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25 April 2026, Volume 26 Issue 2 Previous Issue   

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

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

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Coordinated Optimization of Procurement and Rail-Water Intermodal Transport for Thermal Coal Supply Security HUANG Zhaocha, JIANG Linqiao, WANG Zhimei, CHEN Junhua, ZHANG Xingchen 2026, 26(2): 24-34.  DOI: 10.16097/j.cnki.1009-6744.2026.02.003 Abstract ( )   PDF (2029KB) ( )   To address the dual uncertainties of fluctuating daily coal consumption demand and rail-water intermodal network disruptions in the supply guarantee of thermal coal, this study develops a coordinated optimization model that integrates procurement, transportation, and inventory decisions to achieve a comprehensive balance among price, capacity, and supply security. The model incorporates time- varying coal prices, stochastic demand fluctuations, and port failure probabilities to quantitatively characterize the dynamic coupling between procurement structure and transportation resilience. Considering the high-dimensional stochastic and integer complexity of model, an Integer Branch and Benders Cut (IB&BC) algorithm based on scenario decomposition is designed to enhance the computational efficiency and the stability of large-scale stochastic programming. Using the coastal coal supply network of China Energy Investment Corporation as a case study, the model is validated to yield feasible and optimal solutions under different port failure and demand fluctuation scenarios. Results show that under a complete failure scenario at a key loading port, the total cost increases by 1.2%, while the inventory cost rises by 2.8%. Due to the discrete nature of the physical berth constraints, the response of system to risk parameters exhibits a "step-wise" pattern, where costs remain unchanged within specific risk parameter intervals. On the other hand, when node failure coincides with demand fluctuation, the combined effect amplifies the impact on logistics costs, leading to a 15.5% increase in total cost compared to the baseline scenario. References | Related Articles | Metrics

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Optimization of Train Timetables Considering Vessel Arrival Delays ZHAO Yuan, SHI Baisong, WANG Dan 2026, 26(2): 35-46.  DOI: 10.16097/j.cnki.1009-6744.2026.02.004 Abstract ( )   PDF (2241KB) ( )   To improve the coordination efficiency between liner vessels and rail services in sea-rail intermodal systems, this study develops an approach of timetable optimization that explicitly accounts for the uncertainties in vessel arrival times. The historical data of vessel delay are used to fit the delay distribution, and a scenario set is generated by sampling from this distribution with Monte Carlo simulation. Accordingly, a mixed-integer programming model is formulated with prioritized objectives: maximizing the number of train-vessel direct transfers as the first priority, minimizing the dwell time of containers at the port as the second, and minimizing timetable adjustments as the third. The model is solved by using a hierarchical sequential method, and validated with data from Yantian Port. The results show that the proposed approach effectively mitigates the impact of vessel-schedule disruptions and significantly enhances train-vessel coordination. Compared with the original timetable and a deterministic optimization strategy, the direct transfer rate increases by approximately 39.3% and 16.8%, respectively, while the dwell time of containers decreases by approximately 83.1% and 20.2%. These findings highlight the importance of the increasing timetable flexibility to manage vessel-schedule risks, and provide a methodological support for the coordinated scheduling of container sea-rail intermodal transport. It contributes to the development of“train+liner”fast intermodal networks. References | Related Articles | Metrics

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

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Rural Delivery Logistics Location-Routing Optimization with Cooperative Distribution of Demand-Response Bus and Truck SUN Wenjie, ZHANG Jin, LI Guoqi 2026, 26(2): 60-71.  DOI: 10.16097/j.cnki.1009-6744.2026.02.006 Abstract ( )   PDF (2028KB) ( )   This paper proposes an integrated service mode which combines demand-response bus-truck cooperative distribution with simultaneous home delivery and customer self-pickup. It aims to address the challenge of high operational costs and misallocation of transportation resources effectively associated with utilizing demand-response buses for parcel home delivery in rural areas within mountainous and remote rural areas. Accordingly, a two-echelon location-routing problem model is constructed with the objective of minimizing the sum costs of facility vehicle usage, transportation, self-pickup, and passenger service delay penalty. An improved Benders decomposition algorithm is then designed tailored to the model-specific features, integrating the strategies, such as two-stage, the input ordering, the warm-start, and the effective inequality strategy to achieve rapid solution. The effectiveness of the model and algorithm is validated with a case study from a township in Sichuan Province. The results indicate that the proposed mode exhibits greater economies of scale, reducing the total costs by 31.33% compared to the home delivery mode, and by 9.54% compared to the truck-only delivery mode. The improved BD algorithm offers a superior computational speed and quality, which generates the optimal solution for the case study within 80 seconds. Sensitivity analysis reveals that the accessibility is enhanced by reducing the coverage radius and fixed costs of pickup points. The location of pickup points is minimally affected by the coverage ratio of travel demand. References | Related Articles | Metrics

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Robust Optimization of Emergency Bus Route Design Under Urban Pluvial Flooding with Metro-Bus Joint Evacuation YAN Sanghuiyu, MA Rui, LI Jian 2026, 26(2): 72-80.  DOI: 10.16097/j.cnki.1009-6744.2026.02.007 Abstract ( )   PDF (3020KB) ( )   Climate change has led to increasingly frequent urban pluvial flooding, resulting in road inundation, which subsequently forces buses to detour, skip stops, or suspend services, thereby severely disrupting residents' daily mobility. This study investigates travel time robust optimization for emergency bus route planning with metro-bus joint evacuation under severe rainfall-induced service interruptions. A robust optimization approach based on route-dependent budgeted uncertainty sets is proposed. A multi factor bus speed dataset is constructed using multi-source data. Representative speed scenarios are generated through a Recurrent Temporal Variational Autoencoder combined with k-medoids clustering, and these speed scenarios are converted into travel-time scenarios based on route length to characterize the uncertainty set. A robust optimization model with route-dependent budgeted uncertainty set is then formulated and reformulated into a linear model. The results indicate that incorporating metro-bus coordination into emergency operations can substantially reduce passenger travel time. Compared with conventional bus detour strategies, the coordinated scheme yields a travel time reduction of approximately 15%~20%, and compared with bus short-turn strategies, the reduction expands to approximately 50%~60%. The proposed route-dependent budgeted uncertainty sets capture rainfall-induced variability at the route scale, and it was found that moderate budget levels achieve a favorable balance between robustness and operational efficiency. The proposed framework can support decision-making for emergency bus evacuation planning under urban pluvial flooding. References | Related Articles | Metrics

