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Collaborative Driving Decision-making Method of Unmanned Mining Trucks in Open-pit Mine Operation Areas NI Haoyuan, YU Guizhen, LI Han, CHEN Peng, LIU Xi, WANG Wenda Journal of Transportation Systems Engineering and Information Technology    2024, 24 (3): 277-289.   DOI: 10.16097/j.cnki.1009-6744.2024.03.027 Abstract （128）      PDF （3340KB）（961）       The long parking and waiting time of unmanned mining trucks in open-pit mines during transportation in the loading and unloading operation area is a bottleneck that restricts the efficiency improvement of unmanned transportation systems in open-pit mines. To improve the transportation efficiency of unmanned mining trucks, this paper combines the transportation operation process in the operation area and proposes a multi-vehicle collaborative driving decision-making method based on dynamic travelable distance. The decision-making model was formulated as a mixed integer linear programming (MILP) model to express the optimization objective and problem constraints. Considering the challenge of meeting real-time decision-making requirements in solving the MILP model, the multivehicle conflict resolution was implemented based on Monte Carlo tree search (MCTS). The core idea was to use the derivation capability of the search tree to conduct forward simulation of multi- vehicle driving, calculate the optimal driving priority of multi-vehicle, and thereby dynamically adjust the travelable distance of multi-vehicle. In addition, different MCTS node value functions were designed based on the operating characteristics of unmanned mining trucks in the operation area to achieve driving priority ranking that comprehensively considered transportation efficiency and operating characteristics. A multi- vehicle driving simulation experiment was designed in the scenario of 4, 8, and 12 parking spots in the operation area. Compared with the method based on first-come-first-served (FCFS), the throughput was increased by 22.03% to 28.00% and the average parking waiting time was shortened by 31.71% to 50.79% . In addition, a 6-parking spots operation area scenario experimental platform for miniature intelligent vehicles was built. The total multi-vehicle single-operation time was reduced by 18.84% compared to the FCFS. The results of simulation and miniature intelligent vehicles experiments indicated that the proposed method could enhance the efficiency of multi-vehicle transportation in open-pit mine operation areas.

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Research Review of Influence of Social Network Information on Travel Behavior CHEN Jian, ZHANG Chi, FU Zhi-yan, LIU Ke-liang Journal of Transportation Systems Engineering and Information Technology    2023, 23 (2): 1-10.   DOI: 10.16097/j.cnki.1009-6744.2023.02.001 Abstract （678）      PDF （1616KB）（645）    PDF(English version) （478KB）（118）    To quantitatively review the research results of the influence of social network information on travel behavior, this paper retrieved and screened 133 English and 32 Chinese literatures from 2010 to 2022 based on the database of Web of Science and China National Knowledge Infrastructure. Through the combination of knowledge graph and qualitative literature analysis, the paper quantified and counted three indexes of annual publication volume, research hotspot countries, and keyword graph. The research results were presented in four aspects, including research methodology, social network information behavior, the influence of social network information on travel decisionmaking, and the influence of social network information on travel activities. The results show that: (1) in terms of data sources, the basic data of existing research haven't achieved the integration of feature dimension and decision dimension, and it is necessary to further integrate multi-source data to improve the robustness of research conclusions. (2) In terms of research methods, the existing research lack mutual support among analysis methods, and a variety of research methods can be integrated to analyze the influence of social network information on travel behavior across disciplines. (3) In terms of research content, the existing research results cannot fully reflect the development trend of future travel, and the heterogeneity of travelers can be given more attention. It is necessary to analyze the connection mode between social network information and travel behavior considering traveler heterogeneity in combination with new scenarios such as autonomous driving and shared travel.

