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Review on Application of Truck Trajectory Data in Highway Freight System GAN Mi, QING San-dong, LIU Xiao-bo, LI Dan-dan Journal of Transportation Systems Engineering and Information Technology    2021, 21 (5): 91-101.   Abstract （848）      PDF （1401KB）（718）       With the continuous improvement of the accessibility and accuracy of trajectory tracking data, truck trajectory data has been widely used in the planning and management of highway freight system. At the same time, the rapid development of artificial intelligence and big data analysis technology also brings new opportunities and challenges to the study of highway freight system. This paper comprehensively summarizes the researches on the application of highway freight trajectory data, and reviews the research objectives, main contents and research methods of existing literatures from three aspects: identification of freight travel information, prediction of key features of freight system, and further application of freight trajectory data. Literature analysis shows that the research on freighttravel information identification focuses on hot topics such as freight stop points, vehicles and goods, and activity travel patterns. However, the existing identification methods are mostly transplanted from the research of passenger travel, and more considerations need to be given to the unique characteristics of freight travel. In terms of forecasting the key features of the freight system, researchers mainly conduct research on topics such as freight travel time, spatial location, and travel demand, and proved the feasibility of forecasting freight characteristics based on trajectory data. However, the spatial and temporal range of prediction is relatively limited, further research is needed on specific freight tasks, characteristics of truck drivers, and freight policies. In addition, trajectory data are also further applied to freight travel route choice behavior, freight parking and rest behavior, driving safety, freight emissions and energy consumption analysis, freight policy evaluation. On the basis of analyzing the shortcomings of the existing research, this paper suggests that future research should focus on combining freight trajectory data with other multi-source data, make breakthroughs in three key technologies. First, in view of individuals of freight practice, it is necessary to focus on exploring the travel characteristics and travel patterns of efficient truck drivers and apply them in the freight system. Second, in view of new transportation technologies and new situations, the development and optimization of freight organization models, and strategies under the influence of autonomous driving technology and major emergency events should be focused. Thirdly, in view of freight supply and demand relationship and matching mechanism, the research on freight supply and demand status identification, and prediction of the whole freight process should be focused on, and the intelligent supply and demand matching model combined with deep learning methods should be trained and developed, so as to optimize the freight system scheduling, facilitate the integration of social scattered transportation resources and improve the overall efficiency of the freight system.

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Energy-efficient Timetable Optimization for Urban Rail Transit Considering Difference of Peak and Off-peak Hours ZHANG Bonan, YAO Xiangming, ZHAO Peng, YANG Zhongping, YANG Jianguo Journal of Transportation Systems Engineering and Information Technology    2024, 24 (3): 164-171.   DOI: 10.16097/j.cnki.1009-6744.2024.03.016 Abstract （620）      PDF （2530KB）（211）       Reducing energy consumption is crucial for developing green, low-carbon, and sustainable rail transit. Based on the principle of "running fast during peak hours for transport capacity assurance, running slowly during off- peak hours for energy consumption reduction", this paper proposes an energy-efficient timetable optimization method based on different time criteria during peak and off-peak hours. First, an optimization model considering the characteristics of passenger service demand was developed based on the negative correlation between train running time and traction energy consumption. The objective was to reduce train traction energy consumption. Then, to address the uneven train service issue resulting from different time criteria during the transit period between peak and off-peak hours, this study developed an optimization model for train service to minimize the variability in train arrival intervals. The empirical analysis was conducted for Fuzhou Metro Line 1. The results demonstrate that the energy-efficient timetable based on different time criteria achieved a 12.56% reduction in traction energy consumption, while maintaining the transport capacity and the number of rolling stock unchanged. The proposed method is practical and valuable, providing operational managers with methods for energy-efficient timetabling.