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Integrated Control of Road Internal Boundaries and Signal Timing for Lane free Traffic of Connected Autonomous Vehicles in Urban Networks WANG Yibing, CHEN Anni, PAPAGEORGIOU Markos, YU Hongxin, GUO Jingqiu, ZHANG Lihui 2026, 26(2): 81-90.  DOI: 10.16097/j.cnki.1009-6744.2026.02.008 Abstract ( )   PDF (2302KB) ( )   The problem of road resource allocation and traffic efficiency optimization was addressed for lane- free traffic of connected autonomous vehicles (CAVs) in urban networks. Aiming to solve this problem, the concept of internal boundary control (IBC) was introduced. Both IBC and its integrated control with signal timing was investigated to achieve dynamic adaption between bidirectional road space resources and traffic demands. IBC for bidirectional traffic streams in each road link as well as IBC for through and left-turning traffic streams at each intersection were both considered in this paper, along with the time-delay issue. Based on the store-and-forward model of traffic flow, an integrated control task of IBC and signal timing was investigated to be formulated as a quadratic programming problem. A comparative analysis was conducted to evaluate the performance of three control strategies: signal timing optimization (SGO), IBC, and the integrated control of IBC and signal timing. The simulation investigations demonstrate that IBC can dynamically regulate the resources of road space to effectively accommodate the imbalanced bidirectional traffic demand. Furthermore, the integrated optimization of IBC and signal timing fully leverages the advantages of both strategies, substantially enhancing the utilization of road resource and significantly improving the traffic efficiency. Compared to SGO, IBC and the integrated control of road internal boundaries and signal timing can reduce the total delay of a considered urban network by 42% and 95%, respectively. References | Related Articles | Metrics

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Autonomous Vessel Navigation Decision-Making Methods in Tidal River Reaches Based on Cooperative Reinforcement Learning of World Model WENG Jinxian, DING Haifeng, LIU Wen, SHI Kun, NI Baolong 2026, 26(2): 91-101.  DOI: 10.16097/j.cnki.1009-6744.2026.02.009 Abstract ( )   PDF (2784KB) ( )   Traditional reinforcement learning (RL) methods for autonomous vessel navigation often suffer from environmental evolution modeling deficiency and decision-making short-sightedness. To address these issues, this paper proposes an autonomous decision-making method based on World Model (WM) cooperative Reinforcement Learning (WMRL). First, an RL agent is constructed as the primary decision-maker to generate the candidate actions of vessels. Second, a WM is built based on the Recurrent State Space Model (RSSM). It utilizes an encoder to map high-dimensional observations into a latent space, which integrates the hydrodynamic flow fields and vessel interaction environments to perform temporal feature extraction and environmental evolution modeling. Finally, targeting the short-sighted decision-making of traditional strategies, a gradient projection module based on group risk is constructed. This module validates the safety of RL candidate actions by leveraging the forward-looking inference capability of the WM. By solving a constrained optimization problem, it maps high-risk actions into a safe and feasible region, and achieves the dynamic correction of potential high-risk RL decisions. Closed-loop experiments were conducted by using the typical tidal reaches of the Huangpu River in Shanghai as a case study. The results indicate that the proposed method outperforms other models across multiple evaluation metrics. Compared with the traditional RL methods, the proposed approach reduces the frequency of high-risk groups from 3.2 time·voyage-1 to 0.8 time·voyage-1, optimizes the time deviation rate from 8.15% to 5.56%, and decreases the standard deviation of rudder angle angular velocity from 5.2 (°)·s-1 to 3.2 (°)·s-1. Furthermore, ablation experiments demonstrate that, compared to the baseline, after introducing the projection module, the method reduces the frequency of high-risk groups by 52.11%, optimizes the time deviation rate from 6.20% to 4.32%, and then reduces the standard deviations of rudder angle angular velocity and longitudinal acceleration from 4.21 (°)·s-1 and 0.23 m·s-2 to 2.75 (°)·s-1 and 0.18 m·s-2, respectively. References | Related Articles | Metrics

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Effects of Dynamic Guidance and Voice Prompts on Mainline Lane-changing Behavior of Vehicles Upstream of Expressway Toll Plazas XIANG Wang, TANG Siyang, WANG Yun, XUE Qingwan 2026, 26(2): 102-112.  DOI: 10.16097/j.cnki.1009-6744.2026.02.010 Abstract ( )   PDF (2444KB) ( )   In response to the adaptation issues of driver behavior caused by the configuration changes of lanes of highway toll booths following the increment of Electronic Toll Collection (ETC) vehicles, this paper proposes a dynamic guidance optimization method based on Variable Message Signs (VMS) and voice prompts by taking the manually-tolling vehicles that need to change lanes as the guiding objects. The goal is to help drivers to learn the direction of their target lanes in advance and smoothly switch to the appropriate lane to enter the toll plaza. Three guidance schemes are designed in a driving simulation experiment: Static Guidance (SG), VMS Guidance (VM), and "VMS+Voice" combined guidance (Voice & Variable Message Guidance, VVM). The guidance effects under different traffic flow conditions are tested. The experimental results show that VMS dynamic guidance can reduce lane-change reaction time by 17.09%, while the "VMS+Voice" combined guidance can reduce lane-change reaction time by 24.91%. Furthermore, traffic density does not affect the comparative trend of the guidance effects among different schemes. But under high traffic flow, the lane-change reaction time increases by 19.56%, average speed decreases by 10.29%, and lane-change duration decreases by 15.25%. Lastly, compared to male drivers, female drivers have a 7.48% slower average lane-change speed, and a significantly shorter lane-change duration by 30.58%. The results suggest that the implementation of the dynamic guidance in suitable locations can effectively reduce the vehicle flow conflicts at toll plazas, improve traffic efficiency, and enhance driving safety. References | Related Articles | Metrics