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Prediction of Outbound Transportation Volume of Xinjiang Coal Railway by Integrating Sparrow Search with Long Short-Term Memory LI Haijun, ZHANG Xiaoyang , GAO Ruhu , WEI Dehua, CHEN Xiaoming Journal of Transportation Systems Engineering and Information Technology    2024, 24 (5): 14-23.   DOI: 10.16097/j.cnki.1009-6744.2024.05.002 Abstract （199）      PDF （2275KB）（636）       To enhance the precision of predicting the Xinjiang's coal railway outbound volume transportation, a prediction model integrating the sparrow search algorithm and the long and short-term memory network (SSA-LSTM) is proposed. The model introduces the sparrow search algorithm to optimize the hyper-parameters of the LSTM model in order to improve the model prediction performance. Based on the data of Xinjiang coal rail outbound transportation volume from 2015 to 2022, the gray correlation analysis is employed to comprehensively evaluate the impact of factors, including economic and transportation aspects, ensuring that the selected factors exhibit a strong correlation with the prediction targets. Among the influencing factors, the GDP data is adjusted for Consumer Price Index (CPI) effects, and the refined data are then fed into the model for prediction. Finally, the model is applied to predict the Xinjiang's coal rail outbound transportation volume across short, medium, and long time horizons. The results demonstrate that the SSA-LSTM model outperforms both the BP neural network and the conventional LSTM model, achieving a Mean Absolute Percentage Error (MAPE) of 0.88% and a Root Mean Square Error (RMSE) of 49.9. Furthermore, incorporating CPI processing into the prediction process significantly reduces the prediction error, with MAPE and RMSE decreasing by 75.8% and 56.2%, respectively, compared to non-CPI-processed predictions. This study provides an effective approach for predicting Xinjiang's coal rail outbound transportation volume, offering important data insights that inform the strategic design of coal transportation routes out of Xinjiang.

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Decision Making of Opening Gated Community Considering Traffic Impact on Environment WANG Xiao-ning , CUI Zi-yu , ZHANG Yu Journal of Transportation Systems Engineering and Information Technology    2022, 22 (4): 228-235.   DOI: 10.16097/j.cnki.1009-6744.2022.04.026 Abstract （206）      PDF （1801KB）（627）       Opening gated community can extend urban road network to some extent and help to alleviate traffic congestion. However, the decision-making approach in the current gated community opening plan is single layer and has not consider the vehicle emission and traffic noise pollution brought to the community after the opening. This paper used the minimum cost function value as the optimization goal and developed a bi-level decision-making model with upper-layer as optimal system cost and lower-layer for user balance. Three decision-making methods were introduced in the model: whether the gated community is open, one-way or two-way travel, and speed limit on the road. The genetic algorithm was used to solve the upper-layer model and the Frank-Wolfe algorithm was used to solve the lowerlayer model. The validation analysis of the model shows that the relative deviation of the optimal solution cost value obtained from the model is 0.67%, and the average year-on-year cost saving is 11.8%. The comparative analysis shows that the reasonable setting of three decision-making methods for the opening of gated community can reduce the travel time and detour distance of vehicles, thereby reduce the travel cost and the additional cost considering the impact on the environment. A relatively high posted speed limit helps to reduce the total travel cost.

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Prediction of Transportation Industry Carbon Peak in China LI Ninghai, CHEN Shuo, LIANG Xiao, TIAN Peining Journal of Transportation Systems Engineering and Information Technology    2024, 24 (1): 2-13.   DOI: 10.16097/j.cnki.1009-6744.2024.01.001 Abstract （513）      PDF （2327KB）（609）    PDF(English version) （684KB）（2）    Transportation industry faces a series of challenges under the strategy of "carbon peak" due to the high carbon emissions. This paper analyzes the current situation of the carbon emissions in passenger and freight transportation in China. Based on the statistical data and relevant research results, this study simulates the carbon emissions of the transportation industry including private cars. The carbon emission factors of each transportation mode are calculated. The trend of passenger and freight turnover in 2019 to 2040 is predicted based on the experience of some developed countries. Taking 2040 as the target year, the scenarios of future transportation structure and carbon emission factors were designed, and the time and value of carbon peak for transportation in China are estimated. The results show that the transportation carbon emission, including private cars, is 1.11 billion tons in 2020. It is predicted that the passenger transportation demand will be 8.2 to 8.7 trillion person-kilometers, and the freight transportation demand will be 27.3 to 28.7 trillion tonnage kilometers in 2040. It is verified that it would be difficult to achieve the carbon peak before 2040 only through improving the transportation structure, and it is also significantly important to promote the upgrading of clean transportation technology. The scenario analysis shows that the transportation industry is expected to achieve the carbon peak in 2031 to 2034 by encouraging the transformation of transportation structure such as "road to rail" and "road to water", and promoting the cleanliness of roadway transportation.

