ORCID Profile
0000-0002-2141-0389
Current Organisation
KTH Royal Institute of Technology
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 04-2015
Publisher: Springer Science and Business Media LLC
Date: 05-06-2020
Publisher: SAGE Publications
Date: 12-04-2022
DOI: 10.1177/03611981221086645
Abstract: Short-term passenger flow prediction under planned events is important to reduce passenger delay and ensure operational safety in metro systems. However, most studies make predictions under normal conditions. The study proposes a naïve Bayes transition model for short-term passenger flow prediction under planned events. The target prediction scenario identification is modeled as a binary classification problem using naïve Bayes. The sub-models are developed using gradient boosting decision tree (GBDT) and deep learning (DL) models for normal and planned event scenarios with predictor variables tailored to different passenger demand patterns. The sub-predictor from GBDT or DL is selected based on the inferred prediction scenario. The case study uses automatic fare collection (AFC) data of Shanghai and Hong Kong metro systems. The results show that the proposed model outperforms other representative in idual and fusion models. The results also highlight the effectiveness of the predictive transition mechanism between the normal and planned events and also the event information representation.
Publisher: SAGE Publications
Date: 11-09-2021
DOI: 10.1177/03611981211037553
Abstract: Unplanned events present significant challenges for operations and management in metro systems. Short-term ridership prediction can help agencies to better design contingency strategies under unplanned events. Though many short-term prediction methods have been proposed in the literature, most studies focused on typical situations or planned events. The study develops methods for the short-term metro ridership prediction under unplanned events. It explores event impact representation mechanisms and deals with the imbalanced data training problem in building the prediction model under unplanned events. Typical machine learning and deep learning methods are developed for exploration. A large-scale automatic fare collection (AFC) dataset and event record data for a heavily used metro system are used for empirical studies. The analysis found that the same type of unplanned event shares a similar and consistent demand change pattern (with respect to the demand under typical situations) at the station level. The synthetic minority overs ling technique (SMOTE) can enrich the ridership observations under unplanned events and generate a balanced dataset for model training. Given the occurrence of unplanned events, the results show that a combination of demand change ratio and the SMOTE overs ling technique enables the prediction models to learn the impact of unplanned events and improve the prediction accuracy under unplanned events. However, the overs ling methods (i.e., SMOTE and replication) slightly deteriorate the prediction accuracy for ridership under normal conditions. The findings provide insights into mechanisms for disruption impact representation and overs ling imbalanced data in model training, and guide the development of models for short-term prediction under unplanned events.
Publisher: SAGE Publications
Date: 30-04-2022
DOI: 10.1177/03611981221088221
Abstract: Unplanned disruptions bring challenges to urban railway system operations because of their impacts on safety, operation efficiency, and service quality. Identifying the contributing factors of operation delays and affected areas under unplanned disruptions is critical for agencies to make effective and informed management decisions. Despite its importance, few studies have been reported on unplanned disruption analysis in urban railway systems or they have been limited in their analysis and modeling because of the lack of disruption data. This paper collects a complete set of unplanned disruption data for 7 years in Hong Kong and explores important factors affecting operation delays and affected areas. Quantile regression (QR) models are developed to explore the causes of operation delays under unplanned disruptions. The significant factors include the time of day, weather condition, signal control system (moving/fixed block), line types (urban/suburban), line operation direction, disruption location (underground/ground/elevated), the number of affected stations, and disruption types (e.g., tracing, locomotive and rolling stock, passengers, and operation). A binary logit model is developed to explore the variables contributing to the affected areas (single or multiple stations). The results show that the affected area is significantly influenced by the signal control system, line types, line operation direction, disruption location, terminal/departure station involved or not, transfer station involved or not, and disruption types. The findings provide useful insights into unplanned disruptions and support the development of engineering and policy countermeasures to prevent and mitigate unplanned disruption effects on operations and services.
Publisher: Trans Tech Publications, Ltd.
Date: 09-2011
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.97-98.964
Abstract: In the actual process of navigation, signal interference and inaccurate tracking model can cause inaccurate positioning of navigation. Various filtering algorithms are needed to filter out the interference and improve precision.First of all, this paper elaborates and compares Kalman filtering algorithms and H∞ filtering algorithm, which are both applied in filtering GNSS navigation data. After that, some datas are selected from the vehicle navigation data as the source datas. In the meantime, depend on the ideal model, factors are added into it in consideration of practical application. As a result, the mathematical models for Kalman and H∞ are established. According to the filter algorithm and the mathematic model, simulation programs and their flow charts of Kalman filtering algorithm and H∞ filtering algorithm are all accomplished. Finally, it shows the results of simulation and analyses the problems appeared.
