Table of Content

25 October 2023, Volume 51 Issue 10

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Contents



2023, 51(10):  0. 

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Green, Intelligent Traffic System

Conflict-Free Path Planning For Multi-AGVs in Automated Terminals Considering Road Load Balancing



WEN Huiying, YUAN Yuqing, LIN Yifeng

2023, 51(10):  1-10.  doi:10.12141/j.issn.1000-565X.230227

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With the increasing demand for container transportation and the widespread application of new information technologies, the automation of container terminal operations has become the main development trend in domestic and international ports. It can not only effectively improve the efficiency and safety of terminal operations, but also significantly reduce the demand for human resources and the operational costs. The horizontal transportation system is an essential part of the container terminal handling system and an important link enabling the highly efficient container transportation between the quayside and the storage yard, so its operational reliability and the reasonableness of the scheduling directly affect the operational efficiency of the automated container handling system. The mostly used horizontal transportation equipment in container terminals is the automated guided vehicles (AGVs), which is responsible for horizontal transportation from the front quay crane to the rear yard in automated container terminals. In actual operation process, conflicts and congestion is inevitable when multiple AGVs operate simultaneously. On this basis, this paper used conflict-based search (CBS) to solve the conflict problem arising from the cooperative operation of multi-AGVs at the terminal. The upper layer algorithm searched for conflicts among AGVs, while the lower layer algorithm used the A* algorithm for path planning of AGVs. A load factor was introduced into the heuristic function of the A* algorithm in order to avoid congestion in the path planning and achieve load balancing on terminal roads. Further, a sliding time window conflict resolution (STWCR) based on CBS was adopted to improve computational efficiency for multiple AGVs path planning in the continuous operation scenario of multiple task points at the terminal. Simulation experiments verified that the proposed algorithm in this paper can effectively solve the conflict problem of multiple AGVs path planning at the terminal, while balancing the road network load, alleviating local road congestion, and improving the utilization of road resources. The research results of this paper provide a reference for the optimization of the horizontal transportation system in automated container terminals.

Electric Bus Scheduling Method Considering Differences in the State of Health of Batteries



BIE Yiming, ZHU Aoze, CONG Yuan

2023, 51(10):  11-21.  doi:10.12141/j.issn.1000-565X.230279

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Electric buses (EBs) have the advantages of zero-emission and low energy consumption in operation. The electrification of urban buses is being vigorously promoted in many countries to reduce carbon emissions and promote the realization of the “Carbon peaking and Carbon neutrality” goals. However, due to financial constraints and the fact that fuel buses have not yet reached the end of life and bus companies usually replace fuel buses with EBs in batches, there are differences in the battery health degree and driving range of each bus on the line, which makes the optimization of the vehicle scheduling scheme more complicated. Considering the impact of battery differences in the state of health and time-of-use tariff, this paper proposed an optimized scheduling model for single-route, with the objective of minimizing average daily charging costs, EB acquisition costs, and battery loss costs. Then, the model was transformed into two sub-problems, the vehicle scheduling problem and the charging scheduling problem. In the outer layer, the vehicle scheduling problem was solved by the improved simulated annealing algorithm (ISAA), whose perturbation strategy is designed with the operating intensity differences among EBs. And Gurobi was employed to solve the charging scheduling problem in the inner layer. Finally, an actual EB route was taken as an example to verify the effectiveness of the method, and the method was compared with the simulated annealing algorithm in the perturbation strategy which does not consider differences in vehicle operating intensity. Results show that the ISAA can increase the convergence speed by 31.8% and achieve high-quality solutions in a short time. Moreover, the generated scheduling scheme can not only arrange EBs to be charged preferentially in the off-peak period of electricity prices but also reduce the EB fleet size.

