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Pricing and Subsidizing Mechanism of China Railway Express Considering Shipper's Preference XU Ling, XU Tong, JIANG Wen-hui, XIAO Hua, ZHANG Xiang-hong 2023, 23(1): 2-9.  DOI: 10.16097/j.cnki.1009-6744.2023.01.001 Abstract ( )   PDF (2040KB) ( )   Aiming at the effectiveness of subsidy policies during the development of China Railway Express, this paper proposes a three-level game model composed of government, operating platforms, and shippers. Two subsidy methods (subsidizing shippers' freight rates or platform companies' operating costs) are included in the model based on the differences between high-value and low-value shippers in transport prices and service quality of the operating platform. The optimal decision-making information of each subject is solved under the unified or differential pricing and subsidizing mechanism. By comparing the difference in shippers' order quantity, consumer surplus, and the government subsidy amount under different circumstances, this paper probes into the application scope of each method and mechanism. The numerical analysis is performed forreal situations. The results show that under the differential pricing and subsidizing mechanism, subsidizing shippers' freight rates can bring more consumer surplus to low-value shippers than that of subsidizing operating costs. When the price sensitivity coefficient of shippers is lower than the specific critical value, the subsidy cannot bring subsidy performance to the government and enable this part of shippers to generate order quantity. The subsidy might be cancelled for these shippers. It is also found that when the unit transportation cost of operation platform meets certain conditions, the differential pricing and subsidizing mechanism can achieve the effect of "subsidy recession" and not reduce the order quantity and consumer surplus. References | Related Articles | Metrics

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Division Model of Container Port Hinterland Considering Shippers' Behavior Decisions GAO Tian-hang, XU Xing, WU Hong-yu, BI Shan-shan, TIAN Jia 2023, 23(1): 10-16.  DOI: 10.16097/j.cnki.1009-6744.2023.01.002 Abstract ( )   PDF (1683KB) ( )   A logical chain of shippers' behavioral decisions is proposed based on a path optimization model, in which port constraints, transportation time constraints, and route constraints are introduced. This helps to address the drawback that the existing container port hinterland division model is based on the assumption of complete rationality yet ignores the shippers' behavioral decisions when choosing ports, resulting in a significant difference from the actual situation. Based on the additional constraints, a linear integer programming model is constructed with the objective of reducing the shipper's transportation cost and the traffic volume of each collection and distribution route as the decision variable. For the purpose of model validation using example data, nine cities and eight ports in the Pearl River Delta have been chosen for this research. According to the analysis of throughput indicators over time, the mean squared error and mean percentage error of the model are much lower than the conventional models, and the comparison of OD flow is more accurate. The findings demonstrate that the container port hinterland division model taking the shipper's behavior decisions into account may more precisely match the criteria. References | Related Articles | Metrics

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Evolutionary Game Analysis of Port and Shipping System Emission Reduction Under Government Regulation LI Xiao-dong, KUANG Hai-bo, HE Hong-yua 2023, 23(1): 17-29.  DOI: 10.16097/j.cnki.1009-6744.2023.01.003 Abstract ( )   PDF (2913KB) ( )   PDF(English version) (1841KB) ( 74 )   This paper focuses on the emission reduction problem in China's port and shipping system and proposes a game model with environmental regulation evolutions. The model includes the subjects of local governments, ports, and shipping companies. The study analyzes the strategy selection process and overall evolutionary stability of the three subjects and clarifies the driving mechanism of the evolutionary trend for each subject in the port and shipping system. Based on the numerical simulation analysis, the paper discusses the initial strategy of the three subjects and the strategy choice of the port and shipping system under different incentive and punishment mechanisms of local government. The results show that: (1) The active supervision strategy of local governments is related to the low willingness of ports and shipping companies to actively reduce emissions. (2) The evolution rate of active emission reduction strategies of ports and shipping companies corresponds directly to their mitigation intentions. (3) Under the static incentive and punishment mechanism, the penalty intensity of the local government does not affect the positive emission reduction strategies of ports and shipping companies. Still, it leads to their negative emission reduction if local governments adopt low subsidy measures. (4) Local governments have only a single strategy (high subsidies, no penalties) to enable ports and shipping companies to reach an evolutionary equilibrium (active emission reduction, aggressive emission reduction) under the static incentive and punishment mechanism. (5) Under the dynamic incentive and punishment mechanism, local governments adopt a hybrid regulatory strategy (low dynamic subsidy, high static penalty) to achieve the evolutionary equilibrium of active emission reduction strategies for port and shipping systems with low cost. References | Related Articles | Metrics

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Carbon Emission Efficiency and Influencing Factors Analysis of Urban Rail Transits ZHOU Qi, LIANG Xiao, HUANG Jun-sheng, WANG Hai-peng, MAO Bao-hua 2023, 23(1): 30-38.  DOI: 10.16097/j.cnki.1009-6744.2023.01.004 Abstract ( )   PDF (2015KB) ( )   To explore an efficient and green development pathway of urban rail transit, the carbon emission efficiency of China's urban rail transits is comprehensively analyzed from both static and dynamic aspects. First, we use the "topdown" method to measure the carbon emissions and build a system covering vehicles, human resources, energy, environment, and transport benefits. Then, a super efficiency Slack Based Model (SBM) model considering unexpected output is used to measure the carbon emission efficiency of rail transits in 23 provincial capitals in China, and the Global Malmquist Lounberger (GML) index is constructed using the directional distance function to analyze the dynamic characteristics of carbon emission efficiency. Finally, the panel model is used to clarify the influencing factors of carbon emission efficiency. The results indicate that the carbon emission efficiency of urban rail transit shows a positive correlation with the network scale. The changing characteristics of carbon emission efficiency GML and its decomposition index are different with different types of urban rail transits. Scale efficiency, technological progress and passenger turnover can improve the carbon emission efficiency. An increase of 1% in the growth rate of scale efficiency and technological progress can result in the carbon emission efficiency GML index increased by 1.906% and 2.338%, respectively. The proportion of thermal power generation has a certain inhibitory effect on the improvement of carbon emission efficiency. With the development of the urban rail transits, the improvement of carbon emission efficiency still needs technological progress. Finally, the main directions to improve carbon emission efficiency are proposed for different types of urban rail transits. References | Related Articles | Metrics