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Urban Road Traffic Accidents Prediction Based on Image Sequence Analysis HU Zhenghua, ZHOU Jibiao, MAO Xinhua, ZHANG Minjie Journal of Transportation Systems Engineering and Information Technology    2024, 24 (5): 91-102.   DOI: 10.16097/j.cnki.1009-6744.2024.05.009 Abstract （202）      PDF （3453KB）（593）       To further improve the accuracy of traffic accident prediction in road networks, a short-term traffic accident prediction method based on sequential image analysis is proposed. First, an oversampling technique is applied to interpolate traffic accident data collected from a WeChat mini-program to mitigate the impact of extensive zero values within the data on model training accuracy. These data are then integrated with road network traffic flow and accidentrelated attributes to generate stable time series as input for the model. A Bidirectional ConvLSTM U-Net with densely connected convolutions (BCDU-Net) is constructed. In this model, bidirectional ConvLSTM structures are used to integrate the features from the encoder and decoder layers, comprehensively capturing spatiotemporal correlations in the sequential data. Additionally, densely connected convolutions are employed to concatenate feature maps in the depth dimension, ensuring that each layer can directly access gradients from the loss function. Finally, the performance of the proposed model is evaluated by comparing the predicted results with actual traffic accident data. The results show that, compared to the Fully Connected Long Short-Term Memory (FC-LSTM) model, the Convolutional LSTM (ConvLSTM) model, and the U-Net model, the proposed model achieves reductions in cross-entropy loss of 65.96%, 15.70%, and 3.47%, reductions in root mean square error of 21.48%, 3.13%, and 1.71%, and increases in precision of 75.06%, 11.82%, and 3.08%, respectively. It is demonstrated that the proposed method offers superior performance in predicting urban road traffic accidents.

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Calculation for Carbon Emission Reduction Effect of Urban Rail Transit Based on Carbon Recovery Period Theory YANG Yang, WANG Xue-chun, YUAN Zhen-zhou, CHEN Jin-jie, NA Yan-ling Journal of Transportation Systems Engineering and Information Technology    2023, 23 (5): 1-11.   DOI: 10.16097/j.cnki.1009-6744.2023.05.001 Abstract （514）      PDF （2003KB）（581）    PDF(English version) （1142KB）（33）    Reasonable quantification of the carbon emission reduction effect of urban rail transit has theoretical and practical significance to calculate the external cost of urban rail transit, enrich the theoretical system of carbon trading in the field of transportation, and even formulate subsidy policies for urban rail transit. This paper considered the passengers' travel behavior difference after the construction of urban rail transit, and established the carbon emission model of urban rail transit datum line and project activity from the perspective of life cycle. Furthermore, a theoretical model of carbon recovery period was established as a quantitative indicator of carbon emission reduction effect of urban rail transit. Carbon recovery period refers to the duration of carbon emission recovery toward the construction period through carbon emission reduction during the operation period, which is the time when the cumulative carbon footprint changes from positive to negative for the first time. Then, the urban rail transit data collection was completed in the datum line, project construction and activity period, and the model is calibrated. The Shijiazhuang Subway Line 3 was taken as a case study, the carbon emissions of its datum line, the project construction period and the project activity period were analyzed, and the carbon recovery period was calculated under the two models of future development. The results show that the carbon recovery period is respectively 27 years, 22 years and 29 years under normal, rapid, and slow growth scenarios. Under the scenario of normal development of energy structure and energy efficiency level, rapid development and slow development, the carbon recovery period is respectively 25 years, 24 years and 29 years. The conclusions indicate that large-scale passenger volume and efficient passenger transportation intensity are important elements for the positive impact of carbon emission reduction in urban rail transit. The systematic changes brought about by the adjustment of energy structure and the energy efficiency improvement can have a great positive impact on the carbon emission reduction of urban rail transit.

