflect possible suboptimal project selection when a deterministic modeling approach is used.
Path-constrained traffic assignment: A trip chain analysis under range anxiety
Publisher: Elsevier BV
Date: 07-2016
Fusion based learning approach for predicting concrete pouring productivity based on construction and supply parameters
Publisher: Emerald
Date: 04-04-2016
Abstract: The purpose of this paper is to predict the concrete pouring production rate by considering both construction and supply parameters, and by using a more stable learning method. Unlike similar approaches, this paper considers not only construction site parameters, but also supply chain parameters. Machine learner fusion-regression (MLF-R) is used to predict the production rate of concrete pouring tasks. MLF-R is used on a field database including 2,600 deliveries to 507 different locations. The proposed data set and the results are compared with ANN-Gaussian, ANN-Sigmoid and Adaboost.R2 (ANN-Gaussian). The results show better performance of MLF-R obtaining the least root mean square error (RMSE) compared with other methods. Moreover, the RMSEs derived from the predictions by MLF-R in some trials had the least standard deviation, indicating the stability of this approach among similar used approaches. The size of the database used in this study is much larger than the size of databases used in previous studies. It helps authors draw their conclusions more confidently and introduce more generalised models that can be used in the ready-mixed concrete industry. Introducing a more stable learning method for predicting the concrete pouring production rate helps not only construction parameters, but also traffic and supply chain parameters.
Effect of number of stop-&-gos experienced on the route choice behaviour of car drivers
Publisher: Informa UK Limited
Date: 17-07-2019
A simple contagion process describes spreading of traffic jams in urban networks
Publisher: Springer Science and Business Media LLC
Date: 07-04-2020
DOI: 10.1038/S41467-020-15353-2
Abstract: The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two macroscopic characteristics for network traffic dynamics, namely congestion propagation rate β and congestion dissipation rate μ . We describe the dynamics of congestion spread using these new parameters embedded within a system of ordinary differential equations, similar to the well-known susceptible-infected-recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time.
A scenario-based evaluation of the Middle East respiratory syndrome coronavirus and the Hajj
Publisher: Wiley
Date: 14-07-2014
DOI: 10.1111/RISA.12253
Using Lagrangian Relaxation to Solve Ready Mixed Concrete Dispatching Problems
Publisher: SAGE Publications
Date: 2015
DOI: 10.3141/2498-10
Abstract: The logistics and planning problem of delivering ready mixed concrete (RMC) to a set of demand customers from multiple depots is addressed. The RMC dispatching problem (RMCDP) is closely related to the vehicle routing problem, with the difference that a truck may visit demand nodes in the RMCDP more than once. This class of routing problems can be represented by using mixed-integer programming (MIP) and is known to be NP-hard. Solving RMC delivery problems is often achieved through heuristics and metaheuristic-based methods as exact solution approaches are often unable to find optimal solutions efficiently, in particular when multiple depots are represented in the model. Although a variety of methods are available to solve MIP models, in this paper an attempt is made to solve the RMCDP by using a Lagrangian relaxation technique. Namely, a solution algorithm based on Lagrangian relaxation is derived to reduce the complexity of the initial MIP model and show that the proposed relaxation is able to provide promising computation results on a realistic data set representative of an active RMCDP in the region of Adelaide, Australia.
Estimation of sparse O–D matrix accounting for demand volatility
Publisher: Institution of Engineering and Technology (IET)
Date: 30-07-2018
Identification of competing and feeder links and routes in a toll road context
Publisher: SAGE Publications
Date: 2010
DOI: 10.3141/2196-13
Abstract: Toll road projects have the potential to complement current project procurement practices while lessening the pressure on public finances. In this context, planning and valuation of a toll road project is closely related to analysis of the supporting network. In particular, the contribution of a project to adopted value measures is inherently dependent on the network topology and the influence of competing and feeder links and routes. Research on toll roads often ignores the aspect of evaluating the strategic position of a toll road project in a larger transportation network. This work contributes to filling this gap by proposing a methodology for identifying competing and feeder routes and links in the context of toll roads. Within a traffic assignment framework, the proposed methodology first simulates variations in link capacity and then studies the resulting correlation patterns to identify the impact of capacity variations on link volumes and most likely route flows. The methodology provides planning agencies with a tool for understanding the effects of network actions on competing routes and links versus actions on feeder routes and links, which is helpful in determining which network improvements will add the most value to existing and planned toll road projects.
Maximum entropy method for subnetwork origin-destination trip matrix estimation
Publisher: SAGE Publications
Date: 2010
DOI: 10.3141/2196-12
Abstract: In the context of sketch planning, a simplified network (i.e., an abstract network or subnetwork) model is expected to accurately approximate travel demand patterns and level-of-service attributes obtained from its full-network counterpart. A data prerequisite in this approximation process is the trip matrix of the simplified network. This paper discusses a maximum entropy method for the subnetwork trip matrix estimation problem, relying only on link flow rates estimated with the use of full-network traffic assignment or as observed link-level vehicle counts. A linearization algorithm of the Frank–Wolfe type is devised for problem solutions in which a column–generation approach is used iteratively to solve the linearized subproblem without path enumeration. Encouraging results from s le applications of different size and topology suggest that this method holds much promise for generating trip matrices that can be used to evaluate traffic flow patterns under various network changes.
Modelling and managing the integrated morning-evening commuting and parking patterns under the fully autonomous vehicle environment
Publisher: Elsevier BV
Date: 10-2019
Remote Sensing Methods for Flood Prediction: A Review
Publisher: MDPI AG
Date: 26-01-2022
DOI: 10.3390/S22030960
Abstract: Floods are a major cause of loss of lives, destruction of infrastructure, and massive damage to a country’s economy. Floods, being natural disasters, cannot be prevented completely therefore, precautionary measures must be taken by the government, concerned organizations such as the United Nations Office for Disaster Risk Reduction and Office for the coordination of Human Affairs, and the community to control its disastrous effects. To minimize hazards and to provide an emergency response at the time of natural calamity, various measures must be taken by the disaster management authorities before the flood incident. This involves the use of the latest cutting-edge technologies which predict the occurrence of disaster as early as possible such that proper response strategies can be adopted before the disaster. Floods are uncertain depending on several climatic and environmental factors, and therefore are difficult to predict. Hence, improvement in the adoption of the latest technology to move towards automated disaster prediction and forecasting is a must. This study reviews the adoption of remote sensing methods for predicting floods and thus focuses on the pre-disaster phase of the disaster management process for the past 20 years. A classification framework is presented which classifies the remote sensing technologies being used for flood prediction into three types, which are: multispectral, radar, and light detection and ranging (LIDAR). Further categorization is performed based on the method used for data analysis. The technologies are examined based on their relevance to flood prediction, flood risk assessment, and hazard analysis. Some gaps and limitations present in each of the reviewed technologies have been identified. A flood prediction and extent mapping model are then proposed to overcome the current gaps. The compiled results demonstrate the state of each technology’s practice and usage in flood prediction.
Optimal routing with multiple objectives
Publisher: Wiley
Date: 29-03-2012
A bibliometric analysis and review on reinforcement learning for transportation applications
Publisher: Informa UK Limited
Date: 02-03-2023
An integrated transport and economic equilibrium model for autonomous transportation systems considering parking behavior
Publisher: Wiley
Date: 20-08-2021
DOI: 10.1111/MICE.12611
Abstract: This study investigates whether the expected travelling and parking behavior of autonomous vehicles (AVs) could lead to positive or negative societal impacts, both in terms of transport and the economy, which have been missed in previous studies. To capture transport performance and economic responses in the AV environment, we develop an integrated transport and economic equilibrium model that consists of the proposed autonomous traffic equilibrium and computable general equilibrium submodels. A modeling framework with a proposed solution methodology is presented to investigate the AV situation. A case study on Sydney, Australia shows that AV parking patterns can cause deterioration in traffic conditions with fewer completed journeys and greater travel time costs, which results in significant losses of social welfare. Furthermore, the disbenefit of AV travel behavior would become even larger with population growth.