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Emission Reduction Strategy Research of Port and Shipping Enterprises Considering Carbon Emission Policies LIANG Jing, ZHANG Lin, LIU Yu-xuan 2023, 23(1): 39-47.  DOI: 10.16097/j.cnki.1009-6744.2023.01.005 Abstract ( )   PDF (1456KB) ( )   Considering the two carbon emission policies that the government currently adopts: carbon tax or carbon trading scheme, this paper proposes the emission reduction game model based on the perspective of supply chain, and analyzes the emission reduction strategy choice for port and shipping enterprises and the government's carbon emission policy choice. The results show that when the cost of emission reduction for shipping enterprises is less than the cost of port, the port and shipping enterprises would choose decentralized decision-making to make the best intensity of emission reduction; otherwise, they would choose centralized decision-making. In centralized decision-making, the intensity of emission reduction of port and shipping supply chain is inversely proportional to the cost of emission reduction of port and shipping enterprises, and proportional to the market capacity. In decentralized decision-making, the cost of emission reduction of port will not affect the intensity of emission reduction of port and shipping supply chain. In the case of lower carbon pricing, if the port and shipping enterprises choose decentralized decision-making, they will get the optimal pricing. And if they choose centralized decision-making, they will get the optimal profit. But in the case of higher carbon pricing, the opposite result will be obtained. Regardless of what carbon emission policy the government adopts, if port and shipping enterprises choose centralized decision-making and the government subsidizes port emission reduction, the overall profit and the emission reduction intensity of port and shipping supply chain will simultaneously reach the optimal values. In the case of a substantial increase in carbon pricing in the future, it is better to adopt carbon trading scheme for government to promote the effect of emission reduction of port and shipping enterprises. References | Related Articles | Metrics

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Optimization of Air-rail Transshipment Hub Location Based on Time Value of Goods SHEN Hao, ZHANG Jin, SUN Wen-jie, HONG Zhi-chao, LI Guo-qi 2023, 23(1): 48-57.  DOI: 10.16097/j.cnki.1009-6744.2023.01.006 Abstract ( )   PDF (1894KB) ( )   An efficient and reasonable intermodal transport network is an important support for the innovation of air-rail intermodal express service products. In this paper, considering the time value of goods, an air-rail intermodal transfer hub location model is constructed, which takes the location of the air-rail intermodal transfer hub and the choice of transportation mode as decision variables with the objective of the lowest comprehensive cost. A variable neighborhood genetic algorithm is designed to solve the model. Using 200 prefecture-level cities with high-speed rail and airports as of 2019, alternative cities for air-rail transit hubs were selected using a combination of complex networks and superiordisadvantage solution distance methods. The practicality and effectiveness of the model and algorithm are verified. The results show that the transportation time limit and discount factor are important factors affecting the comprehensive cost of the air-rail intermodal transportation network. With the increase in transportation time limit and the decrease in the discount factor, the integrated cost of the air-rail intermodal transportation network is reduced by 15% and 11% respectively. The spatial layout of air- rail intermodal transfer hubs is mainly influenced by transport time constraints. When the transportation time limit is low, the layout of air-rail intermodal transfer hubs is mainly in the northeast and western regions to improve the timeliness of express transportation. When the transport time limit is high, the layout of air-rail transit hubs shifts to the central and eastern regions to serve the economy. The optimal number of hubs in the airrail intermodal transport network is influenced by the combination of the transport time limit and discount factor, but the transport time limit has a more significant impact on the optimal number of hubs. It is an important way to improve the timeliness and competitiveness of air-rail intermodal transport services to reasonably arrange air-rail intermodal transport hubs and improve the scale efficiency of air-rail intermodal transport. References | Related Articles | Metrics

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Optimization of Reliable Routes for Multimodal Transport of Emergency Supplies Under Dual Uncertainty LIU Song, SHU Wen, PENG Yong, SHAO Yi-ming, LE Mei-long 2023, 23(1): 58-66.  DOI: 10.16097/j.cnki.1009-6744.2023.01.007 Abstract ( )   PDF (2003KB) ( )   In order to deliver emergency supplies to destinations on time and reliably, a reliable path optimization model for multimodal transportation of emergency supplies with the greatest reliability was constructed. The model takes into account the dual uncertainties of demand and transportation environment, the risk of nodal epidemic infection, cost constraints, shift restrictions, and transshipment capacity constraints. According to the NP-hard characteristics of the problem sought, the Monte Carlo adaptive genetic algorithm and simulated annealing genetic algorithm are designed to solve them, and the superior and inferior solution distance method is introduced to analyze the operation results of the study. The results show that the Monte Carlo adaptive genetic algorithm is better than the simulated annealing genetic algorithm in terms of solution quality and solution time, and the maximum reliability of the optimized path is 85% under the optimal parameter combination of 0.80 cross probability, 0.08 mutation probability and 50 population size, and the optimal route solved does not pass through the nodes with epidemic infection risk, and the solution results are better. The parameter analysis shows that under the condition of the same cross probability, the average running time of the two algorithms decreases with the decrease of the variation probability and increases with the increase of the variation probability. The decision to optimize the route of multimodal transport is influenced by the schedule of the water and rail. References | Related Articles | Metrics

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Traffic State Recognition Based on Speed Fluctuation Characteristics of Floating Car CHENG Wei, HUANG Jin-tao, CHEN Yu-guang, GUO Yan-yong, YU Hao 2023, 23(1): 67-76.  DOI: 10.16097/j.cnki.1009-6744.2023.01.008 Abstract ( )   PDF (2511KB) ( )   To realize the accurate identification of the road traffic state and solve the problem that the single parameter cannot directly identify the road traffic state, this paper uses the high-frequency floating vehicle speed characteristic data and the gray co- occurrence matrix eigenvalue contrast and inverse variance to represent the fluctuation characteristics of vehicle driving. Based on the dynamic and continuous changes of urban road traffic state, the average speed, contrast, and inverse variance of vehicles in a fixed time window are analyzed using the Fuzzy c-means (FCM) algorithm, and four state thresholds are obtained: free, smooth, crowded and blocked. A traffic state recognition method is proposed based on a multi-dimensional Gaussian Hidden Markov model. The model is trained with fixed-time Windows of 3 min, 5 min, and 6 min respectively. The state transition matrix of the model shows that the smaller the time window is, it is more likely to keep the original traffic state, and the larger the time window, it is more likely to change the traffic state. Using different sequence lengths to compare the recognition accuracy of the three-time Windows in the test set, the results show that the accuracy increases first and then decreases with the change of sequence length, and the larger the fixed time window, the more uniform the change of recognition accuracy of different sequence lengths. At last, the 5 min fixed time window was used to partition the data, and the proposed method, support vector machine, and random forest were used to identify the road traffic state, and the comprehensive accuracy was respectively 92%, 84.89% and 88.48%. By comparing the precision, recall, and F1 measurement of each state, the proposed method is better than other two models, which indicates that the fluctuation characteristics of road speed can well reflect the road traffic state, and the multi-dimensional Gaussian Hidden Markov model has a good effect on the recognition of road traffic state. References | Related Articles | Metrics