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Research on Energy Consumption and Carbon Emissions in the Whole Life Cycle of Beijing-Xiong'an Intercity Railway CAO Meng, YUAN Zhenzhou, YANG Yang, NIE Yingjie, NA Yanling, SUN Yunchao, CHEN Jinjie Journal of Transportation Systems Engineering and Information Technology    2024, 24 (5): 37-44.   DOI: 10.16097/j.cnki.1009-6744.2024.05.004 Abstract （146）      PDF （1339KB）（572）       As the backbone of green transportation, intercity railways play a supporting role in reducing energy consumption and achieving the goal of "carbon peak and carbon neutrality". This paper analyzes the development trajectory and energy consumption patterns of intercity railways in China, with a particular focus on the entire lifecycle of planning, design, construction, and operation within the context of the "dual carbon" initiative. Utilizing the Beijing-Xiong'an intercity railway as a prototypical case, the paper examines energy consumption and carbon emissions across its lifecycle, incorporating the influence of energy-saving, emission-reduction strategies, and green carbon sink measures. The findings reveal that carbon emissions during the planning and design phases are negligible, whereas the energy consumption during the operation stage dominates, accounting for approximately 74.9% of the total annual lifecycle energy consumption. Additionally, the energy consumption attributed to building materials production (scaled to 100 years) constitutes roughly 22.4% of the total. Notably, the implementation of energy conservation, emission reduction, and green carbon sequestration measures has yielded substantial outcomes, achieving an average annual energy savings of approximately 12%. When compared with similar railway energy consumption indicators globally, the Beijing- Xiong'an intercity railway's unit transportation traction energy consumption of 6.42 tce per million person-kilometer aligns with expectations. Furthermore, its carbon reduction impact is significant in diverting highway passenger traffic, savings approximately 612.67 million yuan in carbon sink transactions and generating substantial societal benefits. This comprehensive analysis offers valuable insights and reference for the establishment of a green and low-carbon intercity railway carbon emission dual control indicator system.

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Analysis of Residents' Travel Mode Choice in Medium-sized City Based on Machine Learning LI Wenquan, DENGAnxin, ZHENGYan, YIN Zijuan, WANG Baifan Journal of Transportation Systems Engineering and Information Technology    2024, 24 (2): 13-23.   DOI: 10.16097/j.cnki.1009-6744.2024.02.002 Abstract （616）      PDF （3143KB）（567）       This paper aims to investigate the characteristics of travel behaviors and the influencing factors on travel mode choice in a medium-sized city. Utilizing travel data from a medium-sized city in China, a random forest model embedded with a particle swarm optimization algorithm adding a variation procedure (PSO-RF) was proposed for travel mode choice prediction, due to the distinctions in prediction accuracy and modeling rationality of discrete choice model and machine learning model, as well as the characteristics and efficiency of hyperparameter optimization algorithms. The predictive accuracy, predictive mode proportion's absolute deviation, and expected simulation error were used to statistically compare the predictive performance among PSO-RF, machine learning models, and the multinomial Logit model. The SHAP (SHapley additive exPlanation) model was employed to thoroughly analyze the nonlinear relationships among individual socio-economic attributes, travel attributes, mode attributes, and residents' travel mode choices. The results indicate that PSO-RF has the highest average overall prediction accuracy (0.856), and the lowest average predictive mode proportion's absolute deviation (0.062) and average expected simulation error (0.306). Statistically significant differences in models' predictions are observed. Distance has the most prominent impact on the choice of different travel modes. The modes of walking and private cars show higher sensitivity to distance, with probability changes exceeding 35% at different distances. Individuals under 30 years old exhibit greater variations in the probability of choosing different travel modes compared to other age groups. Gender, car ownership, and bus IC card ownership notably affect the probability of choosing a bus and a private car.

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Collaborative Lane Change Method for Autonomous Vehicles Based on Dynamic Trajectory Planning LIU Miaomiao, LIU Xiaochen, ZHU Mingyue, WEI Zeping, DENG Hui, YAO Mingkun, WU Silin, LI Ang, SHI Zan, GONG Xiaoyu Journal of Transportation Systems Engineering and Information Technology    2024, 24 (5): 65-78.   DOI: 10.16097/j.cnki.1009-6744.2024.05.007 Abstract （282）      PDF （2891KB）（564）       Traditional multi-vehicle coordination lacks effective utilization of information about target platoons and lane-changing vehicles. To address the impact of dynamic information changes on the lane-changing process, this paper proposes a collaborative lane-changing control method for autonomous vehicles based on dynamic trajectory planning. First, focusing on the scenario of a single vehicle merging into vehicle platoons in autonomous driving environments, a collaborative lane change control framework based on real-time dynamic information is proposed. Considering the cooperation between the lane-changing vehicle and the target platoon vehicles, and the impact of the lane-changing behavior on the target platoon, longitudinal collaborative control models are established for both non-lane changing and lane-changing periods. Second, after the lane-changing vehicle sends a lane-change request and satisfies the lane-change triggering conditions, a dynamic lane-change trajectory planning method using a sinusoidal curve is employed to derive a safe and reliable trajectory. Vertical coordination goals are considered. And based on the dynamic planning of longitudinal speed changes, a sine-curve-based dynamic lane change trajectory planning approach is introduced to derive safe and reliable trajectories. Then, a model predictive control-based trajectory tracking control algorithm is used to achieve real-time trajectory tracking. Finally, by constructing a joint simulation platform of Prescan and Simulink, several sets of simulation experiments under different speed conditions are designed. And traditional control algorithms based on vehicle tracking strategies are compared with the proposed control strategy by analyzing three key indicators: lane change trigger time, train stabilization time, and speed fluctuation amplitude. This comprehensive analysis validates the effectiveness and feasibility of the proposed control strategy. Simulation results show that, compared with traditional methods, the average stable time of the platoon is reduced by 34%, and the speed fluctuation amplitude of the platoon remains stable. In addition, safe and efficient lane changes can be achieved under different relative speed conditions.