Flexible Bus Scheduling Optimization for Integrated Hub Connections in the Context of MaaS



YANG Min, CHEN Shantao, JIANG Ruiyu, et al

2023, 51(10):  22-30.  doi:10.12141/j.issn.1000-565X.230224

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As a crucial complement to conventional public transportation, flexible bus can provide demand-responsive services tailored to specific groups, and it has been successfully implemented and proven effective in foreign countries. However, whether it can be applied to connect passengers at comprehensive transport hubs and alleviate the increasing pressure of passenger flows at these hubs, which has become a prominent issue in the field of urban public transportation in China, warrants further investigation.To address this, this research established a flexible bus dispatching optimization method for comprehensive hub connection. Based on the characteristics of data sharing and flexible response of MaaS system, a MaaS-based flexible connecting bus dispatching service process was constructed. Considering both passengers’ punctuality requirements and the cost considerations of public transit operators, the study developed a multi-objective optimization model by incorporating constraints like time windows, vehicle capacity, and station services. The multiple objective model was transformed into a single objective model by unifying the solution direction, normalization and empowerment. The differential evolution algorithm was designed based on the ideas of encoding, decoding and maximum heap, and the model was verified by taking the railway hub area of Nanjing South Railway Station as a case. Relying on smart card data from selected bus routes in the vicinity of Nanjing South Station in May 2021, the study analyzed the spatial distribution characteristics of passenger travel demands at the hub and established predefined demand sites and passenger travel needs. The model algorithm was iteratively optimized, resulting in a fitness value of 0.921 2 and an average passenger satisfaction of 89.77%. The algorithm converges within 50 iterations, thus verifying the feasibility and effectiveness of the model and algorithm. Sensitivity analysis demonstrates that the model and algorithm remain highly applicable even when passenger demand scales change.

Review of Research Methods, Trends and Hotspots on Rural Residents’ Transportation Behavior



AO Yibin, ZHENG Junjie, XIAO Shan, et al

2023, 51(10):  31-45.  doi:10.12141/j.issn.1000-565X.230103

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In the context of global urbanization, the rural pattern has changed dramatically, and the related issue of daily transportation behavior of rural residents has become a hot research issue concerning society, environment and economy. In the last decade, there has been increasing research on the traffic behavior of rural residents, and the contents and methods of such research are also increasingly rich. These studies have yielded fruitful results and provided valuable references and suggestions for the development and construction of rural areas. Based on 140 literatures related to rural residents’ transportation behavior, this study identifies journals, scholars and countries that have made significant contributions to rural residents’ transportation-related research since 2000 through an integrated method of bibliometrics search, scientific mapping, and qualitative discussion, and reveals the main research topics through keyword analysis. According to statistics, the United States, China and Australia are the three countries with the most published literature. In terms of journals, the Journal of Transportation Geography, the Journal of Transportation Research Records and the Journal of Transportation and Health are the most widely published and cited journals. In terms of scholars, Ao, Wang and Zhao have published abundant literature. In addition, the research results show that the main research directions are green travel and energy-saving emission reduction, youth and elderly groups, built environment and travel patterns, and emerging traffic and transportation disadvantages. Based on literature review, this study constructed a research methodological framework in the field of rural residents’ transportation behavior, summarized the main influencing factors and their influential paths of action, identified the mainstream research topics and existing research gaps in this research field, and proposed future research directions for this field, providing a multidisciplinary guide for practitioners and researchers.

Research Progress on Key Technologies in the Cooperative Vehicle Infrastructure System