Distribution-free travel time reliability assessment with probability inequalities
Publisher: Elsevier BV
Date: 07-2011
Robust Dynamic Continuous Network Design Problem
Publisher: SAGE Publications
Date: 2007
DOI: 10.3141/2029-07
Abstract: A robust optimization model is presented for the dynamic traffic assignment-based continuous network design problem, which accounts for a bilevel objective and long-term origin-destination demand uncertainty. The model also embeds Daganzo's cell transmission model. The objective minimizes the trade-off between expected total system travel time (TSTT) and expected risk. As such, the robust model provides the optimal solution that is least sensitive to the variation of travel demand, given the degree of robustness by transportation planners. The new robust model is compared with the existing network design models on a simple cell transmission test network. The robust model with greater degree of robustness yields less expected risk with the sacrifice of higher expected TSTT. The robust model yields the most robust solution, and no other model provides a satisfactory solution across the budget range. In addition, how a visualized graph may be used to elicit the preference information from transportation planners on the desired degree of robustness is illustrated.
A chance-constrained dial-a-ride problem with utility-maximising demand and multiple pricing structures
Publisher: Elsevier BV
Date: 02-2022
System Optimal Dynamic Lane Reversal for Autonomous Vehicles
Publisher: IEEE
Date: 09-2015
Evaluation of fluctuating speed and lateral movement of vehicles: Comparison between mixed traffic and homogeneous traffic
Publisher: SAGE Publications
Date: 2016
DOI: 10.3141/2581-13
Abstract: Urban roads in developing countries become congested more often because of the substantial lateral movement of vehicles and fluctuations in speed. Popular microscopic models such as car-following and lane-changing models are not suitable for the analysis of such traffic conditions unless some modifications accounting for heterogeneity are included. However, there seems to be an underlying mechanism behind the fluctuations that needs to be investigated thoroughly. The Hurst exponent concept of chaos theory was used to identify the hidden trends in vehicular movement. Real-life trajectory data from an urban arterial in Chennai, India, were analyzed and then compared with a homogeneous data set in the United States. The Hurst exponent for mixed traffic was found to be significantly less than that for the homogeneous data this finding indicated the strong trends of lateral movement and speed in homogeneous traffic. The variation of Hurst exponents with vehicle type and average lateral positions was explored in mixed traffic. Results from this study will help modelers propose better microscopic simulation models accounting for the fluctuations in speed and lateral movement.
Influence of Demand Uncertainty and Correlations on Traffic Predictions and Decisions
Publisher: Wiley
Date: 12-2011
On integrating carsharing and parking sharing services
Publisher: Elsevier BV
Date: 12-2020
Matching experts' decisions in concrete delivery dispatching centers by ensemble learning algorithms: Tactical level
Publisher: Elsevier BV
Date: 08-2016
Column Generation-Based Approach for Solving Large-Scale Ready Mixed Concrete Delivery Dispatching Problems
Publisher: Wiley
Date: 04-12-2015
DOI: 10.1111/MICE.12182
The impact of global on-line information provision on transport networks and how random early detection can help
Publisher: Informa UK Limited
Date: 17-05-2019
Modeling and managing ridesharing in a multi-modal network with an aggregate traffic representation: A doubly dynamical approach
Publisher: Elsevier BV
Date: 08-2020
An Investigation into the Perceived Quality of Communications in Dispersed Construction Project Teams
Publisher: American Society of Civil Engineers
Date: 24-05-2016
Link performance functions for high occupancy vehicle lanes of freeways
Publisher: Vilnius Gediminas Technical University
Date: 10-07-2018
DOI: 10.3846/TRANSPORT.2018.1575
Abstract: High Occupancy Vehicle (HOV) lanes are widely used on freeways and play an important role in network design and management. Likewise, link performance functions serve as an essential tool for transport system analysis. This paper aims to support network analysis by providing a tailored link performance function for HOV lanes contiguous with general motor lanes on freeways. Specifically, real traffic data is used for model calibration and evaluation that was assembled from the Performance Measurement System (PeMS) maintained by the California Department of Transportation. Three alternative models for link performance functions of HOV lanes on freeways are developed, which take traffic performance on both HOV lanes and adjacent sets of general motor lanes into consideration. To calibrate the parameters of the models, linear regression is made through stepwise and enter methods and nonlinear regression is carried out using sequential quadratic programming. Statistical analysis together with an evaluation using real traffic data is conducted to evaluate the validity of the proposed models. Our results show that all the three proposed models for contiguous HOV lanes on freeways are statistically significant and perform better in representing real traffic condition with regards to a traditional link performance function, with one specific nonlinear model best supported.
Surrogate‐based toll optimization in a large‐scale heterogeneously congested network
Publisher: Wiley
Date: 04-2019
DOI: 10.1111/MICE.12444
Integrating the Bus Vehicle Class into the Cell Transmission Model
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2015
Reliable system-optimal network design: Convex mean variance model with implicit chance constraints
Publisher: SAGE Publications
Date: 2009
DOI: 10.3141/2090-08
Abstract: It is critical to account for uncertainty in the design of transportation networks. Various models assuming both user-optimal and system-optimal behavior (as a computationally viable proxy for the more realistic user-optimal design problem) have been proposed. Most of these models do not provide any form of probabilistic guarantee for the obtained capacity expansion decisions. However, for system reliability, it is often useful to know how likely it is that the total system travel time would deviate from a certain value if the prescribed solutions from a specific model are implemented. A new mean-variance type of system-optimal network design model with probabilistic guarantees on systemwide travel time is proposed. The proposed model has several unique features. First, uncertainty in the link performance function is considered. This uncertainty is a result of capacity uncertainty as well as fundamental uncertainty about the functional form of the link performance function itself. Second, instead of imposing an explicit chance constraint–which in general would lead to nonconvexity–probabilistic guarantees on the obtained system travel time are obtained implicitly. More specific, the model yields a one-sided confidence interval for the total systemwide travel time that has an a priori specified confidence level. Finally, it is not necessary to specify an explicit probability distribution to model the uncertainty. Instead, the proposed model is distribution free in that any symmetric probability distribution suffices. Numerical results are presented and discussed.
Impact of network improvement on revenue performance and bonding costs of toll roads
Publisher: SAGE Publications
Date: 2010
DOI: 10.3141/2187-02
Abstract: Increased interest in new and alternative methods for delivering projects places special emphasis on agreements for public–private partnerships. In such arrangements, the public sector must conform to a set of contractual obligations and also is responsible for long-term planning of the transportation network and for providing a better service on the existing public infrastructure. In this context, any changes in the initial network structure represent an additional risk element, acting as an externality to the toll road developer. This paper examines the impact of these changes on the price of selected risk measures (that is, the price of the revenue risk minus the cost of debt). An analytical method is developed that relates network improvement decisions to credit risk measures. The method is applied to two networks to examine the behavior of the credit spread when changes occur in the capacities on feeder and competing links. Sensitivity analyses show that a decrease in the capacity on feeder links might have a higher (adverse) impact on the credit spread than an increase in the capacity on competing links.
Dial-a-Ride Problem with Users’ Accept/Reject Decisions Based on Service Utilities
Publisher: SAGE Publications
Date: 17-08-2020
Abstract: The classic dial-a-ride problem (DARP) aims at designing the minimum-cost routing that accommodates all requests under a set of constraints. However, several modeling and computational challenges have hindered the successful deployment of dial-a-ride solutions. This work proposes incorporating user preference decisions within a rich DARP formulation. Specifically, it is considered that two travel modes are available: a shared mobility (DARP) service and a private travel option. Utility functions for each travel mode are integrated and it is assumed that the utility of the shared mobility service depends on the collective choice of travelers whereas the utility of private travel is fixed. Assuming that travelers are rational and seek to maximize their trip utility, extra variables and constraints are added to ensure that all requests are served by the mode with the higher utility. The behavior of the proposed integrated DARP with formulation of user preference constraints is examined by comparing the optimal solutions and computational time of this model with its classic DARP counterpart. Furthermore, the impact of various formulations of fare and the tolerance of the integrated DARP model is explored. Results show that user-personalized fare formulation improves model tolerance and profit margin, albeit it is rather computationally expensive.
