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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 （1947）      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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A Review of AI-driven Trajectory Prediction Methods for Autonomous Vehicles TIAN Daxin, XIAO Xiao, ZHOU Jianshan Journal of Transportation Systems Engineering and Information Technology    2025, 25 (5): 1-24.   DOI: 10.16097/j.cnki.1009-6744.2025.05.001 Abstract （2007）      PDF （1838KB）（883）       In autonomous driving systems, trajectory prediction plays an important role in connecting vehicle's perception and decision-making, and enhancing driving safety and overall system robustness. In recent years, with the continuous advancement of artificial intelligence (AI), the AI-driven trajectory prediction methods have seen significant progress in terms of accuracy, adaptability, and the capability to model complex traffic environment. This paper provides a systematic review of mainstream trajectory prediction methods in autonomous driving, with a focus on predictive model frameworks. It first revisits traditional physics-based approaches, and then highlights current research trends, including modeling paradigms based on classical machine learning, deep neural networks, and reinforcement learning. Additionally, recent developments in explainable AI techniques aimed at improving model transparency and safety are discussed. Based on comparative analysis, the paper evaluates the strengths and limitations of various models in interaction modeling, multimodal uncertainty, and generalization capability. Furthermore, it organizes trajectory prediction evaluation metrics and publicly available trajectory prediction datasets according to their characteristics and application scenarios, and summarizes representative real-world deployments from both domestic and international sources. At last, considering the existing research bottlenecks and future development trends, the paper outlines potential directions for future studies, such as enhancing model interpretability, effectively integrating multimodal information, and designing unified frameworks for joint prediction and planning. The purpose of this review is to provide insights and references that can be used in the future research and applications.

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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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Intermodal Transportation Network Design Optimization Considering Demand Uncertainty Under "Dual Carbon" Background HUANG Rui, ZHAO Xu, WANG Jingyun Journal of Transportation Systems Engineering and Information Technology    2025, 25 (6): 1-12.   DOI: 10.16097/j.cnki.1009-6744.2025.06.001 Abstract （1130）      PDF （2693KB）（601）    PDF(English version) （1131KB）（14）    A central challenge in modern intermodal transportation planning is the simultaneous consideration of "Dual Carbon" goals and growing fluctuations in freight demand. To address this challenge, this study presents an optimization model for intermodal transport network design. First, a bi-level bi-objective optimization model is developed, with the strategy planner serving as the upper-level leader and shippers as the lower-level followers. The upper level jointly determines the capacity expansion investment, low-carbon investment, and subsidy policies, with the objective of maximizing total revenue while minimizing total carbon emissions. The lower layer solves the network cargo flow allocation under user equilibrium based on generalized transportation costs. Then, the theory of real options is introduced, and geometric Brownian motion is used to describe the stochastic process of transportation demand fluctuations. This enables the quantification of the option value of delayed optimization to determine the optimal timing for strategy implementation. Based on the model characteristics, a nested Frank Wolfe multi-objective evolutionary algorithm based on decomposition (MOEA/D) is designed to solve the deterministic model, combined with a least squares Monte Carlo simulation algorithm to get the optimal implementation timing. Empirical analysis along the Western Land-Sea New Corridor shows that the proposed method simultaneously balances the economic, low-carbon, and operational efficiency optimization goals, which results in a 16.58% decrease in unit transportation costs, a 27.11% decrease in total carbon emissions, and a robust 5.41% increase in total revenue. Under demand uncertainty, delaying the implementation of optimization strategies can generate additional option value. In the case study, delaying to the third period can increase expected revenue by 4.70% and reduce total carbon emissions by 5.03%.

