Article
Economics
Xiaolei Wang, Jun Wang, Lei Guo, Wei Liu, Xiaoning Zhang
Summary: A new modeling approach for ridesharing user equilibrium (RUE) was proposed, which transforms the problem into a convex programming problem by redefining feasible driver trajectories and ridesharing market equilibrium conditions. The algorithm effectively avoids path enumeration and can be implemented on large networks, with theoretical analysis and numerical demonstrations on the impact of problem size on computational efficiency.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)
Article
Computer Science, Artificial Intelligence
Wenlong Zhu, Junting Zhang, Shunqiang Ye, Wanli Xiang
Summary: This paper investigates Braess Paradox under the bi-objective user equilibrium, introducing the definition and occurrence conditions of the paradox. Analytical properties of the bi-objective user equilibrium solutions and the conditions for the occurrence of Braess Paradox are explored on a classical Braess network. The study proves that the occurrence conditions of Braess Paradox are dependent upon link performance parameters and travel demand.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Economics
Tongfei Li, Min Xu, Huijun Sun, Jie Xiong, Xueping Dou
Summary: In this study, a generalized stochastic user equilibrium model is developed to analyze travelers' mode and route choice behavior in urban traffic systems with ridesharing programs. The proposed model considers travelers' heterogeneity in terms of car ownership and value of time, and their limited perceived information based on the stochastic user equilibrium principle. The decision-making problem of ridesharing compensation is also addressed, aiming to minimize total travel cost and vehicular air pollution emissions. A bi-objective optimization model and two single-objective optimization models are proposed, and a genetic algorithm is used to generate Pareto-optimal solutions. Numerical experiments demonstrate the effectiveness of the proposed model and algorithm in mitigating traffic congestion and pollution emissions.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2023)
Article
Computer Science, Hardware & Architecture
Mauro Passacantando, Giorgio Gnecco, Yuval Hadas, Marcello Sanguineti
Summary: This study introduces a new framework to investigate Braess' paradox, by utilizing cooperative games with transferable utility to evaluate the contribution of network resources to overall network performance.
Article
Computer Science, Interdisciplinary Applications
Xiang Zhang, Steven Travis Waller, Dung-Ying Lin
Summary: This study is the first in the literature to examine the Braess paradox considering parking behavior in the autonomous vehicle (AV) environment and model the network design problem for the autonomous transportation system (NDP-ATS). It shows the existence of two distinct Braess paradoxes in AV traffic networks and develops a bi-level programming model to avoid the deterioration caused by these paradoxes. The results highlight the efficacy of the modeling framework for infrastructure development and policy assessment for AV traffic.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2023)
Article
Management
Muqing Du, Heqing Tan, Anthony Chen
Summary: This paper explores a novel step size determination scheme, the Barzilai-Borwein step size, and applies it to solving the stochastic user equilibrium problem. Experimental results demonstrate that the BB step size outperforms current step size strategies in terms of computational efficiency and robustness.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Economics
Ruqing Huang, Lee D. Han, Zhongxiang Huang
Summary: This paper presents an unconventional equilibrium flow model to analyze travelers' route choice behavior, using path residual capacity as the quantity signal. The proposed model and solution algorithm are shown to be feasible and effective, and can capture route choice behaviors that have not been modeled in previous studies.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Transportation Science & Technology
Ze Zhou, Claudio Roncoli
Summary: On-demand ridesharing is seen as an effective way to reduce the number of vehicles needed and improve travel efficiency, potentially impacting urban traffic patterns significantly. A strategy that considers network traffic dynamics to avoid congestion can reduce passenger travel and waiting times, as demonstrated by simulation experiments.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Economics
Yuki Oyama, Yusuke Hara, Takashi Akamatsu
Summary: This study fills the research gap by establishing a Markovian traffic equilibrium assignment based on the network generalized extreme value (NGEV) model. The study provides the necessary theoretical developments for the NGEV equilibrium assignment, including the formulation and solution under the same path algebra as traditional models. Equivalent optimization formulations are also presented, allowing for efficient solution algorithms. The numerical experiments demonstrate the excellent convergence and complementary relationship of the proposed algorithms.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2022)
Article
Computer Science, Interdisciplinary Applications
Sam O'Neill, Ovidiu Bagdasar, Stuart Berry, Nicolae Popovici, Ramachandran Raja
Summary: This paper presents a method of considering multiple objectives simultaneously in selfish routing of network flow. By manipulating free parameters such as speed limits, the behavior of road users is coerced to reconcile conflicts between multiple objectives. The results show that small parameter adjustments can lead to solutions that Pareto dominate other solutions.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2022)
Article
Economics
Terry L. Friesz, Ke Han, Amir Bagherzadeh
Summary: This paper presents sufficient conditions for convergence of projection and fixed-point algorithms used to compute dynamic user equilibrium with elastic travel demand, without the need for strongly monotone increasing path delay operators. Instead, weakly monotone increasing path delay operators and strongly monotone decreasing inverse demand functions are assumed. The Lipschitz continuity of path delay is a mild regularity condition, allowing for convergence even with nonmonotone delay operators under certain conditions.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2021)
Article
Energy & Fuels
Quan Yuan, Yujian Ye, Yi Tang, Yuanchang Liu, Goran Strbac
Summary: This paper proposes a novel deep learning based surrogate modeling method for effective modeling and optimization of EV flows and charging demand. Case studies demonstrate that the proposed method outperforms existing methods in both solution accuracy and computational performance. Coordinated spatial optimization of EV flows and charging demand benefits the operation of both TN and PDN.