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Adaptive Early Warning Intelligent Agent for Inbound Large Passenger Flows at Urban Rail Transit Stations SONG Xuyang, LIU Fangda, PANG Wenfeng, WANG Fangsheng, WAN Huaiyu 2026, 26(2): 113-124.  DOI: 10.16097/j.cnki.1009-6744.2026.02.011 Abstract ( )   PDF (1842KB) ( )   To address the issues of fixed thresholds, response lag, and poor adaptability in early warning of inbound large passenger flow in urban rail transit, this study proposes an adaptive early warning agent method integrated with a Large Language Model (LLM). A technical framework is proposed: "offline clustering-real-time early warning-LLM-based adaptive correction". A two stage algorithm of "hierarchical clustering preprocessing + k-means optimization" is adopted to analyze historical passenger flow patterns, and generate a typical passenger flow pattern library which includes information such as date types and peak characteristics. Then, three types of abnormal indicators (passenger flow extremum, change rate, and cumulative volume) are calculated based on pattern matching. The interquartile range method is used to dynamically determine early warning thresholds under each pattern and defines three early warning levels. The LLM is then introduced to build an adaptive early warning agent, which realizes natural language interactive optimization and dynamic correction of early warning parameters based on the Retrieval-augmented Generation (RAG). The results of empirical study using 373-day inbound passenger flow data from Wusi Square Station of Qingdao Metro show that the system successfully identifies 5 types of typical passenger flow patterns. In a high passenger flow scenario on a day during the May Day holiday, the number of early warnings that might have been triggered was reduced from 24 to 7 valid ones, significantly reducing the false alarm rate. Meanwhile, the case supports parameter adjustment and changes in early warning sensitivity through language instructions, achieving a 28.6% improvement in early warning coverage. This method realizes the dynamic binding of early warning thresholds and passenger flow patterns, and integrates the adaptive optimization capability and natural language interaction advantages of LLMs, effectively solving the lag and adaptability problems of traditional early warning systems. References | Related Articles | Metrics

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Dynamic Macro-Micro Collaborative Deployment Method for Connected and Automated Vehicle Exclusive Lanes GU Yuanli, YU Hongru, CHEN Long, DENG Shejun, LU Wenqi 2026, 26(2): 125-136.  DOI: 10.16097/j.cnki.1009-6744.2026.02.012 Abstract ( )   PDF (3004KB) ( )   Aiming at the problems of poor adaptability, separation of macro-micro decision-making and insufficient real-time response in the deployment of Connected and Automated Vehicle Lanes (CAVL) under the current mixed traffic environment, this study proposes a Dynamic Macro-Micro Collaborative Deployment (DMMCD) method integrating percolation theory with multi agent deep reinforcement learning. At the macro level, the percolation threshold is dynamically updated according to the real-time traffic flow state of the road network, and road segments with high CAV penetration rates are aggregated to form a CAVL backbone network while ensuring the topological connectivity of the mixed traffic road network. At the micro level, a distributed training framework based on the Multi-Agent Deep Q-Network (MA-DQN) is constructed, and the lane-level CAVL deployment strategy is optimized through a multi-objective reward mechanism. The percolation layer enables macro strategies to directly guide micro decision-making via the macro-skeleton strong constraint on the action space; the micro layer realizes dynamic response to real-time traffic flow by virtue of an end-to-end model, and the lane-level traffic state send feedback to the macro layer to drive the iterative update of the backbone network, thus achieving the coupling and linkage of macro-micro decision-making. The simulation verification is carried out based on a 7×7 topological road network with bidirectional four lanes. The results show that within the CAV penetration rate range of 0.1 to 0.9, the proposed method reduces the average travel time by 6.77% and 28.15%, increases the average travel speed by 9.18% and 23.94%, and reduces the system CO2 emissions by more than 11.35%, compared with the hierarchical artificial bee colony algorithm and the scheme without dedicated lanes, respectively. When the CAV penetration rate exceeds 0.5, the advantage of the proposed method is further amplified, which shows a travel time reduction of more than 31.12%. In addition, the average strategy generation time per agent of the proposed model is 0.12 milliseconds, which meets the requirements of real-time CAVL management and control. The study and results provide theoretical basis for the dynamic deployment of CAVLin urban road network environment. References | Related Articles | Metrics

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

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Intelligent Scheduling Optimization for Multi-aircraft Collaborative Maintenance HU Xiaobing, NA Rongfei, LI Hang 2026, 26(2): 148-158.  DOI: 10.16097/j.cnki.1009-6744.2026.02.014 Abstract ( )   PDF (4982KB) ( )   To optimize the collaborative maintenance scheduling for multiple aircrafts, a heuristic-based algorithm is proposed to reduce the maintenance time and achieve the balanced allocation of resources. Considering the complex constraints, a mathematical model is established to minimize the deviation between the expected and actual resource utilization, while it incorporates the weighted aircraft priorities into the objective function. Within a shared resource pool, the algorithm is developed on a genetic framework and customized in population initialization, chromosome encoding, selection, crossover, and mutation to generate optimized schedules. Real C-check data from three B737-800 aircrafts are used for a simulation and comparative analysis. The proposed GA method significantly improves the resource utilization under a shared resource pool. Compared with manual scheduling, it reduces variance by over 76.43% and shortens the critical maintenance duration from 11 to 9 days. References | Related Articles | Metrics

