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Customized Commuter Bus Choice Behavior Under Heterogeneous Car Dependence ZHONG Hua, YAN Xuedong, WANG Yun, LIU Xiaobing, LIU Zile, SUN Yite Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 17-27.   DOI: 10.16097/j.cnki.1009-6744.2026.04.002 Abstract （132）      PDF （2320KB）（159）       The existing studies on the choice behavior of the commuter of customized bus ignore the heterogeneity of car dependence, and lack the strategies of customized bus demand response for commuter groups with different car dependence. Based on the survey data, commuters are classified into three groups—low, medium, and high car dependence. A conditional Logit model is used to systematically examine the effects of socio-demographic and travel attributes on the willingness to choose the customized buses. It is also used to explore the customized bus choice behavior mechanism of commuters considering the car dependence heterogeneity. The results indicate that the user profiles are different among the three groups. The positive impact of education level on the choice of customized bus increases progressively, while the income effects exhibit notable heterogeneity across groups. Flexible working hours significantly influence the willingness to choose customized buses for all groups, and the effect direction reverses depending on car dependence levels. Regarding travel attributes, peak hours have the strongest positive impact on the willingness to choose, especially for the medium dependence group. The key concerns of different groups also vary: low, medium, and high dependence groups prioritize congestion time, commuting costs, and walking time, respectively. The low-dependence group is willing to pay an additional 1.3 yuan per minute to save on congestion time, while the high-dependence group is willing to pay an additional 0.8 yuan per minute to reduce walking time. This paper proposes a stratified, differentiated and precise customized bus promotion and pricing strategy tailored to commuters with varying car dependence levels, aiming to enhance the appeal of customized buses, and reduce the excessive reliance on private cars, alleviate commuting pressure, and promote the sustainable development of urban transportation systems.

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Forecasting Carbon Emissions in Transportation Sector Considering Nonlinear Time-lag Effects WANG Qingrong, ZHANG Jinpeng, ZHU Changfeng Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 1-16.   DOI: 10.16097/j.cnki.1009-6744.2026.04.001 Abstract （162）      PDF （3840KB）（158）       To address the limitations of prediction accuracy caused by high nonlinearity, non-stationarity, and complex driver coupling with information redundancy in transportation carbon emission series, a hybrid prediction model which combines a two-stage feature selection (TLMIC-LASSO), Successive Variational Mode Decomposition (SVMD), Improved Animated Oat Optimization algorithm (IAOO), and Bidirectional Long Short-Term Memory network (BiLSTM) is proposed. The two-stage feature selection strategy based on Time-Lagged Maximal Information Coefficient (TLMIC) and LASSO regression is constructed to identify the time-lag effects of key driver accurately and eliminate redundant variables. The SVMD is introduced to decompose the original series of carbon emission adaptively into multiple stationary modal components, thereby reducing the non-stationarity of data. The animated oat optimization algorithm(AOO) algorithm is improved via hybrid chaotic perturbation, adaptive tdistribution mutation, and dynamic opposition-based learning strategies, and the IAOO algorithm is utilized to optimize the key hyperparameters of BiLSTM network adaptively to prevent the model from falling into the local optima. Finally, an IAOO-BiLSTM prediction model is built for each modal component, and the prediction results are integrated. The model is validated with the data of transportation carbon emission from 1990 to 2023 in China. Results indicate that compared with the optimal contrast model, the proposed model reduces the RMSE, MAE, and MAPE by 21.88% , 23.33% , and 24.32% respectively, which significantly improves the prediction accuracy of transportation carbon emission.