LIN Hongyi, LIU Yang, LI Shen, et al

2023, 51(10):  46-67.  doi:10.12141/j.issn.1000-565X.230200

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With the steady growth of urban car ownership, the issue of traffic congestion is becoming increasingly prominent, bringing great pressure to urban development. To respond effectively to this challenge, it is critical to develop methods that can improve transport efficiency and reduce energy consumption. In current context, the Cooperative Vehicle Infrastructure System (CVIS), an ideal solution for realizing green and intelligent transportation systems, has become an important direction in both transportation research and practice. By integrating and optimizing various traffic resources, CVIS not only enhances traffic efficiency and reduces energy consumption but also provides key technical support for achieving “dual carbon” goals. This paper thoroughly analyzed the fundamental concepts, research methodologies and application scenarios of CVIS, and delved into its four core technological modules: fusion perception, driving cognition, autonomous decision-making, and cooperative control. The paper reviewed and summarized research achievements within these modules, ranging from traditional methods to the latest in deep reinforcement learning techniques. It also explored the potential applications of these technologies and methods for enhancing traffic efficiency, reducing energy consumption, and improving road safety. Finally, the paper scrutinized numerous challenges that CVIS may encounter in practical applications, including the security of information transmission, system stability, and environmental complexity. To overcome these challenges, the paper looked forward to the future development in four areas: developing datasets that integrate vehicle-side and roadside information, enhancing the fusion accuracy of multi-source perception information, improving the real-time performance and safety of CVIS, and optimizing multi-vehicle cooperative decision-making control methods under complex conditions. As a result, this paper not only has important reference value for the advancement of CVIS technology, but also provides important guidance for the future planning and construction of urban transportation systems.

Review of Research on Road Traffic Detectors and Its Optimized Deployment Methods



XU Zhihang, YAO Xinpeng, XU Zhigang, et al

2023, 51(10):  68-88.  doi:10.12141/j.issn.1000-565X.230223

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A widely studied and concerned problem in the traffic network research is how to optimize the location and number of road traffic detectors, so as to obtain real-time and accurate diversified traffic situation information and provide a comprehensive information basis for traffic control departments and as a basis for reasonable decision-making. The key to this problem is to select a suitable detector type and build a decision model according to the research purpose. At the same time, considering the constraints such as the investment cost limit and the number of road sections, appropriate heuristic algorithm should be used to solve the model to get the best number and location of detectors. This paper summarized the optimal layout of road traffic detectors from the types of road traffic detectors, application scenarios, data acquisition indexes and research objectives of various optimization layout studies. Firstly, the detector was divided into two categories according to the installation mode: stationary traffic detector and mobile detector, and the principle, characteristics, advantages and disadvantages of each type of detector were described in detail. Secondly, the application of various types of road traffic detectors in different scenarios and the corresponding data acquisition indicators are given. Then, according to the research purpose of optimization layout methods in the research literature, the optimization layout problems of road traffic detectors were divided into three types: user-oriented travel time estimation, traffic flow observation/estimation, and traffic event detection. And this paper discussed the development course, development direction, problem research model constructed, problem solving methods, and existing shortcomings of these studies. Finally, it summarized a large number of existing studies. And it pointed out that in the complex situation of large traffic network scale, prominent traffic uncertainty and rapid development of wisdom, future research should take the diversity of traffic information detection as the leading factor, fully consider the combination arrangement of different types of traffic detectors, various uncertainties in the traffic network and various scenarios, etc., so as to build a complete optimization model to solve the optimization arrangement of road traffic detectors.

Study on the Multi-Period Allocation Method of Vehicle Resource in Hybrid Service Mode