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A Combination Model for Connected and Autonomous Vehicles Lane-changing Decision-making Under Multi Connectivity Range ZHAO Jian-dong, HE Xiao-yu, YU Zhi-xin, HAN Ming-min 2023, 23(1): 77-85.  DOI: 10.16097/j.cnki.1009-6744.2023 Abstract ( )   PDF (1860KB) ( )   In order to improve the lane-changing efficiency of intelligent connected vehicles (ICV) under different network connection ranges, combined with deep reinforcement learning and molecular dynamics theory, a double deep Q network lane-changing decision model integrating the masking mechanism and attention mechanism (MAQ) is proposed. Firstly, in the Simulation of Urban Mobility (SUMO) simulation environment, the driving status information of connected vehicles and human drive vehicles (HDV) within the network range is collected. Secondly, the MAQ model is built, the mask mechanism and attention mechanism are adopted to achieve fixed model input size and displacement invariance. Thirdly, in order to quantify the degree of influence between vehicles, the relative speed and the relative position between vehicles are used as parameters, and the molecular dynamics theory is used to give weights to HDV information within the connectivity range. Finally, different lane- changing decision models and weighting methods are compared in different traffic density simulation environments. The effect of lane change decision is tested under different connectivity ranges (80~330 meters, with an interval of 50 meters). The simulation results show that, taking 40 HDVs and a 100-meter connectivity range as an example, the MAQ model has a 90.2% improvement in fitting accuracy compared with the DeepSet-Q model; compared with the linear weighting method, the molecular dynamics weighting method increases the total reward value by 5.5% , and the average speed of ICV by 4.4%; with the expansion of the connectivity range, the average speed of ICV shows a change rule of first increasing, then decreasing, and then tending to be stable. References | Related Articles | Metrics

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Algebraic Method of Bidirectional Green Wave of Arterial Road Coordinate Control for Unequal Double-cycle LU Kai, ZHAO Yi-ming, YE Zhi-hong, SHOU Yan-fang 2023, 23(1): 86-96.  DOI: 10.16097/j.cnki.1009-6744.2023.01.010 Abstract ( )   PDF (2343KB) ( )   Considering the shortcomings of the existing algebraic method studies that were mainly applicable to singlecycle control, this paper proposed an algebraic method of bidirectional green wave of arterial road coordinate control for unequal double-cycle. By redefining the double-cycle control mode, the limitation that the signal cycle of a doublecycle intersection was fixed to half of the arterial public signal cycle was broken, and two types of coordination for double-cycle intersections were summarized. The signal cycle control mode for each intersection was determined by calculating the allowable range of the common signal cycle. Then, by analyzing the coordination characteristics of double-cycle intersections, the ideal intersection spacing equation for a double-cycle intersection was derived. The split allocation in the coordination direction was realized by setting the allocation ratio for each split of the double-cycle intersection. At last, the final optimization solution was determined by minimizing the sum of the maximum adjusted bias splits. The experimental results show that, compared with the double-cycle model and the single-cycle algebraic method, the proposed algebraic method reduces the average delay by 16.0% and 19.6%, and the average number of stops by 15.1% and 15.5%, respectively. In particular, the solution scheme of the proposed algebraic method can reduce the average delay of branch traffic and pedestrian crossing respectively by 46.0% and 50.7% compared to the singlecycle algebraic method. It proves that this method can achieve the desired green wave coordinated effect, extend the application of the algebraic method, and effectively reduce the delays. References | Related Articles | Metrics

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Coordination of Signal and Vehicle Trajectory at Intersections for Mixed Traffic Flow SUN Wei, ZHANG Meng-ya, MA Cheng-yuan, ZHU Ji-chen, YANG Xiao-guang 2023, 23(1): 97-105.  DOI: 10.16097/j.cnki.1009-6744.2023.01.011 Abstract ( )   PDF (1976KB) ( )   PDF(English version) (774KB) ( 62 )   The intersection traffic control under the mixed traffic environment can be realized by the coordination of the signal control and the trajectory control of automated vehicles, which can greatly improve the utilization efficiency of road traffic resources. The centralized control strategies in previous studies with the integrated optimization of signal timing and vehicle trajectory are difficult to be applied to the real operations with self-organized vehicles, and often have high computational complexity. In this paper, a logic-based coordinated control of the signal and vehicle trajectory is proposed within a decentralized framework. Based on the active servo control principle of fast and slow variables in the coordination theory, a coordination framework of the slow variables of intersection signal timing and the fast variables of vehicle trajectory strategy is designed. A logic-based signal timing optimization method and a speed control method are proposed for the connected and automated vehicles (CAV). The signal timing at the intersection can adapt to traffic demand dynamically, and the CAVs can optimize their speed strategy based on the prediction of the traffic states to pass the intersection efficiently and smoothly. Based on the reasonable speed control of the leading vehicle in the approach lanes during the green signal, the "leading effect" of the CAV can be utilized to avoid start-up loss and make the platoon pass the intersection efficiently. The simulation results show that the proposed cooperated control method can significantly reduce the average vehicle delay at intersections compared with the traditional control methods, and the logic-based decision making model can be solved efficiently. Based on the sensitivity analysis of the key parameters of the control strategy for the CAV, the fairness of the mixed traffic flow at intersection is further discussed, and the effectiveness of the control methods are compared for the mixed traffic with different penetration rates of CAVs. References | Related Articles | Metrics

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Signal Phase and Timing Optimization Method for Intersection Based on Hybrid Proximal Policy Optimization CHEN Xi-qun, ZHU Yi-zhang, LV Chao-feng 2023, 23(1): 106-113.  DOI: 10.16097/j.cnki.1009-6744.2023.01.012 Abstract ( )   PDF (2368KB) ( )   PDF(English version) (1649KB) ( 63 )   Traffic signal timing is one of the critical measures to alleviate urban traffic congestion from the supply side. With traffic big data technology development, traffic signal control based on deep reinforcement learning has become a key research direction. Most of the existing control frameworks belong to discrete phase selection control, where phase associated duration is obtained by accumulating decision intervals. It may conflict with the agent's exploration for better actions. Therefore, this paper proposes a signal phase and timing optimization method based on hybrid proximal policy optimization for intersection. The study first defines a signal control action as a parameterized action under the constraint of practical application boundary condition of phase duration. Then, the state information is extracted and input into the bi-policy network to adaptively generate the next phase and its associated duration. The reward value of implementing action is evaluated according to the state change of the road network, so as to learn the intrinsic connection between phase and phase associated duration. A simulation platform is built to test the proposed method and compare the algorithms with real traffic flow data. Results show that compared with the discrete control, the proposed method achieves a lower decision frequency and better control effect, and the average travel time of vehicles and average queue length of lanes are reduced by 27.65% and 23.65%, respectively. References | Related Articles | Metrics

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Optimization Mapping Method on Feasible Duration of Service Function Chain in Vehicle-road Cooperative Network MENG Yun, NIU Yong-hao, WANG Ping, DAI Liang 2023, 23(1): 114-122.  DOI: 10.16097/j.cnki.1009-6744.2023.01.013 Abstract ( )   PDF (1976KB) ( )   Considering the difference in the connected duration of physical links in vehicle-road cooperative network, this paper proposes an optimization model of service function chains (SFC) mapping to maximize the feasible mapping duration. First, the physical transmission network model is proposed to represent the vehicle-road cooperative network, and the connected durations are analyzed for the vehicle-to-vehicle link and the vehicle-to-road link. Then, an integer programming problem is established, aiming to maximize feasible mapping duration with the mapping constraints of SFC. To solve the NP-hard problem, this paper provides an approximation technique based on the enhanced subgraph isomorphic and design the pruning based on node characteristics, link attributes, and the connection between links to reduce the complexity. By considering the difference in the connected duration of physical links and optimizing the selection, experimental results show that the feasible mapping duration of the proposed algorithm has increased by 34.4% under the communication range equal to 150 meters and the maximum speed of the vehicle equals 60 km ⋅ h- 1 . Experimental results on the three main influencing factors, including the vehicle-vehicle communication range, the number of vehicles and the maximum vehicle speed, show that the proposed algorithm can effectively improve the feasible mapping duration of service. References | Related Articles | Metrics

