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Method for Identifying Dangerous Driving Behaviors in Heavy-duty Trucks Based on Multi-modal Data WUJianqing, ZHANG Ziyi, WANG Yubo, ZHANGYu, TIANYuan Journal of Transportation Systems Engineering and Information Technology    2024, 24 (2): 63-75.   DOI: 10.16097/j.cnki.1009-6744.2024.02.007 Abstract （673）      PDF （3402KB）（370）       This paper proposes a multi-modal method to identify dangerous driving behaviors of heavy-duty trucks, which integrates driving operation data, eye-tracking data and electrocardiogram (ECG) data in the analysis. A naturalistic driving experiment is designed to collect driving data using three types of devices: vehicle inertial navigation systems, eye-tracking decoders, and physiological data recorders. A multi-modal driving dataset is established through data synchronizing and data cleaning processes. The dangerous driving behaviors are divided into two categories: dangerous manipulation behaviors and fatigue driving behaviors. By extracting data features, nine dangerous driving behavior indicators are defined to represent five types of dangerous driving behaviors, including speeding, unstable speed, rapid speed changing, rapid lane changing, and fatigue driving. For dangerous manipulation behaviors, characteristic thresholds are determined through literature review, indicator calculation, and interquartile range method. For fatigue driving behaviors, fatigue driving levels are identified through factor analysis and K-means clustering methods. A random forest (RF) classification model is then developed to identify dangerous driving behavior. When compared to traditional methods, including back propagation neural network (BPNN), K-nearest neighbors (KNN), support vector machine (SVM), the proposed model surpassed others in terms of accuracy and fitting performance. The model achieved a classification accuracy of over 90% for all types of dangerous driving behaviors. The results prove that the proposed methods are effective in identifying dangerous driving behaviors and it provides a theoretical basis for multimodal warning systems of dangerous driving behaviors.

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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 （583）      PDF （3143KB）（556）       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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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 （552）      PDF （2668KB）（548）    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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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 （440）      PDF （1887KB）（493）       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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Natural Peak Characteristics and Peak Forecast of Carbon Emissions in Transportation Industry YANGDong, LIYanhong, TIAN Chunlin Journal of Transportation Systems Engineering and Information Technology    2024, 24 (2): 34-44.   DOI: 10.16097/j.cnki.1009-6744.2024.02.004 Abstract （402）      PDF （3274KB）（280）       The peak of carbon emissions in the transportation industry is a natural long-term evolutionary process. In order to study the process of carbon peaking in China's transportation industry, this paper adopts the international analogy method, selects typical foreign countries, and compares the time of the peaks of the overall national carbon emissions, transportation industry carbon emissions, and converted turnover. The natural peak characteristics of transportation industry carbon emission are analyzed and the time of the natural peaks of carbon emissions in China's transportation industry is predicted according to transportation demand forecast. Then, the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) prediction model is constructed by introducing the core influencing factors such as carbon emissions per unit of converted turnover and the ratio of railroad to road freight transportation. Finally, through analogical analysis and model prediction, the time and volume of peak carbon emissions of China's transportation sector are obtained. The results of the international analogy show that there is no clear causal relationship between the peak carbon emissions of the transportation industry and the national peak carbon emissions, but it is closely related to the peak converted turnover, and the converted turnover reaches the peak or is close to the peak when the carbon emissions of the transportation industry reach the peak. It is predicted that China's converted turnover will reach a plateau period of 26 trillion ton-kilometers in about 2048. From the perspective of the international analogy, the time of the natural peak of China's carbon emissions from transportation is roughly roughly between 2040 and 2043. The STIRPAT model shows that the carbon emissions of China's transportation industry will increase by 1.201%, 0.259%, 0.454%, and-0.389%, respectively, for every 1% increase in urbanization rate, per capita GDP, carbon emissions per unit converted turnover, and railway- road freight ratio. Based on the combination prediction of international analogy and STIRPAT model, China's transportation industry will achieve peak carbon emissions in 2038-2040, with about 1.3 billion tons.