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Optimization of Multimodal Transport Routes Between Northeast Asia and Europe Considering Time Uncertainty WANG Qingbin, LIANG Yinghao, ZHENG Jianfeng Journal of Transportation Systems Engineering and Information Technology    2026, 26 (2): 1-10.   DOI: 10.16097/j.cnki.1009-6744.2026.02.001 Abstract （628）      PDF （1735KB）（597）       East Asia and Europe maintain close economic and trade ties, and the rapid development of the China-Europe freight train has provided a new land-based transportation option for the two regions beyond maritime shipping. The multimodal transport network between the two regions has developed rapidly, with an increasing diversity of route options. Addressing the issues of uncertainty in transit times and railway border crossing dwell times within the East Asia-Europe multimodal transport network, this paper constructs a multi-objective path optimization model with the objectives of minimizing total cost, total time, and total carbon emissions. Trapezoidal fuzzy numbers are used to characterize the uncertainty in transit time and railway border crossing dwell time, and fuzzy opportunity constraint programming is employed to defuzzify the model. A non- dominated sorting genetic algorithm (NSGA-II) suitable for this problem is designed, and the model is validated with a container transport example from Busan to Hamburg. The results show that the optimized route schemes effectively balance cost, time, and carbon emissions. Further sensitivity analysis of sea freight rates shows that when sea freight rates rise above a specific threshold, the preference of multimodal transport operators for sea-rail intermodal transport significantly increases. Additionally, as the time confidence level increases, the optimal route shifts from sea-rail intermodal transport to all-sea transport. This study provides a reference for cross border logistics companies in addressing uncertainty in multimodal transport route decision-making.

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Electric Bus Rescheduling Method Considering Schedule Consistency Under Operational Disruptions AN Kun, JIA Zuoning Journal of Transportation Systems Engineering and Information Technology    2026, 26 (3): 1-13.   DOI: 10.16097/j.cnki.1009-6744.2026.03.001 Abstract （400）      PDF （3792KB）（416）       Electric bus operations are vulnerable to disruptions, creating complex recovery challenges that necessitate balancing economic costs, battery constraints, and the consistency of driver schedules. To address this, this study proposes a spatiotemporal optimization model for electric bus disruption recovery. Unlike traditional rescheduling frameworks for fuel-based buses, this model accounts for dynamic battery consumption and charging decisions under disrupted conditions. A key innovation of the study is introducing the "spatiotemporal consistency" as a constraint, which is designed to maintain the stability of driver schedules in both time and space. To solve the associated Mixed-Integer Non-Linear Programming (MINLP) problem efficiently, the study proposes a customized gradient heuristic search algorithm. Simulation results based on the real-world bus network of Hengshui City show that the model produces feasible recovery plans rapidly across three typical scenarios: severe weather, sudden accidents, and cumulative disrupted events. The recovery duration was successfully controlled within a range of 95 to 153 minutes. Ultimately, the results demonstrate that the proposed model reduces operating costs and quickly generates recovery plans while safeguarding the spatiotemporal consistency of schedules, proving its adaptability in complex operational environments.

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Optimization of Operation Plans for Full-length and Short-turn Routings of Urban Rail Transit Based on Flexible Train Composition LIU Bin, ZHAO Jinhui, TIAN Zhiqiang, MA Chaofan, LIANG Hui, LI Hebi Journal of Transportation Systems Engineering and Information Technology    2025, 25 (6): 143-152.   DOI: 10.16097/j.cnki.1009-6744.2025.06.013 Abstract （913）      PDF （2072KB）（632）    PDF(English version) （623KB）（184）    To address capacity redundancy or shortages caused by uneven passenger flow distribution in urban rail transit, this study optimizes train operation plan under flexible train composition during peak hours to achieve precise demand-capacity alignment, enhance cost control, and improve resource utilization efficiency. Focusing on asymmetric passenger flow patterns, this study develops a bi-objective nonlinear integer programming model with the goals of minimizing operator costs and maximizing average load factor and the constraints such as standing-passenger density limits. A hierarchical three-stage algorithm and Technique for Order Preference by Similarity to Ideal Solution are designed for solution prioritization. The model and algorithm are validated through case studies of an operational metro line. Results demonstrate that flexible train composition outperforms fixed-composition strategies (single routing and combined large/small routing). During peak hours, the operating costs decreased by 25.67% compared with the single routing method, and 7.34% compared to the combined large/small routing method. The average load factor improvements are respectively 29.55% and 26.02% compared to the single routing and combined large/small routing. Sensitivity analysis indicates that allowing a moderate increase in standing-passenger density for small routing can further reduce operational costs, though requires a balance between passenger comfort and safety. When the standing-passenger density increases from 7 to 9 persons ⋅ m -2, the flexible train composition reduces costs by 15.38%, decreases the load factor ratio 14.83%, and lower the required number of train sets by 15.79%. The flexible train composition effectively adapts to spatiotemporal passenger flow fluctuations, and could achieve synergistic optimization of dynamic capacity allocation and cost control.