Publisher: Elsevier BV
Date: 03-2022
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Elsevier
Date: 2019
Publisher: Elsevier BV
Date: 08-2018
Publisher: SAGE Publications
Date: 23-09-2022
DOI: 10.1177/03611981211045370
Abstract: Data quality is the foundation of data-driven applications in transportation. Data problems such as missing and invalid data could sharply reduce the performance of the methods used in these applications. Although there exist plenty of studies related to data quality issues, they only focus on missing or invalid data caused by infrastructure failures (e.g., loop detector malfunction). In general, there is a lack of attention to data quality issues from insufficient data management. This paper proposes a tensor decomposition based framework to tackle a specific missing data problem which occurs when the machine-station dictionary of an automated fare collection system database is incomplete. In such cases, there is a large amount of loss of origin/destination information as the affected machines are not linked to any station. Consequently, all associated transactions may miss the origin/destination information. The proposed framework recovers the dictionary by capturing features of the passenger flow passing through the unlinked fare machine. Evaluation results show that the proposed approach could recover the missing data with high accuracy even when several fare machines are not linked to a station. The framework could also support other beneficial applications.
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Elsevier BV
Date: 12-2019
Publisher: SAGE Publications
Date: 11-07-2023
DOI: 10.1177/03611981231184237
Abstract: Providing real-time crowding information in urban railways would enable informed travel decisions and encourage cooperative behavior of passengers, as well as improve operating efficiency and safety. However, the problem of real-time crowding prediction is not trivial because of the unavailability of ground-truth crowding data, particularly for the direct impact of crowding on passengers (e.g., denied boarding on platforms). This paper proposes a data-driven method for real-time denied boarding prediction in urban railway systems using automated fare collection (AFC) and automated vehicle location (AVL) data. It predicts the denied boarding probability distribution or its derived metrics as a function of explanatory variables, including demand, operations, and incident-related factors. The method is validated through a case study covering 18 months on Hong Kong Mass Transit Railways. The results highlight the model’s accurate and robust performance in predicting denied boarding on platforms using purely AFC and AVL data (e.g., an average of 6%–7% error) under both recurrent and non-recurrent situations, in which transfer demand-related factors contribute most to the prediction. The prediction model outputs can support proactive operations control and management, as well as provision of customer information without relying on expensive and time-consuming data collection efforts. Customer information, for ex le, can include the average number of trains to wait before boarding or the average waiting time on the platform.
Publisher: Elsevier BV
Date: 03-2023
Publisher: Elsevier BV
Date: 08-2023
Publisher: SAGE Publications
Date: 2015
DOI: 10.3141/2533-03
Abstract: Travel time reliability is an important aspect of bus service quality. Despite a significant body of research on private vehicle reliability, little attention has been paid to bus travel time reliability at the stop-to-stop link level on different types of roads. This study aims to identify and quantify the underlying determinants of bus travel time reliability on links of different road types with the use of supply and demand data from automatic vehicle location and smart card systems collected in Brisbane, Australia. Three general bus-related models were developed with respect to the main concerns of travelers and planners: average travel time, buffer time, and coefficient of variation of travel time. Five groups of alternative models were developed to account for variations caused by different road types, including arterial road, motorway, busway, and central business district. Seemingly unrelated regression equations estimation were applied to account for cross-equation correlations across regression models in each group. Three main categories of unreliability contributory factors were identified and tested in this study, namely, planning, operational, and environmental. Model results provided insights into these factors that affect bus travel time and its variability. The most important predictors were found to be the recurrent congestion index, traffic signals, and passenger demand at stops. Results could be used to target specific strategies aimed at reducing unreliability on different types of roads.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 26-08-2023
DOI: 10.1007/S40864-023-00197-Y
Abstract: Many urban rail systems operate near capacity given the rapid increase in passenger demand, and unplanned disruptions are unavoidable. From a passenger perspective, the duration of trip delays is a major concern, and passenger trip delays may be longer than the train delays. Several studies have focused on predicting train delays, but the research on the duration of the disruption impacts on passenger trips is limited given that the duration is not observed directly. This paper proposes a probabilistic method to estimate the disruption impact duration using smartcard data, explores statistical and machine learning models to predict the duration of impacts on passengers, and identifies influencing factors including incident characteristics, operating conditions, infrastructure, external factors, and demand. The results highlight that prediction accuracies are acceptable for multiple linear regression, accelerated failure time, and random forest models. Disruptions caused by power failures have longer impact durations than other causes, followed by platform screen doors. The fixed block signaling system leads to a larger disruption duration than the moving block system. The study provides, for the first time, a data-driven approach to understanding the duration of the impact of disruptions on passenger trips using smartcard data which can facilitate timely and informed decision-making under unplanned disruptions.