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Path Optimization for Vertical Take-off and Landing Aircraft in Dynamic Urban Airspaces for Urban Air Mobility ZHOU Hang, ZHAO Fengyang, HU Xiaobing Journal of Transportation Systems Engineering and Information Technology    2024, 24 (5): 295-308.   DOI: 10.16097/j.cnki.1009-6744.2024.05.027 Abstract （177）      PDF （4788KB）（554）       To address the current challenges of achieving optimality and computational efficiency in dynamic airspace route optimization for urban air mobility, as well as the inadequacy in addressing mixed urban and suburban operational scenarios, an innovative approach to constructing a combined urban-suburban network is initially proposed to support both urban and suburban operations seamlessly. Based on the flight dynamics model of electric vertical takeoff and landing (eVTOL) aircraft, an accurate eVTOL power consumption model is developed to optimize flight paths. A Dynamically Weighted Routing Network (RSA-DWRN) algorithm for dynamic airspace is introduced by leveraging the Ripple Spreading Algorithm. With a combined urban-suburban network framework that incorporates time-varying airflow patterns and obstacle zones, the optimization performance of the RSA-DWRN's is compared against the traditional DPO-A* algorithm across five scenarios through 600 experiments, considering path power consumption, flight time, computation time, and matching degree as key metrics. Simulation results show that RSA-DWRN algorithm performs best under the four indexes, especially as the complexity of dynamic airspace environmental factors increases. In scenarios with moving obstacles, the DPO- A* algorithm fails to predict their trajectories and requires frequent updates to the network state, significantly increasing the computational cost of path planning. In contrast, the RSA-DWRN algorithm co-evolves with changes in the dynamic environment, finally obtaining optimal solutions that simultaneously ensure optimization results and computational efficiency.

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Impact of Charging and Incentive Strategies on Commuting Mode Choice WANGDianhai, LIYiwen, CAI Zhengyi Journal of Transportation Systems Engineering and Information Technology    2024, 24 (2): 1-12.   DOI: 10.16097/j.cnki.1009-6744.2024.02.001 Abstract （592）      PDF （2668KB）（554）    PDF(English version) （436KB）（4）    This paper investigates the regulatory impact of two traffic demand management strategies, tolls and rewards, on travel mode choices, using the main urban area of Hangzhou as a case study. The stated preference (SP) and revealed preference (RP) surveys were performed to understand the intention of private car commuters' mode choice under parking charge and travel reward scenarios. The disaggregate theory was used to establish Nested Logit (NL) models for commuting mode selection under separate and joint implementation of parking fees and travel rewards. The results indicate that both parking fees and travel incentives can reduce private car travel demand and promote public transportation. Only when the parking price reaches a certain level can private car trips be effectively reduced, and appropriate incentives can actively encourage travelers to switch to other modes of travel. If charging and incentive strategies are implemented simultaneously, it will manifest a joint effect of charging as the main approach and incentive as a supplement. In all three scenarios, income is a significant factor influencing travel mode choices. The higher the income, the more likely the continuation of private car usage. In the scenario with only a parking fee, the elasticity of parking fees increases with the rate; there are limited elasticity when the rate is low. The elasticity of travel rewards initially raises and then drops with the increase in the reward amount; Small rewards also show elasticity.