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

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Optimization of Cross-border Multimodal Transport Routes Under Uncertain Clearance Conditions YANG Yang, ZHOU Sijia, YIN Jijiao Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 65-78.   DOI: 10.16097/j.cnki.1009-6744.2026.04.006 Abstract （85）      PDF （2644KB）（108）       Under the framework of international transportation, cross-border multimodal transport is restricted by customs clearance time and the volatility of tariff levy rates. This paper employs triangular fuzzy numbers to characterize the uncertainties inherent in customs clearance time and tariff rates. A path optimization model for multimodal transport is constructed with the objectives of minimizing transportation cost, transport time, and carbon emissions. Based on the fuzzy chance-constrained programming theory, the uncertain model is transformed into a tractable mixed-integer programming model. An improved multi-objective evolutionary algorithm is designed to solve the model, which mitigates the loss of high-quality solutions and enhances the robustness of the algorithm. Finally, a case study is conducted on cross-border transportation from Chongqing to Singapore, covering 15 nodes across 8 countries. The computational results demonstrate that the proposed method can reasonably generate a set of optimized multimodal transport schemes addressing uncertain cross-border conditions. Compared with other state-of-the-art multi-objective evolutionary algorithms, the hypervolume indicator is improved by 13.21% and the stability indicator by 69.63%, which indicates a significant performance enhancement of the proposed method. Therefore, it can effectively provide high-quality path decision support for relevant carriers engaged in cross-border multimodal transport.

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Airport Departing Passenger Flow Prediction Based on End-to-End Large Language Model ZHAO Xiaoqi, TANG Tieqiao, LI Bin, YAN Na Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 309-320.   DOI: 10.16097/j.cnki.1009-6744.2026.04.027 Abstract （62）      PDF （2307KB）（95）       To improve the accuracy and stability of short-term airport departing passenger flow prediction at an hourly scale, this paper proposes a large language model-based airport departing passenger flow forecasting method, ChatPAX, where Chat denotes a dialogue-/prompt-based large language model modeling approach, and PAX is a commonly used aviation abbreviation for passengers. The proposed method formulates departing passenger flow prediction as a conditional generative sequence forecasting problem constrained by flight schedules, and jointly characterizes demand-side inertia from historical passenger volumes and the constraint effect of future flight supply on passenger flow formation within an end-to-end framework. ChatPAX uses a causal language model as the predictor. Through structured prompts, the historical departing passenger volume sequence over the past 48 h and the forward-looking flight information within the prediction horizon are organized as conditional inputs, and the departing passenger volume sequence for the next 24 h is generated in a fixed format. During training, an answer-masking mechanism is introduced to concentrate supervision signals on the target prediction segment, thereby improving generation stability and convergence efficiency. Meanwhile, parameter-efficient fine-tuning and low-precision quantization are combined to enable a trainable and deployable forecasting workflow under single-GPU memory constraints. Experiments based on multi-month real operational data from a domestic airport show that ChatPAX achieves a mean all- day weighted mean absolute percentage error (WMAPE) of 13.003% on the test set. Compared with the autoregressive integrated moving average model (ARIMA), long short-term memory network (LSTM), Random Forest, and extreme gradient boosting algorithm (XGBoost), the prediction errors are reduced by approximately 53.9%, 29.5%, 26.2%, and 28.4%, respectively, and the proposed method exhibits higher stability in cross-date prediction. Ablation experiments further indicate that introducing flight schedules as a supply-side constraint can bring stable performance improvements, whereas exogenous factors such as holidays and weather do not produce consistent marginal gains.

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Construction and Resilience Recovery of Hierarchical Air-route Network for Urban Low-altitude Logistics GAO Yuan, HUANG Hui, SUN Heying, ZHANG Wenhui Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 52-64.   DOI: 10.16097/j.cnki.1009-6744.2026.04.005 Abstract （105）      PDF （3333KB）（94）       The low-altitude economy in China is rapidly developing but challenges still remain, such as fragmented airspace resources, low utilization efficiency, and high safety risks. To address these issues, this study proposes a method for constructing and restoring urban low-altitude logistics air-route networks, enabling rapid and efficient recovery under disruptive events. An incremental optimization strategy is used to design the spatial layout of nodes and develop a hierarchical topology consisting of trunk, secondary, and branch routes. A multi-objective genetic algorithm is introduced to dynamically prioritize node and edge restoration, yielding an efficient recovery strategy. Using logistics station coordinates and airport no-fly zone data from the cities of Tianjin and Jinan, the study develops a representative urban air- route network with road- freight- based demand and operational constraints. The experimental results indicate that the proposed restoration strategy markedly improves network efficiency and flow resilience, while Pareto frontier analysis confirms the advantage of the multi-objective optimization approach. The framework provides a practical foundation for enhancing Unmanned Aerial Vehicle logistics operations, urban air mobility, and emergency response under the emerging low-altitude economy.