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Intelligent Guidance and Organization Optimization of Subway Security Inspection Passenger Flow DING Xiao-bing, SHI Gan, HONG Chen, LIU Zhi-gang 2023, 23(1): 123-130.  DOI: 10.16097/j.cnki.1009-6744.2023.01.014 Abstract ( )   PDF (1976KB) ( )   To reduce the queuing time, station congestion and even stampede accidents caused by the spatial imbalance of the passenger flow entering the subway station, this paper proposes an intelligent guidance and diversion system for subway security inspection and an optimization method for passenger flow organization. Based on the traditional subway security inspection mode, an intelligent guided diversion system is developed. According to the type of passenger baggage, the security inspection station is divided into big bag, small bag/no bag security inspection channels, and the passenger flow distribution characteristics in the security inspection area are collected in real time. Then, the optimal security queuing decision is calculated based on queuing theory and in consideration of the transfer time and passengers transfer intention. Through guiding equipment to help passengers choose the optimal security inspection channel, the passenger flow data of Shanghai Metro Line No. 1 in the evening peak is selected as the example for simulation analysis, and the calibration verification is carried out through field experiments. The results show that the queuing pressure of passenger flow in each security inspection channel is significantly reduced with the proposed method. The error between the calculated simulation results and the actual security inspection situation is 3.2% , and the average security inspection queuing efficiency of large package passengers is increased by 38% after optimization. The average security inspection queuing efficiency of small package/non package passengers is increased by 16%. The research results provide theoretical basis and method support for improving the efficiency of passengers queuing at security inspection and effectively alleviate subway station congestion. References | Related Articles | Metrics

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Integrated Rolling Stock Scheduling and Maintenance Scheduling Optimization of Urban Rail Transit Lines Based on Shared Train Depot FENG Jia, LI Guo-wei, FU Yi-long, PENG Cheng, WU Yu-hui 2023, 23(1): 131-140.  DOI: 10.16097/j.cnki.1009-6744.2023.01.015 Abstract ( )   PDF (1723KB) ( )   A scientific rolling stock plan contributes to smooth and energy-efficient operation of rail transit. This study integrates the rolling stock scheduling and the maintenance plan of urban rail transit with multi-lines and multi-depots, including a shared depot in the infrastructure network. The integrated problem of rolling stock scheduling and maintenance plan for urban rail transit are established and the aims are reducing the operational and maintenance costs of rolling stock. The mixed integer linear programming model is developed to simultaneously optimize rolling stock and maintenance plan, in consideration of the constraints of rolling stock connection, maintenance and depot capacity. The hybrid heuristic algorithm is used to solve the proposed model and create the strategy to select the rolling stocks with high requirement of maintenance. The numerical experiments were performed in a metro line of China, and the results indicate that the total operating cost of the optimization model under the network operation condition is reduced by 8.1% compared with the single line operation condition. The reasonable allocation of rolling stock can be achieved by using appropriate inspection frequency and maintenance resource arrangement. References | Related Articles | Metrics

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Optimization of Empty Car Distribution in Loading End of Heavy Haul Railway XU He-ying, LV Hong-xia, LV Miao-miao, WANG Meng 2023, 23(1): 141-151.  DOI: 10.16097/j.cnki.1009-6744.2023.01.016 Abstract ( )   PDF (1816KB) ( )   The loading end of heavy haul railway is usually a mix of combined and unit trains, and empty train distribution affects the combined operation of their loaded trains. To enhance the utilization of line capacity and accelerate cargo transportation, the empty car distribution is studied by considering the loaded train combination, based on the loading station demand for arriving empty trains. A multi-objective 0-1 planning model to minimize the arrival penalty of empty trains, maximize the number of loaded train combinations, and minimize the loaded train combination waiting time is constructed by taking the section capacity and operation capacity of technical stations as constraints and solved by non-dominated sorting genetic algorithm with elite strategy (NSGA-II). Finally, through the application of empty car distribution at the east line of the Baoshen railway, the results show that the penalty for arriving empty trains can reach 0. Compared with the traditional empty car distribution method, it effectively reduces the combination waiting time of loaded trains and can be used for empty car distribution in the loading end of heavy haul railways. References | Related Articles | Metrics

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Optimization of Variable Electric Bus Schedule Considering Volatility in Trip Travel Time WANG Yi-ran, MO Peng-li, BIE Yi-ming, XU Zhi-hong, WAN Jian, LIU Zhi-yuan 2023, 23(1): 152-164.  DOI: 10.16097/j.cnki.1009-6744.2023.01.017 Abstract ( )   PDF (2392KB) ( )   A variable bus schedule optimization model based on asymmetric characteristics in two directions, including the number of frequencies and operating time, was proposed to cope with the inaccurate timetable caused by random fluctuations in trip travel time. The model combined bus timetabling and vehicle scheduling under the constraints of trip connectivity and battery state of charge to minimize the number of vehicles and passengers' waiting time. A modified particle swarm optimization algorithm, called modified particle swarm optimization for timetabling and scheduling (MPSO-TS), was tailored to tackle the integrated optimization problem of bus timetabling and vehicle scheduling. Besides, a customized particle encoding and offspring update method was provided. The offspring update method was built on the "dominant vehicle chain", wherein the "vehicle chain" can help to inherit the relevance between the timetable and vehicle scheduling from the parent generation. A bus line in Lianyungang, China, was taken to verify the proposed model and algorithm. The results show that the proposed method can ensure the punctuality of timetable. Moreover, the proposed method generates a tighter vehicle schedule and reduces the number of vehicles used from 35 to 31. The vehicles' utilization is effectively increased by 28.1%. The proposed MPSO-TS has a high level of efficiency and stability, as well as a powerful capacity to avoid from being trapped in the local optimum. References | Related Articles | Metrics

