Article
Energy & Fuels
Ke Liu, Yanli Liu
Summary: As the number of electric vehicles (EVs) connected to the grid increases, there is a need to accurately predict the spatial-temporal distribution of EV charging load synchronized with traffic states. This paper proposes a novel method based on stochastic user equilibrium (SUE) and trip chain to achieve this prediction. The proposed method effectively reflects the charging and trip characteristics of different EV types while ensuring reachability, and accurately predicts overall and individual EV travel costs and total charging loads in detailed synchronous traffic states. The method shows stable convergence and remarkable prediction effectiveness, even during peak load hours and high EV penetration scenarios.
Article
Engineering, Civil
Amir Mirheli, Leila Hajibabai
Summary: This study presents a bi-level optimization program for designing and managing electric vehicle charging infrastructure, which effectively determines the optimal location, capacity, and pricing scheme.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Teng Long, Qing-Shan Jia, Gongming Wang, Yu Yang
Summary: This paper presents an efficient and scalable real-time scheduling method for handling the charging demands of plug-in electric vehicles (PEV), demonstrating through simulations that the proposed method provides high computation efficiency and scalability while reducing operating costs for charging stations. Compared to existing methods, it outperforms in terms of charging policy search capabilities and performance guarantee.
IEEE TRANSACTIONS ON SMART GRID
(2021)
Article
Computer Science, Interdisciplinary Applications
Jangkyum Kim, Hyeontaek Oh, Joohyung Lee
Summary: This paper proposes a novel method to optimize the capacity of EV charging facility by considering various monetary factors and the relationship between Contract power and electricity tariff policies. The proposed scheme achieves a reduction of 24.6% in overall monetary cost compared to other benchmark models.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Environmental Studies
Ramesh Chandra Majhi, Prakash Ranjitkar, Mingyue Sheng
Summary: This paper investigates the allocation problem of dynamic wireless charging (DWC) system to minimize costs for individual users and investment costs for DWC facilities. A mixed-integer optimization model is proposed to achieve a cost-effective solution for the optimal placement of DWC facilities on a large road network, while maintaining an acceptable state-of-charge level. Sensitivity analysis is conducted to examine the feasibility and significance of the model solutions for DWC placements. The results provide useful insights into the optimal settings of DWC facilities under multiple route environments.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2022)
Article
Transportation Science & Technology
Jinhua Ji, Yiming Bie, Linhong Wang
Summary: To address the shortage of charging facilities for electric cars (ECs) and maximize the utilization of charging facilities for electric buses (EBs), a charging facility sharing strategy is proposed. This strategy allows ECs to use EBs' charging piles for a fee during specific time windows. A nonlinear integer programming model is developed to determine the vehicle types allowed to be charged in each time window, allocate daily service trips and charging trips to each EB, and optimize the overall costs and revenues. An algorithm combining enumeration method and branch and price is used to solve the optimization model. The proposed method is verified using a real EB route, demonstrating its effectiveness in increasing revenue for public transit companies and facilitating EC charging without disrupting EB schedules.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Automation & Control Systems
Jangkyum Kim, Hyeontaek Oh
Summary: This article proposes an efficient power operation scheme for EV charging facility by jointly analyzing various power rate policies, battery wear-out costs, and uncertainty issues caused by EV users' behavior. The effectiveness of the proposed scheme is analyzed using real-world EV operation datasets, showing a reduction of 20.5% in overall cost and 17.3% in peak power compared to other benchmark models when applied in an actual power system.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Environmental Studies
Yu-Ting Hsu, Shangyao Yan, Powei Huang
Summary: This study investigates the transition from diesel-consuming buses to electric ones, focusing on determining locations of bus depots, charging and maintenance stations. An optimization model and heuristic algorithm are developed, with a case study in Taiwan. Major cost components are evaluated to help operators/planners make informed decisions during the transition towards electric bus systems.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2021)
Article
Energy & Fuels
Yaseen Alwesabi, Farzad Avishan, Ihsan Yanikoglu, Zhaocai Liu, Yong Wang
Summary: This study aims to address the limitations of battery electric buses (BEBs) in terms of charging time and driving range by utilizing dynamic wireless charging (DWC) technology, and to improve their application in public bus systems. The research analyzes the benefits of joint planning of charging infrastructure and fleet scheduling in a bus network based on Binghamton University using robust planning models.