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Macroscopic Fundamental Diagram-based Optimization Control for a Multi-bottleneck Expressway and Joint Networks HE Zi'ang, HAN Yu, YU Hao, ZHAO Jiahui, ZHU Shunying Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 188-201.   DOI: 10.16097/j.cnki.1009-6744.2026.04.016 Abstract （71）      PDF （3924KB）（94）       To improve the computational time in optimal control for a large-scale mixed road network which consists of a multi-bottleneck expressway and joint urban networks, this paper develops a coordinated optimization method that balances control performance and computational cost. The proposed method extends the existing traffic flow model in the following aspects: (a) the Macroscopic Fundamental Diagram (MFD) model is jointly introduced for both the expressway and urban components of the mixed network; (b) the traffic flow model of multi-region urban networks accounts for complex scenarios where on-ramps have insufficient storage capacity and must relyon upstream urban intersections for control, while incorporating the factor of cordon queues into each network reservoir MFD. The extended traffic model serves as the prediction model within the Model Predictive Control (MPC) framework, with the optimization objective of minimizing the Total Time Spent (TTS) in the system. This enables real-time solution of the total perimeter inflows for the expressway stretch and each urban network. Based on local state feedback, each total perimeter inflow is allocated to individual controlled perimeter links. Simulation results indicate that, by incorporating cordon queues, the proposed method effectively reduces the mismatch between the prediction model and the simulation model, thereby achieving control performance superior to existing related methods and approaching that of the theoretically optimal control method. Leveraging MFD-based modeling for both components of the mixed network, the proposed method significantly shortens computational time and meets real-time control requirements. Specifically, when queue storage space is sufficient, the proposed optimization-based control method reduces the TTS by 8.6% compared to the no-control scenario; under limited queue storage space, the TTS reduction is 7.3%.

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Multi-objective Path Optimization for Multimodal Transport Considering Uncertainty in Transfer Time YAN Shuaishuai, PAN Shuai, HAN Baoming Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 79-88.   DOI: 10.16097/j.cnki.1009-6744.2026.04.007 Abstract （88）      PDF （1936KB）（90）       In practical transportation, the transshipment time associated with different transport mode conversions at each node exhibits significant uncertainty, which increases the complexity of multimodal transport path optimization and poses challenges to carriers' decision-making. This study investigates the multi-objective path optimization for multimodal transport considering uncertain transshipment times, and develops a multi-objective optimization model aiming to minimize transportation cost, transportation time, and carbon emissions. To characterize the uncertainty of transshipment time, sample data are first tested for normality, and uncertain transshipment times are simulated using a combination of Latin hypercube sampling and the inverse cumulative distribution function. Furthermore, to address the issues of premature convergence and loss of population diversity in the traditional NSGA-II algorithm when solving high-dimensional, the multi-objective, and multi-constraint problems, adaptive crossover and mutation operators are introduced to dynamically adjust the crossover and mutation probabilities based on the fitness status of the population. The search flexibility is improved at different evolutionary stages and the quality and convergence performance of the Pareto solution set is enhanced. A case study is conducted based on the multimodal transport network for exports from Heilongjiang Province to Russia. The results show that the compromise solution yields a transportation cost of 52 138.7 yuan, a transportation time of 26.3 hour, and carbon emissions of 4 964.8 kg. Compared with the standard NSGA- II algorithm, the improved algorithm reduces transportation cost and transportation time by 1 690.5 yuan and 13.4 hour, respectively.Moreover, compared with the deterministic transshipment time condition, the model under uncertainty tends to select solutions with fewer transshipments and shorter transportation times, albeit at the expense of increased transportation cost and carbon emissions by 8 705.5 yuan and 1 675.7 kg, respectively. In addition, Jiamusi, Fujin, and Tongjiang are identified as high-frequency transshipment nodes, and rail-to-water transshipment is a key operation. Prioritizing the optimization of these nodes and their transshipment operations can effectively improve the overall efficiency of multimodal transport. The findings provide references for path decision-making in multimodal transport.