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Residents' Travel Mode Choice Behavior in Post-COVID-19 era Considering Preference Differences YANG Ya-zao, TANG Hao-dong, PENG yong Journal of Transportation Systems Engineering and Information Technology    2022, 22 (3): 15-24.   DOI: 10.16097/j.cnki.1009-6744.2022.03.003 Abstract （546）      PDF （2001KB）（550）    PDF(English version) （2509KB）（189）    In order to explore the choice behavior of residents' travel mode in the post-COVID-19 era, a choice behavior experiment was conducted. A mixed Logit model and a latent class conditional Logit model of travel mode choice were constructed based on the data obtained from questionnaire surveys. Stata software was used to calibrate the model parameters, and the main factors influencing residents' travel mode choices were obtained. The results show that both models reflect the heterogeneity of individual travel mode choices. Compared with the mixed Logit model, the latent class conditional Logit model has an improvement of 13% in the goodness of fit and an increase of 3.03% in the prediction accuracy, which provides an effective tool for analyzing individual heterogeneity of travel behavior under public health emergencies. The latent class conditional Logit model divides residents into four and five groups according to the two scenarios of low and medium risk areas. From the perspective of travel mode attributes, the waiting time and the traveling time have become the most important influencing factors for residents to choose the travel modes. From the perspective of personal socio-economic attributes, women with higher incomes are more inclined to choose private cars to travel. The older are more sensitive to travel costs, and men are more willing to choose bus and subway travel.

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Modeling and Simulation of Multi-lane Heterogeneous Traffic Flow in Intelligent and Connected Vehicle Environment SHAN Xiao-nian, WAN Chang-xin, LI Zhi-bin, ZHANG Xiao-li, CAO Chang-heng Journal of Transportation Systems Engineering and Information Technology    2022, 22 (6): 74-84.   DOI: 10.16097/j.cnki.1009-6744.2022.06.008 Abstract （714）      PDF （3265KB）（527）       To explore the operation characteristics of multi-lane heterogeneous traffic flow in mixed Connected and Automated Vehicle (CAV) and Human Driving Vehicle (HDV) environment, this paper analyzes the car-following modes of CAVs and HDVs in heterogeneous traffic flow and proposes two-lane and multi-lane changing models for different vehicle types. The paper establishes a multi-lane heterogeneous traffic flow simulation model and then analyzes the road capacity and lane-changing behavior characteristics under different CAV market penetration rates. The results indicate that with the increase in CAV market penetration rate, the single-lane road capacity increases from 1678 pcu · h-1 to 4200 pcu · h-1 , the critical density changes from 25 pcu · km-1 to 35 pcu · km-1 , which show significant differences for different number of lanes. It is also found that the lane-changing behavior of heterogeneous traffic flow has three-stage characteristics. At low density, vehicles can drive or change lanes freely. When the density is between 20~100 pcu·km-1 , vehicle lane-changing frequency overall follows a convex curve. With the CAV penetration rate increases, the peak value of HDV sees an increase trend, while the peak value of CAV is decreasing. Under highdensity, due to the constraints of available lane-changing space, vehicles cannot complete lane-changing behavior. The benefits of lane-changing behavior are further discussed, with the indicators of the increment of traffic volume and order improvement. The study results help to understand the operation status of multi-lane heterogeneous traffic flow and provides theoretical references for the future management of heterogeneous traffic flow.

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A Train Group Control Method Based on Car Following Model Under Virtual Coupling SHUAI Bina, LUO Jianan, FENG Xinyan, HUANG Wencheng Journal of Transportation Systems Engineering and Information Technology    2024, 24 (3): 1-11.   DOI: 10.16097/j.cnki.1009-6744.2024.03.001 Abstract （410）      PDF （2282KB）（520）    PDF(English version) （1740KB）（1）    There remains a gap between transportation capacity and demand under the high-speed railway moving block mode, prompting the exploration of new approaches such as virtual coupling to enhance transportation capacity. With the concept of virtual coupling and inspired by car-following models utilized in road traffic, we propose a novel acceleration adjustment strategy by train dynamics and multi-agent methods for tracking trains based on the speed and distance relationship between adjacent trains, with the goal of ensuring train safety and passenger comfort while enabling virtual coupling within the train group. A corresponding virtual coupling acceleration adjustment model is established for train groups, aiming to achieve equal speed and distance between all trains in the group. The proposed model is validated using the CRH380A high-speed train as a case study. Simulation results demonstrate that the proposed acceleration adjustment strategies effectively realize the virtual coupling of train groups. Compared to the moving block method, adopting virtual coupling reduces the time required for train collaboration by 9.7% and decreases the distance between trains by 10.1% , thereby improving efficiency. Furthermore, the time required to achieve virtual coupling is shorter when considering the train group as a whole compared to when the group is separated into multiple groups.

