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25 December 2025, Volume 25 Issue 6 Previous Issue   

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Intermodal Transportation Network Design Optimization Considering Demand Uncertainty Under "Dual Carbon" Background HUANG Rui, ZHAO Xu, WANG Jingyun 2025, 25(6): 1-12.  DOI: 10.16097/j.cnki.1009-6744.2025.06.001 Abstract ( )   PDF (2693KB) ( )   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%. References | Related Articles | Metrics

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Capacity Efficiency and Influencing Factors Study of Chinese Listed Airlines LIU Dan, LIN Shanshan, ZHENG Yuting 2025, 25(6): 13-22.  DOI: 10.16097/j.cnki.1009-6744.2025.06.002 Abstract ( )   PDF (1606KB) ( )   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. References | Related Articles | Metrics

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Optimization of Multimodal Transport Considering Coordination of Production and Departure Schedules LI Yajun, XUE Longjiang, ZHENG Jianfeng 2025, 25(6): 23-33.  DOI: 10.16097/j.cnki.1009-6744.2025.06.003 Abstract ( )   PDF (2584KB) ( )   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. References | Related Articles | Metrics

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Drone Delivery: A Systematic Review on Technology, Efficiency, and Applications WU Jingqiong, DIAN Ran, ZI Taisheng, LI Yunqi 2025, 25(6): 34-49.  DOI: 10.16097/j.cnki.1009-6744.2025.06.004 Abstract ( )   PDF (2284KB) ( )   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. References | Related Articles | Metrics

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Safety Optimization Control for Connected Vehicle Platoon Under Acceptable Spacing Policy YANG Haifei, TANG Yong, GUO Yanyong, LI Hongwei, ZHAO Enze 2025, 25(6): 50-61.  DOI: 10.16097/j.cnki.1009-6744.2025.06.005 Abstract ( )   PDF (2581KB) ( )   The existing Three-Phase Adaptive Cruise Control (TPACC) based on the acceptable spacing policy realizes speed synchronization with linear controller, improving stability over the constant headway policy but is more likely to induce traffic conflicts when facing complex traffic disturbances. To improve this, this paper uses Model Predictive Control (MPC) to optimize the safety of Coperative TPACC (CTPACC) with the support of vehicle network technology. A kinematic model of CTPACC is developed considering the system delay. The relationship between stability and safety under acceptable spacing policy is clarified using numerical experiments. Then, a safety optimization policy of MPC based on speed synchronization is proposed, and terminal constraints and Bayesian optimization are introduced to maintain the stability. A delay compensation mechanism is designed to improve the control performance. At last, the effectiveness of the proposed policy is verified by typical working conditions. The results show that, excessively high stability margin of the linear controller will lead to rear-end accidents or emergency safety mode intervention, and safety and stability show a non-monotonic relationship. To address this, the proposed MPC without delay compensation achieves an overall improvement in safety while maintaining stability. The delay compensation mechanism further enables comprehensive optimization of both margins of stability and safety. In the theoretical working condition, the reductions in the safety risk indicators of time-integrated time-to-collision, time-exposed time-to-collision, the platoon oscillation indicators of average maximum overshoot, total absolute jerk reach 24.4%~61.7%, 29.7%~57.4%, 52.7%~90.8%, and 13.9%~81.3%. Moreover, sensitivity experiments with disturbance intensity and actual working condition tests consistently demonstrate the same trend. In addition, the proposed policy suppresses the emergency safety mode intervention and improves the smoothness of acceleration control. Phase transition analysis indicates that the delay-compensated MPC, while mitigating congestion nuclei, reduces two safety risk indicators by 50.4% and 53.3% compared to the typical linear controller. References | Related Articles | Metrics

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Platooning Strategies Study for Connected Autonomous Vehicles on Superhighways HE Yongming, LU Yangpeng, LIU Huiyang, LI Xinran 2025, 25(6): 62-73.  DOI: 10.16097/j.cnki.1009-6744.2025.06.006 Abstract ( )   PDF (3005KB) ( )   Robust platoon control strategies are crucial for enabling safe and efficient operation of Connected and Automated Vehicles (CAV) in superhighway scenarios. This paper proposes a CAV platooning strategy to enhance the stability and safety of platoons on superhighways. First, a comprehensive full velocity difference model considering communication delays is developed as the fundamental car-following foundation. Then, under a leader-follower information topology, a nonlinear platoon control law is designed, where the optimal velocity function of the underlying car-following model is inverted to dynamically compute a speed dependent nonlinear desired spacing. This approach ensures consistency between the steady-state platoon target and individual vehicle driving equilibrium. Furthermore, both local and string stability of the proposed model are rigorously analyzed using transfer function methods, and controller parameters are systematically optimized via the H∞ norm. Simulation results demonstrate that, in dynamic speed scenarios ranging from 120 km·h-1 to 160 km·h-1, the proposed Delayed Leader-Follower Control (DLFC) strategy achieves significantly better string stability and tracking performance compared with the benchmark Cooperative Adaptive Cruise Control (CACC) strategy, with smoother acceleration responses that effectively prevent disturbance amplification along the platoon. In the extreme emergency braking scenarios with random disturbances, the comprehensive Monte Carlo simulations verify that the DLFC strategy provides higher safety margins, effectively avoids collision risks, and reduces the probability of high-risk states to below 20%, thereby substantially improving platoon safety under extreme superhighway conditions. References | Related Articles | Metrics