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Feeder Bus Route Design and Vehicle Allocation Under Influence of Shared Bikes LIU Lu-mei, LIU Zheng-ke, MA Chang-xi, TAN Er-long, MA Xiao-lei 2023, 23(1): 165-175.  DOI: 10.16097/j.cnki.1009-6744.2023.01.018 Abstract ( )   PDF (2157KB) ( )   PDF(English version) (1962KB) ( 62 )   For the "first-and last-mile" of rail transit, feeder buses and shared bikes are two most prevalent modes to provide connection with rail transit for commuters. To understand the impact of bike-sharing on the planning and operation of feeder bus travel demand and route design, this study examines the feeder bus route design and vehicle allocation challenges based on the interaction of demand and supply. From the demand side, the actual travel demand of feeder buses is dynamically estimated depending on the user's mode choice between shared bikes and feeder buses, considering the travel time and travel cost. Comparatively, a mixed-integer non-linear programming model with the objective of minimizing the sum of bus operating cost and user travel cost is developed from the supply perspective to optimize the bus route design and vehicle allocation, including vehicle capacity, vehicle quantity, and flow balance constraints. The Lagrangian relaxation algorithm is used to solve the model. This strategy is applied to the planning of feeder bus routes in the Beijing suburbs surrounding the Huilongguan Metro Station. The actual smart card data and Mobike cycling data are used to obtain the total travel demand. The travel time by various modes between stops is derived from the AutoNavi route planning API (Application Programming Interface). In the case where the total number of vehicles is 10 and the number of lines is 2, the experimental results show that the difference between the assumed bus travel demand and the computed bus ridership can be effectively avoided if the influence of bike-sharing on bus travel demand is considered. The average running time between each bus stop and the station is 15.58 minutes, while the average passenger waiting time is 3.35 minutes. In the case where there are four lines, the average running time from each bus stop to the station is 8.53 minutes, which is almost half of the case with only two lines; the average waiting time for passengers is 3.44 minutes. Nonetheless, the computing time for the model grows exponentially with the increasing of the number of lines. Consequently, from the perspective of model calculation efficiency, both scenarios in which the number of lines is set to 2 or 3 can satisfy the application requirement of updating lines every half hour. References | Related Articles | Metrics

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A Prediction Model of Entry and Exit Passenger Flows of Rail Transit Stations for Group-structured City Based on Attribute Weighted Regression PENG Ting, ZHOU Tao, CAI Xiao-yu 2023, 23(1): 176-186.  DOI: 10.16097/j.cnki.1009-6744.2023 Abstract ( )   PDF (2410KB) ( )   To enhance the adaptability of the regression model of entry and exit passenger flow prediction of rail transit stations for a group-structured city, this paper used multi-source data to refine the statistical indicators of various influencing factors, so as to accurately reflect the differences between different rail transit stations. Since entry and exit passenger flows for a group-structured city have different spatial distribution characteristics at different scales, the attribute differences between samples were used to characterize the heterogeneities of passenger flows, and an Attribute Weighted Regression (AWR) model was proposed by combing the K-nearest neighbors algorithm and Geographically Weighted Regression (GWR) model. The case study in the central area of Chongqing shows that the AWR model can consider the spatial distribution characteristics of sample sets at different scales, and it is more suitable for situations where the samples vary greatly. At the same time, the AWR model has no specific restrictions on spatial correlation characteristics, which makes it more adaptable to group-structured cities. Compared with the Multiple Linear Regression model based on the Ordinary Least Squares (OLS model) and GWR model, the AWR model can significantly improve the goodness of fit and the prediction accuracy of passenger flow demand of rail transit stations for the group-structured city, and the negative spatial correlation of prediction errors is significantly weakened.Therefore, the AWR model proposed is useful for the prediction of entry and exit passenger flow of urban rail transit stations. References | Related Articles | Metrics

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Traffic Flow Forecasting Based on Bi-directional Adaptive Gating Graph Convolutional Networks HE Wen-wu, PEI Bo-yu, LI Ya-ting, LIU Xiao-yu, XU Shao-bing 2023, 23(1): 187-197.  DOI: 10.16097/j.cnki.1009-6744.2023.01.020 Abstract ( )   PDF (1965KB) ( )   Considering the facts that the spatio-temporal dependence of network traffic flow is highly complex and that traffic flow data has noises in practices, this paper proposes a novel spatio- temporal fusion model based on graph neural network for effective traffic flow forecasting. To alleviate negative impacts of data missing, data exception and data noise, a feature fusion block is designed to reconstruct input features and smoothing them within a sliding time window, and then the obtained features are fed into the main body of the proposed model. The main body adopts a design of bi-directional networks to learn respectively the forward and reverse spatio-temporal representation of traffic flow. Both networks share the same structure but with different adjacent matrices. In particular, the causal convolution is used as the temporal feature extractor, and a block of adaptive gated graph convolutional neural network is specially designed for spatial feature extracting, to realize adaptively information aggregation and propagation. Then, a lightweight longitudinal information aggregation layer is constructed to realize information fusion within different local receptive fields. At last, information contributions of the forward and reverse networks are weighed and aggregated with an attention-output module, to establish the expected Bi-directional Adaptive Gating Graph Convolutional Networks (Bi- AGGCN) for traffic flow forecasting. To validate the effectiveness of the proposed model, a series of experiments are carried out on four real traffic flow benchmark datasets, i.e., PEMS03, PEMS04, PEMS07 and PEMS08. Experimental results show that the proposed model Bi-AGGCN can outperform all baseline models over four datasets with three metrics. At the same time, compared with the state-of-the-art baselines, i.e., Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN) and Spatial-Temporal Fusion Graph Neural Networks (STFGNN), Bi-AGGCN is dramatically lighter in parameter scale and faster in training time, and achieves higher prediction accuracy at a significant lower cost. References | Related Articles | Metrics

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Car Operation Rate Prediction Based on Combination Model of Hunter-prey Optimizer Algorithm and Bi-directional Long Short-term Memory Neural Network GAO Yu-hong, , QU Zhao-wei , SONG Xian-min 2023, 23(1): 198-206.  DOI: 10.16097/j.cnki.1009-6744.2023.01.021 Abstract ( )   PDF (2354KB) ( )   The prediction of car operation rate is of great significance for traffic managers to formulate precise control plans, implement coordinated management strategies, and regulate the scale of car ownership in advance. This paper proposes a car operation rate prediction method based on the combination model of hunter-prey optimizer algorithm and bi-directional long short-term memory neural network (HPO-BiLSTM). The key influencing variables of the car operation rate are analyzed, and 17 feature influencing factors are extracted. Combined with the reconstructed time series after normalization, the importance of variables is evaluated based on random forest algorithm, and the optimal feature set is selected as the input of the prediction model. Then, a combined prediction model that fuses the hunterprey optimizer (HPO) and the bidirectional long short-term memory (BiLSTM) is established to solve the problem that the neural network algorithm is prone to fall into local extrema. It utilizes the exploration-exploitation mechanism of the HPO algorithm to realize the dynamic construction and precise parameter adjustment of the BiLSTM framework.The model performance is then verified by combining the data set of car operation rate in the central urban area of Beijing. The results indicate that: compared with the classic algorithms such as auto- regressive integrated moving average model, grey model, convolutional neural network model, long short-term memory model and bidirectional long short-term memory model, the HPO-BiLSTM model is more effective in predicting the car operation rate. The mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) are reduced by 23.85% to 54.38% , 20.67% to 57.40% , 27.48% to 59.32% , and the mean relative error is - 1.57% . The proposed algorithm shows high prediction accuracy and practical performance. References | Related Articles | Metrics