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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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Review of Connected and Autonomous Vehicle Dedicated Lane Setup CHENG Guozhu, WANG Wenzhi, YANG Zihan, WANG Guopeng, CHEN Yongsheng, GU Shuang Journal of Transportation Systems Engineering and Information Technology    2025, 25 (5): 25-39.   DOI: 10.16097/j.cnki.1009-6744.2025.05.002 Abstract （1060）      PDF （2429KB）（506）    PDF(English version) （737KB）（34）    With the advancement of information and communication technologies, autonomous vehicles (AV) and connected and autonomous vehicles have emerged as promising solutions to address traffic congestion, to enhance traffic safety, and to improve overall traffic efficiency. This paper provides a comprehensive review of the methods for setting up dedicated lanes for connected and autonomous vehicle (CAV). It begins by tracing the evolution of CAV dedicated lanes and elaborating on the background and significance of their implementation. Based on relevant literatures, it then delves into the methodologies for calculating road capacity, providing a foundation for predicting the impact of CAV dedicated lanes on traffic operations, evaluating strategies, and making necessary adjustments. Furthermore, the paper conducts an in-depth analysis on the strategies for setting up CAV dedicated lanes, including the conditions based on CAV penetration rates and traffic demand. It also explores the determination of the number and location of lanes, access methods, and lane separation approaches under various influencing factors. Finally, the paper proposes that future research should focus on understanding the changes in influencing factors post-implementation of CAV dedicated lanes and their alignment with real-world traffic conditions. It also emphasizes the need for establishing specific standards for setting up CAV dedicated lanes to ensure their function effectively across different traffic scenarios.

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Identification of Key Nodes and Invulnerability Analysis of Northeast Sea-Land Corridor Transportation Network WU Nuan, LIN Ting, WANG Wanxiang Journal of Transportation Systems Engineering and Information Technology    2026, 26 (1): 2-10.   DOI: 10.16097/j.cnki.1009-6744.2026.01.001 Abstract （524）      PDF （1968KB）（475）    PDF(English version) （489KB）（5）    The Northeast Sea-Land Corridor involves a large number of transportation nodes. Various risk events will not only directly affect the normal operation of these nodes, but also trigger cascading failures in the network through the transfer of freights flow between nodes, which undermine the efficient operation of the corridor. Therefore, the invulnerability of the Northeast Sea Land Corridor transportation network was studied based on a complex network theory. First, a multi-layer composite network was constructed based on the diverse transportation modes of the Northeast Sea-Land Corridor transportation network, and its topological structure was analyzed. Second, metrics, such as degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and clustering coefficient were selected to establish a node importance evaluation system. The improved correlation CRITIC-TOPSIS, incorporating information entropy, was used to quantify the node importance. Finally, a load-capacity cascading failure model was constructed based on node importance. The impact of different parameters and attack modes on network invulnerability was simulated and analyzed. The findings indicate that when the top 10 nodes by node importance fail, the global efficiency of the Northeast Sea-Land Corridor transportation network decreases by 58%, and the network connectivity drops by 30%. When the node load parameter α=2, node capacity coefficient β=1.4, and γ=1.6, the network exhibits strong resilience against various attack modes.

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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 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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Estimating Link Travel Time Under Sparse License Plate Recognition Device Coverage WANG Dianhai, WANG Yifei, HUANG Yulang, LIU Yong, ZENG Jiaqi Journal of Transportation Systems Engineering and Information Technology    2025, 25 (5): 83-90.   DOI: 10.16097/j.cnki.1009-6744.2025.05.007 Abstract （606）      PDF （2206KB）（379）       License Plate Recognition (LPR) devices are crucial traffic state detectors in urban road networks, but the high cost limits their deployment scale and density. This paper proposes a method for urban road network link travel time estimation under sparse devices deployment. Considering the interference of dwell trajectory travel time outliers, this study develops a mixed-integer optimization model for the link travel time measurement, and proposes an alternating solution method based on fixed-point iteration. First, path travel time distributions are calculated based on link travel time distributions to identify abnormal travel times. A travel time assignment method is then designed to allocate normal trajectory travel times to individual links. To ensure computational stability, Bayesian updating is applied to the travel time distribution parameters of high-traffic links, and the parameter proportionality is extended to links with insufficient traffic flow. Link travel times and abnormal trajectories are jointly estimated through iteration procession. Experiments on a real LPR dataset from Hangzhou demonstrate that the proposed method achieves a mean percentage error (MAPE) of 13.29% under a 70% device penetration rate. Compared to the gradient descent method, the MAPE is reduced by 7.69%, and the number of iterations is reduced by 99.4%. Furthermore, for urban scenarios with even sparser LPR device coverage, when the device coverage rate drops to 30%, the proposed method results in the MAPE of 18.51%.