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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 2025, 25(6): 74-86.  DOI: 10.16097/j.cnki.1009-6744.2025.06.007 Abstract ( )   PDF (2456KB) ( )   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. References | Related Articles | Metrics

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Trajectory Control Method for UAV Spiral Search Oriented to High-rise Building Emergency Rescue CHEN Deqi, ZHANG Zishe, ZHANG Wenhui, YAN Xuedong, JIANG Xiancai 2025, 25(6): 87-100.  DOI: 10.16097/j.cnki.1009-6744.2025.06.008 Abstract ( )   PDF (2924KB) ( )   During the golden rescue period following a disaster, unmanned aerial vehicles (UAVs) can be delivered first to reach the damaged buildings for spiral full-coverage scanning and search for survivors. However, due to the complex and dynamic environment at disaster sites, UAVs often encounter challenges such as low trajectory tracking accuracy and high collision risks during close-range three-dimensional (3D) scanning. To address these issues, this paper proposes a Prioritized Experience Replay Soft Actor-Critic (PER-SAC) control model and establishes a high-fidelity simulation platform based on a 6-degree-of-freedom (6 DOF) nonlinear dynamic model. By prioritizing the learning of key experiences with high Temporal-Difference error (TD-error), the model enhances learning efficiency and policy robustness in complex tasks. Comparative simulation experiments demonstrate that the proposed PER-SAC strategy outperforms both Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms in terms of convergence speed and final performance. In static trajectory tracking tasks, PER-SAC achieves a success rate of 99.0%, with an average trajectory error reduced by 66.3% compared to SAC. For dynamic obstacle avoidance tasks, the success rate is 97.0%, exhibiting smoother and more efficient evasion maneuvers, thereby fully validating its robustness. The incorporation of prioritized experience replay significantly improves UAVs' autonomous flight performance in unknown dynamic environments. The proposed PER-SAC strategy represents an advanced control method that effectively balances control precision, flight quality, and safety. It can be directly applied to autonomous spiral scanning of high-rise damaged buildings post-disaster, enabling stable flight attitudes to capture high-definition imagery. This capability assists rescue teams in rapidly locating trapped individuals, thereby enhancing emergency search and rescue efficiency References | Related Articles | Metrics

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Impact of Weather on Invulnerability of Low-Altitude Route Network CHENG Ming, HUANG Hongming 2025, 25(6): 101-108.  DOI: 10.16097/j.cnki.1009-6744.2025.06.009 Abstract ( )   PDF (2120KB) ( )   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 Msi 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 Msi 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. References | Related Articles | Metrics

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Impact Mechanism of Built Environment on Travel Intensity of Residents from a Life Cycle Perspective ZHANG Tao, SONG Tongtong, CHENG Long, JIA Qinglin 2025, 25(6): 109-117.  DOI: 10.16097/j.cnki.1009-6744.2025.06.010 Abstract ( )   PDF (2447KB) ( )   To investigate the impact mechanism of the built environment on the travel intensity of residents from a life-cycle perspective, this study divides resident life into four stages: childless, full nest I, full nest II, and empty nest. The study sets the travel intensity of residents as the explained variable, and combines with the seven explanatory variables derived from the "5D" elements of the built environment. The impact mechanism of the built environment on the travel intensity residents in different life stages is analyzed by using a multi-scale geographically weighted regression model, based on the data of the trip volume, geospatial space, and built environment in Wenling City. The study found that a multi-scale geographically weighted regression model can better reveal the differential impact of built environment variables on the travel intensity of residents across different spatial scales. Overall, the density of residence, bus stop and commercial facility all have a significant positive impact on the travel intensity of residents across the four life stages. The differences of impact between the childless and empty nest stages are similar in spatial distribution pattern, whereas those between the full nest I and full nest II stages are highly consistent. The density of medical facilities has a significant positive impact across all four life stages, and it is concentrated in the areas with abundant medical resources. However, in the full nest I, full nest II, and empty nest stages, the density of medical facilities has a significant negative impact, which is primarily distributed in areas with relatively underdeveloped medical facilities. From the perspective of different life stages, the childless stage has a low rate of private car ownership, a high sensitivity to travel time cost, and relatively significant differences in the impact of bus stop density. In the empty nest stage, short-distance travel is frequent and medical travel is in high demand. The impacts of residence and medical facility densities have a largest difference. Both the residents over the stages of full nest I and full nest II endure the dual pressures from work and family. The distinction is that the full nest I stage is heavily restricted by commuting to and from work, while the full nest II stage focuses more on family life. These results in the stages of full nest I and full nest II show the most significant differences in the impact from the density of bus stop and commercial facility, respectively. References | Related Articles | Metrics