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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 （399）      PDF （2282KB）（517）    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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Traffic Signal Control with Deep Reinforcement Learning and Self-attention Mechanism ZHANGXijun, NIE Shengyuan, LI Zhe, ZHANG Hong Journal of Transportation Systems Engineering and Information Technology    2024, 24 (2): 96-104.   DOI: 10.16097/j.cnki.1009-6744.2024.02.010 Abstract （361）      PDF （2200KB）（290）       Traffic signal control (TSC) is still one of the most important research topics in the transportation field. The existing traffic signal control method based on deep reinforcement learning (DRL) needs to be designed manually, and it is often difficult to extract the complete traffic state information in the real operations. This paper proposes a DRL algorithm based on the self-attention network for the traffic signal control to analyze the potential traffic from limited traffic state information and reduce the difficulty of traffic state design. The vehicle position of each entry lane at the intersection is obtained, and the vehicle position distribution matrix is established through the non-uniform quantization and one-hot encoding method. The self-attention network is then used to analyze the spatial correlation and latent information of the vehicle location distribution matrix which is an input of the DRL algorithm. The traffic signal adaptive control strategy is trained at a single intersection and the adaptability and robustness of the proposed algorithm are verified in a multi-intersection road network. The simulation results show that in a single intersection environment, the proposed algorithm has better performance on the average vehicle delay and other indicators compared with three benchmark algorithms. The proposed algorithm also has good adaptability in the multi-intersection road network.

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Hydrogen Fuel Cell Bus: A Literature Review and Prospects LIU Tao, GUO Jiaxin, HAN Ying, TANG Chunyan Journal of Transportation Systems Engineering and Information Technology    2024, 24 (5): 1-13.   DOI: 10.16097/j.cnki.1009-6744.2024.05.001 Abstract （334）      PDF （1708KB）（295）       The adoption of hydrogen fuel cell buses (HFCBs) contributes to reducing carbon emissions and promoting the sustainable development of transportation systems. This paper systematically reviews the research literature on HFCBs by searching relevant databases. The review covers five main areas: the feasibility and prospects of developing HFCBs, evaluation of HFCB systems and comparison with other transit modes, social acceptance of HFCBs, planning and operations management of HFCBs, and safety analysis of HFCBs. This study reveals that, as HFCBs are still in the exploratory development stage, there are relatively more studies on the feasibility, system evaluation, and social acceptance of HFCBs, whereas studies on the system planning, operations management, and safety analysis are relatively less. Although China's scientific research and practice in the field of HFCBs started later than other countries, it is currently among the world leaders. Driven by both policy support and market demand, HFCBs are rapidly developing in China. Based on the literature review, the paper further analyzes existing research limitations and proposes suggestions for future studies. The research indicates that further in-depth studies can be conducted in four areas: reducing the cost of HFCBs, enhancing infrastructure construction, increasing social acceptance, and strengthening safety management. Particularly, attention should be given to innovations in hydrogen fuel cell battery technology, supporting infrastructure development, and operational safety assurance. In the future, HFCBs have broad application prospects by providing transportation service in various transportation scenarios, such as in tourist attractions, large-scale sports events, urban transportation, or in intercity long-distance transport. Academia and industry should actively align with the relevant policy requirements and practical needs of the hydrogen energy industry and transportation development. Continuous in-depth research should be conducted on the key and challenging aspects of HFCBs development to jointly support its sustainable development.

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Connection Optimization of Container Sea-rail Combined Transport Based on Vehicle-ship Direct Access Mode WANGShuang, SUN Xiang Journal of Transportation Systems Engineering and Information Technology    2024, 24 (2): 53-62.   DOI: 10.16097/j.cnki.1009-6744.2024.02.006 Abstract （317）      PDF （2269KB）（133）       To address the optimization problem of container-sea-rail intermodal connections under the direct access mode of vehicles and ships, this study considers the connection between trains and container ships in terms of time and volume, based on the fixed ship schedule. A mixed-integer nonlinear programming model is constructed for the timetable optimization of sea-rail intermodal transportation, aiming at maximizing the number of connected trains, minimizing the number of ships involved in the connection and minimizing the timetable adjustment. The solution is obtained based on a hierarchical optimization method. The model is verified by using Yantian Port in Shenzhen as an example. The results show that the optimization of train schedule considering time and volume connection can increase the number of connected trains by 6, and the number of directly transshipment containers by 75% with 466 TEU. The number of ships involved in the connection increased by 1, and the number of containers taken directly by the three ships increased by 2.6%, 4.2% and 2.5%, respectively. The total dwell time of the containers in the port was reduced by 26%. The study shows that the model can provide useful references for improving the connection level of sea-rail combined transport direct access mode.