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Optimization Model for Large-scale Events Evacuation Considering Regional Variable Lane Strategies and Bus Transfer TIAN Qiong, WANG Mingyang 2026, 26(2): 159-168.  DOI: 10.16097/j.cnki.1009-6744.2026.02.015 Abstract ( )   PDF (2690KB) ( )   To address the challenges of low efficiency and high costs in large-scale crowd evacuations following large-scale events commonly seen in cities, this study proposes a bi-level optimized multi-modal evacuation model that integrates regional variable lane strategies with bus bridging. The framework aims to achieve integrated decision-making in road network supply and transport capacity allocation. The upper-level model minimizes the weighted average evacuation time by incorporating multiple variable lane strategies, including fleet configuration, lane allocation, lane reversal, and cross elimination. This model is optimized with the Artificial Bee Colony algorithm. The lower-level model, building upon the optimized network conditions from the upper level, minimizes total operating costs by dynamically planning bus dispatch, route connections, and fluctuating passenger demand. This model is solved with a Genetic Algorithm. To validate the proposed model and algorithms, a case study is conducted based on a concert held at the National Stadium. The results demonstrate that, compared to the initial scheme, the final solution obtained through the bi-level optimization model in this paper reduces the weighted average evacuation time in the variable lane area by 30.63 minutes, and lowers the total cost by 2.684 1 million yuan. This optimization significantly improves the evacuation efficiency and reduces the overall costs for bus operations, bus passengers, and private car users. This study verifies the effectiveness and economic benefits of coordinated optimization between variable lanes and bus bridging in managing large-scale events evacuations. References | Related Articles | Metrics

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Integrated Optimization for Sustainable Aviation Fuel, Aircraft, and Routes LI Yanhua, WANG Yuechao, REN Guangjian, WANG Tao, QI Jiayi 2026, 26(2): 169-179.  DOI: 10.16097/j.cnki.1009-6744.2026.02.016 Abstract ( )   PDF (2965KB) ( )   Sustainable aviation fuel (SAF) has substantial decarbonization potential, yet faces practical constraints including high costs and limited supply. To improve the cost-effectiveness of SAF adoption by airlines, this study develops a three-dimensional coupled optimization framework linking SAF allocation, route characteristics, and aircraft types. By considering fuel efficiency differences at the aircraft level and cost and carbon emission variations at the route level, the study proposes an SAF differentiated blending optimization model to minimize airline operating costs and carbon emissions. A three-stage linearization strategy is used to linearize the bilinear objective and constraints, and a Dantzig-Wolfe (DW) decomposition algorithm with ε-constraints is applied to solve the model. A case study using data from the top five Chinese airlines, 40 major airports, and 10 aircraft models, along with 240 000 flight trajectory data points, is conducted. The results show that the DW decomposition algorithm with ε-constraints outperforms direct solving with Gurobi in both efficiency and accuracy. At lower abatement levels, SAF is concentrated on medium-haul operations of new-generation narrow-body aircraft, accounting for 76.1% of total SAF use, where overall mitigation effectiveness is maximized. As the abatement target increases, SAF allocation to wide-body aircraft on long-haul routes rises, with their share of SAF use increasing to 27.1%. Compared with a uniform blending scheme, this differentiated SAF allocation reduces emissions by approximately 3.0%~4.8%. Route characteristics, flight scale, and fleet composition jointly shape the optimal CO2 SAF blending strategy and mitigation contributions for airports and airlines. Sensitivity analysis indicates that increasing SAF supply and reducing SAF prices will further enhance emission reduction benefits. This study provides a quantitative reference for airlines to optimize SAF blending ratios to improve emission reduction outcomes. References | Related Articles | Metrics

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Impact of Weather on Travel OL Choice Behavior Through Psychological Time Perception YU Jiangxia, ZHU Siyue, WANG Xintong, LUO Taibo 2026, 26(2): 180-191.  DOI: 10.16097/j.cnki.1009-6744.2026.02.017 Abstract ( )   PDF (1376KB) ( )   As a key element of the external environment, weather conditions significantly shape the subjective perception of time of travelers, and influence their travel decision-making behaviors. To capture the differences in psychological perception of time under various weather conditions, this study integrates weather factors into a generalized travel cost framework to extend traditional discrete choice models by relaxing the rational agent assumption. A perceived time function covering four stages— access, waiting, in-vehicle travel, and egress—is proposed. Furthermore, it introduces an outdoor walking perception coefficient and a passenger crowding index to quantify the changes in subjective time perception caused by weather-related impacts on walking comfort and in-vehicle crowding. A travel choice model based on Cumulative Prospect Theory is developed. Using commuting behavior in Xi'an as an empirical case, the model parameters are calibrated via a genetic algorithm, and travelers are categorized into two groups: "socioeconomically advantaged family-oriented" and "socioeconomically disadvantaged mobile oriented." The results indicate that the travel choice model based on Cumulative Prospect Theory yields superior predictive accuracy, as it can more precisely capture the decision-making mechanisms of different population segments under various weather conditions. Notably, individuals with higher socioeconomic status demonstrated a significant increase in their loss aversion coefficient during adverse weather, exhibiting a strong tendency for loss avoidance. Conversely, under severe pollution conditions, their risk preference coefficient showed an anomalous rise, reflecting a "responsibility-driven risk-taking" behavior motivated by heightened health concerns. In contrast, the socioeconomically disadvantaged group maintains a high sensitivity to losses under most weather conditions, demonstrating the characteristics of "economically driven" decision-making. However, under heavily polluted weather, their risk preference decreases markedly, while the decision weight coefficient increases sharply, revealing a high level of alertness to health risks and a capacity for rational risk assessment. References | Related Articles | Metrics