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Method for Short-term Passenger Flow Prediction in Urban Rail Transit Networks Considering Data and Model Uncertainty MU Liang, KANG Yurui, YAN Zixu, ZHU Guangyu 2025, 25(6): 118-128.  DOI: 10.16097/j.cnki.1009-6744.2025.06.011 Abstract ( )   PDF (3604KB) ( )   This paper proposes a short-term probabilistic model for forecasting passenger flow in urban rail transit networks, termed PD-STGCN, which considers the uncertainties of both data and model. It aims to obtain the probabilistic information of network-wide passenger volume at future time steps. The model constructs a collaborative framework consisting of the Spatio Temporal Uncertainty Prediction Module (STUPM) and Probabilistic Quantification Module (PQM). In the STUPM, addressing uncertainties inherent in both the passenger flow data and forecasting model, a novel loss function is developed by integrating Gaussian Negative Log-Likelihood (GNLL) and Monte Carlo Dropout (MC Dropout) techniques to quantify these dual uncertainties. Within the PQM, the discrete sample sets are obtained through the random normal sampling based on prediction results, and continuous probabilistic forecasting outputs are generated using the Gaussian Kernel Density Estimation (KDE). Using the data of passenger flow from a urban rail transit system in a large city as a case study, the model is validated under both weekday and non-weekday scenarios. The results demonstrate that, compared to the baseline forecasting models, PD-STGCN improves the Prediction Interval Coverage Probability (PICP) and Continuous Ranked Probability Score (CRPS) by 8.01% and 20.77%, respectively, which provides a better coverage of actual passenger flow values and has higher forecasting accuracy. Ablation experiments confirm that uncertainty is the most significant factor affecting model performance. The model considering dual uncertainties leads to the improvements of at least 1.91% in PICP and 4.02% in CRPS over that considering only a single type of uncertainty. References | Related Articles | Metrics

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Optimization of Train Operation Plans for Through Operation Between Urban Rail and Regional Railways ZHU Changfeng, GAO Shuoyue, WANG Jie, FU Yunqi, CHENG Linna, KUANG Rongjie 2025, 25(6): 129-142.  DOI: 10.16097/j.cnki.1009-6744.2025.06.012 Abstract ( )   PDF (3016KB) ( )   This paper focus on optimizing the operation plan for the through operation of urban rail transit and regional railways to address the issue that the traditional transfer mode is difficult to meet the demand of cross-line passenger flow during peak hours. A passenger travel choice behavior model based on mental account theory and Logit model is proposed to describe the travel choice behavior of passengers under the influence of multiple attribute factors such as time and cost. An optimization model for the through operation plan is developed with the decision variables of operation frequency and location of the return station, and the goals of minimizing passengers travel time and enterprises operating cost. A multi-objective particle swarm optimization solution algorithm is design based on oppositional learning-Arnold chaotic mapping. The results indicate that compared with the transfer connection mode, the total travel time of passengers and the operating costs of enterprises under the through operation mode are reduced by 22.60% and 17.44% respectively. Further analysis reveals that passengers' travel choice behavior exhibits the characteristic of time value preference. High time value passenger flow is highly sensitive to direct services. Additionally, the proportion of rigid cross-line passenger flow is a key factor determining the operating frequency of through trains. The departure frequency of through trains is positively correlated with the proportion of rigid cross-line passenger flow. However, when the departure frequency of through trains exceeds seven pairs per hour, The proportion of rigid cross-line passenger flow shows the law of diminishing marginal benefits. This study provides theoretical support for the integrated operation of urban rail transit and regional railways. References | Related Articles | Metrics

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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 2025, 25(6): 143-152.  DOI: 10.16097/j.cnki.1009-6744.2025.06.013 Abstract ( )   PDF (2072KB) ( )   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. References | Related Articles | Metrics

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Network Performance-oriented Collaborative Optimization of Urban Rail Transit Lines' Capacity Allocation ZHANG Longhao, XU Ruihua, SHAN Yijia, LAN Yangze 2025, 25(6): 153-164.  DOI: 10.16097/j.cnki.1009-6744.2025.06.014 Abstract ( )   PDF (3164KB) ( )   Network-wide synchronous optimization of urban rail transit timetables faces challenges in modeling complexity and high computational demand. To address the practical need for line-by-line adjustment, this paper proposes an inter-line collaborative optimization strategy for capacity allocation in line planning. First, the interaction mechanisms between capacity allocation and network performance are analyzed from the perspectives of passenger flow redistribution synergy and network performance contribution synergy. Next, a dual-dimensional evaluation index system (transport efficiency and service level) is established, with quantitative performance assessment achieved through a self-developed multi-agent network simulation system and the TOPSIS method. Finally, a collaborative optimization model and corresponding algorithms under the line-by-line planning framework are proposed. Validation using real network data involves 363 simulation experiments. Results demonstrate that the proposed method—which targets network performance, follows line importance order, and iteratively adjusts individual lines while keeping other line planning fixed—generates optimal capacity allocation, achieving accuracy comparable to that of network-wide synchronous heuristic search. As a time-efficient alternative to network synchronous optimization, the method improves network performance from 0.519 to 0.588, a 13.4% increase, thereby providing a solution balancing quality and efficiency in large-scale urban rail transit networks. References | Related Articles | Metrics

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A Coordinated Scheduling Model for Train Diagrams of Unban Rail Transit Under Interconnected Operation Mode YAN Fei, YAO Xiangming, HAN Mei, CHEN Chao, ZHAO Peng 2025, 25(6): 165-174.  DOI: 10.16097/j.cnki.1009-6744.2025.06.015 Abstract ( )   PDF (2574KB) ( )   The integrated and coordinated scheduling for train diagrams under the interconnected operation mode is of great significance for improving the train operation efficiency. To address the challenge of high complexity in scheduling caused by the diverse sequencing schemes of local and cross-line train routes, this study analyzes the coupling relationship between departure times and train sequences at cross-line stations. A coordination method for train routes in the shared sections is proposed to determine the order of each train service. An integrated scheduling model for train diagrams and rolling stock circulation plans is developed to minimize the headway deviation, the dwell time redundancy, and the number of entering and exiting depot operations. The model considers constraints, such as train arrival and departure times, train headways, rolling stock circulation, and depot operations, and is linearized to improve solution efficiency. Finally, an empirical analysis is conducted based on an "X" shaped interconnected operation scenario formed by Line 4 and Line 9 of urban rail transit in a certain city. The results show that the proposed method can comprehensively account for the trade-off between diagram stability and rolling stock resources, and enable the integrated scheduling of train diagrams and rolling stock circulation plans. By scientifically sequencing multi-route train services, the headway deviation is reduced by 22.88%, and the dwell time redundancy is reduced by 12.65%. The research findings provide a methodological reference for the coordinated scheduling of train diagrams under interconnected operation, enhancing both scheduling efficiency and diagram quality. References | Related Articles | Metrics