Framework for studying emerging policy issues associated with PHEVs in managing coupled power and transportation systems
Publisher: IEEE
Date: 04-2010
Job Assignments to Construction Workers: Accounting for the Brain Resource Requirements of Activities
Publisher: American Society of Civil Engineers
Date: 24-05-2016
Application of Reactive Tabu Search for Combined Dynamic User Equilibrium and Traffic Signal Optimization Problem
Publisher: SAGE Publications
Date: 2009
DOI: 10.3141/2090-04
Abstract: A new mixed-zero-one continuous linear bilevel formulation is presented. It simultaneously solves the traffic signal optimization problem and the dynamic user equilibrium problem and yields a mutually consistent solution. The upper-level problem finds optimal traffic signal settings (cycle lengths, green times, time offsets, and phase sequences) for prespecified signalized intersections such that the total system travel time is minimized. The lower-level problem is the existing user-optimal dynamic traffic assignment (UODTA) linear program that embeds Daganzo's cell transmission model (CTM). The reactive tabu search (RTS), based on the analogy between the direct search and the dynamical systems theory, is modified to solve the problem. There are three major modifications. First, the binary-string solution representation is chosen, and the associated encoding and decoding procedures are developed for three-, four-, and five-leg intersections. Second, three neighborhood definitions for RTS are proposed they yield three variations of the algorithm: RTS-MT0, RTS-MT1, and RTS-MT2. RTS-MT0 uses the deterministic neighborhood definition, and the others are based on probabilistic neighborhood definitions. Third, the functional evaluation uses the existing simulation-based UODTA that uses the CTM. Comparisons of algorithm performance are conducted on a hypothetical grid network and a modified Sioux Falls, Iowa, network. The performances are compared by using three criteria: solution quality, convergence speed, and CPU time. The CPU times for RTS-MT0, RTS-MT1, and RTS-MT2 on the two test networks are approximately equal. On the other two criteria, RTS-MT2 appeared to be the best, and RTS-MT1 and RTS-MT0 were the second and the third best, respectively.
Adaptive transit routing in stochastic time-dependent networks
Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
Date: 08-2016
Abstract: We define an adaptive routing problem in a stochastic time-dependent transit network in which transit arc travel times are discrete random variables with known probability distributions. We formulate it as a finite horizon Markov decision process. Routing strategies are conditioned on the arrival time of the traveler at intermediate nodes and real-time information on arrival times of buses at stops along their routes. The objective is to find a strategy that minimizes the expected travel time, subject to a constraint that guarantees that the destination is reached within a certain threshold. Although this framework proves to be advantageous over a priori routing, it inherits the curse of dimensionality, and state space reduction through preprocessing is achieved by solving variants of the time-dependent shortest path problem. Numerical results on a network representing a part of the Austin, Texas, transit system indicate a promising reduction in the state space size and improved tractability of the dynamic program.
Bi-level programming formulation and heuristic solution approach for dynamic traffic signal optimization
Publisher: Wiley
Date: 07-2006
A multimodal multi-provider market equilibrium model: A game-theoretic approach
Publisher: Elsevier BV
Date: 2023
A mean-variance model for the minimum cost flow problem with stochastic arc costs
Publisher: Wiley
Date: 03-12-2009
DOI: 10.1002/NET.20374
Image-Based Crack Detection Methods: A Review
Publisher: MDPI AG
Date: 14-08-2021
DOI: 10.3390/INFRASTRUCTURES6080115
Abstract: Annually, millions of dollars are spent to carry out defect detection in key infrastructure including roads, bridges, and buildings. The aftermath of natural disasters like floods and earthquakes leads to severe damage to the urban infrastructure. Maintenance operations that follow for the damaged infrastructure often involve a visual inspection and assessment of their state to ensure their functional and physical integrity. Such damage may appear in the form of minor or major cracks, which gradually spread, leading to ultimate collapse or destruction of the structure. Crack detection is a very laborious task if performed via manual visual inspection. Many infrastructure elements need to be checked regularly and it is therefore not feasible as it will require significant human resources. This may also result in cases where cracks go undetected. A need, therefore, exists for performing automatic defect detection in infrastructure to ensure its effectiveness and reliability. Using image processing techniques, the captured or scanned images of the infrastructure parts can be analyzed to identify any possible defects. Apart from image processing, machine learning methods are being increasingly applied to ensure better performance outcomes and robustness in crack detection. This paper provides a review of image-based crack detection techniques which implement image processing and/or machine learning. A total of 30 research articles have been collected for the review which is published in top tier journals and conferences in the past decade. A comprehensive analysis and comparison of these methods are performed to highlight the most promising automated approaches for crack detection.
Knowledge Adaption for Demand Prediction based on Multi-task Memory Neural Network
Publisher: ACM
Date: 19-10-2020
Robust Transportation Network Design Under Demand Uncertainty
Publisher: Wiley
Date: 2007
Optimal Maintenance and Repair Policies under Nonlinear Preferences
Publisher: American Society of Civil Engineers (ASCE)
Date: 03-2010
Natural History Study of STXBP1-Developmental and Epileptic Encephalopathy Into Adulthood
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 19-07-2022
DOI: 10.1212/WNL.0000000000200715
Abstract: Pathogenic STXBP1 variants cause a severe early-onset developmental and epileptic encephalopathy (STXBP1-DEE). We aimed to investigate the natural history of STXBP1-DEE in adults focusing on seizure evolution, the presence of movement disorders, and the level of functional (in)dependence. In this observational study, patients with a minimum age of 18 years carrying a (likely) pathogenic STXBP1 variant were recruited through medical genetics departments and epilepsy centers. Treating clinicians completed clinical questionnaires and performed semistructured video examinations while performing tasks from the (modified) Unified Parkinson Disease Rating Scale when possible. Thirty adult patients were included for summary statistics, with video recordings available for 19 patients. The median age at last follow-up was 24 years (range 18–58 years). All patients had epilepsy, with a median onset age of 3.5 months. At last follow-up, 80% of adults had treatment-resistant seizures despite long periods of seizure freedom in 37%. Tonic-clonic, focal, and tonic seizures were most frequent in adults. Epileptic spasms, an unusual feature beyond infancy, were present in 3 adults. All in iduals had developmental impairment. Periods of regression were present in 59% and did not always correlate with flare-ups in seizure activity. Eighty-seven percent had severe or profound intellectual disability, 42% had autistic features, and 65% had significant behavioral problems. Video examinations showed gait disorders in all 12 patients able to walk, including postural abnormalities with external rotation of the feet, broad-based gait, and asymmetric posture/dystonia. Tremor, present in 56%, was predominantly of the intention/action type. Stereotypies were seen in 63%. Functional outcome concerning mobility was variable ranging from independent walking (50%) to wheelchair dependence (39%). Seventy-one percent of adults were nonverbal, and all were dependent on caregivers for most activities of daily living. STXBP1-DEE warrants continuous monitoring for seizures in adult life. Periods of regression are more frequent than previously established and can occur into adulthood. Movement disorders are often present and involve multiple systems. Although functional mobility is variable in adulthood, STXBP1-DEE frequently leads to severe cognitive impairments and a high level of functional dependence. Understanding the natural history of STXBP1-DEE is important for prognostication and will inform future therapeutic trials.