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Robust Model Predictive Control of Connected and Automated Vehicle Trajectories on Urban Roads LIU Meiqi, JIN Kairan, LI Yalan, GUO Ge Journal of Transportation Systems Engineering and Information Technology    2024, 24 (4): 31-40.   DOI: 10.16097/j.cnki.1009-6744.2024.04.004 Abstract （272）      PDF （2586KB）（278）    PDF(English version) （3020KB）（3）    To solve the problem of the actuator delay and uncertainties which may cause platoon instability or even destabilization, this paper proposes a robust model predictive control approach for vehicle trajectory optimization on urban roads. A third-order vehicle dynamics model was developed to optimize ride comfort, safety, platoon stability, fuel efficiency, and traffic delay. The behaviors of the red-light violations and the unsafe inter-vehicle distances were penalized, and the speed and acceleration were bounded. The signal changes were treated as system feedback. The proposed vehicle trajectory controller aims to improve the operational efficiency of controlled vehicles. The vehicle trajectory controller was formulated as a Min-Max model predictive control problem to enhance platoon stability by determining the control inputs in the worst case of actuator delays and uncertainties. Then, the iterative Pontryagin's maximum principle was used to solve the control problem, which discretized the control problem and divided the uncertain parameters into multiple intervals. To improve the computational efficiency, the proposed solution approach identified the worst case, iteratively computed the state variables forward in time, and solved the costate variables backward in time. The numerical simulation results demonstrate that the proposed controller performs well on the lane sections with and without signal controllers. The robust model predictive control approach can effectively response to random actuator delays and external vehicle disturbances, such as signal changes, abrupt speed changes, and small trajectory deviations caused by human drivers. The proposed robust Min-Max model predictive controller (MM-MPC) manifests better stability and superiority than the normal MPC controller in riding comfort (improved by 75.7%) and fuel consumption (reduced by 18.4%).

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AVehicle Lane-changing Trajectory Prediction Model Based on Temporal Convolutional Networks and Attention Mechanism YANGDa, LIU Jiawei, ZHENG Bin, SUN Feng Journal of Transportation Systems Engineering and Information Technology    2024, 24 (2): 114-126.   DOI: 10.16097/j.cnki.1009-6744.2024.02.012 Abstract （271）      PDF （2493KB）（212）       An accurate vehicle trajectory prediction model can provide self-driving vehicles with precise information about the motion states of surrounding vehicles in mixed traffic flow environments, allowing it to assess the possibility of conflicts with neighboring vehicles in the short term. This paper proposes a vehicle lane-changing trajectory prediction model based on Temporal Convolutional Networks with Attention Mechanism (TCN-Attention) to improve the accuracy of vehicle lane-changing trajectory prediction. This model uses Temporal Convolutional Networks as the current input's feature extractor and utilizes a temporal and spatial attention mechanism to establish dynamic correlations between different time steps and spatial positions. Specifically, the combination of temporal and spatial attention mechanisms helps the model extract essential semantic features in both the temporal and spatial dimensions before and after lane-changing, enabling it to more accurately capture the dynamic spatiotemporal relationships between vehicles. This enables precise predictions of lane-changing trajectories on highways. Different from the traditional only using a trajectory features as input, our method achieves the multi-dimensional expansion and fusion of the input features, and further improves the accuracy of the trajectory prediction. In addition, this paper proposes a new method to define the start and end time of lane-changing in the dataset more accurately. Experiments show that the proposed model can predict the trajectory of the lane-changing with high accuracy, and the overall effect is better than other deep learning models. Compared with the Convolution Long Short- Term Memory(ConvLSTM), the Mean Absolute Error( EMAE ) of TCN-Attention is reduced by 69.8%, the Root Mean Square Error( ERMSE ) is reduced by 49.15% and the MeanAbsolute Percentage Error( EMAPE ) is reduced by 14.24%.