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Exploration of Transfer Time at Metro Transfer Stations with Automatic Fare Collection Data LIU Chenhui, TAO Mengxin 2025, 25(6): 175-184.  DOI: 10.16097/j.cnki.1009-6744.2025.06.016 Abstract ( )   PDF (2623KB) ( )   Transfer efficiency is a key factor affecting the service quality of urban rail transit systems, and analysis on transfer time is essential to develop effective improvement strategies. This study investigates the transfer time based on the Automated Fare Collection (AFC) data of Changsha Metro system in one week. A breadth-first search (BFS) algorithm with transfer-count constraints was developed to identify shortest transfer paths. Based on the identified trips, a linear regression model was constructed, and parameters were estimated by using the ordinary least squares (OLS) method to quantify transfer times. Transfer characteristics were analyzed from three perspectives: spatial distribution, temporal variation, and relative duration within the entire trip. The results show that the average transfer time in Changsha Metro is 6.2 minutes, accounting for 17.6% of the total travel time. Transfer times on weekdays are generally longer than that on weekends, with noticeable efficiency degradation during peak hours. In terms of spatial distribution, significant differences exist among transfer stations, and K-means clustering was further applied to classify stations by transfer performance. Seven transfer stations with passageway-type layouts were identified as low-efficiency nodes. Based on the findings, this paper proposes optimization strategies including structural improvements, real time passenger guidance, and inter-line interoperability to improve the transfer efficiency in metro system. References | Related Articles | Metrics

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Cascading Failure Analysis of Urban Rail Transit Network with Bus Substitution Effect LI Guiyang, XIE Binglei, LI Xiaodan 2025, 25(6): 185-196.  DOI: 10.16097/j.cnki.1009-6744.2025.06.017 Abstract ( )   PDF (2487KB) ( )   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. References | Related Articles | Metrics

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Coupled Effects of Luggage Abandonment and Companion Behavior on Evacuation Efficiency in Subway Emergencies ZHANG Liye, MA Yuexi, MA Zhicheng, ZHAO Fenglei, JIAO Chunshuo 2025, 25(6): 197-208.  DOI: 10.16097/j.cnki.1009-6744.2025.06.018 Abstract ( )   PDF (2617KB) ( )   The optimization of large-scale evacuation efficiency in subways is crucial to rail transit safety. However, existing models lack research on complex crowds due to their homogenization assumptions and single-type classification of crowds. To address this, this paper proposes an improved social force model and constructs a three-dimensional coupled dynamic simulation framework involving companion behavior, luggage carrying, and luggage abandonment behavior. Parameters are calibrated based on 1081 questionnaire data from Qingdao Railway Station, and simulations are conducted on key bottleneck areas such as corridor corners and carriage entrances/exits using AnyLogic. The luggage abandonment rates and the proportion of pedestrians traveling in groups are defined based on the empirical data. The interaction rules for four types of heterogeneous subjects, namely companion groups, pedestrians carrying suitcases, ordinary pedestrians, and abandoned suitcases, are established to improve the simplified handling of complex crowd behaviors in single-factor models. A simulated evacuation scenario is established based on the geometric characteristics of subway corners and train sizes. The simulation comparison and verification show that in corner and carriage areas, the coupled model has an evacuation time difference of more than 10 seconds compared with the single-factor model. In multi-coupling evacuation simulations, the evacuation of pedestrians traveling in groups is the most affected, with the delay time reaching 1.5 times (in corner areas) and 1.66 times (in carriage areas) of those carrying luggage. References | Related Articles | Metrics

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Subway Station Emergency Evacuation Model Considering Spatial Cognition and Panic Propagation WANG Lianzhen, ZHUANG Hanying, WANG Yuping, WANG Baojie 2025, 25(6): 209-219.  DOI: 10.16097/j.cnki.1009-6744.2025.06.019 Abstract ( )   PDF (3073KB) ( )   To more accurately simulate the emergency evacuation process in subway stations and improve evacuation efficiency and safety, this paper proposes an improved social force model, Cognition Panic Social Force Model (CogPanic-SFM), which integrates pedestrian spatial cognition and panic emotion propagation. The model dynamically simulates the evolution of pedestrians' spatial cognition abilities during the evacuation process, combining the SACR panic propagation model, which depends on spatial cognition features. This achieves dual modulation of spatial cognition ability and panic intensity, and synergistically optimizes the self-driving force and repulsive force in the social force model. Simulation results show that the relative error in total evacuation time of the improved model compared to the emergency drill evacuation time is 7.792%, lower than the 37.083% of traditional models, thus verifying the model's effectiveness and fidelity in complex scenarios. Further, it was found that the distribution of spatial cognition abilities significantly affects the group panic intensity. When the proportion of individuals with high spatial cognition ability (0.85~0.95) is 1, the peak intensity of group panic is 0.49, which is 46.7% lower than that of all individuals have low spatial cognition ability (0.10~0.30). Lower panic intensity means pedestrians remain calmer during evacuation, which helps to alleviate congestion, increase exit utilization, and ultimately reduce overall evacuation time. References | Related Articles | Metrics