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Multimodal Transportation Route Optimization for Long and Bulky Cargo Considering Carbon Emissions WANGJuan, CHENGYuli, YANGYuhan, ZHANGYinggui Journal of Transportation Systems Engineering and Information Technology    2024, 24 (4): 1-11.   DOI: 10.16097/j.cnki.1009-6744.2024.04.001 Abstract （478）      PDF （1887KB）（504）       Long and bulky cargo has the characteristics of large outline, overweight and high cost and cannot be disassembled during the transportation process. Multimodal transportation is becoming the first choice of long and bulky cargo transportation, the core of which is route decision problem. In this paper, an energy consumption factor is introduced, and calculation formulas of carbon emissions during the transportation and reconstruction and reloading process at the node for long and bulky cargo multimodal transportation are all proposed. Then, taking into consideration the following factors, i.e., loading outline, gauge, bridge bearing capacity and reloading capacity at the nodes, and road reconstruction, a multimodal transportation route optimization model for long and bulky cargo with carbon emissions is proposed with the objective of minimizing multimodal transportation cost and carbon emissions. In addition, an adaptive genetic algorithm with an elite retention strategy is designed for the multimodal route decision for long and bulky cargo considering carbon emissions. Numerical results show that, compared with the traditional genetic algorithm and the adaptive genetic algorithm, the objective value of the proposed method is 20% higher and its cost and carbon emissions are 12% and 22% lower, respectively. The route plan by the proposed method can consider transportation cost and carbon emissions simultaneously, which can provide support to solve the multimodal route decision problem for long and bulky cargo and also reduce the cost and increase the efficiency in logistics and achieve the "dual-carbon" target.

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Dynamic Fleet Management of Shared Autonomous Vehicles with Rolling Horizon Optimization CHEN Yao , BAI Yun , ZHANG An-ying , MAO Bao-hua , CHEN Shao-kuan Journal of Transportation Systems Engineering and Information Technology    2022, 22 (3): 45-52.   DOI: 10.16097/j.cnki.1009-6744.2022.03.006 Abstract （636）      PDF （1718KB）（491）       The shared autonomous vehicle (SAV) is an essential component in future urban transportation systems. This paper investigates an optimization approach to the dynamic operationof a SAV fleet with stochastic demand. A timespace network is first constructed to characterize the fleet management problem. Different types of time-space arcs are generated to indicate the vehicle-trip assignment and empty vehicle relocation. Under the framework of approximated dynamic programming, this paper develops a mathematic programming model to maximize the operational profit, in which the flow of nodes is taken as vehicle states and the flow of arcs is taken as decision variables. The rolling horizon optimization, also referred as lookahead policy, is designed for the optimization problem. A stochastic program with a lookahead horizon is developed and solved by the CPLEX solver. A numerical case study is performed with the Sioux Falls network. The rolling horizon optimization approach can provide effective operational decisions of dynamic fleet management. Considering the computational time limit, a long lookahead horizon with a medium- size sample would produce better optimization results. The objective of maximizing the operational benefit while minimizing the passenger waiting time would also result in more effective decisions of the dynamic fleet management.