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Optimization of Multi-system Rail Transit Hub Connection Under Four-network Integration LIANG Hui, JING Yun, ZHANG Ying, XU Jing Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 28-41.   DOI: 10.16097/j.cnki.1009-6744.2026.04.003 Abstract （109）      PDF （3379KB）（89）       Against the background of the four-network integration in rail transit, the connection relationships among multi-system rail transit lines at comprehensive passenger hubs become highly complex. Local-line and cross-line passenger flows are intertwined, which makes the coupling among train timetabling, stop planning, and passenger routing more prominent. This study considers the origin-destination (OD) demand of both local-line and cross-line passengers across multiple rail transit systems. It constructs a multilayer train-passenger space-time network and introduces arc-based decision variables to represent train and passenger route choices.On this basis, a model of multi-commodity network flow is established with the objective of minimizing the total passenger travel time, which is used to address the collaborative optimization of multi-modal rail transit timetabling, stop planning, and passenger routing. Given the computational complexity of the model, this study develops a Lagrangian relaxation algorithm and designs a dual-solution-based heuristic to generate feasible solutions to the original problem. Finally, the effectiveness of the proposed model and algorithm is verified through a real-world case study on a network composed of the Beijing West-Shijiazhuang section of the Beijing-Guangzhou High-Speed Railway, the Beijing-Xiong'an Intercity Railway, and Beijing Sub-Center Line. The case includes 49 trains and 345 passenger groups.The results show that 76% of passengers have a transfer time within 30 minutes, and the average transfer waiting time of passengers is 11 minutes. Sensitivity analyses of relevant parameters are also conducted.

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Site Selection and Route Planning for Urban Low-altitude Commuter eVTOL Systems SONG Cuiying, WANG Jiaxin, NIE Liangtao, CHEN Yunong Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 202-212.   DOI: 10.16097/j.cnki.1009-6744.2026.04.017 Abstract （69）      PDF （2320KB）（87）       Job-housing spatial separation, as a typical feature of modern metropolises, has led directly to the widespread prevalence of long-distance commuting. To overcome this ground transportation bottleneck and promote job-housing balance, the electric vertical take-off and landing (eVTOL) aircraft provides an innovative solution and a new transport mode. With advantages such as high speed, strong flexibility, and independence from ground road networks, eVTOLs are expected to become a key vehicle for future urban medium-distance commuting. However, current research and practices in urban air mobility (UAM) mostly focus on unmanned aerial vehicles, with studies on manned operations still in their early stages. Therefore, this study proposes a systematic method for site selection and route planning based on eVTOL operations. First, by extracting valid commuting trip samples and incorporating no- fly zone constraints, this study applies an improved greedy coverage clustering algorithm based on K-Dimensional Tree (KD-Tree) to achieve eVTOL site selection, aiming at "maximum coverage with a minimum number of sites". A route planning model was then constructed with the objective of minimizing operational costs and the constraints of passenger capacity, flight range, fixed costs, and distance-based costs. A two-stage hybrid strategy combining a greedy algorithm for initial solution generation and an improved genetic algorithm for iterative optimization is adopted for solving the model. The proposed method is validated through a concrete case study, resulting in a feasible route plan. A comparative evaluation is conducted from the perspectives of travel distance and commuting time. The results indicate that, compared with private cars, the eVTOL save on average 28.29% in travel distance and 61.58% in travel time, demonstrating its advantages in improving commuting efficiency and spatial utilization.