Valuation of strategic network flexibility in development of toll road projects
Publisher: Informa UK Limited
Date: 09-2008
Real-time traffic monitoring using wireless beacons with the Cell Transmission Model
Publisher: IEEE
Date: 10-2014
Incorporating environmental justice measures into equilibrium-based network design
Publisher: SAGE Publications
Date: 2008
DOI: 10.3141/2089-08
Abstract: A new variation of the user equilibrium-discrete network design problem (UE-DNDP) is proposed for achieving environmental justice (EJ) or equity among population groups. This research is motivated by the federal requirement that transportation plans using federal money include an evaluation of EJ and that the planning agency mitigate, where feasible, any disproportionate impacts on protected populations (i.e., minority and low-income groups). Eight potential objective functions focused on maximizing equity of congestion and travel time are developed and discussed with regard to their applicability for the upper level of this bilevel problem. On the basis of assumed knowledge of the origin-destination travel matrices by population group, numerical analysis is conducted to assess the performance of each proposed formulation. The lower-level UE problem is solved by using the Frank-Wolfe method, and because of the hard combinatorial nature of EJ-UE-DNDP, a selectorecombinative genetic algorithm is implemented to search the solution space for feasible network improvement strategies efficiently. The results of numerical analysis suggest that Pareto-optimal approaches can be successfully applied and that the most effective formulations minimize the difference between the change in congestion or travel time across population groups due to the selected improvement projects.
Passenger demographic attributes prediction for human-centered public transport
Publisher: Springer International Publishing
Date: 2019
Evaluation of a strategic road pricing scheme accounting for day-to-day and long-term demand uncertainty
Publisher: SAGE Publications
Date: 2014
DOI: 10.3141/2467-02
Reliable evacuation planning via demand inflation and supply deflation
Publisher: Elsevier BV
Date: 11-2010
Spatial centrosome proteome of human neural cells uncovers disease-relevant heterogeneity
Publisher: American Association for the Advancement of Science (AAAS)
Date: 17-06-2022
Abstract: The centrosome provides an intracellular anchor for the cytoskeleton, regulating cell ision, cell migration, and cilia formation. We used spatial proteomics to elucidate protein interaction networks at the centrosome of human induced pluripotent stem cell–derived neural stem cells (NSCs) and neurons. Centrosome-associated proteins were largely cell type–specific, with protein hubs involved in RNA dynamics. Analysis of neurodevelopmental disease cohorts identified a significant overrepresentation of NSC centrosome proteins with variants in patients with periventricular heterotopia (PH). Expressing the PH-associated mutant pre-mRNA-processing factor 6 (PRPF6) reproduced the periventricular misplacement in the developing mouse brain, highlighting missplicing of transcripts of a microtubule-associated kinase with centrosomal location as essential for the phenotype. Collectively, cell type–specific centrosome interactomes explain how genetic variants in ubiquitous proteins may convey brain-specific phenotypes.
Model formulation and calibration procedure for integrated multi-modal activity routing and network assignment models
Publisher: Elsevier BV
Date: 12-2020
Freeway network design with exclusive lanes for automated vehicles under endogenous mobility demand
Publisher: Elsevier BV
Date: 12-2021
Framework for estimating the impact of camera-based intelligent transportation systems (ITS) technology on incident duration
Publisher: SAGE Publications
Date: 22-05-2018
Abstract: Safety is a major motivator of intelligent transportation systems (ITS) projects, and most efforts have addressed the potential to avoid incidents. Managing and reducing the duration of incidents is another key application for ITS despite challenges in distinguishing the true versus the reported duration of an incident. This paper presents a framework for modeling the impact of camera-based (closed-circuit television or CCTV) ITS technology on incident duration including an increase in the reported duration and a reduction in the true duration. The framework is validated against a data set of 121,793 accidents in New South Wales, Australia, covering 4.5 years. The results demonstrate that the use of CCTVs for incident duration contributes a 4.5 min reduction in average duration (as earlier detection can lead to more efficient clearance) and a 9% reduction in variance in the duration (as a uniform detection method supports standardized response procedures). These impacts are only visible when the 8.5 min median detection delay (the difference between the recorded duration and the true duration) is modeled and accounted for. These results offer a quantitative support tool for decision makers wishing to assess the value of incident-detection ITS projects.
BIM-enabled sustainability assessment of material supply decisions
Publisher: Emerald
Date: 17-07-2017
DOI: 10.1108/ECAM-12-2015-0193
Abstract: Enhancing sustainability of the supply process of construction materials is challenging and requires accounting for a variety of environmental and social impacts on top of the traditional, mostly economic, impacts associated with a particular decision involved in the management of the supply chain. The economic, environmental, and social impacts associated with various components of a typical supply chain are highly sensitive to project and market specific conditions. The purpose of this paper is to provide decision makers with a methodology to account for the systematic trade-offs between economic, environmental, and social impacts of supply decisions. This paper proposes a novel framework for sustainability assessment of construction material supply chain decisions by taking advantage of the information made available by customized building information models (BIM) and a number of different databases required for assessment of life cycle impacts. The framework addresses the hierarchy of decisions in the material supply process, which consists of four levels including material type, source of supply, supply chain structure, and mode of transport. The application is illustrated using a case study. The proposed framework provides users with a decision-making method to select the most sustainable material alternative available for a building component and, thus, may be of great value to different parties involved in design and construction of a building. The multi-dimensional approach in selection process based on various economic, environmental, and social indicators as well as the life cycle perspective implemented through the proposed methodology advocates the life cycle thinking and the triple bottom line approach in sustainability. The familiarity of the new generation of engineers, architects, and contractors with this approach and its applications is essential to achieve sustainability in construction. A decision-making model for supply of materials is proposed by integrating the BIM-enabled life cycle assessment into supply chain and project constraints management. The integration is achieved through addition of a series of attributes to typical BIM. The framework is supplemented by a multi-attribute decision-making module based on the technique for order preference by similarity to ideal solution to account for the trade-offs between different economic and environmental impacts associated with the supply decisions.
User Equilibrium with Recourse
Publisher: Springer Science and Business Media LLC
Date: 25-09-2009
Sensor protocols for asset routing via constrained energy (SPARCE): problem definition and example
Publisher: IEEE
Date: 2005
Incorporating equity into the transit frequency-setting problem
Publisher: Elsevier BV
Date: 2012
Reduced value of time for autonomous vehicle users: Myth or reality?
Publisher: Elsevier BV
Date: 09-2020
Modeling carrier collaboration in freight networks
Publisher: Informa UK Limited
Date: 2011
Joint routing and pricing control in congested mixed autonomy networks
Publisher: Elsevier BV
Date: 10-2021
Approximate analytical expressions for transportation network performance under demand uncertainty
Publisher: Informa UK Limited
Date: 04-2010
Special issue on dynamic traffic assignment for general transport networks
Publisher: Elsevier BV
Date: 08-2019
Inferring Infection-Spreading Links in an Air Traffic Network
Publisher: SAGE Publications
Date: 2012
DOI: 10.3141/2300-02
Abstract: The objective of this paper is to present a network-based optimization method for identifying links in an air traffic network responsible for carrying infected passengers into previously unexposed regions. The required data include in idual infection reports (i.e., when the disease was first reported in a region), travel pattern data, and other geographic properties. The network structure is defined by nodes and links, which represent regions (cities, states, countries) and travel routes, respectively. The proposed methodology is novel in its attempt to replicate an outbreak pattern atop a transportation network by exploiting regional infection data. The problem parallels a related problem in phylodynamics, which uses genetic sequencing data to reconstruct the most likely spatiotemporal path of infection.