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Dynamic Passenger Flow Induction in Urban Rail Transit Networks During Train Delays HE Jie, GUO Jianyuan, WANG Ziyu, QIN Yong, WANG Ruoyu, JIA Limin 2026, 26(2): 192-202.  DOI: 10.16097/j.cnki.1009-6744.2026.02.018 Abstract ( )   PDF (3084KB) ( )   In large-scale networks, train delays can cause significant passenger congestion and travel disruptions. It is an essential measure to manage passenger flow through information induction. However, due to the large number of affected passengers and the diversity of their travel plans, it presents a highly challenging problem that how to effectively guide them to improve travel efficiency and alleviate congestion on the network. Accordingly, this paper proposes a induction model of dynamic passenger flow aiming to minimize both the average passenger waiting time and the imbalance of passenger flow distribution across the network. Based on conventional constraints, the model integrates the Random Regret Minimization and Random Utility Maximization theories to establish the route choice constraints under the influence of induction information. A multi-objective asynchronous parallel Whale Optimization Algorithm (NFM_WOA) is designed to propose a high-dimensional variable complex constraint model. The model and algorithm are applied to a case study of actual train delay scenarios in urban rail transit network of Beijing. The results indicate that the average passenger waiting time of affected travelers is reduced by 12.107%, and the imbalance of passenger flow distribution across the network is improved by 8.582%, and the proportion of high-load sections decreases by 39.19% by applying the proposed method to conduct time-dependent dynamic induction for 77 826 affected OD pairs. This validates the effectiveness of the proposed method in guiding passenger flow during train delays. Meanwhile, the proposed NFM_WOA algorithm reduces computation time by 94.02% compared to the traditional Whale Optimization Algorithm. It significantly enhances the efficiency in solving large-scale variable problems under train delay scenarios. References | Related Articles | Metrics

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Extreme Value Model and Risk Assessment for Pedestrian-Vehicle Conflicts at Urban Road Intersections ZHANG Wenhui, LV Jiale, CHEN Deqi, MENG Fengwei 2026, 26(2): 203-211.  DOI: 10.16097/j.cnki.1009-6744.2026.02.019 Abstract ( )   PDF (2166KB) ( )   To explore the underlying mechanisms and evaluate the potential collision risks of pedestrian-vehicle conflicts at urban signalized intersections, this study adopts Post Encroachment Time (PET) as a surrogate safety indicator and establishes a proactive safety assessment framework grounded in Bayesian Extreme Value Theory. With the conflict data which collected from three typical signalized intersections, the tail behavior of PET is characterized by the Generalized Pareto Distribution. Multiple traffic and behavioral covariates including the number of motorized lanes, minimum pedestrian-vehicle distance, pedestrian speed, vehicle speed, and the number of crossing pedestrians are incorporated into the scale parameter to capture the non-stationary and heterogeneous characteristics of extreme conflicts. Three hierarchical models including a stationary model, a non-stationary full model, and a non-stationary significant model are constructed, and Bayesian parameter estimation is performed by using the MCMC (Markov Chain Monte Carlo) algorithm. Model comparison by the DIC (Deviance Information Criterion) reveals that the non-stationary significant model (DIC is 1 811.92) yields the best fitting and predictive performance, which outperforms both the stationary (DIC is 1 827.68) and non-stationary full (DIC is 1 817.17) models. The proposed model quantitatively captures variations in risk levels driven by different influencing factors and enables tail risk estimation under the limited crash data conditions. This approach provides a robust quantitative framework for proactive safety assessment and then delivers scientific insights to support intersection safety management and risk intervention strategies. References | Related Articles | Metrics

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

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Collaborative Optimization of Train Timetabling and Carriage Allocation Based on Flexible Train Formation Strategy WANG Xinwei, XU Yan, SUN Lishan 2026, 26(2): 221-231.  DOI: 10.16097/j.cnki.1009-6744.2026.02.021 Abstract ( )   PDF (2243KB) ( )   To mitigate the peak-hour oversaturation, passenger accumulation, and platform crowding risks arising from spatiotemporally imbalanced demand on urban metro lines, this study proposes a novel distance-based and flexibly coupled train operation strategy. Carriages are flexibly allocated and coupled based on passenger travel distances (long-distance vs. short distance), and a decision on whether to decouple is made when trains enter low-demand sections. This strategy partitions the train into front and rear sets, whose sizes are dynamically determined under the time-varying passenger flow to achieve the precise matching between capacity and demand. In addition, a line-level coordinated boarding control is implemented by regulating long distance boarding at upstream stations to reserve the capacity for downstream short-distance riders. Thereby it improves the passenger exchange rate and effective carrying performance during peak periods, and balance the line-wide of stranded passengers while alleviating congestion at busy stations. With the objective of minimizing total perceived passenger waiting time and vehicle kilometers, a collaborative optimization model for the train timetable and passenger flow control is developed through incorporating flexible marshalling and carriage allocation strategies, and solved using Gurobi solver. Numerical experiments and sensitivity analyses indicate that, relative to the conventional fixed-formation scheme without car allocation or boarding control, the proposed approach improves the overall objective by 6.93%, including a 9.38% reduction in vehicle-kilometers. While it can more effectively balance the distribution of passenger accumulation across stations, and reduce the risk of gathering and get a higher average load factor. Further sensitivity analysis confirms the pronounced impact of the perceived waiting-time coefficient of stranded passengers on model outcomes, highlighting the trade-off between controlling the risks of detention and saving operating costs. References | Related Articles | Metrics