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Drone Delivery: A Systematic Review on Technology, Efficiency, and Applications WU Jingqiong, DIAN Ran, ZI Taisheng, LI Yunqi Journal of Transportation Systems Engineering and Information Technology    2025, 25 (6): 34-49.   DOI: 10.16097/j.cnki.1009-6744.2025.06.004 Abstract （1949）      PDF （2284KB）（914）    PDF(English version) （466KB）（18）    With the rapid development of e-commerce and a surge in demand for instant delivery, drone delivery, as an innovative solution in the logistics sector, is driving profound transformations in the logistics system. This paper synthesizes findings from 74 relevant articles published between 2015 and 2024, comprehensively examining drone delivery research advancements across key dimensions including critical technologies, economic benefits, environmental sustainability, application potential, and system synergy. The results indicate that drone delivery primarily relies on path planning algorithms, energy management, and multi-drone collaboration as its core technologies. Related optimization research has evolved from single-objective to multi-objective coordination, with algorithms transitioning from classical heuristics to intelligent approaches, effectively reducing solution time and optimizing costs. However, nonlinear effects of payload and wind resistance, along with adaptability to harsh weather conditions, remain bottlenecks. In terms of economic benefits, drone-vehicle collaborative systems can significantly reduce customer waiting time, delivery costs, and labor demands through optimized path planning and resource scheduling. Integration with public transit systems (bus/subway) effectively expands service coverage while reducing energy consumption. Multi-objective optimization models dynamically balance energy consumption, cost, and timeliness to further enhance synergistic benefits. Nevertheless, economic viability remains constrained by payload and range limitations, showing greater advantages in short distance, lightweight deliveries, particularly for emergency cargo deliveries. Environmental benefit analyses demonstrate that the operational phase of drone delivery exhibits significantly lower carbon emissions than traditional transportation methods, though a comprehensive lifecycle assessment encompassing manufacturing, operation, and recycling phases is required. Regarding applications, drone delivery technology demonstrates unique value in medical supply distribution, emergency logistics, and urban "last- mile" delivery, with particular advantages in remote areas and urgent emergency scenarios. However, challenges persist regarding safety risks, technological innovation gaps, limited social acceptance, and imperfect policy regulations. Future research should prioritize battery technology breakthroughs, intelligent path planning optimization, privacy/security safeguards, and cross regional policy coordination to accelerate drone delivery commercialization.

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Dynamic Neural NetworkBased Integrated Learning Algorithm for Driver Behavior LIANG Jun, SHA Zhi-Qiang, CHEN Long Journal of Transportation Systems Engineering and Information Technology    2012, 12 (2): 34-40.   Abstract （5324）      PDF （680KB）（1495）       Vehicle driver’s perception, judgment, decision and action towards traffic environment are usually uncertain and inconsistent during their driving processes. Thus, it is difficult to use the traditional driving decision model to accurately predict the driving behaviors under these circumstances. This paper proposes a DNNIA algorithm to describe driving behavior by dynamically integrating ANNs. Specifically, some ANNs are first trained to learn different kinds of driving behaviors based on sample data and small amounts of these ANNs with minimal generalization error E are then selected and integrated to predict the final driving behavior. The Lagrangian function method is used to resolve the coefficientωi for optimal ensemble. Moreover, by introducing the idea of agent alliance, the study takes each individual ANN as an agent in the alliance and outputs the maximal value among all the weighted average outputs of the neuron in each individual ANN. The proposed method is evaluated on some benchmark datasets to show its effectiveness. In addition, the predicted driver’s habitual behavior by DNNIA, such as braking pedal, consistently accord with that revealed by the sample data, which proves its practicality for realworld problems.