Exploring Experts Decisions in Concrete Delivery Dispatching Systems Using Bayesian Network Learning Techniques
Publisher: IEEE
Date: 11-2014
DOI: 10.1109/AIMS.2014.9
Enhancing the safety of construction crew by accounting for brain resource requirements of activities in job assignment
Publisher: Elsevier BV
Date: 04-2018
Consistency Between Convergence of Dynamic Assignment and Stochasticity of Microsimulation: Implication for Number of Runs
Publisher: SAGE Publications
Date: 2017
DOI: 10.3141/2667-09
Abstract: Dynamic transportation models route vehicles by using the principles of dynamic user equilibrium. These models include a dynamic network loading (DNL) module that is used to evaluate link costs. However, an element of stochasticity creeps into the modeling framework when the analytical dynamic assignment (DA) procedure is used along with a stochastic microscopic DNL. A methodologically correct way of approaching this problem is by solving the entire DA with a microscopic DNL (DA-microDNL) model until convergence for a given random seed and then repeating the process with different seed values. This paper proposes an approach to determine the minimum number of replications of the DA-microDNL model to determine a statistically valid estimate of the measure of effectiveness (MOE). The approach was tested on a small and medium-size network having different demand and network characteristics. Results show that running the integrated DA-microDNL framework for a minimum number of replications provides a statistically significant MOE at much lower computation time. The consistent estimates obtained by using this approach would provide robust information to transportation planners and practitioners in evaluating the impacts of several policy decisions on network performance.
Complementing Travel Diary Surveys with Twitter Data: Application of Text Mining Techniques on Activity Location, Type and Time
Publisher: IEEE
Date: 09-2015
DOI: 10.1109/ITSC.2015.43
A branch-and-price algorithm for the bilevel network maintenance scheduling problem
Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
Date: 09-2019
Abstract: We address the network maintenance scheduling problem, which consists of finding the optimal schedule for the coordination of road maintenance projects in a transport network over a planning period. Road works and maintenance operations that require partial or total road closures over a period of time may considerably impact network performance and result in significant delays. In addition, the effects of conducting multiple maintenance projects simultaneously may be nonadditive, hence increasing the difficulty to anticipate congestion effects. In this paper, we propose a new bilevel mixed integer programming formulation for the network maintenance scheduling problem, which relies on the enumeration of maintenance project combinations—patterns—to incorporate congestion effects within the scheduling process. We present a new branch-and-price algorithm that relies on customized branching and bounding rules, and a tailored column generation framework to price patterns. In addition, a statistical regression model is introduced to approximate congestion effects and provide approximate lower bounds on the formulations therein. The proposed branch-and-price algorithm is implemented on instances derived from realistic transport networks and is shown to be able to solve the network maintenance scheduling problem in a reasonable time using only a fraction of the patterns.
Optimality gap of experts' decisions in concrete delivery dispatching
Publisher: Elsevier BV
Date: 06-2015
Modelling the route choice behaviour under stop-&-go traffic for different car driver segments
Publisher: Elsevier BV
Date: 2019
Digitally transferable ownership of mobility-as-a-service systems using blockchain and smart contracts
Publisher: Informa UK Limited
Date: 13-01-2023
Systemic Evaluation of the HERO-Based Ramp Metering Algorithm Using Microsimulation: Demonstration Analysis Using a Sydney Motorway
Publisher: IEEE
Date: 09-2015
Solving Ready-Mixed Concrete Delivery Problems: Evolutionary Comparison between Column Generation and Robust Genetic Algorithm
Publisher: American Society of Civil Engineers
Date: 17-06-2014
A Hybrid Bilevel Model for the Optimal Shelter Assignment in Emergency Evacuations
Publisher: Wiley
Date: 04-05-2010
Freight transportation network design problem for maximizing throughput under uncertainty
Publisher: SAGE Publications
Date: 2009
DOI: 10.3141/2090-12
Abstract: A mathematical programming formulation is developed to determine the throughput of a freight transportation network. The impact of demand and capacity uncertainty on the throughput is systematically studied. Mathematical proofs are provided to show that accounting for capacity uncertainty by using a single-point expected value can lead to systematic overestimation of network throughput. This result is also valid for other measures, such as system capacity. Two s ling-based methodologies–independent random number and common random number–are provided to determine network design decisions in the presence of demand uncertainty. The s ling-based solution methods provide an approximate estimate of optimal solution and provide probabilistic bounds on the optimality gap. The presented methodologies are generic and can be applied even if different functional forms (nonlinear, nonconvex) are used to model various aspects of the freight transportation network. The numerical tests demonstrate that not accounting for capacity uncertainty can result in overestimation of system throughput of up to 40%. A common random number-based s ling strategy was found to significantly outperform the independent random number strategy for all the scenarios tested.
A safety assessment of mixed fleets with Connected and Autonomous Vehicles using the Surrogate Safety Assessment Module
Publisher: Elsevier BV
Date: 10-2019
DOI: 10.1016/J.AAP.2019.06.001
Abstract: The transportation network can provide additional utility by addressing the safety concerns on roads. On-road fatalities are an unfortunate loss of life and lead to significant costs for society and the economy. Connected and Autonomous Vehicles (CAVs), envisaged as operating with idealised safety and cooperation, could be a means of mitigating these costs. This paper intends to provide insights into the safety improvements to be attained by incrementally transitioning the fleet to CAVs. This investigation is done by constructing a calibrated microsimulation environment in Vissim and deploying the custom developed Virdi CAV Control Protocol (VCCP) algorithm for CAV behaviour. The CAV behaviour is implemented using an application programming interface and a dynamic linking library. CAVs are introduced to the environment in 10% increments, and safety performance is assessed using the Surrogate Safety Assessment Module (SSAM). The results of this study show that CAVs at low penetrations result in an increase in conflicts at signalised intersections but a decrease at priority-controlled intersections. The initial 20% penetration of CAVs is accompanied by a +22%, -87%, -62% and +33% change in conflicts at the signalised, priority, roundabout and DDI intersection respectively. CAVs at high penetrations indicate a global reduction in conflicts. A 90% CAV penetration is accompanied by a -48%, -100%, -98% and -81% change in conflicts at the signalised, priority, roundabout and DDI intersection respectively.
Dynamic lane reversal in traffic management
Publisher: IEEE
Date: 10-2011
Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges
Publisher: Elsevier BV
Date: 02-2017
Automated Intersection Control
Publisher: SAGE Publications
Date: 2011
DOI: 10.3141/2259-21
Abstract: Congestion is one of the biggest challenges faced by the transportation community congestion accounted for an estimated $87.2 billion in losses in 2007 alone. Transportation professionals need to go beyond capacity expansion projects and explore novel strategies to mitigate traffic congestion. Automated intersection management is a novel strategy that has the potential to greatly reduce intersection delay and improve safety. Although the implementation of such a system is contingent on the development of automated vehicles, competitions such as the Grand Challenge and Urban Challenge of the Defense Advanced Research Projects Agency have shown that this technology is feasible and will be available. Therefore, the development of the infrastructure and associated control methods required to exploit fully the benefits of such technology at the system level is critical. This research explores one such innovative strategy, an automated intersection control protocol based on a first-come, first-served (FCFS) reservation system. The FCFS reservation system was shown to reduce intersection delay significantly by exploiting the features of autonomous vehicles. Microscopic simulation experimental results showed that the FCFS reservation system significantly outperformed a traditional traffic signal in reducing delay.
A Dantzig-Wolfe Decomposition-Based Heuristic for Off-line Capacity Calibration of Dynamic Traffic Assignment
Publisher: Wiley
Date: 11-2011
Effect of road grade on networkwide vehicle energy consumption and ecorouting
Publisher: SAGE Publications
Date: 2014
DOI: 10.3141/2427-03
Abstract: Because of growing concern about the impact of emissions from the transport sector on global climate change, vehicle energy consumption is a factor of great interest to network planners. In addition, drivers are interested in reducing energy consumption and, thus, fuel costs. However, traditional models of vehicle energy consumption have neglected an important factor: road grade. This assumption has traditionally been supported by the idea that the energy consumed because of the road grade would be reflected in changes in speed and acceleration, but a demonstration of this on an aggregate network in a city of a realistic size has been difficult to show. This work demonstrated the impact of road grade on networkwide vehicle energy consumption by the integration of energy consumption equations based on road load equations, elevation data available from the Google Elevation advanced programming interface, and a dynamic traffic assignment model to capture the effect of user route choice. This work quantified the impact of the energy consumed because of road grades on two city networks, and the results indicate that the effects of grades should not be excluded from evaluations of vehicle energy consumption. In addition, the effects of eco-routing, in which drivers choose the shortest path that consumes the least amount of energy, were explored. The results for the city networks indicate that if drivers do not account for grades, they might choose a route that actually increases vehicle energy consumption. The proposed modeling tool is scalable and easily adaptable to different cities.
