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25 April 2025, Volume 25 Issue 2 Previous Issue   

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

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Flood Evacuation Decision-making Behavior Under Hybrid Utility and Regret Rules JIN Zeqian, YU Chengcheng, YE Xin 2025, 25(2): 16-25.  DOI: 10.16097/j.cnki.1009-6744.2025.02.002 Abstract ( )   PDF (2559KB) ( )   This study aims to investigate the factors that influence urban residents' evacuation decision-making behavior and uncover the heterogeneity between utility-based and regret-based decision-making rules in the face of predictable flood hazards. Considering evacuees' mixed psychology of utility-maximization and regret-avoidance, a framework of the integrated choice and latent variables (ICLV) model was proposed, including individual basic attributes, psychological perception attributes, and evaluation attributes of evacuation decision. By constructing a structural equation model (SEM) reflecting evacuees' psychological preferences and incorporating it into a discrete choice model, an ordered Logit-based ICLV model was established. Based on survey data of residents' evacuation decision behavior from Zhengzhou in China, the results of the SEM show that residents with higher risk perception and self-efficacy are more favorable to evacuation. Through a comparative analysis of the ordered Logit model and the ICLV model, the results indicate that the ICLV model, which takes into account psychological perception attributes, demonstrates the best performance and depicts residents' evacuation decision-making behavior in a more realistic manner. Moreover, both models capture the heterogeneity in residents' evacuation decision behavior between utility and regret decision making rules. Additionally, evacuees tend to choose evacuation options characterized by shorter travel times, lower perceived disaster risks, fewer intermediate trips, and less background traffic. References | Related Articles | Metrics

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Comparison on Influence of Job-housing and Commuting Status on Travel Mode Choice in Multiple Types of Cities ZHOU Yuyang, ZHAO Congying, LI Jingkun, CHEN Yanyan, LIU Di, WANG Shuling 2025, 25(2): 26-35.  DOI: 10.16097/j.cnki.1009-6744.2025.02.003 Abstract ( )   PDF (2873KB) ( )   Establishing a green and efficient travel service system is an important part of China's Green Travel Action Plan. It is necessary to consider the heterogeneity of job-housing status and commuting mode in different levels of cities. Based on 1788 valid questionnaires collected from three types of cities, the SEM-MNL model is constructed to quantitatively analyze the comprehensive impact of job-housing status, commuting attributes and personal economic characteristics on the choice of commuting modes in various types of cities. The findings reveal that the latent variable commuting attribute is the key factor affecting the travel mode, and the restrictive effect is more prominent in ordinary cities than in first-tier and new first-tier cities. Job-housing status indirectly affects commuting mode choice through commuting attributes. The path coefficients of three classes of cities are 0.83, 0.89, and 0.93, respectively. The effects of residential type on commuting distance and mode choice show an opposite trend in first-tier cities and ordinary cities. Highly educated travelers in first-tier cities prefer green travel modes, while in non-first-tier cities, the result is reversed. In new first-tier cities, residents with short commute distances have the highest proportion of renting, nearly half of them choose slow-speed transportation. Adjusting the job-housing distribution to increase the proportion of short-distance commuting can raise the share of green travel mode. As the city level declines, the feedback sensitivity of regulation increases. The research results provide differentiated policy recommendations for job-housing balance and transportation infrastructure planning in multiple types of cities. The results are conducive to promoting the low-carbon travel and contribute to the balance of urban transportation supply and demand and thus sustainable development. References | Related Articles | Metrics

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Collaborative Optimization of Suburban Railway and Metro Train Operation Plans Under Interconnection Operational Mode PENG Qiyuan, LIU Siyuan, JIANG Shan, FENG Tao, CHEN Yao, ZHANG Yongxiang 2025, 25(2): 36-47.  DOI: 10.16097/j.cnki.1009-6744.2025.02.004 Abstract ( )   PDF (2588KB) ( )   The train operation plan plays a crucial role in improving the operational efficiency of regional multi-standard rail under the interconnection operational mode. This study develops an integrated optimization model for the suburban railway and metro train operation plans and passenger flow assignment under the interconnection operational mode to minimize the total of passenger travel cost and the enterprise operational cost. The model simultaneously optimizes the train service routes on the entire line, the frequency, composition types, and stopping plan of trains on each service route, where practical constraints such as carrying capacity, train capacity, rolling stock resources, and the number of train services are considered. An improved adaptive large-scale neighborhood search algorithm is designed and its effectiveness is verified through instances with different passenger demand levels. The results show that: 1) The improved algorithm can provide a satisfactory solution, resulting in an average decrease of 3.6% in the objective function with an average computational time of 234 seconds compared to the two-stage approach;2) Compared to the independent operational mode, the interconnection operational mode reduces the enterprise operational cost by 11.3%, full-line and cross-line passenger travel costs respectively by 3.9% and 10.7%, the transfer times of cross-line passengers by 18.7%, and the number of rolling stocks used by 14.4%. 3) Compared to the all-stop pattern, the passenger travel cost is reduced by 4.2% on average after adopting the flexible-stop pattern for the suburban railway local and cross-line trains. The proposed method provides an auxiliary decision support tool for generating the suburban railway and metro train operational plans under the interconnection operational mode. References | Related Articles | Metrics

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Strategic Approaches for Optimizing Queue Size in Connected and Autonomous Vehicle Platooning SUN Xu, MA Tianxing, WANG Tianshi, WANG Jianyu, LU Huapu 2025, 25(2): 48-57.  DOI: 10.16097/j.cnki.1009-6744.2025.02.005 Abstract ( )   PDF (2665KB) ( )   This study aims to comprehensively explore the effect of queue size for effective mixed traffic flow in Connected and Autonomous Vehicles (CAVs). The study is segregated based on queue size, considering intra-platoon gap organization as well as inter-platoon relative positioning. A two-stage platooning strategy for CAVs is proposed and studied. A three-lane highway operation model is built based on cellular automata with parameters like CAV penetration rate and maximum queue size. The performance of the proposed strategy is compared with free-flow mixed traffic and another two-stage CAV platooning model. Comparison is made with respect to important traffic parameters like traffic flow capacity, density, lane changing frequency, driving speed, and CAVs' safety profile. The outcome demonstrates that, as compared with free-flow mixed traffic, two-stage platooning strategy increases traffic capacity around 16.78% in various CAV penetration rate conditions. In scenarios with moderate CAV penetration levels, the strategy contributes significantly in terms of safety, reducing cumulative collision risk time by 45.45%. Moreover, the platooning strategy demonstrates a critical scale where the optimal platoon size is limited to 6 vehicles at the one-stage and 14 vehicles at the two-stage. References | Related Articles | Metrics