Publisher: Hindawi Limited
Date: 2014
DOI: 10.1155/2014/468563
Abstract: Bus service reliability has become a major concern for both operators and passengers. Buffer time measures are believed to be appropriate to approximate passengers' experienced reliability in the context of departure planning. Two issues with regard to buffer time estimation are addressed, namely, performance disaggregation and capturing passengers’ perspectives on reliability. A Gaussian mixture models based method is applied to disaggregate the performance data. Based on the mixture models distribution, a reliability buffer time (RBT) measure is proposed from passengers’ perspective. A set of expected reliability buffer time measures is developed for operators by using different spatial-temporal levels combinations of RBTs. The average and the latest trip duration measures are proposed for passengers that can be used to choose a service mode and determine the departure time. Using empirical data from the automatic vehicle location system in Brisbane, Australia, the existence of mixture service states is verified and the advantage of mixture distribution model in fitting travel time profile is demonstrated. Numerical experiments validate that the proposed reliability measure is capable of quantifying service reliability consistently, while the conventional ones may provide inconsistent results. Potential applications for operators and passengers are also illustrated, including reliability improvement and trip planning.
Publisher: Elsevier BV
Date: 02-2014
Publisher: Elsevier BV
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: Wiley
Date: 06-2016
DOI: 10.1002/ATR.1314
Publisher: SAGE Publications
Date: 19-06-2019
Abstract: Monitoring rail transit system performance is important for effective operations planning. The number of times passengers are denied boarding is becoming a key measure of the impact of near-capacity operations on customers and is fundamental for calculating other performance metrics, such as expected waiting time for service. This paper reviews existing methods and proposes a denied boarding probability distribution inference method for closed Automated Fare Collection (AFC) urban rail systems. Using AFC (tap-in and tap-out) and Automated Vehicle Location (AVL) data, the method relaxes some of the limitations of existing approaches. The problem is modeled using a mixture distribution framework that incorporates a priori structural information. It is data-driven and requires neither observations of denied boardings, nor assumptions about access/egress time distributions. Also, for comparison purposes, the paper presents an event-based deterministic transit assignment model with explicit capacity constraints. While the network assignment works at the network level and requires train capacity, the mixture model works at the station level, requires no external parameters, and can be easily applied to any station and for any time period. A case study illustrates the application of the proposed methods using actual data and compares the results against existing methods, and also survey data. The results demonstrate the mixture model’s robustness and applicability for monitoring denied boarding.
Publisher: Elsevier BV
Date: 11-2023
Publisher: SAGE Publications
Date: 2017
DOI: 10.3141/2652-02
Abstract: This research considers how one might deduce the set of attractive routes for public transit passengers, as part of a boarding strategy, from passengers’ daily choices of which path to take. From the set of attractive routes (attractive set), a public transit passenger may be assumed to board the first service that arrives at the origin stop. To determine the attractive set, a statistical inference algorithm was developed and tested using a public transit farecard data set. The proposed algorithm was developed from an exact method that investigates the distribution of repeated boarding transactions in a farecard data set and infers the so-called steady-state strategies over the observation period. The advantage of the proposed method is in identifying and eliminating occasional and tried-but-rejected path alternatives recorded during the study period. The method was tested in a case study using 6 months of farecard transactions of regular passengers for multiple major origin–destination pairs in the transit network of Brisbane, Australia. Some behavioral aspects of passengers’ attractive routes are also reported and discussed.
Publisher: SAGE Publications
Date: 2017
DOI: 10.3141/2652-03
Abstract: Transit agencies increasingly deploy planning strategies to improve service reliability and real-time operational control to mitigate the effects of travel time variability. The design of such strategies can benefit from a better understanding of the underlying causes of travel time variability. Despite a significant body of research on the topic, findings remain influenced by the approach used to analyze the data. Most studies use linear regression to characterize the relationship between travel time reliability and covariates in the context of central tendency. However, in many planning applications, the actual distribution of travel time and how it is affected by various factors is of interest, not just the condition mean. This paper describes a quantile regression approach to analyzing the impacts of the underlying determinants on the distribution of travel times rather than its central tendency, using supply and demand data from automatic vehicle location and farecard systems collected in Brisbane, Australia. Case studies revealed that the quantile regression model provides more indicative information than does the conditional mean regression method. Moreover, most of the coefficients estimated from quantile regression are significantly different from the conditional mean–based regression model in terms of coefficient values, signs, and significance levels. The findings provide information related to the impacts of planning, operational, and environmental factors on speed and its variability. On the basis of this information, transit designers and planners can design targeted strategies to improve travel time reliability effectively and efficiently.
