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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 （475）      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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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 （408）      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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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 （344）      PDF （1708KB）（304）       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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Effectiveness of New Energy Vehicle Incentive Strategies Considering Urban and Population Heterogeneity WENG Jiancheng, ZHOU Huiyuan, ZHANG Mengyuan, YU Jiangbo Journal of Transportation Systems Engineering and Information Technology    2025, 25 (1): 2-14.   DOI: 10.16097/j.cnki.1009-6744.2025.01.001 Abstract （299）      PDF （2998KB）（248）       Formulating policies tailored to urban low-carbon development phases and resident characteristics is essential for optimizing incentive structures and promoting green mobility. This study evaluates new energy vehicle (NEV) incentive strategies across four city categories, considering factors such as air quality, NEV penetration, and charging infrastructure maturity. It analyzes social media data using the Latent Dirichlet Allocation (LDA) model and designs user surveys. A Latent Class Ordered Logit Model (LCOL) is employed to assess different urban populations' preferences for vehicle electrification incentives, identifying key impacted groups. The results indicate that immediate incentives, such as driving ban exemptions and significant fiscal subsidies, effectively enhance the purchasing intent of NEVs among less receptive residents. Conversely, more receptive residents respond better to regular, smaller subsidies. Cities with low NEV penetration exhibit a higher probability of purchasing under incentives, highlighting greater potential for improvement. Enhancing charging infrastructure significantly boosts purchasing intentions in infrastructure-deficient cities, with a 1% increase in likelihood for every minute reduction in charging time. However, this effect diminishes in cities with extensive charging networks. In metropolises with vehicle access restrictions, exempting NEVs from these increases purchasing probabilities by 3.5%. These insights guide NEV promotional strategy development in diverse urban settings.

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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 （294）      PDF （2177KB）（242）       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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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 （287）      PDF （2586KB）（280）    PDF(English version) （3020KB）（4）    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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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 （275）      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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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 （271）      PDF （3810KB）（259）       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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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 （268）      PDF （1719KB）（301）    PDF(English version) （1571KB）（6）    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 （258）      PDF （1199KB）（363）       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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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 （247）      PDF （1669KB）（195）       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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Journal of Transportation Systems Engineering and Information Technology    2025, 25 (1): 1-.   Abstract （245）      PDF （761KB）（156）

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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 （233）      PDF （2739KB）（204）    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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Electric Vehicle Ride-hailing Operation and Charging-discharging Dynamic Scheduling Strategy in Vehicle-to-grid Scenario NIU Zhenning, AN Kun, MAWanjing Journal of Transportation Systems Engineering and Information Technology    2024, 24 (4): 50-59.   DOI: 10.16097/j.cnki.1009-6744.2024.04.006 Abstract （229）      PDF （1977KB）（174）       The centralized nature and flexibility of electric vehicle (EV) ride-hailing fleets offer opportunities for vehicles to provide emergency and demand-response services to the grid during peak load periods, when combined with Vehicle-to-Grid (V2G) technology. This study investigates the flexibility of EV ride-hailing fleets participating in V2G systems and aims to make dynamic decisions on vehicle-trip assignment, empty vehicle relocation, and charging/ discharging schedules. First, a time-space-energy three-dimensional network is constructed to depict the vehicle scheduling problem. Then, the rolling horizon optimization model is used to maximize the expected benefits of the fleet. Additionally, the dynamic scheduling decisions of the fleet are obtained by defining feasible arcs. A case study is conducted in Jiading, Shanghai. The results indicate that the proposed strategy for EV ride-hailing fleets can effectively respond to travel requests, balance future travel demand and supply through empty vehicle relocation, and dispatch idle vehicles for discharging. During periods of grid demand response, 10.3% of idle vehicles can be dispatched for discharging, with an average revenue of 104.8 yuan per hour per vehicle. The proposed method helps reduce vehicle idle rates, increase vehicle revenue, and address the issue of the gradually saturated transportation service market.