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Dynamic Operation of Autonomous Shared Electric Vehicles Considering Relay and Ridesplitting JIANG Yangsheng, YE Xiaofu, XU Weiyao, LIU Hongxun, HU Lu 2025, 25(2): 58-68.  DOI: 10.16097/j.cnki.1009-6744.2025.02.006 Abstract ( )   PDF (2154KB) ( )   Autonomous electric shared vehicle is an important intelligent mode for future urban transportation. The traditional single ride-hailing model fails to fully utilize vehicles, resulting in low order fulfillment rates and economic benefits. Therefore, this paper proposes a ride-splitting model and adopts relay strategies for the dynamic operational decision-making of autonomous electric shared vehicles system. The study first constructs a four-dimensional spatiotemporal network based on time, space, battery level, and passengers, which is then simplified to a three-dimensional network through arc merging. Based on this topological network, a pure integer linear programming model is developed to maximize operational profit. A rolling optimization method is introduced with multiple Forward-looking time windows matching heterogeneous services like single ride-hailing, ride-splitting, and relay service. Using GUROBI as the optimization engine, the subproblems for the Forward-looking period are solved quickly to meet practical dynamic operating scenario. A case study demonstrates that, under moderate travel demand, the introduction of ride-splitting and relay strategy increases operational profit by respectively 11.60% and 13.85% under the uniform and non uniform demand distributions. References | Related Articles | Metrics

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Simulation of Driving Strategies for Multi-lane Bottleneck Road Sections in An Environment with Autonomous Vehicles Penetration ZHANGJianxu, WU Chunxiang 2025, 25(2): 69-81.  DOI: 10.16097/j.cnki.1009-6744.2025.02.007 Abstract ( )   PDF (3401KB) ( )   To explore the traffic efficiency and characteristics of multi-lane temporary bottleneck sections in a mixed intelligent connected autonomous vehicle (CAV) penetration environment, this paper designs car-following and lane-changing rules tailored to human-driven vehicles (HV) and CAV based on the continuous cellular automaton model. A car-following model was established based on HV slow start, and a lane-changing model was established considering lane-changing motivation, which can be divided into free lane-changing, inclined lane-changing, and mandatory lane-changing. For CAVs, an active lane-changing with facilitating platoon has been designed. The CAVs in middle lane can cooperate with those in the outer lane for lane-changing, providing lane-changing space for vehicles operating in the inner lane and facilitating the formation of CAV platoons in the outer lane. To verify the impact of this strategy on traffic flow in temporary bottleneck scenarios, a strategy without control experiment was set up to analyze the traffic efficiency of bottleneck sections under different CAV penetration rates and flow levels. The simulation results showed that, compared without strategy, ALC-FP strategy has reduces HV and CAV mandatory lane changing by 75% and 94.45%, respectively; The maximum increase in CAV platoon intensity is 50%, which facilitates the formation of CAV platoons passing through bottleneck sections; The average speed of vehicles passing through bottleneck can be doubled, the average delay of vehicles can be significantly reduced by up to 88.6%. References | Related Articles | Metrics

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Energy-saving Driving Optimization for Connected Electric Buses Considering Station Arrival Strategies NAN Sirui, YU Qian, LI Tiezhu, SHANG Zandi, CHEN Haibo 2025, 25(2): 82-94.  DOI: 10.16097/j.cnki.1009-6744.2025.02.008 Abstract ( )   PDF (2723KB) ( )   Considering the high energy consumption of bus operation especially when stopping at bus stations and signalized intersections, this paper proposes an energy-saving driving optimization method based on stop approach strategies. The SUMO platform is used to build intelligent connected vehicle simulation scenarios. A composite reward function is developed, and the driving efficiency, safety, and energy consumption are factored in. Stop arrival strategies and predefined traffic rules are incorporated as constraints into the Soft Actor-Critic (SAC) deep reinforcement learning framework to optimize vehicle trajectories when bus stops at the stations and approaches signalized intersections. The proposed SAC-ruled algorithm is tested under different scenarios, using real-world driving data and the conventional SAC-based optimization method as baseline methods. Results show that the proposed energy-saving driving optimization method shows a 35.97% reduction in vehicle energy consumption and a 21.67% improvement in travel time compared to the baseline methods. In lane-changing scenarios, energy consumption is reduced by up to 41.40%, with a 16.94% improvement in travel time. The proposed method demonstrates great adaptability to traffic flow fluctuations, as validated by sensitivity analysis. This method can be integrated into energy-saving assistance systems, encouraging drivers to adopt energy-saving behaviors. References | Related Articles | Metrics

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Game Mechanism and Guiding Strategy of Intelligent Connected Transit Signal Priority WEI Liying, FENG Mei, WU Runze 2025, 25(2): 95-107.  DOI: 10.16097/j.cnki.1009-6744.2025.02.009 Abstract ( )   PDF (2006KB) ( )   The ongoing development of intelligent connected technology provides crucial support for achieving transit signal priority (TSP) and assisting the development of the intelligent connected public transit towards "precision public transit" and "safe public transit". This paper starts from the conflict game relationship between different phases, and constructs a TSP guiding strategy based on chicken game in the intelligent connected environment. Firstly, the chicken game theory is used to analyze the game behavior of priority and non-priority phases of public transit, establishing a game model with weighted delay as the benefit matrix. Then, adopting the active priority and speed guidance, a TSP guiding strategy and optimization process based on the proposed game model is proposed by considering factors such as punctuality, limitation of minimum green time, priority and non priority phase delay of priority transit. Finally, to validate the strategy, a case study is conducted using an actual intersection in Beijing, employing SUMO for simulation. The results show that the TSP guiding strategy can effectively improve the traffic efficiency of priority phases and reduce the negative impact on non-priority phases compared to the initial timing; under the condition of 50% penetration rate, compared to the implementation of strategy, 20% of priority buses have been optimized significantly for punctuality, and the traffic efficiency indicators such as average queue length, average parking times and delay are reduced by at least 33.27%. Additionally, fuel consumption and CO2 emissions are reduced by at least 12.20%, and the negative influence of non-priority phase indicators is less than 8%. References | Related Articles | Metrics

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Regional Traffic Signal Control Methods Considering Lane Remaining Capacity DAI Liang, HUANG Zibin, ZHANG Zhonghao, LI Chenfu 2025, 25(2): 108-118.  DOI: 10.16097/j.cnki.1009-6744.2025.02.010 Abstract ( )   PDF (2036KB) ( )   Intersection is the bottleneck of the overall traffic capacity of urban road networks, are the focal points of traffic organization, channelization, and management within the networks. Deep reinforcement learning is widely used in the field of traffic signal control at intersections, as it interacts with the environment to find target strategies, which aligns well with the complex and dynamic characteristics of traffic environments. This paper proposes a regional traffic signal coordination control method that considers lane capacity. By modeling the cooperation relationship between upstream and downstream intersections and introducing downstream lane capacity information into the maximum pressure method to design the reward function, a distributed regional traffic signal coordination control method is proposed based on multi-agent reinforcement learning algorithm. Performance verification is carried out using real road networks and traffic flow datasets from Jinan and Hangzhou. Compared with existing regional traffic signal control methods, the proposed method reduces the average travel time by 6.05%, the average delay time by 18.39%, the average queue length by 21.86%, and increases the throughput by 0.24%. References | Related Articles | Metrics