Integrating a computable general equilibrium model with the four-step framework
Publisher: Springer Science and Business Media LLC
Date: 29-06-2022
DOI: 10.1007/S11116-022-10276-X
Abstract: In the transport policy development process, four-step models are commonly used to estimate transport costs and flows based on representations of travel demands and networks. However, these models typically do not account for broader changes in the economy, which may significantly shift travel patterns in the case of larger transport projects. LUTI models are often applied to simulate changes in land-use patterns, and regional production function models have been used to estimate changes in production, but these methods rely on fixed economic parameters that may not capture the structural economic changes induced by large transport projects. In a separate line of development, computable general equilibrium (CGE) models, which simulate entire economies, have been increasingly applied to estimate the magnitude and distribution of economic impacts from transport improvements both spatially and through markets, including GDP and welfare. Some CGE models are linked with transport network models, but none incorporate detailed networks or generate a complete set of travel demands. This paper presents an integrated CGE and transport model that generates household and freight trips and simulates a detailed road network for different time periods, such that the transport submodel can be calibrated and run as a conventional transport model. The model provides a tool for the rapid strategic assessment of transport projects and policies when economic responses cannot be assumed to remain static. In the model, the CGE submodel simulates the behaviour of households and firms interacting in markets, where their behaviour takes trip costs into account. The model then generates trips as a derived demand from agent activities and assigns them to the road network according to user equilibrium, before feeding back trip costs to the CGE submodel. The model is then tested by simulating the WestConnex motorway project under construction in Sydney, with results showing significant increases in welfare for regions close to the improvements. Further development of the model is required to incorporate land-use and mode choice.
An Internet-based geographic information system that integrates data, models and users for transportation applications
Publisher: Elsevier BV
Date: 02-2000
Recalibration of the BPR function for the strategic modelling of connected and autonomous vehicles
Publisher: Informa UK Limited
Date: 28-02-2022
An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review
Publisher: MDPI AG
Date: 15-07-2021
DOI: 10.3390/SU13147925
Abstract: Rapid advances that improve flood management have facilitated the disaster response by providing first aid services, finding safe routes, maintaining communication and developing flood maps. Different technologies such as image processing, satellite imagery, synthetic imagery and integrated approaches have been extensively analysed in the literature for disaster operations. There is a need to review cutting-edge technologies for flood management. This paper presents a review of the latest advancements in the flood management domain based on image processing, artificial intelligence and integrated approaches with a focus on post-disaster. It answers the following research questions: (1) What are the latest developments in image processing for flood management in a post-disaster scenario? (2) What are the latest techniques for flood management based on artificial intelligence in a post-disaster scenario? (3) What are the existing gaps in the selected technologies for post-disaster? (4) How can the authorities improve the existing post-disaster management operation with cutting-edge technologies? A novel framework has been proposed to optimise flood management with the application of a holistic approach.
An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective
Publisher: MDPI AG
Date: 14-01-2022
DOI: 10.3390/BUILDINGS12010080
Abstract: The Hawkesbury-Nepean Valley, Australia’s longest coastal catchment, is spanned by a river system of more than 470 km, that runs from Goulburn to Broken Bay, covering a total area of over 2.2 million hectares. This region has remained prone to flood events, with considerable mortalities, economic impacts and infrastructural losses occurring quite regularly. The topography, naturally variable climatic conditions and the ‘bathtub’ effect in the region are responsible for the frequent flood events. In response, the Government at the national/federal, state and local level has focused on the design of efficient flood risk management strategies with appropriate evacuation plans for vulnerable communities from hospitals, schools, childcare and aged care facilities during a flood event. Despite these overarching plans, specialized response and evacuation plans for aged care facilities are critical to reducing the loss incurred by flood events in the region. This is the focus of this present paper, which reviews the history of flood events and responses to them, before examining the utilization of artificial intelligence (AI) techniques during flood events to overcome the flood risks. An early flood warning system, based on AI/Machine Learning (ML) strategy is being suggested for a timely decision, enhanced disaster prediction, assessment and response necessary to overcome the flood risks associated with aged care facilities within the Hawkesbury-Nepean region. A framework entailing AI/ML methods for identifying the safest route to the destination using UAV and path planning has been proposed for timely disaster response and evacuation of the residents of aged care facilities.
Reviewing the transport domain: an evolutionary bibliometrics and network analysis
Publisher: Springer Science and Business Media LLC
Date: 29-11-2017
Decision Support System for a Real –Time Field Service Engineer Scheduling Problem with Emergencies and Collaborations
Publisher: SAGE Publications
Date: 2015
DOI: 10.3141/2497-12
Abstract: Repeated replanning with a heuristic for solving a type of vehicle routing problem was used in a dynamic routing and scheduling problem. This problem occurs when field service engineers are assigned a sequence of jobs to attend. The jobs are geographically distributed, and not all jobs to be undertaken are known in advance of planning. This dynamic occurrence of job requests is stochastic. Jobs are assigned an emergency level, which is highest for repair jobs involving a person in danger. In addition, some jobs require two engineers such jobs are referred to as collaborative. The presented approach reschedules the pending jobs in an event-driven manner (i.e., every time a new repair job is required). The event-driven scheduling process ensures that jobs of high importance, with a high emergency level, are completed promptly. This approach to event-driven replanning will allow companies to plan for real-world scenarios with significantly fewer resources than are used in practice.
Stochastic and Dynamic Shipper Carrier Network Design Problem
Publisher: Springer Science and Business Media LLC
Date: 10-01-2009
An ensemble machine learning-based modeling framework for analysis of traffic crash frequency
Publisher: Wiley
Date: 31-07-2020
DOI: 10.1111/MICE.12485
A network traffic assignment model for autonomous vehicles with parking choices
Publisher: Wiley
Date: 30-07-2019
DOI: 10.1111/MICE.12486
Integrated Equilibrium Travel Demand Model with Nested Logit Structure
Publisher: SAGE Publications
Date: 2011
DOI: 10.3141/2254-09
Abstract: This paper presents an integrated travel demand modeling problem that accommodates the traditional travel demand modeling process in a nested logit structure for hierarchical travel choices, including travel, destination, mode, and route choices. The problem is characterized by a closed-form fixed-point model, which properly describes the asymmetric flow–cost effects of different transportation modes in an interactive traffic environment. The method of successive averages is adopted for problem solutions. The proposed model and solution method is further implemented to study travel demand distribution variations caused by model and data uncertainties. Specifically, the Monte Carlo simulation technique is adopted here to simulate and derive solution variations. Through a numerical ex le, the uncertainty analysis exhibits erse demand variation patterns and degrees across different demand aggregation levels under different uncertainty sources.
Integrated Maintenance and Expansion Planning for Transportation Network Infrastructure
Publisher: SAGE Publications
Date: 2011
DOI: 10.3141/2225-07
Abstract: Maintenance of existing road network infrastructure and expansion of networks with new facilities are two major investment categories in the transportation system. Road maintenance projects optimize the scheduling of maintenance activities so that pavement is in good condition and so that road expansion projects add extra capacity to a road network to improve mobility. The two problems are usually considered separately in practice however, an integrated approach to these two problems was proposed. The road maintenance problem and the road expansion problem were formulated together as a mixed-integer, nonlinear, bilevel optimization problem with the objective of optimizing overall system performance. A solution algorithm that was based on the generalized Benders decomposition theory, significantly relaxing computational complexity, was proposed. Finally, a numerical case study demonstrated the proposed problem and the algorithm used to solve it. The benefit of the integrated model over models that consider only maintenance or expansion projects was discussed.