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Safety Evaluation Method for Merging Zones on Expressways in Mixed-traffic Environments Based on Conflict Prediction PENG Bo, XIN Ziyi, CAI Xiaoyu, LEI Cailin, PAN Xumeng 2026, 26(2): 232-243.  DOI: 10.16097/j.cnki.1009-6744.2026.02.022 Abstract ( )   PDF (2870KB) ( )   To mitigate traffic conflicts in expressway merge zones under mixed traffic conditions involving manually driven and intelligent connected vehicles, thereby enhancing driving safety and traffic efficiency, this paper proposes a conflict-prediction based safety evaluation method for expressway merge zones. The micro-level traffic flow simulation models are established for both manually driven and intelligent connected vehicles to analyze factors influencing traffic conflicts. For scenarios dominated by either manually driven or intelligent connected vehicles, corresponding traffic conflict prediction models and safety evaluation indicator systems are developed based on conflict types and severity levels. To address the limitation of existing safety rating evaluation methods in capturing the nonlinear sudden changes in risk characteristics during critical states in merge zones, this study optimizes the traditional whitening weight function using a sine function. By coupling the improved grey clustering method with a comprehensive integrated weighting approach, this paper proposes a comprehensive safety evaluation method for merge zones in mixed-traffic environments. The proposed method was validated using real-world data from the Yanggongqiao Interchange merge zone in Chongqing and 74 sets of cross-experiment simulations. The results show that the mean relative error between predicted conflict values and SUMO simulation conflict counts was 5.24% . As the penetration rate of intelligent connected vehicles increases, the total number of conflicts first grows slowly and then declines rapidly around a penetration rate of 40%. Therefore, a penetration rate of 40% is adopted as the threshold to distinguish between mixed-driving scenarios dominated by manually driven vehicles and those dominated by intelligent connected vehicles. When the penetration rate of intelligent connected vehicles exceeds 40%, the safety level of the Yanggongqiao Interchange merging zone shifts from predominantly Levels 3 and 4 to predominantly Levels 1 and 2. This indicates that increasing the penetration rate of intelligent connected vehicles contributes to enhancing the safety level of the merging zone on expressways. References | Related Articles | Metrics

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Train Timetable Optimization Considering Regenerative Braking Energy Utilization with Flexible Composition Strategies ZUO Jing, PU Xuan, XU Minhao, ZHANG Nenge, SHUAI Bin 2026, 26(2): 244-255.  DOI: 10.16097/j.cnki.1009-6744.2026.02.023 Abstract ( )   PDF (2589KB) ( )   To address the challenges of the significant fluctuations of passenger flow and the high costs of energy consumption in urban rail transit, this paper proposes a train timetable optimization method considering the utilization of regenerative braking energy under the conditions of flexible train composition. By introducing a multi-level speed profile decision mechanism, the method jointly optimizes the train formation configuration, arrival/departure times, speed profiles, and regenerative braking energy recovery to minimize the operational costs, total passenger waiting time, and total passenger in-vehicle time. A mixed-integer nonlinear programming model is formulated and then solved through an improved simulated annealing algorithm with dynamic neighborhood structures. To validate the effectiveness of the proposed model, three comparative experiments were conducted with the actual operational data in urban rail. The results demonstrate that: compared to the fixed formation scheme with the regenerative braking energy recovery, the proposed method reduces the total traction energy consumption by 17.8% and the overall objective by 7.2%; compared to the scheme by using flexible train composition alone, it reduces the traction energy consumption by 13.9% and the overall objective by 14.1%; compared to the fixed formation scheme without regenerative braking energy recovery, the proposed method exhibits the most pronounced energy-saving effect, reducing the traction energy consumption by 31.1% and lowering the overall objective by 20.1%.The comprehensive optimization considering regenerative braking energy utilization under the conditions of flexible train composition achieves the synergistic optimization of train operational costs, service quality, and system energy efficiency, thereby it fulfills the objectives of cost reduction and efficiency enhancement. References | Related Articles | Metrics

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Safety Analysis of Parallel Vehicle Trajectories in Unequal Width Lane Environments HAN Baorui, FAN Jiaqi, JI Yuxuan, WANG Fangxu, ZHAO Yi, ZHU Zhenjun 2026, 26(2): 256-267.  DOI: 10.16097/j.cnki.1009-6744.2026.02.024 Abstract ( )   PDF (3328KB) ( )   To clarify the safety characteristics of parallel driving under unequal lane-width combinations, and explore their implications for cross-sectional design optimization, this study conducted a series of driving simulation experiments with different lane-width configurations, in which the widths of inner lanes were set to 3.50, 3.25, and 3.00 meters, respectively. A signal waveform-based framework for driving safety evaluation was proposed, in which the lateral oscillation trajectories of vehicle and the heart rate data of drivers were treated as non-stationary signals. By applying multilevel wavelet decomposition, Hilbert envelope spectrum analysis, and orthogonal matching pursuit (OMP), the vehicle motion characteristics and the risk perception patterns of drivers under varying levels of lateral pressure were quantitatively revealed from both frequency-domain features and signal composition perspectives. The results indicate that: (1) as the lane width decreases, when driving in parallel with passenger cars, the test vehicle exhibits a trend of reduced oscillation frequency and decreased oscillation amplitude, accompanied by improved driving stability; when driving in parallel with large vehicles, the oscillation spectrum of the test vehicle shifts toward multi-frequency and low-power characteristics, reflecting increased driving tension; (2) the heart rate responses of drivers show a significant "lag effect" relative to driving scenarios, particularly in the interactions with large vehicles, suggesting that the reliance on physiological signals alone has limitations in assessing instantaneous risk; and (3) by decomposing the trajectory signal into five frequency bands, the d3 component shows a relatively significant association with the psychological tension level of drivers, which indicates its potential as the driving characteristic spectrum. This study offers a new perspective for the remote monitoring of driving safety. References | Related Articles | Metrics