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Survival Analysis of Vehicle Lane-changing Duration on Road Segment Adjacent to Freeway Tunnels CHEN Zheng, WEN Hui-ying Journal of Transportation Systems Engineering and Information Technology    2022, 22 (4): 210-217.   DOI: 10.16097/j.cnki.1009-6744.2022.04.024 Abstract （417）      PDF （1984KB）（211）       This paper investigates vehicles' lane-changing behaviorson road segment adjacent to freeway tunnels and aims to improve vehicle's driving safety. Naturalistic driving tests were carried out on the three freeway tunnels' adjacent sections in Guangdong Province. The driving trajectories of the lane-changing vehicle and the surrounding vehicles are collected. Considering the heterogeneity of different drivers' perception of lane-changing risk level, a random parameter accelerated failure time (AFT) model was developed to describe the influence of environment and vehicle movement status on lane-changing duration in the tunnel adjacent sections. The results show that the random parameter in the AFT model has better goodness of fit compared to the fixed parameter AFT model. The significant influencing factors of lane-changing duration include distance to the tunnel, the speed difference to the leading vehicle, lane change direction, and distance to the leading vehicle in the target lane. The closer the position of lane-changing vehicle is to the tunnel and the closer to the vehicle in front of the target lane, the shorter of the lane-changing duration. Compared to the case that the speed of the lane-changing vehicle is greater than the leading vehicle in the original lane, the lane-chancing duration under a non-following state would increase by 7%. When the speed of the lane-changing vehicle is lower than the leading vehicle in the original lane, the lane-changing duration would increase by 20% compared to the case that the speed of the lane-changing vehicle is greater than the leading vehicle in the original lane. The study provides theoretical basis and method guidance for the improvement of traffic safety facilities on freeway tunnel adjacent sections and the development of microscopic driving behavior models.

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Vehicle and Non-motorized Vehicle Traffic Conflict Recognition at Signalized Intersection Based on Vehicle Trajectory LONG Ke-jun, ZHANG Yan, ZOU Zhi-yun, GU Jian, HAO Wei Journal of Transportation Systems Engineering and Information Technology    2021, 21 (1): 69-74.   Abstract （715）      PDF （1460KB）（283）       This study investigated four typical signalized intersections in the downtown area of Changsha, and then used video trajectory tracking software to extract the conflicting trajectory data of right-turning vehicles and through movement non- motorized vehicles. Based on the risk- avoidance behaviors such as decelerating, lane changing and likely colliding (the time from the collision point is less than 2 seconds), the study collected 254 samples of vehicle and non-motorized vehicle traffic conflicts. To improve the traditional conflict recognition model, this study selected the maximum time to collision (MTTC) and the time difference to collision (TDTC) as the evaluation index, and proposed a modified TTC (Time To Collision) model to identify the vehicle and non- motorized vehicle conflict. A real intersection was then used for the empirical study. The number of traffic conflicts calculated by the TTC model, the modified TTC model and the post encroachment time (PET) model were 7, 24, and 22, respectively. The results indicate that the traditional TTC method underestimates the level of conflict between vehicles and non- motorized vehicles at intersections. The accuracy rate of improved TTC method is increased by 2.14 times, which proves the feasibility of the proposed method.

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Spatio-temporal Correlation Analysis of Urban Traffic Congestion Diffusion ZHANG Jing, REN Gang Journal of Transportation Systems Engineering and Information Technology    2015, 15 (2): 175-183.   Abstract （709）      PDF （1702KB）（2374）       The formation and dissipation processes of urban traffic congestions are influenced by shocking waves of different cycles inside the traffic flow. The original factors that lead to traffic congestions are very complicated, the modelling is therefore difficult. This is the main reason that research works about the spatiotemporal dissipation effects for congestions are eventually stopped at the level of qualitative analysis. Some quantitative analysis can be successfully done based on the measured traffic data. However, rare effective knowledge extraction methods can be found to deal with data containing information about multiple time scales and granularities, which however is important to correlation analysis and the direct use of original data leads to unstationary signal features and opposite observation conclusions when putting the data into the discussion of given time scales. Focused on the analysis of spatio-temporal correlation of traffic parameters in congestion areas, a new analyzing method is developed and used based on Pearson's correlation index, which decomposes a measured road speed trajectory into trend and detail components in different time scales. The initial verification and application of this method and the corresponding data segmentation algorithm show the quantitative characteristics of the congestion diffusion in time and space by observing the variation of correlation status.