Multiobjective traffic signal timing optimization using non-dominated sorting genetic algorithm
Publisher: IEEE
Date: 2003
A Spatio-Temporal autocorrelation model for designing a carshare system using historical heterogeneous Data: Policy suggestion
Publisher: Elsevier BV
Date: 08-2022
A linear model for the continuous network design problem
Publisher: Wiley
Date: 07-2006
Graph Neural Network for Robust Public Transit Demand Prediction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Comparing Delay Minimization and Emissions Minimization in the Network Design Problem
Publisher: Wiley
Date: 13-01-2012
Using existing site surveillance cameras to automatically measure the installation speed in prefabricated timber construction
Publisher: Emerald
Date: 30-03-2022
DOI: 10.1108/ECAM-04-2020-0281
Abstract: Measuring onsite productivity has been a substance of debate in the construction industry, mainly due to concerns about accuracy, repeatability and unbiasedness. Such characteristics are central to demonstrate construction speed that can be achieved through adopting new prefabricated systems. Existing productivity measurement methods, however, cannot cost-effectively provide solid and replicable evidence of prefabrication benefits. This research proposes a low-cost automated method for measuring onsite installation productivity of prefabricated systems. Firstly, the captured ultra-wide footages are undistorted by extracting the curvature contours and performing a developed meta-heuristic algorithm to straighten these contours. Then a preprocessing algorithm is developed that could automatically detect and remove the noises caused by vibrations and movements. Because this study aims to accurately measure the productivity the noise free images are double checked in a specific time window to make sure that even a tiny error, which have not been detected in the previous steps, will not been lified through the process. In the next step, the existing side view provided by the camera is converted to a top view by using a spatial transformation method. Finally, the processed images are compared with the site drawings in order to detect the construction process over time and report the measured productivity. The developed algorithms perform nearly real-time productivity computations through exact matching of actual installation process and digital design layout. The accuracy and noninterpretive use of the proposed method is demonstrated in construction of a multistorey cross-laminated timber building. This study uses footages of an already installed surveillance camera where the camera's features are unknown and then image processing algorithms are deployed to retrieve accurate installation quantities and cycle times. The algorithms are almost generalized and versatile to be adjusted to measure installation productivity of other prefabricated building systems.
The short-run and long-run equilibria for commuting with autonomous vehicles
Publisher: Informa UK Limited
Date: 20-07-2022
A chance-constrained based stochastic dynamic traffic assignment model: Analysis, formulation and solution algorithms
Publisher: Elsevier BV
Date: 12-2006
Linear Programming Formulation for Strategic Dynamic Traffic Assignment
Publisher: Springer Science and Business Media LLC
Date: 18-05-2013
Integrating Household Travel Survey and Social Media Data to Improve the Quality of OD Matrix: A Comparative Case Study
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Stochastic traffic assignment, Lagrangian dual, and unconstrained convex optimization
Publisher: Elsevier BV
Date: 09-2012
A genetic algorithm for bi-level linear programming dynamic network design problem
Publisher: Informa UK Limited
Date: 10-2009
Dynamic Programming Approach toward Optimization of Workforce Planning Decisions
Publisher: American Society of Civil Engineers (ASCE)
Date: 02-2018
Parametric search and problem decomposition for approximating Pareto-optimal paths
Publisher: Elsevier BV
Date: 09-2012
Introduction to Astrochemistry
Publisher: Springer Berlin Heidelberg
Date: 2003
Capacitated-Vehicle Routing Problem with Backhauls on Trees
Publisher: SAGE Publications
Date: 2011
DOI: 10.3141/2263-11
Abstract: The capacitated-vehicle routing problem (CVRP) is a classical problem that has been well studied by the transportation science community. A special case of the CVRP in which the network is constrained to have a tree structure (TCVRP) is studied here. Such tree networks arise when the cost of constructing and maintaining roads is much more than the routing cost—or when the transportation network consists of a main highway (e.g., Interstate system) and the customer locations are located off the highway. A new variant of the TCVRP, in which the customers are ided into two subsets—line haul and backhaul customers (TCVRPB)—is introduced in this paper. Line haul customers require delivery from the depot, and backhaul customers have supply that needs to be delivered to the depot. In any vehicle tour, line haul customers must be serviced before backhaul customers. The TCVRPB's relationship to the two-dimensional bin-packing problem is studied and, with the use of that problem, conditions for the lower bound on the problem are derived. An integer-programming (IP) formulation of the problem is presented. A two-approximation algorithm, which gives a feasible set of vehicle routes and customer assignments, is also presented. The problem is tested on two real-world treelike networks of varying sizes and on a randomly generated test network. The IP formulation works exceptionally well on small and medium-sized networks, and the gap between the approximation algorithm and the IP solution did not exceed 2% for nine of the 12 problem instances tested.
A maximum entropy-least squares estimator for elastic origin–destination trip matrix estimation
Publisher: Elsevier BV
Date: 11-2011
Disaster Region Coverage Using Drones: Maximum Area Coverage and Minimum Resource Utilisation
Publisher: MDPI AG
Date: 13-04-2022
Abstract: The purpose of this study is to develop a design for maximum area drone coverage in a post-disaster flood situation. When it comes to covering a disaster-region for monitoring and detection of the extent of damage and losses, a suitable and technically balanced approach is vital to achieving the best solution while covering the maximum affected area. Therefore, a mathematical optimisation model is proposed to effectively capture maximum images of the impacted region. The particle swarm optimisation (PSO) algorithm is used to solve the optimisation problem. Modern relief missions heavily rely on drones, specifically in the case of flooding, to capture the damage due to the disaster and to create roadmaps to help impacted people. This system has convincing results for inertia, exploration, exploitation, velocity, and determining the height of the drones to enhance the response to a disaster. The proposed approach indicates that when maintaining the flight height of the drone above 120 m, the coverage can be enhanced by approximately 34% compared with a flight height of 100 m.
Continuous Network Design Using Partial Linearized Subgradient Methods
Publisher: SAGE Publications
Date: 21-03-2023
DOI: 10.1177/03611981231157723
Abstract: Although demand management has shown to be a vital tool in managing congestion, many metropolitan planning organizations (MPOs) and departments of transportation (DOTs) still pursue network modification and capacity increase as a congestion relief and a mitigation measure. The network design problem (NDP) still has, therefore, an essential and significant role in shaping and sizing urban transportation networks. The literature has traditionally treated the NDP as a bi-level mathematical programming problem or a mathematical program with equilibrium constraints (MPEC). In the bi-level optimization setting, the problem is approached as a leader-follower problem in which the lower level is a user equilibrium (UE) assignment problem as the follower, and the upper level is the network sizing problem as the leader problem. NDP has long been known as a challenging problem, and many solution algorithms have been proposed to solve it. This study proposed an efficient solution algorithm for the continuous network design problem (CNDP). The solution algorithm has been shown empirically to solve the CNDP in a shorter time using partial linearized subgradient methods. The proposed method was applied to a small network that was traditionally used to evaluate the performance of solution algorithms versus Braess’s paradox. It was then applied to the Sioux Falls network as a well-known benchmark network to compare the results with previous studies. The proposed method has been shown to run much faster than all the previous studies reviewed in this paper with minimal degradation of accuracy (0.52% lower than the best solution).
