Table of Content

15 June 2023, Volume 27 Issue 2

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A systematic review of researches and applications of bi-level programming in the context of urban transport

He WEI, Haofei LIU, Dandan XU, Xuehua HAN, Liang WANG, Xiaodong ZHANG

2023, 27(2):  1-26.  doi:10.15960/j.cnki.issn.1007-6093.2023.02.001

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Bi-level programming is a typical NP-Hard problem. It is a nonconvex optimization problem with upper and lower hierarchical structure and contains optimization problems in constraint conditions. This paper systematically reviews the researches and applications of bi-level programming in the context of urban transport, focusing on transportation network design problem and OD estimation/adjustment problem. Firstly, the domestic and international research topics and evolution progress are summarized by bibliometrics. Secondly, it takes pioneering research as the clue to look back upon important researches, the first systematic review paper, the first doctoral dissertation, the first Transportation Research Part-B's issue, and the first review paper in Chinese are introduced. Thirdly, the recent development of network design problems including road, transit and multi-modal, and the static and dynamic OD estimation problems are expounded. Fourthly, some general solutions are concluded, and the trends of solutions are discussed, the relationship between bi-level programming and MPEC is expressed. Finally, it points out three opportunities and challenges in the future should be addressed, including exploring and revealing of smart transportation, the optimization of modeling architecture, and building a computing platform to share and interact.

Operational research methods for urban traffic flow estimation

Hu SHAO, Yue ZHUO, Pengjie LIU, Feng SHAO

2023, 27(2):  27-48.  doi:10.15960/j.cnki.issn.1007-6093.2023.02.002

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With the development of the social economy and the progress of human production mode, the traffic management system provides a series of subjects for operations research. The operational research methods are widely applied in the field of traffic network modeling, and they also occupy some important positions in the intelligent traffic management system. To solve the problems existing in the traffic system, we can make full use of various branches of operations research, which can effectively ensure the efficiency and orderliness of transportation in real life. In this paper, we first introduce several solution models for solving traffic flow estimation problems and then review the existing research from seven aspects: linear programming, integer programming, dynamic programming, graph theory, statistical, heuristic approach, and machine learning method. Finally, to provide more references for transportation managers and researchers, we discuss the development directions and related problems for traffic flow estimation models and propose the potential problems that need to be further investigated and solved.

Multi-agent deep reinforcement learning-based urban traffic signal management

Yun HUA, Xiangfeng WANG, Bo JIN

2023, 27(2):  49-62.  doi:10.15960/j.cnki.issn.1007-6093.2023.02.003

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With the rapid improvement of the national economy in recent years, people's travel demand has increased, bringing increasingly severe pressure on the current urban traffic signal system relying on traditional non-intelligent traffic lights. The significant increase in the complexity of the traffic network has led to the development of traffic signal control from a single-point problem to a system engineering problem, and the development of artificial intelligence technology brings more methods to dealing with urban traffic signal control. Swarm intelligence methods, represented by multi-agent reinforcement learning, have been widely used in traffic signal control and optimization, including traffic light control, autonomous driving, and vehicle-road collaboration. Compared to traditional methods, multi-agent reinforcement learning can empower the intelligence of traffic signal systems while implementing large-scale traffic signal system collaboration to improve the efficiency of urban traffic operations. The various components involved in urban transportation must collaborate in the vision of intelligent urban traffic. Multi-agent reinforcement learning is of great research value in urban traffic signal control and optimization. This paper will systematically introduce the basic theory of multi-agent deep reinforcement learning and its use in urban traffic signal optimization, summarize the existing approaches and analyze the drawbacks of each method. In addition, this paper will outline the challenges of multi-agent reinforcement learning methods for urban traffic signal optimization. Then the paper points out possible future research directions to promote the development of multi-agent reinforcement learning methods in urban traffic signal optimization.