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Multi-vehicle Collaborative Platooning and Distributed Optimization Methods Under Expected State Constraints YUAN Zhenzhou, ZHOU Bohan, CHEN Mo, YANG Yang 2025, 25(2): 119-127.  DOI: 10.16097/j.cnki.1009-6744.2025.02.011 Abstract ( )   PDF (2471KB) ( )   Traditional studies on vehicle platooning mainly focused on vehicle interactions and the analysis of platoon formation, lacking a systematic exploration of the platoon formation from an overall state perspective. Therefore, this study investigates the optimization mechanism of the vehicle platooning process under the constraint of the overall desired state, and proposes a longitudinal platoon control optimization method based on desired platoon state constraints. First, the desired platoon state is defined, covering five dimensions: platoon completion time, distance, speed, acceleration, and spacing. Then, in the ideal scenario where no vehicles interfere with the leading vehicle, a platoon control optimization model without the front vehicle constraint is established based on model predictive control. Further, for the common scenario where the front vehicle is interfering, a corresponding platoon control model is proposed. This study adopts a distributed computation mode based on adjacent vehicle pairs, which reduces computational pressure while enhancing the safety and robustness of the platooning process. Finally, a Python based visualization simulation program is developed to verify the effectiveness of the algorithm. The results show that excessively short desired distances and expected times are the main factors hindering platoon feasibility, and as the desired distance and speed increase, the minimum feasible platoon time threshold also increases accordingly. In terms of execution, the actual performance of the platoon under feasible desired states is satisfactory, with the deviation from the initial platoon target being less than 1‰ , while ensuring the safety and smoothness of the vehicle trajectories. In terms of computational efficiency, the distributed strategy outperforms the centralized strategy, with this advantage becoming more pronounced as the number of platooning vehicles increases. In the larger-scale platooning task consisting of 9 vehicles, the maximum computation time does not exceed 0.3 seconds. References | Related Articles | Metrics

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Vehicle Intention Recognition and Trajectory Prediction Based on Probabilistic Fusion XIN Song, LIU Han, WANG Ke, QUYirun, SONG Xinyu 2025, 25(2): 128-137.  DOI: 10.16097/j.cnki.1009-6744.2025.02.012 Abstract ( )   PDF (1611KB) ( )   Traditional maneuver-based trajectory prediction models need to identify factors such as the scenario where the vehicle is located and the driving intention in order to select the corresponding trajectory prediction model. However, the recognition error of this framework may be propagated to the trajectory prediction model when the scenario is complex or the driving intention is unclear, thus affecting the prediction accuracy. Therefore, this study proposes a probabilistic fusion approach and introduces a probabilistic calibration technique to fuse the results of multiple vehicle trajectory prediction models through the intention recognition prediction probabilities to mitigate the impact of intention recognition errors in the maneuver-based model framework on the trajectory prediction accuracy. First, the input and output modules are extracted based on the historical trajectories of the target vehicle and the surrounding vehicles. Second, the XGBoost (Extreme Gradient Boosting) algorithm is used for intention recognition and intention probability prediction, and a three-layer Gated Recurrent Unit (GRU) is constructed separately for each recognized intention. The trajectory prediction model is constructed for each identified intention, probability calibration is performed using both Platt scaling and isotonic regression, and probabilistic fusion based on results of probabilistic calibration and trajectory prediction. Finally, the model is trained and validated using the CQSkyEyeX dataset. The experimental results show that the combined model based on Platt scaling and probabilistic fusion outperforms the other models in all evaluation metrics. When using a time window of 4 s and a prediction step of 2 s, the model has a Mean Absolute Error (MAE) of 0.87 m. Compared with the model without probabilistic fusion (1.32 m), the error is reduced by 34%. Compared to the model using only weighted fusion (1.18 m), the error is reduced by 26%. References | Related Articles | Metrics

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Estimation of Mainline Freight Delivery Time Reliability from an Abstract Network Perspective LUAN Jianlin, YOU Ruiqi, WANG Sini, JIA Peng 2025, 25(2): 138-145.  DOI: 10.16097/j.cnki.1009-6744.2025.02.013 Abstract ( )   PDF (1693KB) ( )   To accurately estimate the delivery time reliability of mainline trunk freight transportation, this paper proposes a non parametric estimation method for delivery time reliability based on an abstract network, utilizing truck trajectory data. Firstly, parking points are identified using the ADF-3sigma method, and the HDBSCAN algorithm is employed to construct the abstract network vertices. Subsequently, the TRACLUS algorithm is used for trajectory clustering to construct the abstract network edges, forming the abstract network of mainline trunk freight. Then, a non-parametric estimation method, the Improved-Cornish-Fisher (ICF) approach, is applied to estimate the travel time distribution of the abstract network edges. Finally, the Fourier transform of the travel time distribution on consecutive edges of the transport path is performed to estimate the delivery time reliability. The proposed framework is validated using real truck trajectory data from Zhejiang Province. The results demonstrate that in terms of the accuracy of edge travel time distribution estimation, the ICF non-parametric estimation method outperforms existing methods based on assumed distributions on 68% of the edges. Furthermore, the use of an abstract network for delivery time reliability estimation reduces the average computation time by 80% compared to methods based on real networks, while improving the accuracy of delivery time reliability estimation by 60%. References | Related Articles | Metrics