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Urban Rail Transit Timetable Optimization with Flexible Train Compositions and Multiple Service Routes Under Depot Conditions BAO Xinyu, ZHANG Qi, XIAO Yaling, LI Tao Journal of Transportation Systems Engineering and Information Technology    2026, 26 (2): 212-220.   DOI: 10.16097/j.cnki.1009-6744.2026.02.020 Abstract （184）      PDF （2207KB）（202）       The integration of flexible train compositions and multiple service routes allows urban rail trains to dynamically adjust their compositions at specific timestamps and stations, optimizing transport capacity in real time. This study optimizes train timetables incorporating these strategies under depot constraints. Considering constraints on the turnaround operations, train circulations, and depot layouts, the model minimizes total operating costs and passenger waiting time by adaptively determining train compositions, as well as turnaround directions, timings, and frequencies based on passenger demand and infrastructure conditions. It jointly optimizes the train timetables and rolling stock circulation plans through a mixed-integer linear programming model that can be solved with commercial solvers. The case studies demonstrate that the proposed method accommodates tidal passenger flows, enhances rolling stock utilization, and reduces the objective function by 39.0% and 4.3% relative to scenarios without flexible train compositions and without multiple service routes, respectively. Enhanced depot configurations can further increase transport capacity and operational rationality, and balance constraints of rolling stock utilization can enhance plan stability and continuity. The method provides efficient and flexible decision support for operational planning.

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Optimization of Yangtze River Multimodal Transport Based on Joint Data and Decision-Making WANG Jing, LEI Deming, ZHAI Jing, CHEN Shumei, LIU Linfan Journal of Transportation Systems Engineering and Information Technology    2026, 26 (2): 11-23.   DOI: 10.16097/j.cnki.1009-6744.2026.02.002 Abstract （316）      PDF （2204KB）（391）       This paper proposes a collaborative optimization method for the Yangtze River multimodal transport system to help improve efficiency while reducing costs and carbon emissions. To address challenges of data quality and dynamic decision making, this study introduces a two-stage collaborative framework with hybrid data preprocessing and reinforcement learning. First, based on the characteristics of small-sample trend data, sequential data, and statistical data, grey prediction, interpolation, and mean imputation methods are respectively used for targeted data governance, that can provide high-quality input for subsequent decision-making. Then, a dynamic decision-making model is developed with reinforcement learning as the core component. The real-time intelligent optimization of path and mode combinations is realized through a 12-dimensional state space and a composite reward function. The proposed method was validated using the actual operational data from 2019 to 2024. The results show that the proposed model reduces total logistics costs by 16.8%, saves RMB 2.016 billion in carbon emissions, and converges faster compared to conventional methods. The experimental results can provide reliable decision support for enhancing the efficiency of the Yangtze River shipping system in complex data environments.

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Collaborative Deep Reinforcement Learning Method for Expressways Integrating Dual Attention Mechanism SUN Jian, JI Yuwei, YU Kewei, LI Zihao, ZHAO Yulin Journal of Transportation Systems Engineering and Information Technology    2026, 26 (2): 137-147.   DOI: 10.16097/j.cnki.1009-6744.2026.02.013 Abstract （280）      PDF （3049KB）（293）       On urban expressways, ramp merging areas are prone to become traffic bottlenecks. The mixed traffic flow composed of connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) brings new challenges to traffic control. This study proposes a variable speed limit (VSL) control method for mixed traffic flow that considers ramp queuing spillover impacts, so as to alleviate congestion and enhance traffic flow stability. The merge area collaborative optimization control problem is formulated as a Markov decision process (MDP), and an integrated method DDQN-CBAM is proposed by combining the double deep Q-network (DDQN) and convolutional block attention module (CBAM). Specifically, an extended state space is constructed, including multi dimensional core parameters such as ramp queue length and merging area density, as well as grid-based features. The CBAM dual attention mechanism is introduced to strengthen the extraction of key features. A reward function integrating multi-objectives such as traffic efficiency and queue control is designed, and the training process is optimized by combining strategies such as prioritized experience replay and progressive traffic input. Taking the North Third Ring Road Expressway of Xuzhou, China as a case study, validation is completed on the Simulation of Urban Mobility (SUMO) platform. Experimental results show that compared with traditional control strategies, the proposed method reduces the total travel time by 26.49%, increases the total travel distance by 35.95%, decreases the standard deviation of traffic flow by more than 22.5%, and stabilizes the hourly control frequency and speed adjustment rate at approximately 10 times and 0.14, respectively. This method possesses both engineering applicability and robustness, and provides reliable support for traffic control of ramp merging areas on urban expressways.