Quantitative Approaches to Resilience in Transport Networks
Publisher: Informa UK Limited
Date: 05-10-2015
A Combinatorial user optimal dynamic traffic assignment algorithm
Publisher: Springer Science and Business Media LLC
Date: 04-2006
Optimal Information Location for Adaptive Routing
Publisher: Springer Science and Business Media LLC
Date: 10-09-2009
Mixed-Vehicular Aggregated Transportation Network Design Considering En-route Recharge Service Provision for Electric Vehicles
Publisher: Springer Science and Business Media LLC
Date: 26-05-2018
A High-Impact Journal for Novel Real-World Implementable Methodologies
Publisher: Wiley
Date: 22-12-2015
DOI: 10.1111/MICE.12120
Design and management strategies for mixed public private transportation networks: A meta-heuristic approach
Publisher: Wiley
Date: 05-2009
An endogenous lottery-based incentive mechanism to promote off-peak usage in congested transit systems
Publisher: Elsevier BV
Date: 02-2016
A combinatorial algorithm and warm start method for dynamic traffic assignment
Publisher: IEEE
Date: 10-2011
Solution Methods for Robust Pricing of Transportation Networks under Uncertain Demand
Publisher: Elsevier BV
Date: 10-2010
Modeling transit user stop choice behavior: Do travelers strategize?
Publisher: University of South Florida Libraries
Date: 09-2016
Congestion pricing under operational, supply-side uncertainty
Publisher: Elsevier BV
Date: 08-2010
Trialing Virtual Intercultural Learning With Australian and Hong Kong Allied Health Students to Improve Cultural Competency
Publisher: SAGE Publications
Date: 20-07-0001
The development of an Ontology for driving Context Modelling and reasoning
Publisher: IEEE
Date: 11-2016
A Computational Method for Estimating Travel Frequencies in Site Layout Planning
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-2016
Incorporating Multiskilling and Learning in the Optimization of Crew Composition
Publisher: American Society of Civil Engineers (ASCE)
Date: 05-2016
Deployment and Calibration Considerations for Large-Scale Regional Dynamic Traffic Assignment
Publisher: SAGE Publications
Date: 2016
DOI: 10.3141/2567-09
Abstract: Dynamic traffic assignment (DTA) has received increasing attention in recent years, and there are numerous ex les of practical implementations. This work adds to the literature by describing the ongoing experience of building the first large-scale simulation-based DTA model in Australia. The input data for the model are summarized, and an in-depth discussion and an analysis of model output and the calibration process are presented. Current results put 80% of the 322 calibration points spread across the network within an acceptable bound of error, but the project found that alternative metrics of network performance also must be considered so that other aspects of model realism are not neglected. The described DTA model could be used for evaluating important policy decisions and infrastructural development in the context of the macro- and mesoscale network operation. Additionally, this project is a proof of concept for the Australian region and may provide insight to practitioners interested in emerging areas of transport planning and traffic modeling.
A review on flood management technologies related to image processing and machine learning
Publisher: Elsevier BV
Date: 12-2021
Optimal long-term infrastructure maintenance planning accounting for traffic dynamics
Publisher: Wiley
Date: 10-2009
How Should Travel Demand and Supply Models Be Jointly Calibrated?
Publisher: SAGE Publications
Date: 15-05-2018
Abstract: Calibration is a critical aspect of model development that has long been recognized by researchers as a challenging issue. In particular, difficulties arise when the observed data used for calibration do not match the model output, which is the case in the majority of transport planning models. In the traditional calibration process, the origin–destination (OD) matrices are the key interface between demand and supply models, which could lead to issues when observed traffic link counts are used to update the OD matrix, causing a loss of key demand characteristics in the process. Developing a unified structure for modeling both demand and supply requires a calibration process that meets the requirements of both types of models, a serious issue which has received less attention in the literature. In this paper, the existing processes of developing and integrating demand and supply models are discussed and then examined using a case study in the Melbourne area. The numerical results show that the standard OD calibration procedure causes unrealistic changes in the OD matrix. Finally, some possible solutions to address the current limitations in development of a unified structure are discussed.
A Dynamic Route Choice Model Considering Uncertain Capacities
Publisher: Wiley
Date: 07-06-2011
Utilising Location Based Social Media in Travel Survey Methods
Publisher: ACM
Date: 03-11-2015
A Dual Variable Approximation Based Heuristic for Dynamic Congestion Pricing
Publisher: Springer Science and Business Media LLC
Date: 07-01-2010
Strategic dynamic traffic assignment incorporating travel demand uncertainty
Publisher: Informa UK Limited
Date: 25-09-2018
The price of uncertainty in pavement infrastructure management planning: An integer programming approach
Publisher: Elsevier BV
Date: 12-2011
Quantifying the benefit of responsive pricing and travel information in the stochastic congestion pricing problem
Publisher: Elsevier BV
Date: 03-2011
Sequential Meta-Heuristic Approach for Solving Large-Scale Ready-Mixed Concrete–Dispatching Problems
Publisher: American Society of Civil Engineers (ASCE)
Date: 2016
A network equilibrium analysis on destination, route and parking choices with mixed gasoline and electric vehicular flows
Publisher: Elsevier BV
Date: 06-2014
A dynamic evacuation network optimization problem with lane reversal and crossing elimination strategies
Publisher: Elsevier BV
Date: 05-2010
Geometric Congestion Detection Algorithms in the Speed-Flow and Flow-Density Spaces
Publisher: IEEE
Date: 09-2015
Transportation application of social media: Travel mode extraction
Publisher: IEEE
Date: 11-2016
The evacuation optimal network design problem: Model formulation and comparisons
Publisher: Informa UK Limited
Date: 04-2009
Prediction of Pavement Performance: Application of Support Vector Regression with Different Kernels
Publisher: SAGE Publications
Date: 2016
DOI: 10.3141/2589-15
Abstract: The pavement performance model is a basic part of the pavement management system. The prediction accuracy of the model depends on the number of effective variables and the type of mathematical method that is used for modeling the pavement performance. In this paper, the capability of the support vector machine (SVM) method is analyzed for predicting the future of the pavement condition. Five kernel types of SVM algorithm are formed and nine input variables of the proposed models are extracted from the range of effective variables on the pavement condition. The international roughness index is used as the pavement performance index. The results show that the Pearson VII Universal kernel can accurately predict pavement performance in its life cycle.
Implications of link-based equity objectives on transportation network design problem
Publisher: Springer Science and Business Media LLC
Date: 17-05-2018
Rapidex: A novel tool to estimate origin–destination trips using pervasive traffic data
Publisher: MDPI AG
Date: 10-10-2021
DOI: 10.3390/SU132011171
Abstract: A traffic assignment model is a critical tool for developing future transport systems, road policies, and evaluating future network upgrades. However, the development of the network and demand data is often highly intensive, which limits the number of cases where some form of the models are available on a global basis. These problems include licensing restrictions, bureaucracy, privacy, data availability, data quality, costs, transparency, and transferability. This paper introduces Rapidex, a novel origin–destination (OD) demand estimation and visualisation tool. Firstly, Rapidex enables the user to download and visualise road networks for any city using a capacity-based modification of OpenStreetMap. Secondly, the tool creates traffic analysis zones and centroids, as per the user-specified inputs. Next, it enables the fetching of travel time data from pervasive traffic data providers, such as TomTom and Google. With Rapidex, we tailor the genetic-algorithm (GA)-based metaheuristic approach to derive the OD demand pattern. The tool produces critical outputs such as link volumes, link travel times, OD travel times, average trip length and duration, and congestion level, which can also be used for validation. Finally, Rapidex enables the user to perform scenario evaluation, where changes to the network and/or demand data can be made and the subsequent impacts on performance metrics can be identified. In this article, we demonstrate the applicability of Rapidex on the network of Sydney, which has 15,646 directional links, 8708 nodes, and 178 zones. Further, the model was validated using the Household Travel Survey data of Sydney using the aggregated metrics and a novel project selection method. We observed that 88% of the time, the “estimated” and “observed” OD matrices identified the same project (i.e., the rapid process estimated the more intensive traditional approach in 88% of cases). This tool would help practitioners in rapid decision making for strategic long-term planning. Further, the tool would provide an opportunity for developing countries to better manage traffic congestion, as cities in these countries are prone to severe congestion and rapid urbanisation while often lacking the traditional models entirely.
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