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Campus Bus Route Design Considering Passenger Transfers ZHAO Ying, LIANG Jinpeng, BAO Yue 2025, 25(6): 220-228.  DOI: 10.16097/j.cnki.1009-6744.2025.06.020 Abstract ( )   PDF (2365KB) ( )   In response to the growing internal mobility challenges caused by the expansion of university campuses, this paper proposes an optimization model for campus bus route design and departure frequency setting that incorporates the transfer behavior of passengers. By constructing a bi-layer network structure consisting of travel nodes and transfer nodes, the formulation of flow balance constraints that accurately capture transfer dynamics is designed. The model is formulated as a mixed-integer programming problem, aiming to minimize the travel time of total passengers and the operation costs of buses. It also optimize the decisions of route selection, frequency setting, and passenger path allocation jointly. In the route generation stage, a constrained depth-first search algorithm is proposed to construct a set of candidate routes based on the spatial distribution of travel demand. A case study based on the Xiong'an campus of Beijing Jiaotong University demonstrates the effectiveness of the approach: three optimized routes achieve full coverage of 11 stations, with 70.8% of travel demands satisfied via direct service and the remainder completed with only one transfer. Additionally, 75% of trips incur less than 5 minutes of additional travel time, and over 52% of route segments operate with a load factor above 70%. The results provide both theoretical support and practical guidance for the scientific planning and efficient operation of the campus bus systems. References | Related Articles | Metrics

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Nonlinear Effects of Built Environment on Bus Usage of Different Traveler Groups LIU Lu, ZHENG Haolong, LI Minggao, ZHU Yuting, WU Keqi 2025, 25(6): 229-238.  DOI: 10.16097/j.cnki.1009-6744.2025.06.021 Abstract ( )   PDF (2487KB) ( )   This paper analyzes the generation mechanism of peak-hour public transport trips for school commuters, work commuters, and elderly travelers, and to examine the heterogeneous effects of the built environment on different groups within the same spatiotemporal dimension. A random forest model is developed to quantify the contribution of key factors, identify thresholds and marginal effects, and analyze multi-factor interaction effects. The proposed model aims to reveal the non-linear influence mechanism of the built environment on public transport trips among different groups. The findings are as follows: from the perspective of the individual importance of influencing factors, the number of public transport routes plays a dominant role for all three groups, while there are significant group differences in secondary influencing factors. The work commuters are highly dependent on the density of bus stops and distance to the city center. The elderly travelers are doubly constrained by medical and health care facilities as well as science, education, and cultural facilities. The school commuters are mainly affected by the distance to the city center. From the perspective of the collective importance of influencing factors, school commuters and elderly travelers are unilaterally dominated by land use factors, work commuters are co-driven by land use factors and bus stop attribute factors, and the importance of urban design factors is relatively low and stable. From the perspective of non-linear influence, the thresholds and marginal effects of the same factor on different groups are significantly different, and there are also differences in multi-factor interaction effects. In addition, the impact of the same factor on school commuters and elderly travelers shows obvious temporal differentiation. References | Related Articles | Metrics

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Integrated Optimization of Electric Bus Timetable and Vehicle Scheduling with Piecewise Charging Strategy GAO Wanchen, LU Shichang, ZHAO Yatong, LIU Kai 2025, 25(6): 239-248.  DOI: 10.16097/j.cnki.1009-6744.2025.06.022 Abstract ( )   PDF (2366KB) ( )   To reduce the costs of bus enterprises and enhance passenger satisfaction, this paper proposes a dual-objective model to optimize the timetable and vehicle scheduling considering multiple depots and multiple vehicle types. The model combines the piecewise charging strategy, the time-of-use pricing, and time-dependent parameters such as dwelling time, travel time, passenger boarding rate, and passenger alighting rate. The objectives of the model include minimizing the passenger travel time and the cost of bus enterprises. The former includes passenger waiting time and the riding time, while the latter includes fixed costs, deadheading costs and charging costs. The improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) was designed to solve the model. The bus line No. 26 in Zhuhai City was selected for case analysis. The findings indicate that during the morning rush hour, the noon off-peak hour, and the evening rush hour, the solutions derived from both the current plan employed by bus companies and the sequential method are inferior to the Pareto optimal solutions achieved through an integrated optimization approach. Compared with the current plan of bus enterprises, the integrated optimization approach reduces the cost of bus enterprises by an average of 5.1% and the travel time of passengers by 4.9%. Compared with the sequential method, the average travel time of passengers decreased by 1.6%, among which the waiting time of passengers decreased by 1.8% and the riding time of passengers decreased by 1.6%. This research provides references for the scientific dispatching and management of urban bus transit. References | Related Articles | Metrics