Publisher: IEEE
Date: 05-2011
Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
Date: 03-2023
Abstract: This paper proposes an ex post path choice estimation framework for urban rail systems using an aggregated time-space hypernetwork approach. We aim to infer the actual passenger flow distribution in an urban rail system for any historical day using the observed automated fare collection (AFC) data. By incorporating a schedule-based dynamic transit network loading (SDTNL) model, the framework captures the crowding correlation among stations and the interaction between the path choice and passenger left behind, which is important for the path choice estimation in a “near-capacity” operated urban rail system. The path choice estimation is formulated as an optimization problem, which aims to minimize the difference between the model-derived and observed information with path choice parameters as decision variables. The original problem is intractable because of nonlinear (logit model) and nonanalytical (SDTNL) constraints. A solution procedure is proposed to decompose the original problem into three tractable subproblems, which can be solved efficiently. Solving the decomposed problem is equivalent to finding a fixed point. We prove that the solution to the original problem is the same as the decomposed problem (i.e., the fixed point) when passenger path choices follow the predefined behavior model. If this condition does not hold, the solution of the original problem is proved to be an “almost fixed point” for the decomposed problem. The model is validated using both synthetic and real-world AFC data from a major urban railway system. The analysis with synthetic data validates the model’s effectiveness in estimating path choice parameters and left behind probabilities, which outperforms state-of-art simulation-based optimization methods and probabilistic models in both accuracy and efficiency. The analysis using actual data shows that the estimated path shares are more reasonable than the baseline uniform path shares and survey-derived path shares. The model estimation is robust to different initial parameter values and AFC data from various dates. Funding: The project is partially funded by the TRENoP Strategic Research funding at KTH. Supplemental Material: The online appendices are available at 0.1287/trsc.2022.1177 .
Publisher: Elsevier BV
Date: 04-2019
Publisher: Elsevier BV
Date: 2012
Publisher: Hindawi Limited
Date: 27-02-2021
DOI: 10.1155/2021/5597130
Abstract: Transit network simulation models are often used for performance and retrospective analysis of urban rail systems, taking advantage of the availability of extensive automated fare collection (AFC) and automated vehicle location (AVL) data. Important inputs to such models, in addition to origin-destination flows, include passenger path choices and train capacity. Train capacity, which has often been overlooked in the literature, is an important input that exhibits a lot of variabilities. The paper proposes a simulation-based optimization (SBO) framework to simultaneously calibrate path choices and train capacity for urban rail systems using AFC and AVL data. The calibration is formulated as an optimization problem with a black-box objective function. Seven algorithms from four branches of SBO solving methods are evaluated. The algorithms are evaluated using an experimental design that includes five scenarios, representing different degrees of path choice randomness and crowding sensitivity. Data from the Hong Kong Mass Transit Railway (MTR) system is used as a case study. The data is used to generate synthetic observations used as “ground truth.” The results show that the response surface methods (particularly constrained optimization using response surfaces) have consistently good performance under all scenarios. The proposed approach drives large-scale simulation applications for monitoring and planning.
Publisher: Institution of Engineering and Technology (IET)
Date: 24-10-2022
DOI: 10.1049/ITR2.12300
Abstract: Reinforcement learning (RL)‐based models have been widely studied for traffic signal control with objectives, such as minimizing vehicle delay and queue length, maximizing vehicle throughput, and improving road safety, through tailored reward designs. Despite the advancements in RL‐based signal control models for car traffic, limited research focused on multimodal traffic (e.g. car, bus, pedestrian). The simplified environment of unimodal traffic restrains these models from applications in real‐world cases. In this paper, the authors propose an RL‐based human‐centric multimodal deep (HMD) traffic signal control method to coordinate multimodal traffic at an intersection, with the objective of minimizing the waiting time per capita for multiple traffic modes by taking consideration of the mode capacity to ensure social equity. The method is validated in the simulation of urban mobility (SUMO) simulation environment using real traffic data. The results show the superior performance of HMD over vehicle‐centric (VC) RL‐based methods and traditional signal control schemes. HMD reduces the waiting time per capita by up to 19.2% and 6.3% compared with the fixed timing and VC RL‐based methods. In addition, the experiment results show that the HMD method assigns a higher priority to public transport over low‐occupancy travel modes in passing through intersections compared with unimodal traffic signal control strategies.
Publisher: Springer Science and Business Media LLC
Date: 10-01-2022
Publisher: Springer Berlin Heidelberg
Date: 2012
Location: United States of America
No related grants have been discovered for Zhenliang Ma.