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Optimization for Train Plan of Full-length and Short-turn Routing in Urban Rail Transit XU De-jie, MAO Bao-hua, LEI Lian-gui Journal of Transportation Systems Engineering and Information Technology    2017, 17 (1): 120-126.   Abstract （571）      PDF （1201KB）（1122）       According to the properties of the full- length and short- turn routing mode in urban railway transit, we develop a multi- objective optimization model of train plan for a single transit line, where the objective functions are minimum passenger waiting time, running kilometers of rolling stocks, and operation time of trains, and the decision variables are frequencies, train formation plans (full- length and short- turn train), and positions of turn-back stations. The linear weighted method is applied to reduce the above model into a single objective model, then the controlled random search algorithm (CRS) is designed to solve it. The validity of the model is verified by a case, and the sensitivities of frequency of short-turn train and positions of turn-back stations are also analyzed. The results indicate that the full-length and short-turn routing mode can significantly reduce the spatial inequality of load factor and the total number of rolling stocks; the shorter length of short- turn routing is not conducive to the passengers, and it can not lead to a more saving of operator’s cost.

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Collaborative Optimization of Emerging Mixed Traffic Evacuation Considering Flexible Lane Allocation LIU Jialin, XU Zhiran, JI Hao, JIA Bin, ZHANG Meng, SU Bing Journal of Transportation Systems Engineering and Information Technology    2026, 26 (3): 25-35.   DOI: 10.16097/j.cnki.1009-6744.2026.03.003 Abstract （228）      PDF （4040KB）（203）       Focusing on the emerging mixed traffic environment where connected and automated vehicles (CAVs) and human driven vehicles (HVs) coexist, this paper studies the cooperative evacuation problem under flexible lane allocation strategies. First, considering the platooning behavior of CAVs, the cell transmission model is adopted to simulate the dynamics of the mixed traffic flows. Then, to minimize total evacuation time, the cooperative evacuation problem of mixed traffic is formulated as a mixed integer nonlinear programming model, and an improved Benders decomposition algorithm is designed to solve it. Numerical experiments are conducted on the road network of the core urban area in Xi'an to analyze the evacuation efficiency, lane allocation schemes, and optimal platoon sizes under different evacuation demands and CAV penetration rates. The results indicate that: (1) compared with the fixed lane allocation, the flexible lane allocation can improve the evacuation efficiency by more than 10% by reusing released road resources; (2) the total evacuation time decreases with the increase of CAV penetration rate. When the CAV penetration rate exceeds a certain threshold, flexible lane allocation is equivalent to the CAV priority strategy; (3) when the evacuation demand or CAV penetration rate is high, allocating more lane resources to CAVs can reduce the total evacuation time; (4) there is an optimal CAV platoon size, which is dynamically adjusted with the changes in the evacuation demand and CAV penetration rate. In this paper, the optimal value is mostly 5 or 6.

