ORCID Profile
0000-0001-6743-0193
Current Organisations
Hong Kong University of Science and Technology
,
University of New South Wales
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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 4 Research Topics
ANZSRC Field of Research (FoR)
Road Transportation and Freight Services | Transportation and Freight Services
ANZSRC Socio-Economic Objective (SEO)
Road Freight | Road Passenger Movements (excl. Public Transport) |
Related Links
Publications
Quantum Fourier transform to estimate drive cycles
Publisher: Springer Science and Business Media LLC
Date: 13-01-2022
DOI: 10.1038/S41598-021-04639-0
Abstract: Drive cycles in vehicle systems are important determinants for energy consumption, emissions, and safety. Estimating the frequency of the drive cycle quickly is important for control applications related to fuel efficiency, emission reduction and improving safety. Quantum computing has established the computational efficiency that can be gained. A drive cycle frequency estimation algorithm based on the quantum Fourier transform is exponentially faster than the classical Fourier transform. The algorithm is applied on real world data set. We evaluate the method using a quantum computing simulator, demonstrating remarkable consistency with the results from the classical Fourier transform. Current quantum computers are noisy, a simple method is proposed to mitigate the impact of the noise. The method is evaluated on a 15 qubit IBM-q quantum computer. The proposed method for a noisy quantum computer is still faster than the classical Fourier transform.
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
Modeling the impact of vehicle-to-grid discharge technology on transport and power systems
Publisher: Elsevier BV
Date: 04-2022
Dynamic optimal vehicle relocation in carshare systems
Publisher: SAGE Publications
Date: 2016
DOI: 10.3141/2567-01
Abstract: One-way carsharing, providing users with more flexibility on returning stations, has attracted larger market demand than has the traditional round-trip service. However, the main challenge faced by a one-way carsharing system is the vehicle stock imbalance due to the uneven distribution of user demand. This study attempted to address this problem by proposing an optimization model that integrated with a discrete choice model. The model accounts for the interdependent relationship between carshare demand and supply. User demand is influenced by the availability of carshare vehicles meanwhile, conversely, the demand further changes vehicle availability as well as vehicle stock distribution. The model determines the optimal relocation decisions to maximize the profit for carshare operators that offer both one-way and round-trip services. This model was applied to the network of a carshare operator in Australia to evaluate the impacts of different pricing and capacity policies on the system profit. The results indicate that the one-way trip price has a more significant impact on system profit than does vehicle pod capacity. Further, the maximum profit occurs when the price of a one-way trip is around four times higher than that of the round trip.
Quantifying service-reliability-based day-to-day evolution of travel choices in public transit systems with smart transit card data
Publisher: Informa UK Limited
Date: 2021
Few-Shot traffic prediction based on transferring prior knowledge from local network
Publisher: Informa UK Limited
Date: 31-07-2023
An Integrated Supply-Demand Approach to Solving Optimal Relocations in Station-Based Carsharing Systems
Publisher: Springer Science and Business Media LLC
Date: 15-05-2019
One-way carsharing service design under demand uncertainty: A service reliability-based two-stage stochastic program approach
Publisher: Elsevier BV
Date: 03-2022
A Spatio-Temporal autocorrelation model for designing a carshare system using historical heterogeneous Data: Policy suggestion
Publisher: Elsevier BV
Date: 08-2022
Eliciting perceptions of travel time risk and exploring its impact on value of time
Publisher: Elsevier BV
Date: 10-2019
Mobile phone conversation distraction: Understanding differences in impact between simulator and naturalistic driving studies
Publisher: Elsevier BV
Date: 08-2019
DOI: 10.1016/J.AAP.2019.04.017
Abstract: A current issue within the driver distraction community centres around different findings regarding the impact of mobile phone conversation on driving found in driving simulators versus instrumented vehicles employed in real-world naturalistic driving studies (NDSs). This paper compares and contrasts the two types of studies and aims to provide reasons for the differences in findings that have been documented. A comprehensive review of literature and consultations with human factors experts highlighted that simulator studies tend to show degradation in driving performance, suggestive of increased crash risk as a result of mobile phone conversation. Whilst NDSs, at times, present data suggesting that mobile phone conversation distraction actually reduces crash risk. This study identifies that these differences may be attributed to behavioural hypotheses associated with driver self-regulation, arousal from cognitive loading, task displacement and gaze concentration - all of which need to be explicitly tested in future driving studies. Metric estimation and application was also revealed to be polarising results and the subsequent assessment of the crash risk. A common metric applied in this domain is the 'Odds Ratio', particularly prevalent in NDSs. This study presents a detailed investigation into the assumptions and application of the Odds Ratio which revealed the potential for over- and under-estimation of the metric depending on the core data and s ling assumptions. Furthermore, this research presents a comparative analysis of select driving simulator studies and an NDS considering only driving behaviour data as a means to consistently compare the findings of both methodologies. The findings from this investigation implores the need for greater consistency in the application of analysis methods and metrics across both simulator and NDSs. Improvements can yield a more robust platform to systematically compare and interpret data across both approaches, ultimately leading to enhanced planning and safety regarding mobile phone use while driving.