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Cascading Failure and ResilienceAssessment of Urban Traffic Congestion Risk Fields ZHAO Xueting, HU Liwei, ZHOU Jun 2025, 25(2): 146-156.  DOI: 10.16097/j.cnki.1009-6744.2025.02.014 Abstract ( )   PDF (3919KB) ( )   This paper applies the information physics system to analyze the cascading failure process of urban traffic congestion risk field and proposes the quantitative method for toughness risk size assessment. A CPS(Cyber Physical Systems) control model is developed with mutual coupling of real traffic road network, traffic congestion prevention, and control type zoning of urban traffic congestion risk field in consideration of the multidimensional coupling characteristics of urban traffic congestion risk. The structural characteristics of the traffic domain is examined through the complex network theory. The CPS characteristic parameters are redefined, the four processes of CPS cascade failure are defined, and the CPS cascade failure model is developed with urban traffic congestion risk field. The risk factor is defined as the intervention point and the risk perturbation mechanism is elaborated through the network topology theory. The CPS connectivity is examined through the normalized quantitative node connectivity, delay time, average operating speed, average congestion length and other toughness indexes. The CPS damage perturbation and toughness are evaluated by different failure-recovery strategies, the indicators of the robustness, damage/recovery rate, and recovery capability. The results from case studies show that: (1) Guiyang city urban traffic congestion risk field CPS real traffic network consists of 170 intersections and 231 edges, and the traffic congestion prevention and control type zoning network consist of 21 traffic command areas and 41 edges. (2) The maximum and minimum degree value of the CPS model is respectively 22 and 1. The degree value obeys the power rate distribution function, has characteristic scale-free network features, and the mediator shows the exponential distribution. The degree value perturbation has the greatest influence on the real traffic network of Guiyang city, and the mediator perturbation has the greatest influence on the type of sub-districts of Guiyang city's traffic congestion prevention and control. (3) When t=2 , the network performance starts to decline under both meso and degree perturbation, and the meso perturbation affects the performance more than the degree perturbation, and both reach the lowest at t=7 . (4) The median recovery effect is better than the degree value recovery, under the median perturbation, the degree value recovery and median recovery effect of Guiyang city's real traffic network is poor, the toughness value is respectively 0.01123 and 0.01252, which is significantly lower than that of the toughness value of the traffic congestion prevention and control type of partitioning (0.1355). The proposed model provides reference for the quantitative evaluation and initiation of the traffic congestion control strategy at different stages. References | Related Articles | Metrics

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Accident Risk Identification and Impact Mechanisms in Nighttime Highway Maintenance Work Zones ZHANG Heshan, ZENG Changkun, WANG Haiyang, TIAN Fengming, ZHENG Zhanji, ZHANG Yu 2025, 25(2): 157-168.  DOI: 10.16097/j.cnki.1009-6744.2025.02.015 Abstract ( )   PDF (3810KB) ( )   The complex and variable nighttime environment and constricted driving space significantly increase the risk of traffic accidents in maintenance work zones. To identify risk characteristics and capture the underlying relationships between influencing factors and accident risks, this study employs drone aerial footage to capture traffic flow videos of a nighttime maintenance work zone on a highway segment in Chongqing, China. The Tracker software is used to extract microscopic vehicle trajectory data, thereby revealing traffic flow characteristics such as spatiotemporal trajectories, vehicle speed, and headway distribution in nighttime maintenance work zones. The probability of accidents and the severity of potential accident consequences are evaluated based on time-and energy-based safety surrogate measures. Furthermore, an Extreme Gradient Boosting (XGBoost) algorithm was utilized to construct a Loss Energy Index (ZLEI ) prediction model for vehicle collisions, and the SHAP (SHapley Additive exPlanations) algorithm was applied to quantify and interpret the impact mechanisms of features such as speed, headway, traffic conflicts, and deceleration rates to avoid collisions on the ZLEI . The results indicate that headway distance shows a decreasing-then increasing trend from the warning zone to the transition zone. Severe rear-end collisions are highly prone to occur when headway distances are between 2.5 and 3.0 meters. Additionally, large trucks have a higher lane-changing risk compared to smaller vehicles. The key factors influencing ZLEI include deceleration rate to avoid collisions, lane-changing conflicts (tc TTC ) , headway, speed, and travel time. When traffic flow exceeds 1000 pcu·h-1 and vehicle speed falls within the range of (1.25, 2.50) m·s-1, ZLEI increases as the inter-vehicle distance decreases and the deceleration rate to avoid collisions rises. When traffic flow is below 1000 pcu·h-1, and headway is within the range of (3.0, 8.0) seconds, ZLEI increases with higher inverse time-to-collision ( 1 /tc TTC ) . For vehicle speeds within (12.5, 20.0) m·s-1, ZLEI increases with a rising Deceleration Rate to Avoid a Collision (ADRAC ) . References | Related Articles | Metrics

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Expressway Traffic Flow Prediction Method for Holidays Based on Diffusion Model LIN Peiqun, CHEN Zemu, ZHOU Chuhao 2025, 25(2): 169-179.  DOI: 10.16097/j.cnki.1009-6744.2025.02.016 Abstract ( )   PDF (1859KB) ( )   Traffic management authorities require accurate traffic demand forecasts to implement effective traffic control strategies. However, the high uncertainty and suddenness of holiday traffic flows present significant challenges in generating precise pre holiday predictions. This research introduces a diffusion framework for predicting holiday traffic flow, grounded in diffusion probabilistic model theory, and further develops a Conditional Diffusion Model with Multi-feature Extraction (CDMME). The proposed CDMME integrates spatio-temporal characteristics of traffic flow, holiday attributes and meteorological factors to predict long-term traffic flow for holidays. Experiments are conducted using 15-minute and one-hour traffic flow data from 28 busy expressway segments in Guangdong Province, focusing on holidays such as New Year's Day, the Dragon Boat Festival and the Mid-Autumn Festival for model training and validation. The experimental results indicate that, for 15-minute and hourly total flow predictions, compared to the random forest (RF) model, the CDMME reduces the Weighted Mean Absolute Percentage Error (WMAPE) by 12.98% and 34.88%, respectively, while the Mean Absolute Error (MAE) increases by 1.47% for 15-minute prediction and decreases by 23.54% for hourly prediction. In comparison to the long short-term memory (LSTM) model, the CDMME reduces the WMAPE by 16.10% and 32.39% , respectively, and the MAE by 9.42% and 27.55% respectively. Additionally, when comparing hourly total traffic prediction with 15-minute total traffic prediction, hourly passenger traffic prediction and hourly truck traffic prediction, the WMAPE decreased by 29.57%, 12.23% and 30.42%, respectively, indicating that it has superiority in tasks with larger magnitude. Visualization result demonstrates that the CDMME effectively captures traffic peaks. Furthermore, the CDMME achieves peak average accuracy with a one-day advance forecast, with the accuracy of hourly total traffic prediction reaching 87.27%. References | Related Articles | Metrics

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Risk Identification Method for Abnormal Driving Behavior Based on Interpretable Ensemble Learning Model DENG Yuanchang, JIANG Yunxuan, TAO Shengqin 2025, 25(2): 180-189.  DOI: 10.16097/j.cnki.1009-6744.2025.02.017 Abstract ( )   PDF (2329KB) ( )   In order to accurately identify the risk of abnormal driving behavior and overcome the limitations of poor interpretability of existing models, this study collected vehicle motion data through natural driving test. Five types of abnormal driving behaviors were investigated: speeding, rapid shifting, sharp turning, short distance following, and dangerous lane changing. The risk coefficients of these behaviors were quantified using a threshold method, and the risk levels were classified using the CRITIC (Criteria Importance Through Inter-criteria Correlation) weight method and quantile method. A Stacking based ensemble learning identification model was constructed to identify the abnormal driving behaviors. The model combined training results from different learners. GBDT (Gradient Boosting Decision Tree), AdaBoost, and XGBoost that have the best comprehensive performance, selected as the primary learner combination, and logistic regression was used as the secondary learner. The SHAP (Shapley Additive exPlanation) algorithm was then used to analyze the influence of feature variables on the recognition results of the optimal Stacking model. Results indicate that the optimal Stacking model has an identification accuracy of 92.68%, achieving high precision in identifying abnormal driving behavior risks. Vehicle speed and time-to-collision of lane changing were identified as key features significantly impacting model recognition. Specifically, vehicle speed exceeding 95 km·h-1 and time-to-collision of lane changing less than 2.8 s both increase behavioral risk, and the risk level is higher when the vehicle speed exceeds 150 km·h-1. This study provides a feasible framework for identifying and interpreting the risks of abnormal driving behavior, which is expected to provide technical support for improving traffic safety levels. References | Related Articles | Metrics