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Urban Rail Transit Passenger Flow Induction Optimization Under Event Interference ZHAOMingxi, MAChangxi, MACunrui Journal of Transportation Systems Engineering and Information Technology    2024, 24 (6): 76-85.   DOI: 10.16097/j.cnki.1009-6744.2024.06.007 Abstract （585）      PDF （1688KB）（263）    PDF(English version) （580KB）（163）    Urban rail transit systems often experience operational disruptions or reduced service capacity during peak hours, major events, and adverse weather conditions. To effectively mitigate the negative impacts of these disruptions on passenger flow and enhance the resilience of urban rail transit systems, this paper proposes an optimization method for passenger flow guidance in response to disruptive events. First, considering the impact of disruptive events and the compliance rate of passenger guidance, this paper develops a rail transit passenger flow guidance model with the goal of minimizing the total travel time of passengers in the system. Then, a column generation-based exact algorithm is designed, and Gurobi is used to solve the restricted master problem. The A* algorithm is applied to solve the pricing subproblem, and the branch-and-bound algorithm is utilized to find integer solutions. Through actual case analysis, it is found that the acceleration strategies designed in this paper can improve the solving efficiency by 66%~89%, with performance significantly superior to using Gurobi alone. Simulations of scenarios ranging from minor to severe disruptions demonstrate that the proposed optimization method is applicable to urban rail transit passenger flows of varying scales, effectively guiding passenger travel paths under various disruption intensities.

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Resilience Evolution and Improvement Methods of Regional Road Traffic Networks Under Uncertain Disturbances WANG Xiaorong, LI Yinzhen, SUN Yingjie, XIAN Yong, LI Wen, JU Yuxiang Journal of Transportation Systems Engineering and Information Technology    2026, 26 (2): 47-59.   DOI: 10.16097/j.cnki.1009-6744.2026.02.005 Abstract （291）      PDF （3240KB）（316）       This paper investigates the resilience evolution law and improvement methods of regional road traffic networks under uncertain disturbance conditions. Given the time-varying characteristics of regional road traffic flow, this paper uses the real-time utilization rate of traffic capacity as an important factor to reflect the effects on network resilience and considers the network topological structure in the analysis. A weighted network model for regional road traffic is constructed. Four dynamic resilience indicators—preparedness, disturbance resistance, adaptability and recoverability—are adopted as the optimization metrics for measuring network resilience. On this basis, a multi-objective bi-level programming model based on uncertain probabilistic risks is developed, and an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm integrated with dynamic simulation and parallel computing is designed to solve the model. The regional backbone road network of Gansu Province is used for the case study. The disaster scenarios of regional coordinated failure are simulated to analyze the evolution laws of investment costs and traffic network resilience. The capacity expansion investment schemes for improving network resilience present a staged negative-slope concave function pattern with a diminishing marginal return on investment, and differentiated investment decisions should be implemented in phases according to the budget scale, which is verified by specific improvement schemes. Further sensitivity analysis of the basic recovery rate reveals that capacity expansion investment and recovery capacity exhibit a nonlinear coupling characteristic under different disturbance intensity levels and sustained disturbance durations. The targeted decision-making suggestions are proposed: for networks with a low recovery rate, priority should be given to investment in emergency management. The range of 0.10 to 0.15 for the basic recovery rate represents the stage with the highest return on investment, where a combination of management measures and engineering construction investment can be adopted to achieve maximum benefits. For networks with a high recovery rate, resilience can be improved through road reinforcement and optimization. The revealed evolution laws and proposed decision-making suggestions provide a reference for the collaborative optimization of emergency resource allocation and facility investment.

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Resource Allocation and Process Optimization of Unloading Operations in Dry Bulk Ports LI Haijiang, ZHAO Jiapeng, GUO Jingyi, MA Qianli, JIA Peng Journal of Transportation Systems Engineering and Information Technology    2026, 26 (2): 309-317.   DOI: 10.16097/j.cnki.1009-6744.2026.02.029 Abstract （251）      PDF （2098KB）（224）       The optimization of unloading operations in dry bulk ports is crucial for enhancing the overall efficiency of ports. To address the integrated resource allocation and the optimization of unloading operations through entire process of berths, unloaders, yard storage slots, and horizontal transportation, this paper proposes a graph-based representation method for port operational elements. A two-stage mixed-integer programming model is developed with the objective of minimizing the total operational cost. First, for the quayside unloading stage, a collaborative "berth-unloader" configuration model based on an asynchronous operation strategy is proposed. A joint solution method utilizing a Non-dominated Sorting Genetic Algorithm is designed. Experimental results demonstrate that this model significantly reduces the unloading energy consumption by 14.3% . Second, for the yard operations stage, a joint scheduling model for "storage slot allocation and horizontal transportation flow" is constructed, fully considering the uncertainty of cargo dwell time and transport path constraints. A solving algorithm integrating a dynamic masking mechanism is proposed within a Deep Deterministic Policy Gradient framework. Results show that this algorithm notably improves the solution speed, while reducing the operational time cost by 21% and the energy consumption cost by 26.4%. The proposed comprehensive solution can significantly reduce the total handling cost for the entire operational process in dry bulk ports.