SHEN Yutong, LI Ming, CUI Zhiyong, et al

2023, 51(10):  89-98.  doi:10.12141/j.issn.1000-565X.230182

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Ride-sharing is one of the typical models in the context of the sharing economy. With the advantages of reducing travel costs and carbon emissions, it has occupied an important share in the urban travel market. Vehicle resource allocation is the core link in optimizing carpooling services. To realize the overall management of travel modes, this paper comprehensively considered the influence of multiple travel modes and passengers’ selection behavior on the allocation scheme. Focusing on the supply and demand matching of the mixed travel mode based on ride-sharing travel, the internal correlation between the passenger travel mode selection behavior and the allocation of vehicle resources between regions was analyzed. A passenger-to-driver matching function was introduced to describe the relationship between vehicle supply and passenger demands. Then the passengers’ travel selection behavior was simulated by the Logit model, and an inter-regional resource allocation optimization model was constructed. In solving the optimization model, the allocation problem was divided into two stages: passenger travel mode allocation and vehicle supply allocation. The Lagrangian relaxation and the gradient descent algorithms were integrated to solve the decision-making problems involved in each stage. The solutions of the two stages were updated based on the feedbacks from passenger selection preference and vehicle allocation, so as to establish the regional multi-period traffic resource allocation algorithm. Finally, the resource allocation algorithm was tested by random generation of examples on a road network with 100 sub-regions. Results show that the method proposed in this paper can effectively balance the mobility allocation among regions with mixed travel modes. As the number of ride-sharing vehicles decreased, the decreasing proportion of total revenue increased from 7.32 % to 25.37 %. Compared with the vehicle allocation method based on mileage, the vehicle allocation algorithm proposed in this paper helps to improve the total revenue.

Repositioning Strategy for Ride-Hailing Vehicles Based on Geometric Road Network Structure and Reinforcement Learning



XU Lunhui, YU Jiaxin, PEI Mingyang, et al

2023, 51(10):  99-109.  doi:10.12141/j.issn.1000-565X.230148

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The inefficient and inaccurate bidirectional search by both ride-hailing drivers and passengers leads to a mismatch between supply and demand. Ride-hailing vehicle repositioning strategy can pre-dispatch vehicles to areas with future demand, improving supply-demand matching. However, existing research mostly uses network grids to represent the urban road environment, lacking geometric topological information and reducing the dispatch accuracy. To address this issue, a ride-hailing vehicle relocation algorithm called GA2C was proposed based on Graph Neural Networks (GNN) and Actor-Critic reinforcement learning algorithm. This algorithm has a smoother learning process and can perform high-dimensional sampling, and it is suitable for learning the best relocation strategy for a large number of ride-hailing vehicles as multi-agent systems. Moreover, the geometric network structure was used to represent the urban road environment by using a GNN as a function approximator to learn the geometric information of the road network. Additionally, an action sampling strategy based on action value function was introduced to increase the randomness of action selection, effectively preventing competition. A ride-hailing vehicle relocation simulation experiment was conducted using Python, and the results are as follows: (i) the order response rate of the GA2C algorithm is 84.2%, significantly higher than all the comparative experimental results; (ii) in the order distribution comparative experiment, GA2C’s relative improvements in uniform distribution, central distribution layout, block distribution layout, and checkerboard distribution layout are 1.17%, 6.02%, 13.12%, and 14.55%, respectively. The above experimental results demonstrate that the GA2C algorithm can effectively relocate ride-hailing vehicles. When the order distribution presents significant differences, and the distance between different demand areas is relatively close, it can better learn dynamic demand changes, and achieve maximum order response rate by relocating ride-hailing vehicles.

Vehicle Trajectory Tracking at Intersections Based on Millimeter Wave Radar Point Cloud