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Dynamic Clearance Control Method for Reusing Bus Lanes Under Vehicular Networking DONG Hongzhao, YANG Jiawei, QUAN Cheng Journal of Transportation Systems Engineering and Information Technology    2024, 24 (3): 12-20.   DOI: 10.16097/j.cnki.1009-6744.2024.03.002 Abstract （223）      PDF （2405KB）（215）       Traditional dynamic control methods for dedicated bus lanes can be improved to ensure both the bus priority and the lane utilization rates. To address this issue, this paper proposes a dynamic clearance control method for the reutilization of dedicated bus lanes with the support from vehicular networking, which is also referred to as Dynamic Clearance Bus Lane (DCBL). This method establishes a clearance framework model that dynamically adjusts the speed of connected buses and the lane-changing time of connected private vehicles. Additionally, it defines a lane change urgency coefficient and uses the fuzzy control theory to design a lane change probability output algorithm in consideration of drivers' lane-changing psychology to simulate the actual lane-changing process. The simulation analysis was conducted to verify the effectiveness of the DCBL control method. The results indicate that the DCBL control method expands the applicable range of traffic density to 0~71 pcu · km- 1 , an increase of 9~21 pcu · km- 1 compared to traditional BLIP(Bus Lane with Intermittent priority) and IBL(Intermittent Bus Lane) control methods. In the mid-to-high-density range of 40~70 pcu · km-1 , the DCBL control method maintains the average speed of private vehicles at 45.86 km·h-1 , an improvement of 17.9%~24.7% compared to traditional control methods. The average speed of buses is maintained at 33.68 km· h-1 , only decreasing by 6.4% compared to the expected speed of buses. The DCBL control method results in a bus travel delay of less than 25 seconds mid-to-high-density range, leading to an increase in roadway throughput by 8.0%~18.3% compared to traditional control methods.

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Stackelberg Game-based Control Method for Driver-automation Collaboration in Ship Remote-control LI Chen, YAN Xinping, LIU Jialun, HUANG Yamin, LI Shijie Journal of Transportation Systems Engineering and Information Technology    2024, 24 (3): 21-31.   DOI: 10.16097/j.cnki.1009-6744.2024.03.003 Abstract （223）      PDF （2855KB）（199）       To solve the problem of human-machine control objective non-consistency in the ship remote-control process, this paper proposes a shared steering control method within the Stackelberg framework and considers the human-dominated and machine-auxiliary operating mode of the system. The human-machine interaction in ship collision avoidance collaborative steering task is described as a non-cooperative game relationship under complete information conditions. By constructing the state space of the driver and the co-pilot controller, the differential strategy is derived for Stackelberg game, and the uniqueness and existence of Nash equilibrium solution is proved with FanGlicksberg fixed points theorem. Based on model predictive control method, the trajectory tracking controller is designed with pre-allocating driving weight for different driving style and maneuvering skills, rolling and optimization in a finite time domain through feedback correction. And the control authority will be adjusted online in combination of the safety navigation boundary, collision risk and degree of human-machine conflicts. Taking the lateral displacement and driver's operational load as evaluation indexes, the effectiveness of method is verified in inland maneuvering scenarios. Simulation results show that the proposed method could provide personalized assistance for remote operators with different driving styles and maneuvering skills, while there exists intention conflict between the driver and the co-pilot controller, it can adjust the pre-allocated weight in accordance with the navigating risk dynamically, so as to make the ship motion more compliant with driver's maneuvering intentions under the premise of ensuring navigating safety.

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Computer Vision-based Fire Detection and Localization Inside Urban Rail Transit Stations ZHANG Jinlei, YANG Jian, LIU Xiaobing, CHEN Yao, YANG Lixing, GAO Ziyou Journal of Transportation Systems Engineering and Information Technology    2024, 24 (3): 53-63.   DOI: 10.16097/j.cnki.1009-6744.2024.03.006 Abstract （222）      PDF （3076KB）（218）       To efficiently address the occurrence of in-station fire incidents in rail transit, this paper proposes a computer vision-based model for fire detection and precise fire localization within the rail stations, which is referred to as Fire-Detect. First, this study created the Fire-Rail dataset using the Unity simulation and collecting internet images, which established the dataset to train the fire detection and precise localization algorithms. Then, a fire detection algorithm was developed to integrate convolutional neural networks, residual structures, and channel attention mechanisms. This algorithm classifies each frame of surveillance video within the station as either "normal" or "suspected fire" status. In the "suspected fire" status, the model activates the precise localization algorithm. It processes the "suspected fire" image along with subsequent frames, providing real-time, detailed fire localization information to station attendants. Experimental results on the Fire-Rail dataset demonstrated a fire detection accuracy of 95.12% on the test set. Furthermore, hierarchical experiments with convolutional neural network layers balance the resource consumption and accuracy. Ablation experiments confirmed the effectiveness of individual components, and robustness experiments indicated the algorithm's ability to handle most noise. The overall model achieves an average fire localization detection accuracy (mAP) of 77.3% and is suitable for deployment in video surveillance equipment within rail transit stations.