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Airport Disruption Recovery Considering Passenger Cancellation Behavior and Flight Structural Adjustment WANG Tao, WEI Tingting, WANG Yuechao, LI Yanhua Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 332-343.   DOI: 10.16097/j.cnki.1009-6744.2026.04.029 Abstract （60）      PDF （2707KB）（82）       Extreme weather events and other emergencies may disrupt airport operations. The conventional recovery strategies typically resume flight schedules through time extension after disruptions, but often overlook passenger cancellations caused by prolonged delays and the flexibility of transport capacity structures. To address this, this study develops a dual-layer optimization model for departure recovery that incorporates both passenger cancellations and flight structure adjustments. The upper layer optimizes capacity allocation through structural strategies such as flight consolidation, aircraft swaps, and standby replacement to minimize operational costs. The lower layer optimizes departure times under constraints like capacity limits, balancing cancellations with total delay duration. Using the Weibull distribution to characterize cancellation probabilities under delays, the model introduces flight duration and high-speed rail accessibility parameters to reflect flight heterogeneity. A co-evolutionary algorithm integrating simulated annealing, constraint methods, and non-dominated sorting is designed to solve the model. The validation was performed using Kunming Changshui International Airport as an example, and the result demonstrates that compared to baseline recovery plans based on time extension and departure scheduling optimized solely for takeoff times, the proposed method reduced total operational costs by 37.27% and 11.51%. Cancellations decreased by 42.08% and 13.50%, and total delay duration is reduced by 36.46% and 11.21%. With consideration of passenger behavior responses and departure decisions, this study provides actionable decision-making for coordinated recovery during large-scale operational disruptions at airports.

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Connected and Automated Vehicles Overtaking Response Strategy Considering Heterogeneous Driving Behaviors WANG Hao, CHEN Xumei Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 173-187.   DOI: 10.16097/j.cnki.1009-6744.2026.04.015 Abstract （65）      PDF （3688KB）（75）       At signalized urban roads, smooth trajectories planned for Connected and Automated Vehicles (CAVs) often involve relatively low local speeds, which can respond to overtaking by following human-driven vehicles (HVs). To enhance the operational efficiency and energy economy of mixed traffic flow in overtaking scenarios, this paper proposes a CAV trajectory planning method that responds to overtaking by rear HVs is proposed. First, HVs are categorized into general HVs and following HVs based on their willingness to follow CAVs and mixed platoons led by CAVs. A simulation framework is designed for mixed traffic on signalized road segments, which uses finite state machines to model vehicular interactions. A mixed-integer quadratic programming model is developed to plan CAV trajectories, aiming to minimize travel time and fuel consumption while maximizing driving comfort. A trajectory prediction and overtaking detection algorithm is developed and an improved passive yielding strategy (IPYS) is proposed to update CAV trajectories and alleviate the negative effects of HV overtaking. Simulation results demonstrate that the IPYS consistently outperforms benchmark strategies across various CAV penetration rates and traffic demand levels. The differences among strategies are most pronounced when CAV penetration ranges between 20% and 30%. An increase in the proportion of following HVs contributes to improved traffic efficiency. Sensitivity analysis further indicates that road segment length and signal cycle length significantly influence optimization outcomes. When the road segment length is between 500 and 700 meters, the frequency of CAVs being overtaken varies considerably with changes in cycle length.

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Theoretical System of Integrated Transport Development ZHANG Guo-wu Journal of Transportation Systems Engineering and Information Technology    2009, 9 (4): 1-9.   Abstract （3599）      PDF （640KB）（1202）       The fifteenth conference of “Transport 7+1 Forum” sets its theme as “theoretical system of the integrated transport development”. The integrated transport system, an essential subsystem of the whole national economy system, has its own development rules and generally consists of railway, highway, waterway, air, and pipeline transport. This conference discusses relevant issues about building theoretical system of the integrated transport development. To effectively prompt integrated transport development and to train advanced talents for interegrated transport planning, construction, and management, it analyzes the curriculum design and trainning program for traffic and transportation education both in demestic and international colleges and universities. It further discusses the probable curriculum design and knowledge framework for the integrated transportation education in China. During this conference, the concept of establishing the platform with theoretical research and practical application is put forward, and some suggestions are also proposed for further development.