Article
Green & Sustainable Science & Technology
Le Zhang, Lijing Lyu, Shanshui Zheng, Li Ding, Lang Xu
Summary: Route game is an effective method to alleviate traffic congestion, but traditional methods based on potential functions are not suitable for real-time traffic. This paper proposes a matched Q-learning algorithm to generate approximate Nash equilibrium for the classic route game in real-time traffic.
Article
Transportation Science & Technology
Nam H. Hoang, Manoj Panda, Hai L. Vu, Dong Ngoduy, Hong K. Lo
Summary: This study focuses on a transport network with two types of users, selfish and cooperative. Selfish users aim to minimize their travel time, while cooperative users aim to maximize their class's aggregate throughput or minimize their total travel time. A new framework is proposed to study the route choices and network performance of the two classes of users.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Green & Sustainable Science & Technology
Ruixiao Sun, Xuanke Wu, Yuche Chen
Summary: This study proposes an integrated framework to analyze the efficiency and environmental benefits of ridesharing on a regional scale. The results show that ridesharing services can reduce traffic delays, improve arrival times, and decrease vehicle emissions and energy consumption on a city scale.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Engineering, Civil
Lin Liu, Shuo Feng, Yiheng Feng, Xichan Zhu, Henry X. Liu
Summary: This paper proposes a learning-based stochastic driving model for AV testing, which can reproduce trajectories more similar to human drivers and generate a naturalistic driving environment, showing significant importance for AV testing and evaluation.
TRANSPORTATION RESEARCH RECORD
(2022)
Article
Engineering, Civil
Shuo Feng, Yiheng Feng, Haowei Sun, Yi Zhang, Henry X. Liu
Summary: A customized testing scenario library for a specific CAV model is generated through an adaptive process to compensate for the performance dissimilarities and leverage each test of the CAV.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Yiheng Feng, Shihong Ed Huang, Wai Wong, Qi Alfred Chen, Z. Morley Mao, Henry X. Liu
Summary: This study proposes a comprehensive analysis framework to address the cybersecurity problem in the traffic signal control system with connected vehicle technology. A case study is conducted to demonstrate the impact of data spoofing attacks and the corresponding mitigation measures.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Economics
Jing-Peng Wang, Hai-Jun Huang
Summary: This paper proposes an analytical model to investigate the operating strategies of an on-demand ride service system that coordinates express and limousine services. It finds that customer preference for express over limousine plays a crucial role in the model solutions. Additionally, improving the quality of express service benefits both customers and drivers.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2022)
Article
Economics
Zhi Chen, Wen-Xiang Wu, Hai-Jun Huang, Hua-Yan Shang
Summary: This paper constructs a dynamic model to capture the interactions between peripheral traffic and downtown, and examines the competition for road resources in the downtown area. It proposes optimal time-varying cordon pricing schemes and analyzes the tolls to support the system optimum. The analytical results are verified through numerical experiments.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2022)
Article
Engineering, Civil
Xingmin Wang, Zachary Jerome, Chenhao Zhang, Shengyin Shen, Vivek Vijaya Kumar, Henry X. Liu
Summary: This paper proposes a trajectory data processing pipeline for urban traffic network applications, which includes matching, splitting, and distance extraction steps, as well as smoothing and filtering algorithms to reduce noise and errors. The processed data is used to calculate various mobility performance indices for comprehensive evaluations. The proposed methods are efficient, robust, and scalable, and can be applied to large-scale urban traffic networks.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Operations Research & Management Science
Ren-Yong Guo, Hai Yang, Hai-Jun Huang
Summary: We study a departure time choice model for commuters in a bottleneck system with heterogeneity in travel time and schedule delays. A Walrasian toll charge scheme is used to control traffic flows. The scheme is anonymous and does not require information on travel time and schedule delays. The theoretical analysis proves that the toll charge scheme can achieve the system optimum flow pattern. The distributions of traffic flows and toll charges at the system optimum state are shown analytically, and the scheme's effectiveness is examined through numerical analyses.