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Joint Optimization of Multi-line Bus Scheduling and Berth Settings with Overlapping Sections HU Baoyu, LIU Wenlei, CHENG Guozhu 2025, 25(6): 249-264.  DOI: 10.16097/j.cnki.1009-6744.2025.06.023 Abstract ( )   PDF (3316KB) ( )   To address the congestion resulting from multiple overlapping bus routes, this paper proposes a joint optimization method for bus vehicle scheduling and bay allocation to enhance the operational efficiency of multi-route. It is established with the goals of minimizing both the weighted sum costs of vehicle and passenger waiting time, and the number of bays in the overlapping sections. The model, which considers constraints such as timetables, bus operations, and berth allocation at separated stops, is reformulated using a robust optimization approach. A hybrid adaptive large neighborhood search algorithm (MODE-ALNS) is designed for the solution. The multi-objective differential evolution (MODE) algorithm is responsible for global search and generating initial solutions, while the adaptive large neighborhood search (ALNS) algorithm is responsible for locally optimizing the solutions generated through the multi-objective differential evolution algorithm. By combining the two, an organic integration of global and local searches is achieved to improve the solution quality. A case study of six overlapping bus routes in Harbin shows that the optimized plan reduces vehicle costs and passenger waiting time costs by 3.17% and 7.19% respectively, compared to the current scenario. It also achieves a 16.67% reduction in the total number of bays within the overlapping section, benefiting both operators and passengers. The robust optimization model proves its superiority over the deterministic model by its ability to cope with various perturbation scenarios with a lower total cost and a more stable number of berths. References | Related Articles | Metrics

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Optimization of Collaborative Distribution of Light Trucks and Buses Considering Carbon Emission Costs ZHANG Zhijian, ZHANG Ting, DI Zhen, GUO Junhua 2025, 25(6): 265-275.  DOI: 10.16097/j.cnki.1009-6744.2025.06.024 Abstract ( )   PDF (2229KB) ( )   In order to cope with the traffic pressure and carbon emission caused by the expansion of the network in urban logistics and distribution, this paper proposes an optimum model of light trucks-public transportation collaborative distribution considering the cost of carbon emissions. The model integrates the fixed bus routes, customer demand time windows, carbon emission factors and transportation costs. It systematically depicts the complex constraints and multi-factor decision-making problems in the collaborative distribution of light truck-public transportation with the optimization goal of minimizing the total cost. In view of the characteristics of the problem, such as high dimensionality, complexity, and easy to fall into local optimum, this paper designs an improved genetic algorithm. It generates high-quality initial populations through the mileage-saving method, and introduces championship selection, adaptive crossover and mutation strategies, which significantly improves the convergence speed and global search ability of the algorithm. Experimental results show that the proposed algorithm achieves a cost optimization of 19.48% on the 60-node scale problem. Under four different node sizes, the maximum, minimum and average costs of running data for 20 times are effectively reduced. Compared with the mode of single distribution, the mode of public transport coordination can effectively reduce the costs of transportation, carbon emission and time window penalty within a certain range, and improve distribution efficiency and service quality. References | Related Articles | Metrics

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Nonlinear and Interactive Effects of Built Environment on Electric Vehicle Charging Behavior WU Jingxian, GUAN Houjie, LI Xiao, ZHAO Jing 2025, 25(6): 276-284.  DOI: 10.16097/j.cnki.1009-6744.2025.06.025 Abstract ( )   PDF (2244KB) ( )   This study aims to investigate the nonlinear effects and interactions of built environment on the charging behavior of electric vehicles (EVs). Using the data on the EV charging order and multi-source built environment, an empirical study was made in Shanghai. To account for the differences between weekday and non-weekday charging patterns, two Gradient Boosting Decision Tree (GBDT) models were developed. Model interpretations was performed using the SHAP (SHapley Additive exPlanations) framework, which decomposes the output of model into the marginal contributions of built environment factors. The key determinants of EV charging behavior for each period were identified, and their nonlinear relationships with EV charging intensity were analyzed. Compared to the linear regression models, the GBDT-based approach achieved superior performance, with R2 values of 0.333 and 0.573 for weekdays and non-weekdays scenarios, respectively. The results indicate that, the density of main and secondary roads, proximity to the city center, and enterprise density are the core factors which affect EV charging at public stations, and exhibit significant nonlinear and threshold effects, whenever on weekdays and non-weekdays. The influence mechanisms of variables are different. The density of main and secondary roads and enterprise density positively promote the intensity of EV charging, whereas the proximity to the city center and bus stop density exhibit inhibitory effects. Additionally, the significant interactive effects were observed among variables such as the density of main and secondary roads. References | Related Articles | Metrics

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Truck Service Area Parking Selection Behavior Modeling Considering Perceived Fatigue QIN Wenwen, SAI Jinhong, JI Xiaofeng, JI Xuan, CHEN Fang, LI Wu 2025, 25(6): 285-293.  DOI: 10.16097/j.cnki.1009-6744.2025.06.026 Abstract ( )   PDF (1483KB) ( )   To analyze the key factors influencing truck drivers' parking behavior in service areas and their underlying mechanisms, a model for truck parking behavior selection in service areas was developed with emphasis on incorporating drivers' perceived fatigue. First, based on the Theory of Planned Behavior, three latent variables—parking attitude, parking behavior intention, and parking behavior—were selected as the basic model A. Then, latent variables for parking space preference and service area information transparency were introduced to construct the initial model B and the extended model C incorporating perceived fatigue for truck parking behavior selection in service areas. Finally, the model was applied to a case study in Yunnan Province, yielding 320 valid questionnaires. Using partial least squares structural equation modeling, the three trucks service area parking behavior selection models were validated. The results indicate that the expanded model C, which incorporates the perceived fatigue variable, improves the model's fit and explanatory power. Besides, the fit of model C is 26.53% higher than that of the basic model A and 8.16% higher than that of the initial model B. Parking behavior intention and parking space preference are the most significant factors influencing truck service area parking behavior selection. In particular, parking attitude and service area information transparency indirectly influence parking behavior by affecting parking behavior intention and parking space preference. Perceived fatigue also indirectly influences parking behavior intention by affecting parking attitude. References | Related Articles | Metrics