First-order splitting algorithm for multi-model traffic equilibrium problems

Maoran WANG, Xingju CAI, Zhongming WU, Deren HAN

2023, 27(2):  63-78.  doi:10.15960/j.cnki.issn.1007-6093.2023.02.004

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In this paper, we study the multi-model traffic equilibrium problem of private transportation and public transportation, which is modeled as a separable monotonous variational inequality problem with linear inequality constraints. We propose a modified alternating direction method of multipliers in a parallel way for the linear inequality constraint problem by modifying the subproblem appropriately and adding a simple correction step. Under general hypothetical conditions, the global convergence and sublinear convergence rate of this new algorithm are proved. Applying the algorithm to the traffic equilibrium shows its effectiveness.

Station location of intercity high-speed rail based on spatial equilibrium analysis of two cities

Xingqi YANG, Haijun HUANG

2023, 27(2):  79-94.  doi:10.15960/j.cnki.issn.1007-6093.2023.02.005

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This paper proposes a novel spatial equilibrium model integrating station locations of intercity high-speed rail (HSR), which allows for households.intracity commuting, intercity migration, and intercity commuting. The proposed model explicitly investigates the effects of station location on urban spatial structure, households. choices of residence and workplace, and housing market. We systematically analyze and summarize all spatial structures for two cities, as a result of a great diversity of station location. The analysis result reveals that station location will affect households.choices of residence and workplace, and administration will underestimate travel demand when ignoring intercity commuting due to population migration. Numerical results indicate that although the reconstruction of existing stations is not the best scheme for household utility, it may be the best one for social welfare from the perspective of saving demolition costs. It is also found that the improvement of intercity transport between specific cities may lead to population migration to lower-income cities and then flatten the urban population and housing rental prices of higher-income cities.

Charging and discharging scheduling for electric bus charging station with energy storage system

Wei XU, Yuefeng HUANG, Caihua CHEN

2023, 27(2):  95-109.  doi:10.15960/j.cnki.issn.1007-6093.2023.02.006

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A charging and discharging scheduling strategy for electric bus charging station considering the configuration of energy storage system is proposed to address the management difficulties of high load pressure and high charging operation cost caused by disorderly charging at electric bus charging station. Firstly, a mixed integer programming model is established to minimize the overall daily cost of the charging station and to coordinate the charging of the electric bus and the charging and discharging of the energy storage system. The capacity of energy storage system is optimized and sensitivity analysis is performed. Secondly, the nonlinear charging characteristic of the onboard lithium battery is fully considered and the piecewise linear approximation method is used to describe the charging profile of battery SOC. Finally, model verification and case study are made based on the historical travel and charging records of an electric bus charging station in Chengdu. The findings demonstrate that the suggested charging scheduling strategy can successfully lower the overall cost of the charging station, ease load pressure of the grid, prolong the life of the lithium battery and enhance the economy of bus charging management and power grid stability.

Analysis of travel behavior and optimization of parking fare in a commute corridor with park and ride-sharing

Jiancheng LONG, Xinyi ZHANG, Jianxun DING

2023, 27(2):  110-124.  doi:10.15960/j.cnki.issn.1007-6093.2023.02.007

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Ride-sharing can effectively alleviate traffic congestion and parking pressure by improving the utilization of vehicle seat capacity and reducing the number of traffic travels. Considering setting parking and ride-sharing meeting points, a traffic management scheme for a linear monocentric city was proposed. Under the situation of parking and ride-sharing, the travel costs of solo driver, ride-sharing driver and ridesharing passenger were analyzed, and a route choice model based on stochastic user equilibrium was constructed. A bi-level programming model was proposed to minimize the total travel cost by optimizing the ride-sharing parking charge. Based on the method of sensitivity analysis, the Frank-Wolfe and BFGS algorithms were applied respectively to solve the proposed bi-level programming model. Finally, the results of numerical analysis show the effectiveness of the proposed model and algorithm.