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Intelligent Vehicle Trajectory Prediction Considering Dynamic Interactions WENHuiying, ZHANG Xinyi, HUANG Junda, XU Pengpeng Journal of Transportation Systems Engineering and Information Technology    2024, 24 (4): 60-68.   DOI: 10.16097/j.cnki.1009-6744.2024.04.007 Abstract （267）      PDF （2177KB）（234）       For dynamic scenarios involving interaction among multiple vehicles, intelligent vehicles should be able to predict the future trajectories of surrounding vehicles for safe and efficient driving. This paper proposes a trajectory prediction method that considers dynamic interactions among vehicles. First, based on the historical trajectory information of the target and surrounding vehicles, a dynamic spatio-temporal correlation graph is constructed as the input for the interaction feature extraction module. The graph attention mechanism is then used to capture the temporally varying interaction feature parameters. Second, the historical temporal information of the target vehicle is fused with the variable interaction feature parameters. A context vector is obtained by an LSTM encoder embedded with a temporal attention mechanism, followed by using the LSTM decoder to output the future trajectory of the target vehicle. Finally, the proposed model is trained and validated on the CitySim dataset, and transfer experiments are conducted using the CQSkyEye dataset. The results show that the model achieves an RMSE of 0.82 m in a 5 s prediction horizon, demonstrating a 15% improvement in accuracy compared to other popular models. The model also demonstrates the ability to make predictions with less than 2 s lead time. In terms of transferability, the proposed model outperforms others with an RMSE of 6.43 m in the 5 s prediction horizon after adjusting the distance threshold parameter for graph construction, showing an improvement of over 48% in transfer prediction capability.

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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 （256）      PDF （2891KB）（557）       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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Car-following Model Construction and Behavior Analysis of Connected Vehicles in Foggy Conditions HUANGYan, LI Haijun, YAN Xuedong, DUAN Ke Journal of Transportation Systems Engineering and Information Technology    2024, 24 (4): 41-49.   DOI: 10.16097/j.cnki.1009-6744.2024.04.005 Abstract （247）      PDF （1719KB）（291）    PDF(English version) （1571KB）（5）    Connected vehicle (CV) has been proven to effectively improve traffic safety under fog weather conditions in microscopic driving behavior analysis. A microscopic car-following model is important for simulating the trajectory of CV in fog weather. Based on the traffic information perception mode and car-following behavior characteristics of CV in fog weather, this paper proposes a fog-related intelligent driver model of connected vehicle (FIDMCV) considering factors such as time headway, weighting, and compliance, based on the fog-related intelligent driver model. To evaluate the effectiveness of the FIDMCV model and assess the traffic impact of CV in fog weather, the cumulative reciprocal of Time-to-collision (1/TTC) and throughput were selected as analysis indicators, and numerical simulation scenarios with different CV penetration rates and decelerations of the leading vehicle were established. Before conducting numerical simulations, sensitivity analyses were performed on key parameters of time headway and compliance. The simulation results show that with the increase in the penetration rate of CV, mixed traffic flow more effectively improved traffic safety in fog weather. However, it also led to an increase in car-following distances of vehicles, thereby reducing road throughput and decreasing traffic efficiency. The proportion of reduction in cumulative 1/TTC values for CV in a high risk scenario (deceleration of 6 m⋅s²) is 14.3%, and in medium-low risk scenarios (decelerations of 4 m ⋅ s² and 2 m ⋅ s²) is 5.6% and 6.3%, respectively, indicating that the improvement of traffic safety for CV is more significant in the high risk scenario. The proposed FIDMCV model can effectively reflect the traffic safety improvement effect and car-following distance increase characteristics of CV in fog weather conditions, and can be used as a microscopic simulation tool for CV.

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Tasks and Measures of Carbon Emission Reduction for China's Traffic and Transportation Industry in the New Period  DUPeng Journal of Transportation Systems Engineering and Information Technology    2024, 24 (6): 1-4.   DOI: 10.16097/j.cnki.1009-6744.2024.06.001 Abstract （245）      PDF （1199KB）（353）       The carbon emission of the traffic and transportation industry accounts for 10% of the total emission throughout the country. The industry, which provides support for the development of national economy, has its own responsibility of carbon emission reduction in the meantime, and is one of the important arenas of carrying out carbon peaking and carbon neutrality strategy. With the subject of Tasks and Measures of Carbon Emission Reduction for China's Traffic and Transportation Industry in the New Period, and taking the strategy of carbon peaking and carbon neutrality as the flag, it is specified in the session both tasks and key fields of carbon emission reduction, based on making fully use of comparative advantages of each transportation mode. It is also discussed in the session the measures and feasible roadmaps in key fields, with comprehensive considerations of social and economic development in the new period, substitution degree of new energy products, and economic feasibility of new technologies.