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Dynamic Spatiotemporal Priority Control of Connected Vehicles Public Transport System LI Zhe, GOU Yangyang, LI Zhenyao, LI Ao, CEN Wei, GAO Jianping Journal of Transportation Systems Engineering and Information Technology    2024, 24 (5): 56-64.   DOI: 10.16097/j.cnki.1009-6744.2024.05.006 Abstract （186）      PDF （2303KB）（489）       To improve the utilization efficiency of bus lanes and reduce vehicle delays at intersections of continuous bus lanes, this paper investigates dynamic spatiotemporal priority control of connected public transport systems from spatial and temporal dimensions and analyzes the applicable traffic flow conditions. In the spatial dimension, intermittent bus entrance lanes are introduced and vehicle operation control strategies are formulated for four dynamic intervals, including clearance distance. In the temporal dimension, based on deep reinforcement learning, signal timing is dynamically adjusted through time extension of the green light and time interruption of the red light. A simulation verification platform is constructed using SUMO and Python, and comparative simulation experiments and three saturation scenarios are designed for four control schemes concluding the original scheme, spatial priority scheme, temporal scheme, and spatiotemporal collaborative priority scheme. The results show that at saturation levels of 0.2, 0.5, and 0.8, the spatiotemporal collaborative priority scheme reduces the average delay compared to the original scheme by respectively 40.96% , 39.93% , and 28.20% . At low saturation, the spatial priority effect is obvious; at medium saturation, the temporal effect is obvious. Using intermittent bus entrance lanes may lead to a slight increase in bus delays, but the average delay at the entire intersection is significantly reduced. The proposed dynamic spatiotemporal priority control method for connected vehicle bus systems can effectively improve intersection traffic efficiency while ensuring bus priority.

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Impact of "Star-Type" High-speed Railway Network on High-quality Development of Regional Social Economy YUE Guoyong, HU Hao Journal of Transportation Systems Engineering and Information Technology    2024, 24 (5): 24-36.   DOI: 10.16097/j.cnki.1009-6744.2024.05.003 Abstract （163）      PDF （2915KB）（486）       This paper focuses on the first "Star-type" high-speed railway network in China and analyzes its impact on regional high-quality development from four dimensions: spatial pattern, economic development, social development and ecological environment. The study constructs a comprehensive impact evaluation model and establishes a multidimensional indicator system to evaluate the effects. It culminates in a detailed quantitative analysis of the outcomes to provide a nuanced understanding of the impacts. The result indicates that the opening of the "Star-type" high-speed railway network has significantly reduced the weighted average travel time between and within Henan province to 4.90 hours and 1.62 hours, with improvements rate of 65.6% and 37.8% . The intensity of regional connections has been significantly enhanced, gradually forming a "center-periphery" development structure which focuses on intra-provincial connections and steadily expands to the energy consumption optimization and comprehensive operational emission reduction of railway northeast and southeast. The primacy index of Zhengzhou high-speed railway hub has increased from 1.90 to 2.83, further consolidated its position as a core hub. The differencein-differences model is used to verify that the "Star-type" high-speed railway network has a positive promotion on economic and social development indicators of Henan，such as social fixed assets investment, foreign capital utilization, per capita Gross Domestic Product (GDP), urbanization rate, employment upgrading index, etc. The amount of optimized comprehensive energy consumption and comprehensive operational emission reduction in railway passenger transport has steadily increased, and industrial SO2 emissions reduced, with significant ecological environment effects.

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Urban Logistics Unmanned Aerial Vehicle Vertiports Layout Planning ZHANG Hong-hai , FENG Di-kun, ZHANG Xiao-wei, LIU Hao, ZHONG Gang, ZHANG Lian-dong Journal of Transportation Systems Engineering and Information Technology    2022, 22 (3): 207-214.   DOI: 10.16097/j.cnki.1009-6744.2022.03.023 Abstract （661）      PDF （1919KB）（480）       This paper focuses on the layout planning of urban logistics Unmanned Aerial Vehicle (UAV) vertiports. In consideration of different types of logistics UAV vertiports, this paper proposes a vertiports layout planning model with the objective of minimizing the total economic cost and maximizing the customer satisfaction. The constraints of the model involve no-fly zone, UAV performance, vertiport capacity, and other factors. The human learning optimization algorithm (HLO) is designed and the random learning operator, individual learning operator and social learning operator are introduced in the algorithm to solve the model. The simulation experiment is then performed with real geographic data and logistics data to verify the effectiveness of the model and algorithm. The experimental results show that the proposed model can generate reasonable layout planning of vertiports, which is suitable and effective for large-scale resource allocation problem. The HLO algorithm shows better solution accuracy and convergence speed than the genetic algorithm (GA) The parameter analysis shows that the optimal economic cost weight is 0.4 and the optimal customer satisfaction weight is 0.6 based on the simulation environment. The optimal algorithm learning probability parameters are 5/n and 0.8+2/n. The study results could provide decision-making support for the layout planning of the actual urban logistics UAV vertiports.