Article
Engineering, Electrical & Electronic
Ke Li, Chengcheng Shao, Hongcai Zhang, Xifan Wang
Summary: Electric vehicles (EVs) are rapidly developing and EV charging stations (EVCS) are essential for meeting charging demand and promoting transportation electrification. This study presents a pricing method to maximize profits for EVCS owners, the charging service providers. A three-level pricing framework is established, where EVCSs set charging prices, EVs make route and charging choices, and electricity prices are determined in the power distribution network. An optimal pricing model is developed, considering traffic flow, power generation, and electricity prices, and a decomposition algorithm is proposed to address computational burdens and feasibility issues. Real-world case studies validate the effectiveness and benefits of the proposed method.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Automation & Control Systems
Tianyi Zhao, Na Li, Nan Kong, Xiaoqing Xie
Summary: This paper addresses the optimal electric vehicle charging station location problem with two types of customers. A bi-level location optimization model is formulated, and an adaptive large neighbourhood search algorithm and a construct-improve heuristic are designed to solve the problem. Numerical experiments demonstrate the efficiency of the solution method, and a need-inspired case study provides practical insights.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Thermodynamics
Chengzhe Li, Libo Zhang, Zihan Ou, Qunwei Wang, Dequn Zhou, Jiayu Ma
Summary: This article studies the optimization of electric vehicle charging station in combination with distributed renewable energy, proposing a robust optimization model based on road network and grid, considering uncertainties in EV charging demand and renewable energy output, and using kernel density estimation to address over-conservatism. Simulation results on a network show the robustness and economy of the model.
Article
Transportation
Cong Quoc Tran, Dong Ngoduy, Mehdi Keyvan-Ekbatani, David Watling
Summary: This paper presents a bi-level optimization framework to identify the optimal locations of fast-charging stations in urban networks, addressing the barriers to the mass adoption of Electric Vehicles (EVs) while reducing total system costs.
TRANSPORTMETRICA A-TRANSPORT SCIENCE
(2021)
Article
Thermodynamics
Ning Wang, Hangqi Tian, Huahua Wu, Qiaoqian Liu, Jie Luan, Yuan Li
Summary: This study proposed a multi-stage optimization strategy to optimize the location and capacity of electric vehicle charging stations for the Robotaxi fleet. The strategy included fleet sizing, charging demand simulation, model construction, and solution. The effectiveness of the proposed model and algorithm was analyzed using real data from Chengdu, China.
Article
Engineering, Electrical & Electronic
Brian S. Gu, Tharindu Dharmakeerthi, Seho Kim, Michael J. O'Sullivan, Grant A. Covic
Summary: This article proposes a reduced ferrite inductive power transfer system for electric vehicle charging, which uses ferrite-based soft magnetic composites to reduce system cost and improve mechanical robustness. The optimized system reduces ferrite volume by 63% and exhibits minimal deterioration in terms of coupling reduction and magnetic field leakage.
IEEE TRANSACTIONS ON POWER ELECTRONICS
(2023)
Article
Engineering, Civil
Meng Xie, Michael Winsor, Tao Ma, Andreas Rau, Fritz Busch, Constantinos Antoniou
Summary: This paper evaluates the sensitivity of a proposed cooperative dynamic bus lane system using microscopic traffic simulation models. The system is sensitive to variations in the penetration rate and communication range of connected vehicles (CVs) in congested traffic conditions, with buses benefiting most at a communication range of 150 m. Safety concerns related to cooperative driving behavior are also discussed in the study.
TRANSPORTATION RESEARCH RECORD
(2022)
Article
Economics
Georgios Grigoropoulos, Axel Leonhardt, Heather Kaths, Marek Junghans, Michael M. Baier, Fritz Busch
Summary: The popularity of utilitarian bicycling is increasing in urban areas, impacting traffic flow and capacity at intersections. This study quantifies the impact of bicycle traffic on signalized intersections and proposes factors for the reduction in vehicular capacity. Empirical studies and traffic simulation models are used to assess the effects of bicycle infrastructure on traffic efficiency.
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE
(2022)
Article
Economics
Chang Zhou, Qiong Tian, David Z. W. Wang
Summary: This study proposes a novel control technique to mitigate bus bunching by providing passengers with real-time wait time information and degrees of in-vehicle congestion. The numerical results show that providing in-vehicle congestion information is as effective as the schedule-based and headway-based control methods in achieving mitigation of bus bunching.
Article
Engineering, Civil
Frederik R. Bachmann, Lars Briem, Fritz Busch, Peter Vortisch
Summary: This paper reveals the dynamics and processes of operations control centers in public transport, providing insights into potential improvements in reliability such as enhanced communication training and increased use of information technology.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Environmental Studies
Roy Tan, Harilaos N. Psaraftis, David Z. W. Wang
Summary: This paper revisits speed optimization and speed reduction models for liner shipping in a multi/flexible fuel context, analyzing the influence of a maximum average speed limit on optimal speeds, carbon intensity and emissions for dual fuel Neopanamax container vessels utilizing liquefied natural gas.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2022)
Article
Economics
Qingyun Tian, David Z. W. Wang, Yun Hui Lin
Summary: This paper proposes a multi-stage mathematical modeling framework to optimize the deployment strategy of autonomous buses in public transit service operations. Passenger acceptance attitudes and the diffusion model are considered to forecast the passengers' adoption rate. The optimal deployment strategy that minimizes the total travel cost is determined through solving a mixed-integer nonlinear program.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Liang Du, Ruobin Gao, Ponnuthurai Nagaratnam Suganthan, David Z. W. Wang
Summary: Traffic forecasting is crucial for smart city development, and this study proposes a novel graph ensemble deep random vector functional link network (GEdRVFL) to achieve node-wise traffic forecasting, which outperforms state-of-the-art models in most cases.