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Modeling andAnalysis of Driver's Short-term Response Behavior Heterogeneity Under Cut-in Scenarios GONG Zhe, YANG Zhen, ZHENG Ruiping, YUAN Fang 2025, 25(2): 190-200.  DOI: 10.16097/j.cnki.1009-6744.2025.02.018 Abstract ( )   PDF (2047KB) ( )   Cut-in scenarios are common in traffic flow and pose risks to driving safety, especially when the lead vehicle cuts in at low speeds and short distances, which may force the following driver to take emergency braking or other actions, affecting the stability and safety of traffic flow. To address this issue, this study collected data from 360 driving simulation experiments under different lead vehicle cut-in speeds and distances, and analyzed the short-term response behavior of drivers and its influencing factors. A correlated random parameters ordered Probit model considering heterogeneity and parameter correlation was developed to identify the key factors affecting the decision-making of the following driver. The results show that the average marginal effects indicate that when the lead vehicle cuts in at distances of 50 m and 100 m, the probability of the following vehicle braking increases by 47% and 33%, respectively. Similarly, when the lead vehicle cuts in at speeds of 60 km · h-1 and 80 km · h-1, the probability of braking increases by 31% and 24%. In contrast, two random variables (experienced following drivers and the low speed of the following vehicle before the cut-in) reduce the braking probability by 6% and 29%, respectively. In addition, the variable of high acceleration noise significantly affects the means of these two random parameters. Compared to traditional models, the correlated random parameters model reveals the correlation among random parameters and identifies that the unobserved heterogeneity captured by these parameters significantly reduces the following driver's braking decision. The findings of this study reveal the driving characteristics of following drivers in cut-in scenarios and provide both theoretical foundations and data support for control strategies in autonomous driving systems and for traffic safety management in such scenarios. References | Related Articles | Metrics

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Urban and Rural Bus Scheduling with Alternating Segment Demand Response Based on Passengers' Autonomous Choices JIANG Xiaohong, LI Jiawei, HUA Jingwen, ZHONG Yunhao, XING Jiping 2025, 25(2): 201-213.  DOI: 10.16097/j.cnki.1009-6744.2025.02.019 Abstract ( )   PDF (2528KB) ( )   To effectively tackle the issue of inconvenient resident travel caused by the low coverage of public transportation services in rural areas and enhance passengers' option as the main travel entities, this paper proposes a section alternating response based scheduling approach which utilizes the existing fixed route resources of urban and rural bus and considers passengers' autonomous choices. This approach divides the rural operation sections into two alternating response subsections and determines the demand response stations through the vector product method. A two-stage scheduling model is designed, adopting the method of instantaneously processing orders to achieve real-time responses. The demand response stations that have been served are set as "temporary fixed stations" to accelerate the model solution. An autonomous choice mechanism is developed, covering the entire process from the generation of travel demands to the selection of departure time, boarding stations, service trips, and secondary choices after order refusals. Taking the No. 4 urban and rural bus line in Siyang County, Jiangsu Province as an example, numerical experiments under different passenger flow intensities are designed. The experiments indicate that in the scenario of a large passenger flow, the average number of alighting passengers at each demand response station is 3.76. Under different passenger flow intensities, the autonomous choice mechanism enables 15.79% to 34.50% of passengers to make personalized choices, with the highest increase in fare revenue by 13.51%. By charging service fares to balance the additional costs, an average increase of 1.53% in fare revenue can be achieved for every 1% increase in driving distance. The results indicate that the section alternating response can effectively concentrate passengers with similar travel times and arrival stations within the same section, reducing operating costs. Considering passengers' autonomous choices can fully depict the travel behavior of passengers and increase system revenue. References | Related Articles | Metrics

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Urban Freight Bus Route Planning Model Considering Dynamic Demand WU Huirong, WANG Yuying, SHENG Chunting 2025, 25(2): 214-226.  DOI: 10.16097/j.cnki.1009-6744.2025.02.020 Abstract ( )   PDF (2468KB) ( )   To alleviate urban traffic congestion and reduce freight delivery costs while meeting the demands of urban freight delivery characterized by high volume, small package sizes, frequent batches, and strict timeliness, many cities have implemented freight bus services. The rational design of freight bus delivery routes is crucial for the cost reduction and efficiency improvement in urban freight transportation. This study considers real-time freight demand at dynamic stations and divides the planning of freight bus routes into two phases: static route planning and dynamic route adjustment. The study develops a two-phase freight bus route planning model that aims to minimize total operating costs, including vehicle transportation costs, fixed costs, and time penalty costs, while taking dynamic demand into account. The particle swarm optimization sparrow search algorithm(SSAPSO) and improved greedy order insertion algorithm(IGOIA) are used to solve the two-phase model, create optimal static and dynamic route plans for freight bus delivery. The case study was performed for Harbin city freight bus route planning. The results indicate that compared to the current freight delivery mode, the static and dynamic freight bus delivery modes can reduce the costs respectively by 43.77% and 45.81%, while improving vehicle utilization. Maintaining the same number of stops, the dynamic freight bus delivery mode outperforms the static mode; the value of dynamic ratio has a certain impact on the operational efficiency of the dynamic freight bus mode. The proposed method provides a reasonable routing scheme and serves as a theoretical reference for governments to develop operational modes and implementation strategies for freight bus services, further promoting the optimization of urban freight delivery. References | Related Articles | Metrics