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Improved Lane Line Detection in Autonomous Driving Based on Anchor Point Classification HUANG Kai, XIE Zijun, LI Haoyu , LIU Xintong , LIU Zhiyuan Journal of Transportation Systems Engineering and Information Technology    2026, 26 (3): 14-24.   DOI: 10.16097/j.cnki.1009-6744.2026.03.002 Abstract （215）      PDF （2125KB）（191）       In recent years, lane detection has become a key technology in autonomous vehicles. However, lane detection is difficult to achieve accurate recognition because it greatly affected by complex environmental factors. In order to detect the lane markings with high accuracy and effectiveness in various complex environments, this paper innovatively proposes a lane markings detection model DG-UFLD based on anchor point classification. Firstly, a novel Global Spatial Channel Attention Module (GSCA) is designed to improve the overall performance of the model, which can uniquely capture multidimensional global information and enhance feature representation. Secondly, dynamic snake convolution (DSConv) and triplet loss function are introduced into the model to further enhance its detection capability for slender targets. Finally, the integrated model adopts coarse-grained lane grid classification instead of fine-grained segmentation, which achieves extremely high detection speed with high accuracy. To verify the effectiveness of the proposed DG-UFLD algorithm, case studies are conducted on two publicly available lane detection datasets (TuSimple and CULane) in this paper. The results show that compared to the original algorithm, the proposed DG-UFLD algorithm improves the detection accuracy on the TuSimple dataset from 96.05% to 96.45% at extremely high detection speed; the detection accuracy on the CULane dataset increases from 68.4% to 75.3%. Meanwhile, compared with other mainstream lane detection networks, the improved network achieves better results in lane detection under extreme conditions. The verification results demonstrate that this method can quickly and accurately detect the lane markings.

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Research Progress and Challenges on Equity in Flight Slot Allocation HU Rong, ZHANG Yutong, DING Jiahao, WANG Yiren, ZHANG Junfeng Journal of Transportation Systems Engineering and Information Technology    2025, 25 (2): 1-15.   DOI: 10.16097/j.cnki.1009-6744.2025.02.001 Abstract （989）      PDF （1969KB）（527）    PDF(English version) （607KB）（57）    To further improve the feasibility of slot allocation results and reduce the unfairness among the participants in slot allocation, much literature has been studied on the fair allocation of flight schedules. By searching relevant databases at home and abroad, this paper systematically sorts out the individual fairness indicators and overall fairness goals in the optimization of existing slot allocations. Firstly, the development process and metrics of the concept of "fairness" are summarized, and the connotation of fairness in slot allocation is analyzed from three perspectives: horizontal/vertical, individual/overall and absolute/ relative. Secondly, the individual fairness index of each participant in the slot allocation are sorted out and compared based on the two dimensions of the number of slot adjustment and slot displacement. Then, from the perspectives of absolute fairness, relative fairness and Gini index, the overall fairness optimization objectives of the slot allocation model are summarized. The results show that the current fairness indicators are mainly constructed based on the principle of proportionality, while the weighted construction method is limited due to the difficulty of data acquisition and strong subjectivity. The research on the fairness goals has been relatively well-developed, and the Gini index has been widely used because of its global characteristics. Based on the content of the literature review, this paper further analyzes the shortcomings of existing studies and provides suggestions for future research. The study concludes that, in-depth research should be carried out in four aspects in the future: quantitative calculation of flight value, expansion of fairness research objects, construction of environmental fairness indicators and evaluation of the impact of dynamic parameters, to help the healthy and sustainable development of the civil aviation industry.