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Multi-objective Optimization of Truck-Speedboat Coordination for Emergency Material Distribution in Mountainous Floods CHENG Jiahao, HAO Zhidan, LI Guoqi, LIU Sijing 2025, 25(6): 294-304.  DOI: 10.16097/j.cnki.1009-6744.2025.06.027 Abstract ( )   PDF (2460KB) ( )   Floods in mountainous areas often disrupt roads, create complex networks of land and water routes and challenge emergency material distribution. This paper develops a bi-objective mixed-integer programming model considering warehouse storage limits and post-disaster network functional differentiation. A land-water intermodal distribution network is established with warehouses, land, and water routes. The proposed model uses the lateral transshipment and the truck-speedboat collaborative strategy to minimize total transportation time and maximize the average demand satisfaction rate. An improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) is designed to solve the model, which includes a two-phase heuristic, hybrid genetic operators, and variable neighborhood search. A case study of a large-scale construction project shows that a hybrid collaborative strategy, allowing speedboats to depart from warehouses, improves the maximum average satisfaction rate by 4.85% and reduces the shortest transportation time by 2.17% compared to a dependent collaborative mode. References | Related Articles | Metrics

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Collaborative Routing Optimization for Multiple Trucks and Robots with Multiple Mobile Satellites CHEN Junxi, WEI Zhenlin 2025, 25(6): 305-316.  DOI: 10.16097/j.cnki.1009-6744.2025.06.028 Abstract ( )   PDF (2693KB) ( )   This paper focuses on the challenges of actual accessibility in truck-prohibited areas and the flexible deployment and retrieval of robots by proposing a collaborative routing optimization model for multiple trucks and robots with multiple mobile satellites (MMS-MTRCRP). The model aims to minimize the total cost comprising routing costs of trucks and robots and the fixed cost of trucks, incorporating constraints such as truck routing, robot travel and endurance, dynamic resource allocation, and coupled temporal, spatial, and capacity constraints between trucks and robots. For the complex decision-making resulting from these coupling constraints, the study proposes a greedy path initialization algorithm based on deployment and retrieval operations (DRGPIA), along with an improved adaptive large neighborhood search algorithm (IALNS) integrated with optimization operators. Comparative experiments on 12 Solomon instances with solver GUROBI 12.0.1 were conducted to validate the effectiveness of the proposed algorithm. The IALNS outperforms GUROBI in both solution quality and computation time for small-scale instances and demonstrates competitive performance in medium-and large-scale instances. Furthermore, ablation studies and comparative analysis with other modes reveal that the optimization operators positively contribute to the algorithm's performance. The optimization module reduces the solving cost of IALNS by an average of 8.9% in three large-scale instances. In cases where neither the single-satellite mode nor the multi-satellite same-station retrieval mode can achieve a feasible solution, the multi-satellite collaborative optimization strategy successfully obtains feasible solutions that satisfy all hard time window constraints, yielding average cost reductions of 20.6% and 17.1%, respectively. References | Related Articles | Metrics

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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 2025, 25(6): 317-326.  DOI: 10.16097/j.cnki.1009-6744.2025.06.029 Abstract ( )   PDF (2707KB) ( )   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. References | Related Articles | Metrics

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Joint Scheduling of Automated Cross-docking Equipment with Flexible Charging for Less-than-truckload Express TANG Weilin, LANG Maoxiang, CHEN Xinghan 2025, 25(6): 327-340.  DOI: 10.16097/j.cnki.1009-6744.2025.06.030 Abstract ( )   PDF (2963KB) ( )   As a critical coordination node in trunk-branch transportation, the less-than-truckload (LTL) express hub is responsible for the rapid collection, distribution, and efficient transshipment of goods. This imposes higher requirements on the joint scheduling and seamless coordination among automated handling equipment. Aiming at the“fast-in, fast-out”cross-docking characteristics of LTL operations, this paper proposes a mixed-integer programming model to minimize the makespan by jointly scheduling automated guided vehicles (AGVs) for horizontal transport and automated forklifts for vertical handling. A flexible charging strategy is further introduced to optimize both task assignment and charging decisions in an integrated manner. Based on the problem's decomposable structure, a logic-based Benders decomposition algorithm is developed to enhance the solution efficiency for large-scale instances. Numerical experiments on instances of varying sizes are conducted to validate the effectiveness of the proposed model and algorithm. The results show that the proposed approach outperforms the Gurobi solver in terms of both computational efficiency and solution quality. Moreover, joint scheduling of AGVs and automated forklifts significantly improves the fluidity and resource coordination of cross-docking operations compared to independent scheduling. The incorporation of the flexible charging strategy enables dynamic optimization of charging timing and energy replenishment, reducing charging time by an average of 24.3% and decreasing the total operation time by 7.62% compared to the full-charging strategy, thereby improving overall system efficiency. References | Related Articles | Metrics