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Impact Model of COVID-19 and Built Environment on Bus Passenger Flow FU Zhi-yan, GAO Yu-yue, CHEN Jian, CHEN Qi 2023, 23(1): 207-215.  DOI: 10.16097/j.cnki.1009-6744.2023.01.022 Abstract ( )   PDF (1921KB) ( )   In this study, the factors of COVID-19 and the built environment are used to examine variations in bus passenger flow. The study aims to reveal the influencing mechanism of bus passenger flow in the context of epidemic prevention and control, thereby providing strategic support for the quick recovery of bus passenger flow in the postepidemic period. This study focuses on Guangzhou City, and the data are collected from the bus IC card, point of interest (POI), and road network. The ordinary least squares (OLS) model and gradient boosting regression tree model (GBRT) are constructed to analyze the passenger flow of bus stops. The results show that the fitness of the GBRT model, which takes into account nonlinear effects, is superior to that of the OLS model. The key factors influencing changes in bus passenger flow during the epidemic period are the number of bus lines (which accounts for 22.02%) and the distance to the city center (which accounts for 13.56%). The findings indicate that the impact of COVID-19 on bus passenger flow is not crucial. With the normalization of epidemic prevention and control, people's demand for bus travel is recovering. References | Related Articles | Metrics

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Traffic Flow Assignment Method Considering Travelers' Different Rational Degree Under Congestions LONG Xue-qin, WANG Rui-xuan, WANG Han 2023, 23(1): 216-223.  DOI: 10.16097/j.cnki.1009-6744.2023.01.023 Abstract ( )   PDF (2222KB) ( )   To investigate the influence of traveler' perceptual preference on traffic assignment results, this paper proposes a microscopic route selection model and a traffic assignment algorithm constrained by road capacity under congested conditions. Travelers' regret and indifference threshold are introduced into the decision-making process. The route selection probability model under different rational degree is developedin consideration of the psychological perception difference of normal travel time and queuing time. At the aggregate level, the inflow and outflow correction method is proposed based on the capacity limitation of the current road and its upstream and downstream sections, as well as the space queue and overflow of vehicles. The incremental traffic loading assignment method is adopted to study vehicles' dissipation characteristics, and the evolution process from individual route decisions to macroscopic road network states is reproduced. Based on the Nguyen-Dupuis simulation network, the traffic congestion and inflowoutflow are compared using different algorithms. The results show that travelers' personal preference perception significantly affect the cost function of congested road sections, but has minimum impact on vehicles' inflow and outflow in non-congested sections. The bounded rationality traffic assignment method considering individual preferences can reduce the average saturation of the road network. The proposed traffic assignment method can be applied to the traffic guidance of the congested road network, which is beneficial to the rational utilization of road resources. References | Related Articles | Metrics

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Cooperative Merging Strategy for Freeway Ramp in a Mixed Traffic Environment HAO Wei, GONG Ya-xin, ZHANG Zhao-lei, YI Ke-fu 2023, 23(1): 224-235.  DOI: 10.16097/j.cnki.1009-6744.2023.01.024 Abstract ( )   PDF (3012KB) ( )   In order to improve traffic efficiency and reduce traffic accidents, a hierarchical cooperative merging framework was proposed under a mixed traffic condition that is composed of both connected and automated vehicles (CAV) and human-driven vehicles (HDV). The framework integrated the merging sequence scheduling algorithm and the cooperative merging algorithm and adjusted it in real time according to the vehicle type and state. Firstly, a realtime merging sequence scheduling algorithm based on heuristic was proposed to optimize the merging sequence, which can address the drawback that traditional merging sequence scheduling algorithms cannot adapt to the random disturbance of HDV driving behavior. Next, Using the merging sequence scheduling algorithm, the cooperative merging vehicle group as well as the vehicle type was determined according to the position of the vehicles. The cooperative merging algorithm of CAV- CAV, CAV- HDV, and HDV- HDV was established to describe the merging strategy under the mixed traffic flow by mathematical models. Simulation investigations demonstrate that compared with the no-control situation and the "first-in-first-out" strategy, the total delay is reduced by 21.66% and 39.88%, respectively. The length of the cooperative control area has a influence on the fuel consumption, and the energy consumption decreases with the increase of the distance, and there is a minimum value, namely 300 m, after reaching the minimum value, the energy consumption will gradually increase; the increase of the time headway has a certain influence on reducing the energy consumption of the vehicles. Among them, time headway between HDVs has a greater influence on fuel consumption than time headway between CAVs. References | Related Articles | Metrics

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Dual Attention Guided Cross-layer Optimized Traffic Scene Semantic Segmentation XIE Xin-lin, LUO Chen-yan, XU Xin-ying, XIE Gang 2023, 23(1): 236-244.  DOI: 10.16097/j.cnki.1009-6744.2023.01.025 Abstract ( )   PDF (2494KB) ( )   This paper proposes a dual attention guided cross-layer optimized traffic scene semantic segmentation to solve the problems that edge of object segmentation is not smooth and small objects are difficult to be accurately segmentedin the traffic scene. A coding network is established with multi-branch feature extraction. The assignment of dilation rate of atrous convolution is non-proportional and can extract spatial context information whichavoids the loss of small object information. Then, a cross-layer feature fusion decoding network based on spatial alignment is constructed to fuse semantic information and detail information, thereby enhancing the expressive ability of objects of different scales. At last, the channel and spatial attention mechanisms are proposed to model global channel correlations and long- distance location correlations, which enhances the ability to learn key features of the network. The experimental results on the traffic scene datasets Cityscapes and CamVid show that the proposed feature extraction encoding network, cross-layer feature fusion decoding network, and attention mechanism are effective.The proposed semantic segmentation algorithm achieves the mean intersection over union ratio of 77.79% and 78.66% , and can smooth the edge of object segmentation, especially for the long and thin objects. References | Related Articles | Metrics

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Adaptability of Expressway Full Time Emission Model Based on Dimensionality Reduction TURSUN Mamat, MA Jie, LIU Zhi-cheng, CHEN Jun-hao 2023, 23(1): 245-253.  DOI: 10.16097/j.cnki.1009-6744.2023.01.026 Abstract ( )   PDF (2179KB) ( )   To meet the needs of urban expressway vehicle pollutant emission control and consider the differences of emission models in mapping the relationship between emission influencing factors and emission rates in different periods, this paper uses the real condition emission rate data and develops a dimensionality reduction model based on the Back propagation neural network (BPNN), General regression neural network (GRNN), Radial Basis Function (RBFNN) and Mean Impact Value (MIV). To reduce the dimension of the input of the emission prediction model with 95% cumulative contribution rate as the threshold, the study analyzes the adaptability of the neural network in different periods before and after the dimension specification. The results indicate that after dimensionality reduction, the prediction performance of BPNN and GRNN models in R2 and MSE evaluation dimensions in the full-time emission dataset is respectively improved by 1.19% , 10.14% , 6.51% and 15.56% . The RBF model is not sensitive to dimensionality reduction. The Full time GRNN model R2 is respectively 10.18% and 7.68% higher than BPNN and RBFNN, the MSE is respectively 0.0396 and 0.0446 lower than the other two models, and the MAPE is respectively 7.38% and 3.86% lower than the other two models. It also reveals that the GRNN model is more robust than BPNN and RBFNN in predicting expressway pollutant emissions. By analyzing the prediction performance of GRNN in different periods, the predicted R2 in the normal peak period is respectively 3.10% and 4.37% higher than that in the early peak and late peak. The MSE is respectively 0.0303 and 0.0157 lower than that in morning peak and evening peak, and the MAPE is respectively 0.4117 and 0.2857 lower than other two periods. The abnormal fluctuation of emission time series under the influence of traffic status, traffic flow and driving behavior in different periods of expressway has a significant impact on the robustness and generalization ability of the emission model, which provides a basis for the emission model to include the emission period, traffic status and other parameters in the future. References | Related Articles | Metrics