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A Free Lane Change Intention Recognition Model Considering Vehicle Motion State Information Characteristics XIN Qi, WANG Yanfeng, WANG Zhilong, WANG Chang, NIU Shifeng Journal of Transportation Systems Engineering and Information Technology    2025, 25 (6): 74-86.   DOI: 10.16097/j.cnki.1009-6744.2025.06.007 Abstract （748）      PDF （2456KB）（368）       To accurately identify the driver's lane changing intention in the scene of free lane changing, this paper proposes a free lane changing intention recognition model considering the characteristics of vehicle's motion state information by analyzing the change law of vehicle's motion state information in lane changing. First, the vehicle movement status information under free lane changing was collected based on the human-machine co-driving real vehicle system platform. The distance between the vehicle center and the lane center was obtained through lane detection to determine the key time nodes of the free lane changing process. The collected data were divided into three categories: lane keeping, left lane changing and right lane changing, and the lane changing intention dataset was constructed. Then, the influence weight of vehicle motion state information on lane change intention recognition is analyzed using the SHAP (SHapley Additive exPlanations) global interpretability method, and the difference of each variable is illustrated by the independent sample T test, which verifies the feasibility of each variable as the input of lane change intention recognition model. To address challenges in recognizing free lane-changing intentions: such as unstable vehicle movement, the Gibbs phenomenon in data sampling, and interference from lane departure data, a new model was built based on the Informer network, incorporating several key techniques: RevIN (Reversible Instance Normalization), FECAM (Frequency Enhanced Channel Attention Mechanism), ETTA (Efficient Temporal Trend-Aware Attention mechanism), and U-Net structure. This model is named RF-EUInformer (RevIN FECAM-ETTA Unet Informer). The Monte Carlo Cross Validation was used to evaluate the generalization ability of the model. The ablation test showed that the contribution of each module to the accuracy was respectively 1.4%, 0.5%, 0.6% and 1.2% in 1.5 s prediction time, which verified the effectiveness of each module. Compared with Bi-LSTM (Bidirectional Long Short Term Memory), ConvLSTM (Convolutional Long Short-Term Memory), TCN (Temporal Convolutional Network) and TCN-Attention models, the accuracy of the proposed model in 0.5 s, 1.0 s and 1.5 s prediction time is improved by 3.9%, 4.5% and 5.8%, respectively.

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Cooperative Optimization of Lane Allocation and Vehicle Trajectory at Intersections Under Connected-and-Automated-Vehicle Environment SONG Lang, HU Xiaowei, YU Shanchuan, AN Shi Journal of Transportation Systems Engineering and Information Technology    2025, 25 (5): 59-71.   DOI: 10.16097/j.cnki.1009-6744.2025.05.005 Abstract （934）      PDF （2673KB）（351）    PDF(English version) （1984KB）（35）    In the collaborative optimization of intersection signal timing and Connected and Automated Vehicle (CAV) trajectory planning, the CAV exit, left turn, through, and right turn lanes can be assigned dynamically in the operation period. Based on the characteristics of CAV technology, this paper proposes a set of dynamic control rules for lane assignment under CAV, named as "flexible lane strategy". Compared to the existing fixed lane strategy, the proposed strategy can adjust the number of exit lanes and entrance lanes (including left turn, through, right turn) for different directions of traffic flow during operation. Lane assignment, signal timing and CAV trajectory planning are incorporated into a unified optimization framework to build a mixed integer linear programming optimization model. Meanwhile, feasible phase and sequence schemes can be automatically generated according to lane assignment in each direction, and the effectiveness of the model is verified through a case study. The results show that the optimization model can generate the optimal lane assignment scheme according to the traffic demand of each flow direction, especially when the lane assignment of the fixed lane strategy does not match the traffic composition of each flow direction, the flexible lane strategy helps to improve the intersection traffic efficiency. In low flow scenario, the flexible Lane strategy reduces average vehicle delay by 4.08%. In high-traffic scenarios, the fixed lane strategy at the intersection will be in a supersaturated state, while the flexible lane strategy can still meet the demand.