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Quantitative Analysis of Mental Workload in Closely Spaced Tunnel-Interchange Sections Based on on-road Driving Tests LUO Shuang, CHEN Kuan, LI Shanxing, XU Jin 2025, 25(6): 341-349.  DOI: 10.16097/j.cnki.1009-6744.2025.06.031 Abstract ( )   PDF (2326KB) ( )   To quantify the mental workload of drivers in closely spaced tunnel-interchange sections on diverging freeways, electrocardiogram data of 33 participants were collected through on-road driving tests along the Huangcao to Baojia Interchange section of Chongqing-Hunan Expressway. The distribution characteristics of the mean heart rate increasing rate, maximum increasing rate of heart rate, standard deviation of R-R intervals (the time intervals between adjacent R waves on the electrocardiogram) and root mean square of continuous R-R intervals of drivers were analyzed. A comprehensive quantitative model of mental workload based on factor analysis method was constructed through using these indicators as observation valuables. The mental workload level of the closely spaced tunnel-interchange section and its differences between general tunnel and interchange were revealed. The influences of clearance distance and driver factors on the mental workload were discussed. The results show that the mental workload of the four closely spaced tunnel-interchange sections ranges between 0.23~1.00, 0.00~0.87, 0.15~0.76, and 0.03~0.65 respectively, which is significantly higher than that of the general tunnel ( p<0.05). The mental workload of the closely spaced tunnel-interchange sections with clearance distance of 410 meter or less is significantly higher than that of the general interchange ( p<0.05). As the clearance distance decreases, the mental workload gradually increases. There are significant differences in mental workload between each pair of the closely spaced tunnel-interchange sections ( p<0.05). There are also significant differences in mental workload among different types of drivers ( p<0.05). The mental workload of female drivers is 41.0% higher than that of male drivers. The drivers who are not familiar with the route have a 37.8% higher mental workload than those who are familiar with it, and non-experienced drivers have a 59.0% higher mental workload than experienced drivers. References | Related Articles | Metrics

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Analysis of Safety Utility of Head-up Display Pedestrian Warning System Based on Conflict Theory LI Xuewei, SUN Qi, LIU Xiaomeng, KANG Xuejian, ZHAO Xiaohua 2025, 25(6): 350-359.  DOI: 10.16097/j.cnki.1009-6744.2025.06.032 Abstract ( )   PDF (2023KB) ( )   This paper aims to quantify the safety utility of in-vehicle head-up display systems in pedestrian conflict events. First, pedestrian conflict events under two weather conditions (sunny/foggy) were designed based on a driving simulator experiment, and driving maneuvers and vehicle operation data were acquired from 34 participants utilizing three human-computer interaction systems: Baseline, HDD (Head-Down Display), and HUD (Head-Up Display). Then, the speed characteristics of vehicles during human-vehicle conflicts were analyzed, and the driver's braking reaction time, minimum time-to-collision and post-encroachment time were extracted based on the traffic conflict theory, in order to characterize the pattern of variation of temporal safety margins before, during, and after the conflict. A Cox proportional hazard model was developed for the three time-dimensional indicators to explore the mechanism of the system conditions, weather conditions and individual driver attributes on safety margins. The results show that, compared to the HDD and Baseline groups, drivers in the HUD group exhibited significantly lower vehicle speeds before and during the conflict, shorter braking reaction times, longer minimum time-to-collision, and significantly higher post conflict recovery speeds. However, there was no significant difference in post-encroachment time compared to the other two groups. Further analysis showed that driver gender and driving frequency were related to post-encroachment time, with male drivers and drivers with lower driving frequency exhibiting longer post-encroachment time. The research results can provide theoretical support for the optimization design of the HUD warning system and enhance the safety performance of the HUD system in complex risk-driving environments. References | Related Articles | Metrics

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News Event-driven Prediction of Unidentified Flight Activities in Oceanic Areas MENG Linghang, ZHANG Linhai, CHEN Min 2025, 25(6): 360-372.  DOI: 10.16097/j.cnki.1009-6744.2025.06.033 Abstract ( )   PDF (3210KB) ( )   In recent years, with the increasingly complex of international geopolitics, the unidentified flight activities (UFAs) in ocean areas have a rising frequency. These activities severely impact the safety and operational efficiency of civil aviation flights flying over ocean areas. This study constructs an attention-based convolutional memory network prediction architecture to explore the relationship between GDELT (Global Database of Events, Language, and Tone) news events, the counts of UFAs, and their temporal lag effects. Initially, the Granger causality test and correlation analysis are employed to identify news event types that are significantly correlated with UFA counts, and to construct the input feature space of the proposed hybrid deep learning network. Subsequently, a hybrid deep learning framework combining with the Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), and Multi-Head Attention (MHA) mechanism is developed. The CNN-LSTM-MHA framework utilizes the CNN to extract local spatio-temporal correlation features between GDELT events and UFA counts, explores the LSTM to capture the lagged influence of GDELT events on UFA counts, and incorporates the MHA to optimize learning weights of LSTM outputs and UFA counts. Finally, the proposed framework was validated by using the UFA data in Sanya Flight Information Region (FIR) from 2015 to 2024. The results indicate that: The predictive model achieved excellent performance on the test set, with a mean absolute error (MAE) of 0.6049, root mean square error (RMSE) of 0.7642, and coefficient of determination (R²) of 0.8103; It maintained high predictive accuracy for both normal and anomalous samples, while closely matching training set performance, demonstrating strong generalization capability and reliable prediction stability. References | Related Articles | Metrics