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Identification of Road Rage Aggressive Driving Behaviors Based on Gaussian Mixture-Hidden Markov Model WAN Ping, CHEN Ying, DENG Xinyan, MA Xiaofeng 2026, 26(2): 268-279.  DOI: 10.16097/j.cnki.1009-6744.2026.02.025 Abstract ( )   PDF (3598KB) ( )   To effectively intervene in aggressive driving behaviors induced by road rage, this paper proposes a road rage driving behavior identification model considering vehicle motion state features. An anger emotion induction experiment was conducted based on road event stimuli on busy traffic sections to obtain data on aggressive and normal driving behaviors. Then, the samples of normal driving behavior and aggressive driving behavior were calibrated through the time series data change point detection method based on the Relative Unconstrained Least-Squares Importance Fitting (RuLSIF) density ratio estimation and the short form of the Simple Differential Emotions Scale (SDES). Welch's t-test analysis was conducted for the driving behavior data and verified that the four features, including speed, longitudinal acceleration, lateral acceleration, and yaw rate, have significant differences under different driving states. A road rage aggressive driving behavior identification model based on the Gaussian Mixture-Hidden Markov Model (GHMM) was then developed. The Viterbi algorithm and Baum-Welch algorithm were adopted to optimize the model parameters. The Synthetic Minority Over-sampling Technique combined with Edited Nearest Neighbors (SMOTE-ENN) hybrid sampling strategy was used to balance the samples. The likelihood ratio test and threshold setting methods were introduced to enhance the model's recognition ability. The results show that the model has an overall accuracy of 84.17%, which is respectively 6.90% and 6.27% higher than that of the Logistic Regression (LR) model and Support Vector Machine (SVM) model. Feature analysis verifies that the model shows significant identification effects on key features such as lateral acceleration and yaw rate. The research results provide theoretical support for the development of a road rage aggressive driving behavior detection and early warning system based on vehicle motion state features. References | Related Articles | Metrics

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Reliable Route Optimization Model for Dynamic and Uncertain Road Networks Considering Punctuality WANG Ziji'an, HUANG Ailing, YANG Liu 2026, 26(2): 280-288.  DOI: 10.16097/j.cnki.1009-6744.2026.02.026 Abstract ( )   PDF (1679KB) ( )   The dynamic and uncertain nature of road networks poses significant challenges for path planning schemes to simultaneously achieve punctuality and optimality, while also escalating the computational complexity when solving such problems in large-scale networks. To tackle this issue, this paper proposes a reliable route optimization model for dynamic and uncertain road networks that incorporates punctuality requirements, striving to minimize travel time while fulfilling specified on time arrival rates. Based on the robust optimization paradigm, an adjustable robust shortest path model is developed for time dependent and uncertain networks, which considers the adaptability to various punctuality demands for reliable route identification. A corresponding plan generation algorithm is designed to empower users to intuitively generate travel plans according to their punctuality preferences. By integrating interval search with Monte Carlo simulation, this algorithm establishes an explicit mapping between an adjustment parameter and the punctuality rate, thereby eliminating the need for users to set the parameter directly. For the reliable route solving subroutine, an efficient sub-algorithm incorporating the time-dependent Dijkstra method is developed to reduce computational complexity. The model and algorithms are validated through performance tests on multiple real-world urban road networks. Experimental results demonstrate that the proposed generation algorithm can output travel plans that align with users' punctuality expectations. When guaranteeing a 100% punctuality rate, the travel time is reduced by 10.6% compared to conventional robust optimization models. Moreover, the reliable route solving sub-algorithm proves to be highly efficient, achieving computation times ranging from milliseconds to seconds on large-scale networks, which enables its support for both online and offline large-scale route planning tasks. References | Related Articles | Metrics

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Crew Pairing Optimization Method Considering Flight Crew Size ZHANG Baocheng, XIE Yachen 2026, 26(2): 289-299.  DOI: 10.16097/j.cnki.1009-6744.2026.02.027 Abstract ( )   PDF (1756KB) ( )   Traditional two-stage solutions to the crew scheduling problem typically neglect the connection between the first stage (crew pairing) and the second stage (crew assignment). However, under the existing CCAR-121-R8 regulations, the result of crew pairing determines the size of the flight crew, which affects the cost of crew assignment. To prevent the increased costs and unnecessary resource consumption due to the expansion of flight crew size, this paper proposes a crew pairing model that considers the size of the flight crew. Taking the column generation algorithm as the main framework, and considering the characteristics of the pricing subproblem, an improved dynamic programming algorithm incorporating state compression and vector domination strategies is designed for solving the problem. Results show that: 1) Compared with the traditional dynamic programming algorithms, using the improved dynamic programming algorithm designed in this paper to solve the subproblem reduces the computation time by 48.43%~81.65%. 2) Compared with the existing crew pairing methods, the pairing generated by the model in this paper reduces the average number of pilots required by 3.12% and the average total cost by 5.53%. In addition, the model in this paper reduces the size of the flight crew in the pairing by compressing the upper limit of daily flight time and daily duty period, which reduces the number of duties with flight time greater than 9 hours and duty period greater than 14 hours by 9.10% and 23.53% respectively, and the potential fatigue risk of some pilots is reduced. References | Related Articles | Metrics