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Optimization of Multimodal Transport Considering Coordination of Production and Departure Schedules LI Yajun, XUE Longjiang, ZHENG Jianfeng Journal of Transportation Systems Engineering and Information Technology    2025, 25 (6): 23-33.   DOI: 10.16097/j.cnki.1009-6744.2025.06.003 Abstract （812）      PDF （2584KB）（344）       In order to solve the problem of the disconnection between production and transportation, this paper constructs a bi objective optimization model which integrates the allocation of production line, production sequence and multimodal transportation route selection. With the purpose of minimizing the total cost and time, two railway departure modes, scheduled departure and fixed-frequency departure, are introduced into the model to truly describe the characteristics of multimodal transportation. To solve the model, an enhanced algorithm of multi-objective particle swarm optimization is proposed, which improves the global search capabilities and convergence speed by dynamically adjusting the parameter of algorithm and combining with the simulated annealing mechanisms. Finally, the numerical experiments are carried out with an example from Busan to Hamburg/Rotterdam. The results show that the optimal scheduling scheme can reduce the total cost by 11.4% and the total time by 580 hours. Compared with the other multi-objective optimization algorithms, the proposed algorithm has significant advantages in the diversity and accuracy of solutions. Scenario analyses reveal that optimizing either production or transportation scheduling separately fails to achieve overall optimality, and increase costs by over 18% potentially. Sensitivity analyses indicate that the moderate adjustments of departure schedule and unit storage costs can effectively influence the selection of transportation modes under the balance cost and time.

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Capacity Efficiency and Influencing Factors Study of Chinese Listed Airlines LIU Dan, LIN Shanshan, ZHENG Yuting Journal of Transportation Systems Engineering and Information Technology    2025, 25 (6): 13-22.   DOI: 10.16097/j.cnki.1009-6744.2025.06.002 Abstract （728）      PDF （1606KB）（323）       Under carbon emission constraints, enhancing capacity efficiency to gain a competitive edge in the market has become a common focus among airline executives. This paper selects the panel data of 6 listed airlines in China from 2017 to 2021, incorporates carbon emissions as an undesirable output into the indicator system, and uses a window-based network DDF (Directional Distance Function) model to measure the capacity efficiency of listed airlines. Capacity inefficiency is decomposed into technical inefficiency and capacity utilization inefficiency to identify the key constraints behind the low capacity efficiency of listed airlines in China. Additionally, the dual methodology of panel regression and threshold effect analysis is used to investigate the impact of government subsidies on the capacity efficiency of listed airlines under the regulatory effect of equity concentration. The results show that the capacity efficiency of the 6 listed Chinese airlines under carbon emission constraints is generally low, jointly influenced by both technological level and capacity utilization. All listed airlines need to reduce carbon emissions during flight operations. The impacts of various factors on the capacity efficiency of Chinese listed airlines exhibit heterogeneity, with government subsidies, ownership concentration, enterprise age, and flight hours are the key driving factors in improving capacity efficiency of listed airlines in China. Furthermore, under the regulatory effect of equity concentration, the impact of government subsidies on the capacity efficiency of listed airlines exhibits a single-threshold regulatory effect, which maintains a promoting effect when the ownership concentration is less than or equal to 86.79%, but becomes an inhibitory effect when it is greater than 86.79%. Therefore, listed airlines should advance technological innovation, optimize input allocation, and maintain a reasonable level of ownership concentration. The government should facilitate technological upgrades, foster energy conservation and carbon reduction, and optimize the subsidy allocation mechanism.