LIN Yongjie, CHEN Ning, LU Kai

2023, 51(10):  110-125.  doi:10.12141/j.issn.1000-565X.230100

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As an emergent traffic detection device, millimeter wave radar is little affected by environmental factors (e.g., light and weather) and can provide reliable data support for road traffic sensing, safety control and signal timing optimization. Vehicle trajectory data collected by millimeter wave radar contains rich traffic information, reflecting spatial-temporal characterization of vehicle motion, which is critical in traffic parameter extraction, abnormal detection, driving behavior analysis, signal timing optimization, etc. Aiming at solving the problems such as trajectory fragmentation and poor valid tracking rate caused by the vehicle data loss and easy occlusion of vehicles detected by millimeter wave radar in the intersection, this paper proposed a continuous tracking method of vehicle trajectory based on short trajectory fragment associations. Firstly, the 2D point cloud data with high frequency collected by millimeter wave radar at the intersection was acquired and cleaned to obtain valid target information. Secondly, short track fragments were extracted from 2D point clouds by inter-frame association, and multiple movement sequence feature was used for track fragment correction to reject split trajectories. Thirdly, the fuzzy correlation function was constructed based on the motion characteristics of the spatiotemporal dimension to describe the correlation among multiple short track fragments. Hungarian algorithm was employed to solve the set of target short track with the highest correlation. Finally, the missing trajectory points in the vehicle tracklet set were repaired based on the piecewise cubic Hermite interpolation, which derived complete trajectories and achieved continuous tracking. The experiments were conducted using 6 627 frames of 2D point cloud data collected at the real intersection. The results indicate that the proposed method achieves better tracking performance under different traffic densities, monitoring directions, and occlusions than the traditional trajectory tracking algorithm. Specifically, the trajectory tracking accuracy is 92.4%, the number of fragmentations is 4.5, and the accuracy of estimated vehicle volume is significantly improved.

Location and Capacity Optimization Model of Battery-Swapped Electric Bus Charging Station



ZHANG Wenhui, SU Jiaqi, HA Zihong, et al

2023, 51(10):  126-134.  doi:10.12141/j.issn.1000-565X.230198

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In order to improve the electricity exchange efficiency of urban pure electric bus and reduce the construction and operation costs of charging stations, this paper studied the optimization of urban battery exchange pure electric bus charging station siting and capacity selection. Firstly, considering the operation guarantee capacity of pure electric bus exchange stations, the study established a model of the number of exchange facilities and battery reserve capacity and obtained the number of exchange stations and optimal battery configuration. Then, based on the pure electric public exchange demand, the charging station operation conditions were modeled by using queuing theory, and penalty factors were set to ensure the service quality of charging stations. A site selection and capacity model with service radius, service intensity and supply and demand balance as constraints and minimum annual total cost as the object was established, and GA, PSO and PSO-GA algorithms were applied to solve it. Finally, a sensitivity analysis of the siting and capacity model was performed to obtain the effects of parameters such as charging rate and rated driving range on the siting results. The results of the case application show that the PSO-GA algorithm is better than the GA and PSO algorithms in terms of objective function and convergence speed, and the optimal number of charging stations is 30, the number of charging piles is 943, and the lowest total cost is 12 151 429 000 RMB. The rated driving range of pure electric buses is negatively correlated with the number of stations, transportation cost and construction cost; the charging rate is negatively correlated with the number of stations and construction cost and positively correlated with transportation cost, and increasing the charging rate will accelerate battery aging and reduce battery life. The research results can provide theoretical basis for the reasonable planning and operation of urban pure electric bus charging stations.

Modeling Method for Optimizing Dynamic Wireless Charging Lane of Electric Vehicles



PEI Mingyang, ZHU Hongyu

2023, 51(10):  135-151.  doi:10.12141/j.issn.1000-565X.230261

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With the aggravation of energy crisis and environmental pressure, electric vehicles （EVs） are considered as an effective measure to reduce petrochemical resource dependence, combat climate change, maintain sustainable energy and environmental development and achieve the carbon peaking and carbon neutrality goals. The growing EV market, fueled by policy incentives, has led to increasing charging demands, posing challenges for charging infrastructure deployment and operation. “Range anxiety” caused by limited battery capacity and difficulties in finding available charging stations contribute to low accessibility and inconvenience for users. This issue has become the pain point of the EV industry, hindering the popularization of EVs. However, the maturation of dynamic wireless charging (DWC) technology has brought very promising solutions to the above problems.To provide a more comprehensive review perspective of the research field, this paper provided a comprehensive overview of DWC development and summarized the technical characteristics according to the advantages and their challenges. Then, from the dimensions of modeling method, decision variables, optimization objectives, constraints, model assumptions, solution algorithm and so on, this paper analyzed the optimal configuration model of dynamic wireless charging lanes (DWCLs) and enumerated the research results and test conditions at home and abroad. Finally, it summarized existing methods and technical issues in the dynamic wireless charging domain and offered insights into future prospects, including its impact on the power grid, charging load management, electricity market analysis, charging strategies, battery management systems, battery capacity prediction, battery equalization control, and interdisciplinary integration with autonomous driving technologies.