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Optimizing Modular Bus Route Operation Considering Spatially Uneven Demand YI Hongbo, LIU Yugang, WANG Tongyu Journal of Transportation Systems Engineering and Information Technology    2024, 24 (4): 166-175.   DOI: 10.16097/j.cnki.1009-6744.2024.04.016 Abstract （219）      PDF （2056KB）（208）       Traditional fixed-capacity buses struggle to meet the varying demand distribution on bus routes. To tackle this challenge, modular buses are introduced, allowing for dynamic adjustments in platoon capacity through joining and detaching, thus better accommodating spatial demand variations. An optimization model is developed to describe the operational scheme of modular bus routes, based on the reconstruction of spatiotemporal graphs. The formulated model, a Mixed Integer-Nonlinear Program (MINLP) model, includes decision variables such as platoon schemes and modular bus unit schemes. To facilitate the model solution, time discretization is applied, which transforms the MINLP model into a Mixed Integer-Linear Program (MILP). A case study is performed using real bus routes and passenger demand data from Chengdu, China. Experimental results demonstrate that the use of modular buses reduces passenger costs by 11.44% and operating costs by 31.35% compared to traditional fixed-capacity buses, resulting in an overall decrease of 20.32% in total system costs. Sensitivity analysis experiments examine the effect of system supply and demand changes on system costs.

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An Equity-oriented Planning Method for Freight Carbon Tax Base on Integrated Modelling Framework WANGZongbao, ZHONG Ming Journal of Transportation Systems Engineering and Information Technology    2024, 24 (4): 12-22.   DOI: 10.16097/j.cnki.1009-6744.2024.04.002 Abstract （215）      PDF （2384KB）（145）       This paper proposes an equity-oriented planning method for the freight carbon tax, which is developed based on an integrated modelling approach to study its potential impacts on regional equity. The proposed method uses a bi-level programming model. The upper-level model uses the Dagum Gini coefficient to evaluate the equity of the freight carbon tax policy on industrial locations across regions. Based on this evaluation, it optimizes the freight carbon tax rates for each region to enhance their equity. The lower-level model simulates the interaction among regional socio economic activities, transportation, and environment by adapting an integrated land use-transportation model. Due to the complex interaction between decision variables and objective functions, this study proposes a Bayesian optimization method to solve the above model. Extending from the above integrated land-use and transportation model, the proposed model not only provides a comprehensive impact analysis of the freight carbon tax policies on freight emission and industrial locations, but also takes the equity of such policies onto regional multimodal transportation networks and industrial locations into policy formulation. Taking the Yangtze River Economic Belt as an example, the findings suggest that a freight carbon tax policy of 80 Yuan per ton could intensify the adverse effects on the utility of industrial locations. Specifically, the net difference between regions accounts for 74.63% of the total Gini coefficients. In contrast, the differentiated freight carbon tax policy formulated by the model can not only effectively reduce the net difference between regions under the same freight emission reduction target, but also decrease the total Gini coeffifrom 0.180 to 0.115, which balances the equity impact of the freight carbon tax policies on the location of manufacturing industries in different regions.cient

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Coordinated Sequential Optimization for Network-wide Traffic Signal Control Based on Heterogeneous Multi-agent Transformer CHEN Xiqun, ZHU Yizhang, XIE Ningke, GENG Maosi, LV Chaofeng Journal of Transportation Systems Engineering and Information Technology    2024, 24 (3): 114-126.   DOI: 10.16097/j.cnki.1009-6744.2024.03.012 Abstract （212）      PDF （4128KB）（185）       Focusing on the complex traffic signal control task in an urban network, this study proposes a coordinated sequential optimization method based on a Heterogeneous Multi-Agent Transformer (HMATLight) to optimize network-wide traffic signals and improve the performance of signal control policy at intersections within the urban network. Specifically, considering the spatial correlation of multi-intersection traffic flow, a value encoder based on a self-attention mechanism is first designed to learn traffic observation representations and realize network-level communication. Secondly, in response to the non-stationary environment for multi-agent policy updates, a policy decoder based on the multi-agent advantage decomposition is constructed, which can sequentially output the optimal responsive action on the basis of the joint actions of preceding agents. Besides, an action-masking mechanism based on effective driving vehicles, adapting the decision frequency within the time-adequate interval, and a spatio-temporal pressure reward function considering the waiting fairness are constructed, which further enhance policy performance and practicality. A series of experiments are carried out on Hangzhou network datasets to validate the effectiveness of the proposed method. Experimental results show that the proposed HMATLight outperforms all baselines on two datasets with five metrics. Compared with the best-performed baseline, HMATLight decreases the average travel time by 10.89%, the average queue length by 18.84% and the average waiting time by 22.21%. Furthermore, HMATLight is dramatically higher in generalization and significantly reduces instances of long vehicle waiting times.