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Impact of Weather on Invulnerability of Low-Altitude Route Network CHENG Ming, HUANG Hongming Journal of Transportation Systems Engineering and Information Technology    2025, 25 (6): 101-108.   DOI: 10.16097/j.cnki.1009-6744.2025.06.009 Abstract （807）      PDF （2120KB）（322）       To accurately quantify the impact of weather factors and node failures on the destructivity of low-altitude logistics route networks, this paper proposes an evaluation framework that integrates meteorological data spatial interpolation and complex network cascade failures. Using the published elevation data (DEM) as a covariate, the study uses the local thin disk smooth spline method to perform high-precision spatial interpolation of wind speed and rainfall data on low-altitude routes, which overcomes the sparsity of station data and accurately depicts the distribution of meteorological risks on the route. Based on six indicators, including the centrality of the degree of entropy weight method and the centrality of the intermediary, the M si is introduced to identify the key nodes of the network. The comprehensive destructibility index of convergence network efficiency, maximum connected subgraph size, and network density is defined, H , and a cascading failure model considering node failure and load redistribution is developed. The low-altitude logistics route network built by 88 operating stations in Shenzhen is used as the object for simulation. The simulation results show that the comprehensive destructive resistance index, H can fully reflect the change of network destructivity, and the entropy-weighted node importance index M si has a significant effect on identifying key nodes, and the removal of the first 10 nodes leads to a decrease of 80% in H and the network collapse. The quantitative analysis of meteorological impact shows that the constructed network effectively avoids the high wind speed area in Shenzhen, and the rainfall in 60% of the area is lower than the UAV operation threshold, and when the top 30 nodes affected by weather are removed, H only decreases by 28.3%, which proves that the impact of weather on the overall destructive resistance of the network is limited. The resulting destructive optimization scheme are adding 4 alternate landing fields near key nodes. Simulation verification shows that the destructive resistance of the network is significantly improved after optimization, and when the first 10 nodes are removed according to the degree value, H increases from 0.08 to 0.25 before optimization. This study provides safety evaluation tools and optimization strategies for the safe operation and destructive resistance improvement of low-altitude logistics networks in bad weather.

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Traffic Flow Characteristics of Large-flow Expressway Based on Electronic Toll Collection Data XU Jin, ZHANG Gaofeng, JIN Yong, WANG Tao Journal of Transportation Systems Engineering and Information Technology    2026, 26 (2): 328-341.   DOI: 10.16097/j.cnki.1009-6744.2026.02.031 Abstract （283）      PDF （4534KB）（320）       In order to clarify the characteristics of expressway traffic flow under the condition of large flow, and reveal the dynamic change law and correlation of traffic flow parameters, This paper takes the Yongguan-Guanfo Expressway (Changhu Expressway) in Dongguan City, Guangdong Province as the research object. Based on a week of high-precision ETC (Electronic Toll Collection) gantry data, the spatial and temporal characteristics of expressway traffic flow, the differentiated speed and flow distribution characteristics of vehicle types, and the speed-flow correlation under large flow conditions are studied. The results show that the traffic flow presents a significant 'M'-type morning and evening peak characteristics. The peak traffic flow is prominent during the peak hours of the weekday commute (8:00-10:00,16:00-18:00), while it is relatively flat on the weekend. The traffic distribution is obviously different among different models. The proportion of passenger cars is the highest (69.4%), and it shows a typical 'bimodal' mode, which is highly consistent with the commuting demand, while the truck is more active at night. The analysis of travel speed shows that the speed of passenger cars is significantly higher than that of medium and large buses and various trucks. By classified the vehicle according to the peak speed, the piecewise function is used to fit the velocity-flow of steady flow and congested flow respectively, and the fitting result is closer to the real situation of large flow expressway. The research results can provide a theoretical basis and data basis for fine management and dynamic traffic control of large-flow expressways.