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Hybrid Scheduling Method for Conventional Bus and Demand-responsive Transit Based on Eco-driving Technology XU Weihan, LI Xin, WANG Tianqi 2025, 25(2): 227-240.  DOI: 10.16097/j.cnki.1009-6744.2025.02.021 Abstract ( )   PDF (2540KB) ( )   In response to the insufficient energy efficiency in the joint operation mode of conventional bus and demand responsive transit, this paper proposes an energy-saving oriented transit scheduling decision and ecological speed collaborative optimization method by introducing eco-driving technology. With the purpose of minimizing total operational cost and the penalty cost caused by asynchronous schedules, a mixed integer optimization model was developed with the constraints of passenger travel time window preferences and schedule coordination. Feasible range of conventional bus eco-speed are derived for four signal phase and timing scenarios, and the conventional bus eco-speed, actual arriving time, and demand responsive bus fleet size, route, schedule, and eco-speed are synthetically optimized. A three-stage mixed heuristic algorithm is designed based on the model properties. To verify the effectiveness of the proposed method, a case study is conducted in the Chongqing University town area, simulating the actual scenario of joint operation of conventional and demand-responsive buses. The results show that compared with traditional scheduling method, the proposed method can reduce the total system cost by more than 16.2%. Through adjusting eco-speed, the idle waiting at signalized intersection is avoided, and the arrival punctuality and the goal of energy saving are taken into account. In addition, this method also improves the turnover rate of demand responsive-bus, then reduces the fleet size, increases the timetable synchronization by 81%, fully meeting the passengers' time window preference and hybrid scheduling requirement. References | Related Articles | Metrics

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ImpactAnalysis of Non-bus Vehicle Stopping on Delays at Main-auxiliary Bus Platforms WANG Jianjun, WU Jing, LI Jingtao, ZHOU Yingjie 2025, 25(2): 241-252.  DOI: 10.16097/j.cnki.1009-6744.2025.02.022 Abstract ( )   PDF (3046KB) ( )   In order to analyze the delay at main-and-auxiliary bus platforms under the interference of non-bus vehicles, such as ride hailing vehicles, this paper constructs a delay model based on impact time and flow reduction. Building upon traditional delay models, this delay model considers the movement characteristics of buses. First, delay models for both linear and bay platforms are developed, considering the micro-behaviors of buses at stops influenced by non-public vehicle parking. Second, a comprehensive delay model for main-auxiliary platforms, accounting for non-bus vehicle stops, is established using the movement patterns at main-and-auxiliary platforms and the single-platform delay models. The feasibility of the model is verified using VISSIM software, with the mean absolute percentage error (MAPE) as a validation index. Finally, a case study is conducted at a typical bus stop in Jiaozuo City, Henan Province, examining the suitability of different main-auxiliary platform types under varying outer lane traffic flows and percentages of buses affected by non-bus vehicle stops (α) . The results show that under the interference of non bus vehicle stopping, the maximum errors of the impact time and the flow reduction models for main-and-auxiliary platforms are 33% and 7%, respectively. The bay-bay parallel and series main-and-auxiliary platforms cause the least delay, with the impact time reduced by up to 70% and flow reduction increased by up to 30%, which is applicable for scenarios where the outer lane traffic flow exceeds 400 pcu⋅h-1 or α is greater than 0.1. The study demonstrates that the proposed delay model is suitable for calculating platform delays under the interference of non-public vehicles, and the main-and-auxiliary platform design provides a reference for the design and optimization of bus stops with non-bus vehicle stopping. References | Related Articles | Metrics

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Urban Rail Transit Crew Rostering Optimization Method Considering Cross-line Operation and Crews Capacity Difference LI Hao, CHEN Shaokuan, XU Bin, SHI Mengtong, XIAO Di, CHEN Ziqi 2025, 25(2): 253-260.  DOI: 10.16097/j.cnki.1009-6744.2025.02.023 Abstract ( )   PDF (2040KB) ( )   Aiming at the cross-line operation mode of metro trains, this paper investigated the matching problem between crew capacity and duty assignment in cross-line operation and analyzed the influence of crew heterogeneity on crew rostering. The mathematical model was proposed in consideration of the difference of crew capacity, and the network graph based on shift path and taboo sequence was described to simplify the model. The network search method and improved particle swarm optimization algorithm were designed to solve the proposed model in stages. A case study was performed based on some urban metro lines crew data. Compared with the crew who were only on duty of single line under the cross-line operation mode, the number of duties of the crew who were qualified to take cross-line tasks can be reduced by 1.58% to 6.79%. The matching degree between the two types of crew capacity and duty task is not less than 95.77%. Compared with the split duty mode, the average duty intensity of the crew in the cross-line duty mode is reduced by 3.79% to 4.78%, and the standard deviation of work intensity is reduced by 22.67% to 26.16%. The duty balance of the crew can be improved. References | Related Articles | Metrics

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Optimization of Metro Crew Scheduling Plans Based on Traveling Salesman Problem Modeling XUE Feng, XIAO En, YANG Ying, WANG Jincheng, LUO Jian 2025, 25(2): 261-272.  DOI: 10.16097/j.cnki.1009-6744.2025.02.024 Abstract ( )   PDF (2325KB) ( )   This paper addresses the scheduling problem for subway crew by modeling it based on the characteristics of the multiple traveling salesmen problem. Specifically, crew segments in the scheduling problem are analogous to cities in the multiple traveling salesmen problem, and the succession time between crew segments represent the distances between cities. Constraints such as the maximum working time per shift, continuous duty time, interval time, and meal time are considered. A nonlinear 0-1 integer planning model is developed with the optimization objectives of shortest and minimum variance of crew working time. A genetic simulated annealing hybrid algorithm is designed to solve the model. The algorithm is validated through the case study for Chengdu Metro Line 5. The results show that compared with the Alternating Direction Method of Multipliers(ADMM) algorithm and Generalized Shortest Path Faster Algorithm(G-SPFA), the proposed method has optimization rates of 17.9% and 23.1% in the number of passenger tasks, and 15.8% and 12.1% in the succession time, which can effectively reduce the enterprise's manpower cost and improve the efficiency of the driver and passenger on duty. References | Related Articles | Metrics

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Quasi-dynamic Stochastic User Equilibrium Assignment Method for Urban Rail Transit SU Huanyin, MO Shanglin, DAI Huizi 2025, 25(2): 273-281.  DOI: 10.16097/j.cnki.1009-6744.2025.02.025 Abstract ( )   PDF (2227KB) ( )   This paper proposes a quasi-dynamic stochastic user equilibrium assignment model and algorithm to grasp the temporal and spatial distribution of passenger flow in the urban rail transit network. The Origin-Destination (OD) passenger flow is allocated to different periods of the line interval, and the passenger flow distribution is calculated in each period of the line interval in the network. The transfer network is constructed to describe the passenger travel process at different departure periods. The route cost is designed to consider the waiting time, stopping time, running time, congestion benefit, transfer time and transfer penalty cost, which changes with the departure time. On this basis, a quasi-dynamic stochastic user equilibrium allocation model is designed, and the optimal solution of the model is proved to reach the stochastic user equilibrium state in each period of departure. A dynamic successive averaging algorithm based on the effective path is designed to solve the model, and the experiment is carried out in the actual Guangzhou Metro network. The algorithm convergence speed is fast, and the convergence accuracy is high. Based on the analysis of passenger route selection under different random parameters, most passengers tend to choose the least cost efficient route, which often has the least transfer times, but not necessarily the shortest travel distance. By analyzing the spatial and temporal distribution characteristics of passenger flow on the line sections, the results show that the distribution of passenger flow on some line sections has significant morning and evening peak characteristics and tidal phenomena, which reflects the regional characteristics of the line and the commuting habits of urban passengers. The study result confirms the rationality of the passenger flow distribution results and has certain practical application value. References | Related Articles | Metrics