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Prediction of CO2 Emission Reduction State of Ridesplitting Based on Machine Learning LI Wen-xiang, LI Yuan-yuan, LIU Hao-de, YI Mao-mao, HAN Yin 2023, 23(1): 254-264.  DOI: 10.16097/j.cnki.1009-6744.2023.01.027 Abstract ( )   PDF (3085KB) ( )   Ridesplitting can effectively improve the transportation efficiency of vehicles and has great potential for emission reduction compared with regular ridesourcing. However, in reality, whether a ridesplitting trip reduces CO2 emissions, is determined by many factors with heterogeneity and uncertainty. To identify the ridesplitting trips with greater carbon emission reduction potential, this study proposes a machine learning-based model for predicting the CO2 emission reduction state and interpreting the CO2 emission reduction mechanism of ridesplitting. First, the CO2 emissions of shared rides (ridesplitting) and their substituted single rides (regular ridesourcing) are calculated based on the COPERT (COmputer Program to calculate Emissions from Road Transport) model using the real-world order data and trajectory data of ridesplitting in Chengdu City. Then, the actual CO2 emission reduction of each ridesplitting trip compared with regular ridesourcing trips is quantified. Given the CO2 emission reduction and order attributes of ridesplitting trips, the XGBoost (eXtreme Gradient Boosting) model is trained to predict the CO2 emission reduction states of potential ridesplitting trips in the future. Finally, the ALE (Accumulated Local Effects) analysis method is used to analyze the mechanism of the prediction model to identify the key factors influencing the CO2 emission reduction state of ridesplitting trips. The results showed that the average CO2 emission of each ridesplitting trip is 307.23 g in the study area. However, there are still 15% of ridesplitting trips even increasing CO2 emissions. The XGBoost model can effectively predict the CO2 emission reduction state of ridesplitting trips. In addition, the detour rate, the number of shared rides, and the overlap rate are identified to be the three key factors that determine the CO2 emission reduction state of ridesplitting trips. This study provides a theoretical basis for the ridesourcing platform to optimize the matching algorithms of shared rides. It can also realize more efficient and low-carbon ridesplitting and further improve the environmental benefits of ridesplitting. References | Related Articles | Metrics

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Optimization of Dynamic Collection Route of Smart Garbage Bins Considering Negative Environmental Effects YAN Fang, DENG De-ping, CHAI Fu-liang, MA Yan-fang 2023, 23(1): 265-274.  DOI: 10.16097/j.cnki.1009-6744.2023.01.028 Abstract ( )   PDF (1658KB) ( )   With the smart city construction advancement, the smart garbage bins, which are the basic supporting facilities of the smart city, are becoming more and more popular. The fill-level sensors inside the smart garbage bins can transmit the real-time waste data which is likely to be used in reducing the negative environmental effects and inefficient collection caused by the randomness of urban domestic garbage generation. This paper proposes an optimization strategy for the dynamic municipal waste collection with smart garbage bins. Considering the negative environmental effects caused by the waste overflow, the study develops a pre-optimization model and a dynamic optimization model of waste collection vehicle routing problem to minimize the total cost of collection and transportation. Then, the particle swarm algorithm is used to pre-plan the collection routes. A strategy combining periodicity and continuity is designed and heuristic rule with continuity triggering are constructed based on the realtime data transmitted by the smart bins. The standard and simulated arithmetic experiments show that the strategy combined periodic and continuous optimization exceeds the periodic optimization strategy on total cost, penalty cost and distance metrics, and especially in penalty cost. This research is helpful in reducing the negative environmental effects caused by the improper or delayed collection, and provides a theoretical support for related enterprises in making reasonable and efficient waste collection plan. References | Related Articles | Metrics

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Construction of Port and Shipping Logistics Information System Based on Production Berth Resource Sharing LI Zhi-ping, ZHAO Nan, YIN Ming, ZHEN Hong 2023, 23(1): 275-283.  DOI: 10.16097/j.cnki.1009-6744.2023.01.029 Abstract ( )   PDF (2291KB) ( )   In order to solve the problem of structural berth resource excess in port groups in adjacent regions, a new type of port and shipping logistics information system for ship carriers, cargo owners, and ports is built based on the sharing of berth resources in the port group. By introducing the queuing theory and stochastic process theory, a simulation model is established, which includes the building of trunk and branch route network of the port group (by a space-time allocation model), hinterland cargo flow allocation, and ship docking port selection (in a bi-level programming model). The simulation model is solved by using the designed spatio-temporal network analysis method and the two-level iterative algorithm with double threshold values. The sensitivity of model parameters is verified, and a specific example is analyzed. Simulation results show that changes in the observation period, hinterland cargo arrival frequency, and trunk/branch line pass- ability thresholds affect the route network structure within the port cluster by influencing the number of cargo arrivals or trunk/branch line opening conditions. Changes in vessel arrival frequency affect the transport capacity of the port cluster. Changes in berth sharing waiting time and sharing cost thresholds determine whether sharing can be achieved. When sharing is achieved through the logistics information system, the total berthing time and logistics costs of 3 trunk routes and 1 feeder route in the port cluster, observed in four periods, are 23% and 16.4% lower than those without sharing. References | Related Articles | Metrics

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Optimization of Multi-sector Transfer Strategy Based on Complexity WANG Hong-yong, ZHANG Jia-hao, XU Ping 2023, 23(1): 284-294.  DOI: 10.16097/j.cnki.1009-6744.2023.01.030 Abstract ( )   PDF (2595KB) ( )   The complexity of the sector is often neglected in the existing research of multi-sector transfer interval management, which may lead to some problems, such as unbalanced local sector complexity, and thereby cause security risks to airspace. This paper fully considers the complexity of multiple control sectors, and proposes a new optimization method of minute-in-trail. First, a multi-sector network model of a large regional control center is established based on the interaction of complexity between sectors. Then, three strategies are put forward, i.e., the time adjustment of aircraft entering multi-sector boundary, the time adjustment of aircraft entering each sector and the reassignment of flight level. A transfer strategy optimization model is established, which take the average of the complexity of multi-sector, the degree of equilibrium, and the aircraft delay as the objective. A multi-objective genetic algorithm is adopted to solve the model. Finally, the simulation analysis is carried out based on the actual operation data of the Beijing Regional Control Center (ZBAAAR). The results showed that: the three strategies can reduce the average complexity by 2.2% , 2.8% , and 6.0% respectively, and the complexity equilibrium is increased by 1.76% , 1.83%, and 1.38%, respectively. The two strategies based on aircraft entry time control result in an average flight delay time of -295 s and -214 s, respectively, which verifies the effectiveness of the model and algorithm. References | Related Articles | Metrics