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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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Cascading Failure Analysis of Urban Rail Transit Network with Bus Substitution Effect LI Guiyang, XIE Binglei, LI Xiaodan Journal of Transportation Systems Engineering and Information Technology    2025, 25 (6): 185-196.   DOI: 10.16097/j.cnki.1009-6744.2025.06.017 Abstract （807）      PDF （2487KB）（316）       This paper proposes an extended coupled map lattice (SCML) model that incorporates the bus substitution effect to evaluate the mitigating impact of bus networks on cascading failures triggered by disruptions in urban rail transit stations. The bus substitution effect is characterized in terms of transportation accessibility and quantified as a weighted combination of pedestrian transfer convenience and station access opportunities through the CRITIC-TOPSIS method. A mapping function is formulated to depict the regulatory role of the bus substitution effect on station states, and it is integrated into the CML framework to simulate the dynamic evolution of cascading failures. Several vulnerability indicators are defined, including the stepwise proportion of failed stations. The Shenzhen metro-bus system is used for the empirical validation. The results show that the bus substitution effect exhibits spatial heterogeneity, with higher values in the urban core and lower values in peripheral areas. Under low to moderate disturbance intensity, it substantially slows down and reduces the spread of failures, while its effect becomes limited under high-intensity disturbances. Compared with the CML model, the SCML model more accurately captures the vulnerability characteristics of urban rail transit network, raising the average perturbation intensity threshold for network collapse from 1.7 to 2.2. The findings provide quantitative evidence for assessing and enhancing the resilience of multimodal transportation systems.

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Integrated Location-Routing Optimization for Two-echelon Logistics Network with Heterogeneous UAVs GENG Shaoqing, ZHAI Yibing, CAO Yunchun Journal of Transportation Systems Engineering and Information Technology    2026, 26 (1): 34-44.   DOI: 10.16097/j.cnki.1009-6744.2026.01.004 Abstract （573）      PDF （2066KB）（316）    PDF(English version) （1118KB）（6）    To address the bottlenecks faced by UAVs in conducting trunk and last-mile delivery operations across complex terrains, this paper investigates the location-routing problem for a two-echelon logistics network composed of regional hubs and local distribution centers. A critical gap in existing research is the tendency to overlook the functional and cost heterogeneity among UAVs operating at different echelons. To fill this gap, we formulate a mixed-integer programming model with the objective of minimizing the total system cost, which comprises the facility construction, differentiated two-echelon transportation, and time penalty costs. The model is designed to jointly optimize the distribution of two-echelon facilities, UAV delivery routes, and timeliness of customer service. To solve this problem, a hybrid algorithm is designed, in which a Genetic Algorithm is employed for global facility location and customer allocation, while a Variable Neighborhood Tabu Search is utilized for local route optimization. A case study of Yunlong County, Yunnan, demonstrates that, compared to a sequential decision-making approach, the proposed joint optimization method reduces the total cost of system by 88.3%. Furthermore, in contrast to a single-echelon direct delivery network, the on-time delivery rate is enhanced to 91.9%, achieving an effective balance between cost and service quality. This research provides an effective decision-making model and methodology for the planning and operation of distributed UAV logistics networks.

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Resource Allocation and Multi-machinery Cooperative Scheduling Optimization in Automated Dry Bulk Terminals JI Mingjun, LI Jiawei, HU Hanlin, GAO Zhendi Journal of Transportation Systems Engineering and Information Technology    2025, 25 (6): 317-326.   DOI: 10.16097/j.cnki.1009-6744.2025.06.029 Abstract （852）      PDF （2707KB）（313）       This paper addresses the difficulties of multi-resource and multi-equipment coordination scheduling in automated dry bulk terminals by proposing a berth-ship loader-stockyard collaborative scheduling model with dual objectives of minimizing ship port time and terminal operation costs. Due to the large-scale and nonlinear characteristics of the problem which bring significant computational challenges, this paper proposes a two-stage algorithm based on grey wolf optimization for model solution. The first stage employs an improved algorithm of grey wolf optimization to solve the berth-ship loader allocation scheme, while the second stage uses an algorithm of distribution machine-stockyard allocation to screen feasible solutions that satisfy operational line and stockyard constraints. Finally, the feasibility and algorithm superiority of model are validated using the data from Anhui Changjiu Inland River Terminal. The numerical results demonstrate that the established optimization model is highly suitable for the operation scenarios in automated dry bulk terminal involving multi-resource and multi-equipment collaborative operations, which conforms to actual operational constraints, and can fully utilize terminal resources and mechanical equipment. The proposed algorithm effectively solves this optimization problem, achieving the solution improvements of 8.1%, 8.7%, 6.5%, 2.4%, and 4.5% in total cost through comparing with the algorithms of particle swarm optimization, genetic, grey wolf optimization, whale optimization, and Harris hawks optimization, respectively.