A Spatial Hazard-Based analysis for modelling vehicle selection in station-based carsharing systems
Publisher: Elsevier BV
Date: 11-2016
Characterizing car-following behaviors of human drivers when following automated vehicles using the real-world dataset
Publisher: Elsevier BV
Date: 07-2022
DOI: 10.1016/J.AAP.2022.106689
Abstract: As the market penetration rate of automated vehicles (AVs) increases, there will be a transition period when the traffic stream is composed of both AVs and human-driven vehicles (MVs) in the near future. However, the interactions between MVs and AVs, especially whether MVs will behave differently when following AVs compared to following MVs, have not been fully understood. Previous studies in this field mainly conducted traffic/numerical simulations or field experiments to investigate human drivers' behavior changes, but these approaches all have critical drawbacks such as simplified driving environments and limited s le sizes. To fill in the knowledge gap, this study uses the high-resolution (10 Hz) Waymo Open Dataset to reveal differences in car-following behaviors between MV-following-AV and MV-following-MV cases. Driving volatility measures, time headways and time-to-collision (TTC) are adopted to quantify and compare MV-following-AV and MV-following-MV interactions. The principal component analysis (PCA) is applied on the high-dimensional feature space, followed by the hierarchical clustering on the dimension-reduced feature set to categorize MV driving styles when following AVs. The comparison results indicate that MV-following-AV events have lower driving volatility in terms of velocity and acceleration/deceleration, smaller time headways and higher TTC values. Furthermore, the clustering results reveal that human drivers when following AVs exhibit four different car-following styles: high-velocity-non-aggressive, high-velocity-aggressive, low-velocity-non-aggressive, and low-velocity-aggressive. These findings highlight the vital importance of taking into account the heterogeneity of MV-following-AV behaviors when designing mixed traffic control algorithms and can be beneficial for AV fleet operators to improve their algorithms.
Mechanism design for Mobility-as-a-Service platform considering travelers’ strategic behavior and multidimensional requirements
Publisher: Elsevier BV
Date: 07-2023
Panel data analysis of Chinese households’ car ownership and expenditure patterns
Publisher: Elsevier BV
Date: 10-2023
Optimal pricing and fleet management for shared electric vehicle in coupled power and transport networks
Publisher: Elsevier BV
Date: 08-2022
Strategic collaboration between land owners and charging station operators: Lease or outsource?
Publisher: Elsevier BV
Date: 12-2022
On integrating carsharing and parking sharing services
Publisher: Elsevier BV
Date: 12-2020
Pre-Disaster Resilient Road Network Investment Strategy With Uncertainty Quantification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Risk of automated driving: Implications on safety acceptability and productivity
Publisher: Elsevier BV
Date: 04-2019
DOI: 10.1016/J.AAP.2019.02.005
Abstract: Autonomous Vehicles have captured the imagination of our society and have promised a future of safe and efficient mobility. However, there is a need to understand behaviour and its consequences in the use of autonomous vehicles. Using paradigms of behavioural and experimental economics, we show that risk attitudes play a role in acceptability of autonomous vehicles, productivity in autonomous vehicles and safety under risk of failures of autonomous systems. We found that risk attitudes and age have a significant impact on these. We believe these findings will help provide guidance to insurance agencies, licensing, vehicle design, and policies around automated vehicles.
A Clustering Algorithm for Bi-Criteria Stop Location Design with Elastic Demand
Publisher: Wiley
Date: 31-08-2016
DOI: 10.1111/MICE.12162
Modeling the Effects of Autonomous Vehicles on Human Driver Car-Following Behaviors Using Inverse Reinforcement Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Understanding the impact of occasional activities on travelers’ preferences for mobility-as-a-service: A stated preference study
Publisher: Elsevier BV
Date: 10-2023
An analysis of carsharing vehicle choice and utilization patterns using multiple discrete-continuous extreme value (MDCEV) models
Publisher: Elsevier BV
Date: 09-2017
Related Organisations
Related Funding Activities
Discovery Projects - Grant ID: DP210103138
Start Date: 05-2021
End Date: 04-2024
Amount: $516,500.00
Funder: Australian Research Council
View Funded Activity