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Human-machine Collaborative Decision-making for Transportation Scheduling Optimization LIU Tao, YOU Hailin Journal of Transportation Systems Engineering and Information Technology    2024, 24 (2): 136-148.   DOI: 10.16097/j.cnki.1009-6744.2024.02.014 Abstract （241）      PDF （3361KB）（165）       The paper proposes a human-machine collaborative decision-making methodology based on the deficit function model to solve transportation scheduling problems with flexible constraints. The methodology mainly consists of two stages. In the first stage, by making use of mathematical programming models and the powerful computing capacity of computers, a feasible solution is quickly obtained. In the second stage, with the help of the deficit function model, human beings' own knowledge and experience are employed to further optimize the feasible solution obtained in the first stage, while taking into account flexible constraints. The two stages interact in real time through a graphical user interface composed of deficit function figures, thereby realizing human-machine collaborative decision-making. The effectiveness of the proposed human-machine collaborative decision-making methodology based on the deficit function model is demonstrated through two case studies, i.e., app-based customized bus scheduling problem and civil aviation aircraft scheduling problem. Computation results show that the proposed methodology can realize the automatic construction of vehicle/flight chains and the automatic insertion of deadheading trips. It is useful for solving complex transportation scheduling problems with flexible constraints.

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Segmented Cooperative Control Method for Urban Road Traffic Flow in Connected Vehicle Environment JIANG Xiancai, GUO Zihao, SONG Chengju Journal of Transportation Systems Engineering and Information Technology    2024, 24 (6): 47-62.   DOI: 10.16097/j.cnki.1009-6744.2024.06.005 Abstract （233）      PDF （3810KB）（254）       The lane-changing behavior of vehicles approaching intersections will constrain the improvement of intersection traffic efficiency. Based on this, this paper proposes a Segmented Cooperative cOntrol Method for Urban Road Traffic Flow (SCOM-URTF) in a connected vehicle environment, which adopts a bi-level optimization model to achieve dynamic division of road section functional zones and collaborative optimization of traffic flow between road section and intersection. The upper-level model designs a Misaligned Lane-changing with Separated Lane Speed Guidance (ML-SLSG) to promote rapid lane changes for left and right turning vehicles through rearranging the vehicles entering from upstream intersection in longitudinal space, minimizing the vehicle lane-changing zone length, and balancing lane group traffic flow. The lower-level model uses dynamic programming to optimize the trajectory of connected vehicles and intersection signal timing parameters with the goal of minimizing average vehicle delay. The simulation results show that ML-SLSG can effectively shorten the total length of lane-changing. At the same time, the longitudinal trajectory optimization model proposed in this paper can reduce average vehicle delays at intersections by 5.9% ~8.0% under low, medium and high traffic demands. And after further collaborative optimization of vehicle trajectory and signal timing, the average vehicle delay can be further reduced by 3.7%~22.8%. Comparative studies with similar methods have shown that SCOM-URTF is more suitable for traffic environments where multiple driving behaviors are coordinated with each other. Sensitivity analysis shows that higher connected and automated vehicle penetration rates and road speed limits can help reduce average vehicle delays, and increasing the spacing between intersections can initially reduce average vehicle delay, but there may be a delay rebound after reaching the critical point. However, the coordinated optimization of trajectories and signals can effectively curb the rebound of delays.