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Optimization of Train Scheme for Provincial Railway Network GONG Shuaiyu, XU Xingfang, LU Yu 2025, 25(2): 282-292.  DOI: 10.16097/j.cnki.1009-6744.2025.02.026 Abstract ( )   PDF (2405KB) ( )   In a railway network, the train operation schedule involves multiple factors such as line specifications, grades, and types of trains, which are further complicated by the diversity of passenger flow choices. To depict the coupling between passenger flow and train flow in the network context, this paper used a depth-first search (DFS) algorithm to construct a set of alternative passenger flow routes, and established a set of alternative train flow routes based on the principles of train operation schedule preparation. The optimization objective is to minimize the total running cost of trains and the total travel time of passengers, and a bilevel nonlinear optimization model is established. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm is used to solve the model, and a multi-criteria evaluation index system is established from the perspectives of train operation, passenger travel, and enterprise operation. The Pareto frontier is evaluated using the entropy weight-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, and the solution with the highest relative proximity is selected as the recommended solution. Based on a large-scale example of the Z province railway network, the results show that the Pareto frontier obtained by the model has good distribution and convergence, which has strong robustness and better solution effect than the multi objective particle swarm optimization algorithm (MOPSO). The recommended solution II is obtained through the entropy weight TOPSIS multiple criteria method. 660 train pairs per day are operated in the province, and the travel time and operating cost of passengers and trains are significantly reduced compared to before optimization. The proportion of sections with low or high utilization rates is greatly reduced, and the congested sections are effectively alleviated. References | Related Articles | Metrics

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An Optimization Model for Minimizing Arterial Red Wave Bandwidth with Mixed Release at Intersections ZHANG Peng, WU Zhaojie, SUN Chao, LI Wenquan 2025, 25(2): 293-303.  DOI: 10.16097/j.cnki.1009-6744.2025.02.027 Abstract ( )   PDF (3403KB) ( )   Existing arterial coordination control methods primarily focus on maximizing the green wave bandwidth to facilitate the continuous flow of traffic. However, the potential impact of the additional red wave bands resulting from this expansion in the coordination control effectiveness has not received adequate attention. To address this issue, this paper establishes an optimization model for minimizing the red wave bandwidth on arterials based on mixed release strategies at intersections. The model minimizes the weighted sum of the ratios of red wave bandwidth for through and left-turn movements to the cycle length, and adopts both the NEMAdouble-ring phase and split phase release strategies, and provides a unified linear calculation formula for the internal phase difference for both release methods. Intersections can be specified to use the release method or choose freely. The model is developed by integer linear programming for the optimal solution. An analysis was conducted using five consecutive intersections on Songling Avenue in Suzhou as a case study. The results indicate that, compared to the Multiband, Pband, and All-direction optimization schemes, the model proposed in this paper effectively reduces the average delay time and stop occurrences for through and left-turn vehicles on arterial corridors. The proposed model can achieve better control of the corridor progression coordination. References | Related Articles | Metrics

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Living Materials Temporary Distribution Points Location-routing Optimization Under Transportation Capacity Shortage LI Guoqi, HAO Zhidan, YANG Jiaxin, CHENG Jiahao 2025, 25(2): 304-313.  DOI: 10.16097/j.cnki.1009-6744.2025.02.028 Abstract ( )   PDF (2262KB) ( )   Major emergency disasters can lead to shortages of transportation resources, which poses a challenge to the supply work of living materials in disaster-stricken areas. This paper develops a three-tier delivery network consisting of material transfer stations, temporary distribution points, and demand points to efficiently deliver living materials to disaster-stricken areas. A multi trip and facility-collaborative distribution strategy is designed in consideration of the capacity limitations of the temporary distribution points for living materials and the transportation capacity shortage that arise post-disaster. A mixed-integer programming model is developed with the objective of minimizing deprivation costs. To solve this model, an Improved Ant Colony Optimization (IACO) algorithm is used, incorporating max-min, pseudo-random transfer, and multi-dimensional pheromone strategies. Numerical experiments validate the effectiveness of the proposed method in terms of computational efficiency and solution quality. A case study of Songjiang District in Shanghai is performed to demonstrate the applicability of the model. The results indicate that, compared to the independent distribution, the facility-collaborative distribution approach can reduce deprivation costs by 40.68%, increase the total material distribution by 7.42%, and decrease the variance of residual demand by 13.18%. References | Related Articles | Metrics

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Joint Optimization of Berth-quay Crane-shore Power Allocation Under Time-of-use Pricing WANG Xiaokun, DONG Zejin, WANG Yuwei, XIAO Hong 2025, 25(2): 314-327.  DOI: 10.16097/j.cnki.1009-6744.2025.02.029 Abstract ( )   PDF (2379KB) ( )   In the context of the national drive to promote shore power applications at ports and the implementation of time-of-use electricity pricing mechanisms in various regions, this study investigates the continuous berth-quay crane-shore power joint allocation optimization problem, which considers terminal operational efficiency, ship energy costs, carbon emissions, and the physical requirements for ships to connect to shore power under a time-of-use electricity pricing regime. A bi-objective optimization model is constructed to minimize the total comprehensive cost and total carbon emissions. Specifically, the total comprehensive cost comprises waiting costs, delay costs, and penalty costs based on the ship's total stay time at the port, which reflect terminal operational efficiency. Ship energy costs consist of fuel costs and electricity costs. Total carbon emissions include emissions generated by auxiliary engines during docking, indirect emissions from using shore power, and emissions from quay crane operations. To solve this model, an improved NSGA-II algorithm is designed, integrating heuristic methods, a gene repair strategy, progressive elimination, and an alternate group population. A case study based on a real container terminal is conducted to test the model and analyze the impacts of electricity prices, peak-valley electricity price differences, and the proportion of retrofitted vessels and the shore power coverage rate at the terminal. The results indicate that the improved NSGA-II algorithm is better than traditional NSGA-II algorithm in terms of calculation results and performance, and can effectively solve the model. The off-peak electricity price was reduced by 45.45%, resulting in a 20.33% decrease in total costs and a 6.33% reduction in total carbon emissions, while the number of vessels using shore power increased by 23.81%. When the peak-to-valley electricity price difference increased from 3∶1 to 5∶1, time and energy costs rose by 7.69 and 4.49%, respectively, leading to an 5.16% increase in total comprehensive costs. An excessively large peak-to-valley electricity price ratio is not recommended. Increasing the shore power coverage rate to 50% and the proportion of retrofitted vessels to 70% is more beneficial for enhancing the port's economic and environmental benefits. References | Related Articles | Metrics