APPLIED SOFT COMPUTING
(2022)
Article
Engineering, Civil
Zhen Di, David Z. W. Wang, Lixing Yang
Summary: The high-speed rail plays a pivotal role in intercity commuting, but there are issues with ticket pricing and seat allocation. This study proposes a new ticketing/exchanging scheme to address demand fluctuation and validates its effectiveness through numerical experiments.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
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
Operations Research & Management Science
Gabriel Tilg, Lukas Ambuehl, Sergio F. A. Batista, Monica Menendez, Ludovic Leclercq, Fritz Busch
Summary: A semi-analytical methodology is proposed to estimate the macroscopic fundamental diagram (MFD) for realistic urban networks, which is more accurate than existing methods. This methodology can be applied to networks with irregular topologies and considers different spatial demand patterns.
TRANSPORTATION SCIENCE
(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)
Proceedings Paper
Computer Science, Artificial Intelligence
Liang Du, Ruobin Gao, Ponnuthurai Nagaratnam Suganthan, David Z. W. Wang
Summary: This paper proposes a novel online performance-based ensemble deep random vector functional link neural network model for time series forecasting tasks. The model supports non-iterative online learning and dynamic ensemble method, and outperforms existing statistical, machine learning-based, and deep learning-based models in extensive experiments.
2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2022)
Article
Green & Sustainable Science & Technology
J. Zhang, M. Meng, David Z. W. Wang, B. Du
Summary: This study develops a methodology to determine the optimal allocation position to deploy bikes in a competitive dockless bike sharing market. Two different scenarios are considered, one with a potential competitor and one without. The study proposes two different heuristics to handle these scenarios based on different design objectives.
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION
(2022)
Article
Transportation Science & Technology
Yue Zhao, Liujiang Kang, Huijun Sun, Jianjun Wu, Nsabimana Buhigiro
Summary: This study proposes a 2-population 3-strategy evolutionary game model to address the issue of subway network operation extension. The analysis reveals that the rule of maximum total fitness ensures the priority of evolutionary equilibrium strategies, and proper adjustment minutes can enhance the effectiveness of operation extension.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Hongtao Hu, Jiao Mob, Lu Zhen
Summary: This study investigates the challenges of daily storage yard management in marine container terminals considering delayed transshipment of containers. A mixed-integer linear programming model is proposed to minimize various costs associated with transportation and yard management. The improved Benders decomposition algorithm is applied to solve the problem effectively and efficiently.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Zhandong Xu, Yiyang Peng, Guoyuan Li, Anthony Chen, Xiaobo Liu
Summary: This paper studied the impact of range anxiety among electric vehicle drivers on traffic assignment. Two types of range-constrained traffic assignment problems were defined based on discrete or continuous distributed range anxiety. Models and algorithms were proposed to solve the two types of problems. Experimental results showed the superiority of the proposed algorithm and revealed that drivers with heightened range anxiety may cause severe congestion.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Chuanjia Li, Maosi Geng, Yong Chen, Zeen Cai, Zheng Zhu, Xiqun (Michael) Chen
Summary: Understanding spatial-temporal stochasticity in shared mobility is crucial, and this study introduces the Bi-STTNP prediction model that provides probabilistic predictions and uncertainty estimations for ride-sourcing demand, outperforming conventional deep learning methods. The model captures the multivariate spatial-temporal Gaussian distribution of demand and offers comprehensive uncertainty representations.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Benjamin Coifman, Lizhe Li
Summary: This paper develops a partial trajectory method for aligning views from successive fixed cameras in order to ensure high fidelity with the actual vehicle movements. The method operates on the output of vehicle tracking to provide direct feedback and improve alignment quality. Experimental results show that this method can enhance accuracy and increase the number of vehicles in the dataset.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Mohsen Dastpak, Fausto Errico, Ola Jabali, Federico Malucelli
Summary: This article discusses the problem of an Electric Vehicle (EV) finding the shortest route from an origin to a destination and proposes a problem model that considers the occupancy indicator information of charging stations. A Markov Decision Process formulation is presented to optimize the EV routing and charging policy. A reoptimization algorithm is developed to establish the sequence of charging station visits and charging amounts based on system updates. Results from a comprehensive computational study show that the proposed method significantly reduces waiting times and total trip duration.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