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Joint Optimization of Train Timetabling and Rolling Stock Circulation Planning in Urban Rail Transit Line with Multiple Train Compositions RAN Xinchen, CHEN Jian, CHEN Shaokuan, LIU Gehui, ZOU Qingru Journal of Transportation Systems Engineering and Information Technology    2024, 24 (3): 184-193.   DOI: 10.16097/j.cnki.1009-6744.2024.03.018 Abstract （227）      PDF （2739KB）（203）    PDF(English version) （894KB）（2）    To address the issues of peak-hour congestions and off-peak underutilization of transportation capacity on an urban rail line, a joint optimization method of train timetabling and rolling stock circulation planning with multiple train compositions is proposed. Based on dynamically changing OD passenger demand and multiple types of line resource, a two-objective optimization model is constructed to minimize the total passenger waiting time and the train operating cost. The total number of operating trains, the timetable, the train types, the entry and exit of trains from depots, and the train succession relationship are taken as decision variables. Timetable-related constraints, rolling stock circulation- related constraints, fleet size constraints, turnaround constraints, and train capacity constraints are considered in this model. Since the total number of trains is not determined, a NSGA-II (Non-dominated Sorting Genetic Algorithm-II) with variable-length chromosomes is designed to solve for the Pareto optimal solution of the twoobjective optimization model. A case study conducted on a subway line demonstrates the effectiveness of this modelling and solution approach. The results show that the optimized multi-train composition strategy simultaneously reduces the total passenger waiting time by 26.16% and the train operating costs by 25.75%. Moreover, the optimized average load factor of trains is increased by 1.3% ~9.6% , further improving the matching between transportation capacity and passenger flow demand.

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Optimization of Urban Emergency Vehicle Rescue Route Considering Dynamic Impact of Rainstorm Disasters HU Xiaowei, LU Hongbo, AN Shi Journal of Transportation Systems Engineering and Information Technology    2024, 24 (3): 75-82.   DOI: 10.16097/j.cnki.1009-6744.2024.03.008 Abstract （226）      PDF （1669KB）（191）       In recent years, the frequency and intensity of extreme weather events show an increasing trend, in which the urban inland flooding caused by heavy rains escalates the likelihood of traffic emergencies. To accelerate emergency rescue during the rainstorm disasters, this study focuses on the optimization of emergency vehicle rescue routes under such conditions. To minimize the passage time considering the dynamic impact of road water on vehicle passing speed, this paper develops an emergency rescue path optimization model. A dynamic shortest path optimization algorithm is proposed to solve the model. The northeast of Changning District of Shanghai is chosen as the study area. According to the water accumulation of the urban road surface under the condition of a rainstorm in 50 years simulated by the Storm Water Management Model (SWMM), the paper sets the emergency rescue scenario and solves the emergency rescue path. By comparing the path solved by the proposed algorithm with the traditional static shortest path algorithm, the traffic time is reduced by 25.42%. Furthermore, this paper also considers the emergency supplies reserve to allocate emergency rescue tasks, expands the application scenarios of the algorithm, and forms a reliable and efficient emergency response scheme, which provides a reference for improving the efficiency of emergency response under rainstorm disasters.

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Assessment of Drivers' Potential Hazard Perception at Unsignalized Intersections PENGJinshuan, CHENG Jiajia, ZHAO Liuchang, LUO Shuang, YUAN Hao, XU Lei Journal of Transportation Systems Engineering and Information Technology    2024, 24 (2): 127-135.   DOI: 10.16097/j.cnki.1009-6744.2024.02.013 Abstract （224）      PDF （2593KB）（143）       To quantify the potential hazard perception ability of drivers at unsignalized intersections, this paper conducted a real vehicle driving test to collect real-time parameters of drivers' eye-movement characteristics. The fixation and saccade law of skilled and unskilled drivers were analyzed when they drive straight through the unsignalized intersections. Based on the Markov chain model, the driver's fixation transfer characteristics were illuminated to reveal the internal relationship between drivers' visual characteristics and potential hazard perception characteristics. The characteristic parameters of drivers' potential hazard perception ability were extracted combining with the statistical analysis under each mapping index of skilled and unskilled drivers. Based on the gray near-optimal comprehensive evaluation method, this study evaluated driver's potential hazard perception ability at unsignalized intersections. The results show that when driving straight through the intersections, the horizontal search breadth, vertical search depth and saccade intensity of unskilled drivers are significantly lower than those of skilled drivers. The ability of unskilled drivers searching for information on both sides of the road is weaker than that of skilled drivers, and timely allocation mechanism of fixation probability is not flexible. Potential hazard perception score of skilled drivers at unsignalized intersection is 31.2% higher than that of unskilled drivers. Among skilled drivers, men's potential hazard perception ability is significantly higher than that of women, while gender has no significant effect on the perceived performance of unskilled drivers. The research results can enrich the theoretical system of defensive driving, and provide important reference for the optimization and improvement of traffic facilities at unsignalized intersections, as well as driver safety education and evaluation.