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Velocity Behavior and Road Alignment Safety Evaluation in Complex Scenarios of Bridge and Tunnel Combination BAI Jing-rong, TANG Bo-ming, LIU Rui-hang, BI Hui-yun 2023, 23(1): 295-304.  DOI: 10.16097/j.cnki.1009-6744.2023.01.031 Abstract ( )   PDF (2697KB) ( )   To clarify the "vehicle-road" coupling driving environment and alignment coordination of bridge and tunnel combination scenario of expressways in mountain cities, this paper conducted a natural driving test in the 3-tunnel-2- bridge combination scenario of the expressway in the main urban area of Chongqing. The real-time running speed of 18 drivers and the local speed of 13 cross-sections of vehicles were collected. An integrated alignment evaluation model was developed based on the road conditions and running speed data. The test results show that: in the tunnel-bridgetunnel multi-scene switching connection mode, the average speed distribution of the mainline section is 50.00 to 64.25 km·h-1 , drivers are most alert in the bridge section, and when driving from the bridge into the articulated ramp or tunnel entrance, the vehicle speed decreases significantly. Less than 15% of the vehicles can pass at a low speed or experience serious traffic congestions. The speed distribution is from 8.00 to 39.50 km· h-1 , The overall running safety of the test section is in good level and linear condition. The speed behavior control of the mountainous urban expressway bridge-tunnel combination scenario cannot merely rely on the traffic management of the single tunnel or bridge, but also need to consider the distance and the duration of signal control of the upstream road section. References | Related Articles | Metrics

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Evaluation of Market Share of New International Land-sea Trade Corridor in Different Periods of COVID-19 GUO Shu-juan, WEI Zi-feng, SUN Zheng-yu, LI Yi-yi, LI Gang 2023, 23(1): 305-313.  DOI: 10.16097/j.cnki.1009-6744.2023.01.032 Abstract ( )   PDF (1983KB) ( )   This paper investigates the market share of the New International Land-Sea Trade Corridor (ILSTC) with the fluctuation and change of the global COVID-19 pandemic. The container shipping liner transport routes, China Railway Express (CR Express) transport routes and the ILSTC routes are selected as the alternative based on the collected data of the container transportation between China and Europe. The Multinomial Logit model and Mixed Logit models are established for comparative analysis. The route selection decision-making mechanism of shippers is analyzed for the China-Europe container transport in three stages of the pandemic and the subjective value of the transportation attributes of ILSTC are estimated for different stages. The results show that the shippers have different individual heterogeneity according to the route attributes of container transport between China and Europe in different pandemic stages. The time value of goods increases steadily with the severity of the pandemic, and the delay time value first increases fast and then slow down. During the slack period of the pandemic, the market share of ILSTC is mainly affected by the transport costs and liner transport time. In the stable period of the pandemic, the market share of ILSTC is mainly affected by the liner transport time. The market share is affected by the delay time in the severe period. During the pandemic, the container freight volume undertaken by the new land sea channel in western provinces and cities experienced continuous and rapid growth. The growth was fast in the southwest region during the pandemic stable period, and the growth in the northwest region was obvious in the pandemic severe period. References | Related Articles | Metrics

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Nonlinear Influence of Built Environment on Pedestrian Traffic Accident Severity JI Xiao-feng, QIAO Xin 2023, 23(1): 314-323.  DOI: 10.16097/j.cnki.1009-6744.2023.01.033 Abstract ( )   PDF (2935KB) ( )   The study on the impact of the built environment on pedestrian traffic accidents could provide a theoretical basis for accident prevention. This paper constructed a day-night built environment index system, based on three dimensions of land use, urban design, and transportation system in the "5D" elements. Using the light gradient boosting machine, a day-night pedestrian traffic accident severity model was constructed to explore the influence mechanism of the urban built environment on the severity of pedestrian traffic accidents. Combined with the SHAP attribution analysis method, the nonlinear relationship was revealed. Taking Shenzhen City as an example, the results show that there is significant temporal heterogeneity in the impact of the built environment on pedestrian traffic accidents. The severity of daytime pedestrian traffic accidents is mainly affected by factors such as sidewalk accessibility, subway station accessibility, and school proximity. At night, it is mainly affected by sidewalk accessibility, entertainment point of interest (POI) indicators, road lighting conditions, and other factors. The built environment has a conspicuous nonlinear effect on the severity of pedestrian traffic accidents. When the proximity of schools is between zero and three kilometers during the daytime and the accessibility of subway stations is less than three kilometers, it has a great effect on the severity of pedestrian accidents. When the accessibility of entertainment POI is less than 0.5 kilometers at night, it has a significant effect on the severity of pedestrian accidents. The accessibility of sidewalks can reduce the severity of pedestrian accidents both day and night, and the area with a low density of courtyard gates on the street has higherdegree accidents. Lastly, the model shows excellent results, with classification accuracies of 96.38% and 92.08%. References | Related Articles | Metrics

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Vehicles Trajectory Oscillation Characteristics and Passenger Cars' Lane Width for Freeways ZHUANG Jia-feng, LI Zheng-jun, DING Rui, XIONG Wen-lei, ZHANG He-shan, XU Jin 2023, 23(1): 324-336.  DOI: 10.16097/j.cnki.1009-6744.2023.01.034 Abstract ( )   PDF (4015KB) ( )   To clarify the trajectory lateral oscillation behavior of vehicles during lane-keeping driving on freeways, vehicle trajectories and driving speed were extracted using the dataset of the vehicle position coordinates from drones. The index of vehicle trajectory oscillation characteristics under normal driving conditions was then calculated, including the lateral amplitude of trajectory oscillation and traveled distance within the oscillation cycle. The speed distribution characteristics of different vehicle types and the effects of speed and lane positions on the trajectory lateral oscillation indexes were analyzed. The results show that: although there are significant differences in body size and power performance between light vehicles and large vehicles, their trajectory oscillation amplitude is nearly the same. The average oscillation amplitudes of the two types of vehicles are 0.587 m and 0.560 m, and the traveled distances within the oscillation cycle are 252.95 m and 251.99 m, respectively. The lateral oscillation amplitude of vehicle trajectory is insensitive to the speed change and will not increase with the increase of speed. It tends to be smooth or even decrease under high- speed conditions. Similarly, there is no significant correlation between travel distance and travel speed during the oscillation cycle. Different lane positions have an impact on the trajectory oscillation behavior, for light vehicles, trajectory oscillation increases when the vehicle position changes from the inside lane to the outside lane; while for large vehicles, the trajectory oscillation is the smallest in the middle lane. The trajectory oscillation of the Chinese freeway measured in Chongqing is slightly higher than the one calculated from the German HighD data set, but they are very close on the whole. According to the characteristics of the lateral amplitude of the vehicle trajectory, the proposed values of lane width for light cars dedicated lanes or passenger car freeways can be determined, the minimum and general value of lane width for passenger car dedicated lane with a design speed of 100~120 km· h-1 is 3.0 m and 3.25 m. References | Related Articles | Metrics