Research on Space-Time Taxiing Optimization of Aircraft Based on Carbon Emission



JIANG Yu, WANG Yasha, XUE Qingwen, et al

2023, 51(10):  152-159.  doi:10.12141/j.issn.1000-565X.230220

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The aviation transportation industry is one of the largest industries of carbon emission. With the development of the aviation transportation industry and the strengthening of environmental regulations, the industry is facing enormous environmental pressure. How to reduce the carbon emissions of the air transport industry has attracted wide attention. Aircrafts taxiing in airport scene emits a lot of carbon. Therefor, optimizing aircraft taxiing is an important way to reduce aviation transportation carbon emissions. In addition, to ensure the safety and efficiency of aviation transportation, taxiing on the airport apron must also consider factors such as taxiing waiting time and taxiing conflicts. To solve this problem, this paper proposed an optimization model of space-time taxiing of aircraft considering carbon emission. By deciding the taxiing path in the upper model and the taxiing time in the lower model, the upper and lower interactive optimization minimizes the total carbon emission of aircraft taxiing, the taxiing waiting time of approach aircrafts and the conflict of aircraft taxiing. To improve the quality of the model solutions, an iterative neighborhood search algorithm based on the interaction optimization of upper and lower models was designed to solve the model and the conflict-free neighborhood operator and stochastic perturbation operator were introduced. The proposed model and algorithm were validated with actual operational data from Guangzhou Baiyun International Airport. The experimental results show that compared with the shortest taxiing strategy, the proposed model reduced the total carbon emission for 47 aircrafts by 971 kg, accounting for 1.8% of the total carbon emissions, in the planning period of one and a half hours under the condition of avoiding aircraft taxiing conflict. This research can provide a more environmentally friendly taxiing strategy while improving taxiing efficiency and safety, helping the aviation transportation industry reduce carbon emissions and achieve greater sustainability.

Optimization Model of Bus Priority Control Considering Carbon Emissions with Stochastic Characteristics



HU Xinghua, CHEN Xinghui, WANG Ran, et al

2023, 51(10):  160-170.  doi:10.12141/j.issn.1000-565X.230178

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In the context of the construction of a country with a strong transportation network, vigorously developing urban public transportation and promoting sustainable urban development has become an inevitable requirement for urban transportation development. Transit signal priority control, as an active priority strategy, can effectively reduce the carbon emissions and delays generated by buses at signal intersections, and improve the quality of bus service. A bus speed probability density function was introduced to study the effect of bus priority control strategy on traffic carbon emission, based on the speed stochastic characteristics of intersection. The effect of main parameters such as delay, stopping times, and speed on traffic carbon emission was analyzed. A bi-level optimization model of single-intersection bus priority control was established using the combination strategy of speed guidance and green extension. The model took the optimal carbon emission reduction of buses and cars with different fuel types in the upstream section of the intersection and the intersection control area as the upper-level objective, the optimal total people delay reduction as the lower-level objective, and the guidance speed as well as the compressed green time of the non-bus-priority phases as the decision variables. The Gauss-Seidel iterative algorithm was used to solve the model. Finally, the established model was applied to the calculation cases for analysis, and the results indicated that under the guidance acceleration and green extension strategy, the overall carbon emission and total passenger delay reduction of the intersection could reach 25.63% and 36.27%, respectively. The model effectively reduced carbon emissions and total passenger delays in the upstream sections of the intersection and the intersection control area, and optimized the overall traffic benefit of the intersection while promoting sustainable development.