TRANSPORTATION SCIENCE
(2023)
Article
Multidisciplinary Sciences
Shuo Feng, Haowei Sun, Xintao Yan, Haojie Zhu, Zhengxia Zou, Shengyin Shen, Henry X. Liu
Summary: A critical bottleneck for autonomous vehicle development and deployment is the high costs required to validate safety in real-world driving. Researchers have developed an intelligent testing environment using AI-based agents to accelerate the safety validation process without bias. Their approach reduces testing time by orders of magnitude and can also be applied to other safety-critical autonomous systems.
Article
Engineering, Civil
Zhen Yang, Rusheng Zhang, Gaurav Pandey, Neda Masoud, Henry X. Liu
Summary: This work proposes a hierarchical vehicle behavior prediction framework that incorporates traffic signal information and models the interaction between vehicles. The framework predicts vehicle behaviors in two stages: discrete intention prediction and continuous trajectory prediction. It is designed to capture the difference among human drivers with parameterized driver characteristics.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Zhen Yang, Jun Ying, Junjie Shen, Yiheng Feng, Qi Alfred Chen, Z. Morley Mao, Henry X. Liu
Summary: This paper proposes a method to detect GPS spoofing attacks on connected vehicles (CVs) and autonomous vehicles (AVs) using domain knowledge in transportation and vehicle engineering. A computational-efficient driving model is constructed by learning from historical trajectories, and a statistical method is developed to measure the dissimilarities between observed and predicted trajectories for anomaly detection. The proposed method is validated on real-world datasets and shown to detect almost all attacks with low false positive and false negative rates.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Yujie Feng, Jiangtao Wang, Yasha Wang, Xu Chu
Summary: As a crucial part of public health system, population health monitoring plays a significant role in shaping health policies. However, the high cost of traditional data collection methods has led to the proposal of sparse-sampling-completion algorithms. Existing data-completion methods primarily focus on adjacent-spatial correlations, which may not accurately infer missing prevalence data in neighboring areas due to cost constraints. To address this problem, we propose a novel deep-learning-based prevalence inference model, SDA-GAIN (Spatial-attention and Demographic-augmented Generative Adversarial Imputation Network), which improves accuracy by learning health semantic space similarities between cross-space areas. SDA-GAIN utilizes a Transformer-based model to learn healthy semantic similarities and a GAN-based model for high-accuracy completion, with the addition of demographic data to enhance the model's ability in learning better health semantic representation through CNN. Extensive experiments demonstrate that SDA-GAIN outperforms other state-of-the-art approaches at low sampling rates (<30%), leading to significant cost savings. Moreover, the visualization of health semantic similarity learned by SDA-GAIN closely resembles real-life situations.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Article
Environmental Studies
Shaopeng Zhong, Ao Liu, Yu Jiang, Simon Hu, Feng Xiao, Hai-Jun Huang, Yan Song
Summary: This study analyzes the long-term effects of shared autonomous vehicles (SAVs) from the perspective of land use and transportation integration. Different SAV pricing scenarios are developed to explore the optimal pricing strategy for low carbon-oriented SAVs. Moreover, the study assesses the effect of vehicle electrification on vehicle emissions and energy consumption. The results show a significant reduction in PM2.5 emissions and energy consumption under an appropriate pricing strategy for SAVs, with further improvements achievable through vehicle electrification.
NPJ URBAN SUSTAINABILITY
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Jingxuan Yang, Honglin He, Yi Zhang, Shuo Feng, Henry X. Liu
Summary: This paper proposes an adaptive testing method using sparse control variates, which evaluates the performance of CAVs by adaptively utilizing testing results. It reduces estimation variance by adjusting testing results based on multiple linear regression techniques and optimizes regression coefficients for the CAV under test. The method applies sparse control variates to critical variables of testing scenarios and has been validated in high-dimensional overtaking scenarios, achieving a 30 times acceleration in the evaluation process.
2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
(2022)
Review
Transportation
Chuan-Zhi Xie, Tie-Qiao Tang, Peng-Cheng Hu, Hai-Jun Huang
Summary: The study examines new management strategies for the civil aircraft deplaning process in response to disease transmission. The proposed strategies reduce infection risks but sacrifice deplaning efficiency.
JOURNAL OF TRANSPORTATION SAFETY & SECURITY
(2022)
Article
Transportation
Liang Chen, Tie-Qiao Tang, Ziqi Song, Ren-Yong Guo, Hai-Jun Huang
Summary: The study found that children behave differently during emergency and non-emergency evacuations, with teachers playing a positive role in guiding children during emergencies. The level of emergency significantly affects the behavior and choices of children during evacuation.
JOURNAL OF TRANSPORTATION SAFETY & SECURITY
(2022)