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Short-Term Passenger Flow Prediction for Airport Express Buses by Integrating Bayesian Optimization and Deep Learning Models LIU Tao, LI Lin 2026, 26(2): 300-308.  DOI: 10.16097/j.cnki.1009-6744.2026.02.028 Abstract ( )   PDF (2491KB) ( )   Considering the significant external disturbances and high randomness of airport express bus passenger flow, this study proposes a hybrid deep learning model for forecasting the short-term passenger flow. This model employs a convolutional neural network to extract the local features from passenger flow data. Then it introduces an attention mechanism to dynamically weight critical time steps, which significantly enhances its capability to predict complex passenger flow patterns. And it utilizes a long short-term memory network to capture long-term temporal dependencies. To address the limitations of manual hyperparameter tuning, a Bayesian optimization algorithm is employed to automatically search for the global optimal hyperparameter combinations, which significantly improves the prediction accuracy of this model. This paper takes the passenger booking data from Line 4, which collected from January to October 2024, an airport express bus service operating at Chengdu Tianfu International Airport as an example. Four temporal granularities (5, 10, 15, 20 minutes) are analyzed to assess their effect on prediction accuracy. The experimental results show that at a 20-minute granularity, the model achieves the best overall performance, with an R2 of 0.794 2, root mean square error (RMSE) of 3.836 9, mean absolute error (MAE) of 3.132 3, and mean absolute percentage error (WMAPE) of 19.041 8%. Compared to benchmark models like Transformer, Random Forest, XGBoost, spatial-temporal synchronous graph convolutional network (STSGCN), graph multi-attention network (GMAN), and spatial temporal attention fusion network (STAFN), the proposed model demonstrates a better prediction performance. This validates its effectiveness and advantages in short-term passenger flow forecasting for airport express bus services. References | Related Articles | Metrics

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

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Efficiency Assessment and Repair Decision-Making of Traffic Road Networks Under Urban Waterlogging Disasters ZHANG Xuequan, GUO Yuying, HUANG Zhen, ZHANG Shuyang, DU Zhigang 2026, 26(2): 318-327.  DOI: 10.16097/j.cnki.1009-6744.2026.02.030 Abstract ( )   PDF (3249KB) ( )   Urban waterlogging disasters are sudden highly and extensive spatially, which severely threaten the road traffic safety. The rapid post-disaster assessment of road network performance and formulation of effective repair strategies is essential for restoring the traffic functionality. Considering both the travel time and usage frequency under inundated conditions, this study develops an improved evaluation model of network efficiency by incorporating the betweenness centrality of path and weighted population. On this basis, a road network objective function for performance recovery is constructed to analyze the optimal repair sequence of the disrupted nodes and links under different preference strategies. Using the Zhengzhou Fourth Ring Road network during the catastrophic“7·20”rainstorm and flooding event as a case study, the experimental results indicate that urban waterlogging causes the traffic interruptions or speed reductions on certain road segments and intersections, and the improved model of network efficiency effectively quantifies the overall impact of the inundation on road network performance. The fully blocked segments with high-betweenness impose the greatest reduction in network efficiency; moreover, the partially blocked segments with high-betweenness can, to some extent, exert a greater impact than which in fully blocked segments with low betweenness. The recovery curves of network efficiency further show that the efficiency-first strategy outperforms the type-first and location-first strategies. The repair at intersection is not necessarily superior to that along the road segments, but the fully blocked disruptions generally warrant a higher repair priority compared with the partially blocked ones. References | Related Articles | Metrics

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

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Sample Expansion for Household Travel Survey Data Using Improved Population Synthesis Method WU Huan, CAO Chengtao, LIN Xiaohui, WU Fan 2026, 26(2): 342-353.  DOI: 10.16097/j.cnki.1009-6744.2026.02.032 Abstract ( )   PDF (2000KB) ( )   This paper proposes a sample expansion method for household travel survey data using improved population synthesis and the existing iterative proportional updating (IPU) algorithm. To address the defects in existing population synthesis methods, the systematic enhancements are introduced. A virtual population encoding and children's information table reconstruction technique are developed to process the non-categorical data and information of young children. Then, the sample expansion schemes are developed, which encompasses multi-tiered structures, multi-regional coverage, diverse control variables, and the evaluation system. The mobile phone signaling data and motorized operation data are integrated to identify the unreported trip records and adjust expansion weights. To validate the effectiveness of the proposed method, an analysis was conducted using the Foshan household travel survey data and the results were to compare with the existing population synthesis techniques. The results demonstrate a marked superiority of the proposed method in expanding key policy-sensitive disaggregated indicators, with the mean absolute error across these indicators reduced by approximately 60%. The proposed framework proves to be reliable and effective, offering a solid reference for expanding urban household travel survey data. References | Related Articles | Metrics

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Impact of Short Trip Duration Thresholds on Driving Cycle Construction YANG Yang, WEI Qian, YU Qian, WU Fang 2026, 26(2): 354-362.  DOI: 10.16097/j.cnki.1009-6744.2026.02.033 Abstract ( )   PDF (2536KB) ( )   Accurately capturing transient driving behaviors is essential for constructing driving cycles. Existing research primarily focuses on clustering algorithms and data stitching methods, lacking systematic analysis of the short trip duration threshold ( tmin ). This paper aims to investigate the mechanism by which tmin influences the representativeness of driving conditions, providing a scientifically quantifiable basis for regionalized tmin optimization. Based on one year of in-vehicle measurement data from 200 passenger vehicles in Xi'an city, this paper proposes a driving condition construction method integrating principal component analysis, k-means++ clustering, and Markov chain Monte Carlo algorithms. A multi-dimensional verification system is established to systematically analyze the effects of tmin values (20, 60, 100, 140, 180 s) on driving condition construction. The results indicate that tmin =20 s yields optimal clustering performance, enabling precise differentiation of seven diverse driving modes. The constructed driving conditions maximally preserve transient driving characteristics, with an average relative error ( δPV ) of 4.48%, KL divergence ( DKL ) of 0.36, and total variance in specific power ( Ps ) across the range of 9.10%, which all significantly outperformed other threshold conditions. Thresholds below 20 s introduced fragmented noise, degrading clustering stability. The 20 s operating condition exhibited an average jerk of 0.11 m·s-3, significantly below the industry-practiced threshold of 0.20 m·s-3, demonstrating excellent bench reproducibility. This study confirms tmin =20 s as the optimal threshold for constructing passenger vehicle operating conditions in Xi'an and similar congested cities. The findings provide theoretical and practical support for regionalized operating condition development, vehicle performance evaluation, and energy-saving technology research. References | Related Articles | Metrics