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Multi-berth Joint Allocation Strategy in Inland Port Group Considering Ship Transfer Scheduling GAO Pan, HUANG Liusen, ZHAO Xu 2025, 25(2): 328-337.  DOI: 10.16097/j.cnki.1009-6744.2025.02.030 Abstract ( )   PDF (2118KB) ( )   In order to alleviate the time-space mismatch between the supply and demand of berth resources in inland ports, the allocation of single port berths is extended to inland ports with highly overlapping hinterlands. The optimization strategy of multi berth joint allocation is explored by considering the transfer scheduling operations between different ports. With the aim of minimizing the total cost and the total time in port, a multi-berth joint allocation optimization model for port groups is established. According to the characteristics of the model, an improved non-dominated sorting genetic algorithm is designed to solve the model, and the optimization effect before and after scheduling is discussed. Then, taking a port group in an inland river basin in China as an example, 4 groups of simulation experiments were set according to the ship arrival scale, and 12 numerical examples were randomly generated to verify the effectiveness of the model and the configuration strategy. The experimental results show that the total cost and the total time in port under the joint allocation strategy are lower than those under the independent allocation strategy, and when the size of ships arriving at port increases from 20 to 80, the reduction ratio of cost and time before and after the implementation of the joint allocation strategy increases to about 24% and 40%, respectively. Meanwhile, when the proportion of the number of ships allowed to transport increases from 0 to 20%, the total cost of ships and the total time in port decrease greatly. After the proportion exceeds 20%, a marginal diminishing effect occurs. Therefore, considering the cost of port transfer, setting an appropriate transfer quantity threshold can improve the operation efficiency of the port group. References | Related Articles | Metrics

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Resilience Assessment of China Railway Express Transport Network from Perspective of Risk Disturbances FENG Fenling, FANG Yuan, ZHANG Ze, DONG Kaiyun 2025, 25(2): 338-351.  DOI: 10.16097/j.cnki.1009-6744.2025.02.031 Abstract ( )   PDF (2440KB) ( )   The China Railway Express involves multi-regional cross-border transportation, posing high uncertainty and complexity in security management. To enhance the risk prevention and control capacity of the China Railway Express transportation network, this paper combined network flow theory and resilience theory, employing the entropy-weighted TOPSIS method to comprehensively evaluate node risk and incorporate it into the network-weighted attack process. Virtual arcs are used to represent disturbance-induced unmet transportation demand, and a disturbed cargo flow allocation model was constructed. Global network efficiency was used as the structural metric, while the freight flow retention ratio is adopted as the functional metric. The resilience performance of the China Railway Express transportation network under different disturbance nodes and scales was quantified from both structural and functional perspectives, and corresponding resilience optimization strategies were proposed. The results indicate that: (1) The transportation network of the China Railway Express demonstrates a certain level of risk mitigation capability. When a disturbance occurs at a single node, 74.36% of the nodes cause less than 1% structural loss to the network, and 53.45% cause less than 1% functional loss. Nodes with low resilience in the network are mainly distributed in the western exit channels, assembly centers, and along the route of the New Eurasian Continental Bridge. (2) The functional resilience of the China Railway Express transportation network is more sensitive to disruption than its structural resilience, and its recovery process exhibits non-linear characteristics. (3) Regarding resilience enhancement, the comprehensive protection strategy proposed in this paper based on risk and resilience clustering can improve the resilience of the network by 38.24% when responding to a large-scale disturbance. This strategy outperforms the traditional node-protection strategy based on the magnitude of node risks. The research findings provide theoretical support and practical insights for the comprehensive evaluation and improvement of resilience in the China Railway Express network. References | Related Articles | Metrics

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Heterogeneous Effects of Built Environment on Ridership of Integrated Use of Bus and Metro XU Qi, QIN Beining, REN Peng, CHEN Yue, LAI Jinxuan 2025, 25(2): 352-363.  DOI: 10.16097/j.cnki.1009-6744.2025.02.032 Abstract ( )   PDF (3792KB) ( )   Metro and buses both play important roles in urban public transportation system. It is of great significance to investigate the bus and metro connections and the influencing mechanism to enhance an integrated public transit system. This paper uses the smart card data of Beijing to analyze four types of bus connecting metro ridership during morning and evening peak hours. Based on the 5D principle, the built environment index system is constructed to describe the characteristics of metro stations. The Multiscale Geographically Weighted Regression (MGWR) model is used to compare and analyze the differences in the impact of the built environment. The results show that the MGWR model can well reflect the impact of the built environment on different connection conditions. The distance from the city center has the greatest impact on the total number of connections. The residential point of interest (POI) density and land use mixed entropy are sensitive to time, and the effect of the two is more significant in the evening peak period. The number of bus stops is sensitive to the connection mode. The public transport accessibility and closeness centrality are sensitive to time and connection mode. Both show a restraining effect on the sensitive ridership in the central area of the city. In the peripheral area of the city, there is a promoting effect. Therefore, when considering the optimization of the connection target of the bus and metro system, it is necessary to fully consider the heterogeneous effects of the built environment on spatial, temporal and connection mode, and formulate strategies according to local conditions and time to promote the integrated development of public transportation. References | Related Articles | Metrics

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Pedestrian Movement Characteristics with Mobile Phone Distractions YAO Ming, WANG Yuhang, CAO Shuchao, MA Luhan 2025, 25(2): 364-372.  DOI: 10.16097/j.cnki.1009-6744.2025.02.033 Abstract ( )   PDF (2883KB) ( )   To investigate the impact of smartphone distraction on pedestrian movement trajectories, gait characteristics, and walking speed, this paper designed a controllable experimental scheme, and performed series of walking experiments under normal and three distraction conditions (reading, sending messages, watching videos). Based on image processing technology, high precision pedestrian trajectories were obtained, and the effects of different distractions were analyzed for trajectory, step length, step width, step time and walking speed. The results indicate that distracted behaviors lead to more disordered movement trajectories, with the video group showing the largest deviation distance, which is 33.98% greater than the normal group. Distraction also impairs pedestrian acceleration abilities, reducing walking speed. The video group shows a 26.78% decrease in acceleration, a 44.88% increase in relaxation time, and a 28.24% reduction in walking speed. As for gait characteristics, step length decreases as the level of distraction increases, while step width and stride time show opposite trends. Notably, the messaging group exhibits the shortest step length, the widest step width, and the longest stride time. A linear regression analysis reveals that step length increases with walking speed, while step width and stride time are inversely related to speed. References | Related Articles | Metrics