Article
Transportation Science & Technology
Tingting Xie, Yang Liu
Summary: This paper investigates the impacts of fully connected and autonomous vehicles (CAVs) on vehicle market penetration and travelers' route choices from the perspective of transportation planning. A two-sided market equilibrium model is proposed, taking into account the interaction between the vehicle market and the road traffic equilibrium market. The model considers factors such as market penetration, information quality, and spatial distribution of congestion, which are all endogenously determined. Through numerical experiments, it is found that CAV technology and high-quality information have a greater effect on travel time savings for long trips and congested networks.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Transportation Science & Technology
Shuang Yang, Jianjun Wu, Huijun Sun, Yunchao Qu, David Z. W. Wang
Summary: This study proposes an integrated framework combining pricing and relocation strategies to help carsharing companies determine their optimal operational strategies. A game-theoretical multi-leader-follower model is used to maximize profit and minimize disutility.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Transportation Science & Technology
Hao Guo, Yao Chen, Yang Liu
Summary: This paper explores the optimization of operational decisions for shared autonomous vehicles (SAVs) and tests the proposed solution approaches with a case study in Singapore. Results demonstrate that SAV systems can achieve higher performance under elastic demand through active relocation activities, and a higher level of service can lead to increased operating profits if users are more sensitive to service quality.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Transportation Science & Technology
Qingyun Tian, Yun Hui Lin, David Z. W. Wang, Yang Liu
Summary: This paper studies the optimal planning of public transit services with modular vehicles and proposes two solution methods. A case study is conducted on the proposed Singapore DART line. The results show that modular-vehicle transit service can significantly reduce operating costs and passengers' travel time costs, providing a useful tool for determining optimal operation strategies for future transit service systems.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Transportation Science & Technology
Ruixue Gu, Mark Poon, Zhihao Luo, Yang Liu, Zhong Liu
Summary: This paper proposes an efficient hierarchical solution evaluation method for a general VRPD problem with multiple visits (VRPD-MV), which accelerates the feasibility evaluation of a solution and reduces the time complexity to O(1). The computational results show a positive impact of powerful drones on reducing solution costs, and the proposed algorithm outperforms a state-of-the-art algorithm on the multi-visit traveling salesman problem with multi-drones in terms of solution qualities and computational times required.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Transportation Science & Technology
Zhihong Guo, David Z. W. Wang, Danwei Wang
Summary: With the emergence of automated vehicles, the future traffic system is expected to consist of a mix of self-driving autonomous vehicles (AVs) and human-driven conventional vehicles. Therefore, it is crucial to propose new traffic management measures to handle this future traffic system. This study aims to utilize the controllable property of AVs' routing choices to develop a daily routing allocation scheme for a certain number of autonomous vehicles, in order to achieve the desired traffic state in the mixed traffic system.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Engineering, Electrical & Electronic
Wenbin Zhang, Zihao Tian, Lixin Tian, David Z. W. Wang, Yi Yao
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2022)
Article
Engineering, Civil
Hui Zhao, Willy Gunardi, Yang Liu, Christabel Kiew, Teck-Hou Teng, Xiao Bo Yang
Summary: This study develops a clustering-based machine learning model to predict the duration of traffic incidents. Compared to traditional fixed-cluster models, this model demonstrates superior and diverse prediction performance. Additionally, the study analyzes the influence of different variables on traffic incident duration using a random forest feature importance function.
JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS
(2022)
Article
Operations Research & Management Science
Yao Chen, Yang Liu
Summary: This study investigates an integrated optimization problem for shared autonomous electric vehicle systems. It determines the long-term charging facility deployment at the planning level and optimizes vehicle assignment, relocation, and charging decisions at the operational level. A two-stage stochastic integer program is formulated to capture demand uncertainty and an event-activity space-time-battery network is proposed for optimal decision making. The proposed approach is tested on a large-scale case in Shanghai City and shows efficient and high-quality solutions.
TRANSPORTATION SCIENCE
(2023)
Article
Green & Sustainable Science & Technology
Jie Zhang, Meng Meng, David Z. W. Wang, Li Zhou, Linghui Han
Summary: This paper investigates the problem of bike allocation in a competitive bike sharing market. A continuum approximation (CA) approach is used to handle computational challenges by assuming that allocation points and user demand are continuously distributed in a two-dimensional region. Bike sharing companies bear allocation and bike depreciation costs while earning revenue from fare collection. User's choice of bike service depends on walking distance and bike quality preference. The demand elasticity is considered in relation to the density of allocation points. A leader-follower Stackelberg competition model is developed to derive the optimal allocation strategy for the market leader. Numerical studies are conducted for both hypothetical and real cases to examine the impact of parameters on model performance and demonstrate the application of the proposed model in decision making.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Transportation Science & Technology
Qingyun Tian, Yun Hui Lin, David Z. W. Wang
Summary: This paper focuses on the operation design of a future public transit service adopting modular vehicles. The unique feature of modular vehicles allows for assembling and disassembling operations along each trip to dynamically adjust the vehicle formation at stations. A mathematical model is proposed to determine the optimal scheduling and modular vehicle formation, considering time-dependent travel demand and module availability. The model is solved using exact reformulation techniques and a two-step heuristic approach, showing the validity and efficiency of the formulation and solution methods. It is found that modular transit services have remarkable advantages in reducing both operator's and passengers' costs.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Engineering, Electrical & Electronic
Wenbin Zhang, Zihao Tian, Lixin Tian, David Z. W. Wang
IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE
(2023)
Article
Transportation
Yuanyuan Wu, David Z. W. Wang, Feng Zhu
Summary: This study proposes a deep reinforcement learning approach to address the issue of optimizing traffic efficiency at congested major-minor intersections, which can negatively impact vehicle fairness. The proposed method optimizes both efficiency and fairness by measuring traffic fairness using the difference between the crossing order and the approaching order of vehicles, and measuring traffic efficiency using average travel time. The effectiveness of the method is evaluated in a simulated real-world intersection and compared with benchmark policies, and it shows outstanding performance in balancing traffic fairness and efficiency.
TRANSPORTMETRICA A-TRANSPORT SCIENCE
(2023)
Article
Operations Research & Management Science
Yao Chen, Yang Liu
Summary: Shared autonomous electric vehicle systems have great potential for sustainable urban mobility. This study proposes an integrated optimization problem that addresses both long-term charging facility deployment and short-term vehicle assignment, relocation, and charging decisions. The proposed two-stage stochastic integer program captures demand uncertainty and uses an accelerated two-phase Benders decomposition-based algorithm for solving. Experimental results demonstrate that the deployment of both normal- and fast-charging infrastructure can improve system profit and operational performance.
TRANSPORTATION SCIENCE
(2023)