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Resilience Recovery Strategy of Multimodal Transportation Network of Urban Agglomeration ZHOU Xueyan, SUN Chenxing, ZHOU Xin Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 42-51.   DOI: 10.16097/j.cnki.1009-6744.2026.04.004 Abstract （83）      PDF （2077KB）（72）       To enhance the resilience of multi-modal transportation networks in urban agglomerations, this study investigates resilience recovery strategies for multi-modal transportation networks with the goal of maximizing resilience. This paper develops an undirected weighted multi-modal transportation network for urban agglomerations. A resilience assessment method is proposed for multi-modal transportation networks with consideration of performance resilience and connectivity resilience. The performance resilience consists of structural performance and functional performance. A bi-level programming model for resilience restoration is established with the objective of maximizing resilience, in which the upper-level model is a bi-objective mixed-integer programming model, and the lower-level model is a user equilibrium assignment model. The Reinforced Learning Artificial Hummingbird Algorithm (RLAHA) and the Frank-Wolfe algorithm are designed to solve the upper-level model and the lower-level model, respectively. The results show that the RLAHA algorithm exhibits superior convergence performance and solution quality. Under the optimal recovery strategy obtained by the resilience restoration decision model, resilience is improved by 15% and 20% respectively compared with the degree-priority recovery strategy and the random recovery strategy. The optimal repair scheduling scheme derived from this model enhances resilience from the commencement of repair operations. The allocation of maintenance teams has a considerable impact on the resilience and overall repair duration of multi-modal transportation networks in urban agglomerations in western China. The maintenance duration of a single transportation network decreases with an increase in maintenance teams, yet the overall repair duration does not decline monotonically with the growing number of maintenance teams. The structural performance weight has a minor effect on performance resilience and connectivity resilience, the average rates of change both are 1.28% . The decision maker's preference exerts a significant influence on performance resilience, but a slight impact on connectivity resilience, their average rates of change are 12.37% and 3.39% , respectively. The research findings can provide a decision-making basis for resilience assessment and emergency repair recovery of multi-modal transportation networks in urban agglomerations across China.

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On Integrated Train Operational Scheme Drawing-up Platform of China MAO Bao-hua, WANG Bao-shan, XU Bin, LIU Hai-dong, CHEN Jian-hua, DU Peng Journal of Transportation Systems Engineering and Information Technology    2009, 9 (2): 27-37.   Abstract （4830）      PDF （2112KB）（1256）       The paper analyzes the historical development of railway train schemes since 1949. Four stages have been summarized to embrace different characteristics and issues existed in different times according to their procedures. From the viewpoints of methods and techniques involved in above four stages, the authors describe the key models and algorithms applied in train scheduling, locomotive and crew work plan, yard operational plan and experimental analysis of train timetables. Based on the studies delivered in the field during the past decade, authors advance an integrated framework which deals with its major functions, key theoretical and technological progresses and several applications of the platform, including ones in improvements of railway station design, signal layout optimization, train scheduling evaluation at microscopic level and possible other applications in the construction of high-speed railways of China.

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Effects of Built Environment on Bike-Sharing Feeder Ratio Considering Metro Station Types CHEN Yue, JIA Shunping, JI Qianxi, DAI Siwei, XU Qi Journal of Transportation Systems Engineering and Information Technology    2026, 26 (3): 134-143.   DOI: 10.16097/j.cnki.1009-6744.2026.03.013 Abstract （152）      PDF （2215KB）（154）       Under the background of the integration of metro and active mobility, this study uses the data of 2025 Beijing metro smart-card transactions and dockless bike-sharing trip in one week to identify bike-metro feeder trips. Then it develops a method to delineate the bike-sharing catchment areas around metro stations. This paper quantifies the bike-sharing feeder ratio across time periods and feeder modes. And then it integrates the Fuzzy C-Means (FCM) clustering with Multiscale Geographically Weighted Regression (MGWR) to investigate the spatiotemporal heterogeneous effects of built environment on transfer shares for different station types. The case study of Beijing shows that bike sharing expands the traditional walking catchment radius of metro stations, reaching an average distance of approximately 1.6 km. During peak hours, the average bike-sharing feeder ratio is about 16%, with low-ridership stations showing a slightly higher proportion than other station types. The MGWR results indicate that high job housing density suppresses the bike-sharing feeder ratio, whereas a high level of land-use mix promotes it. Sufficient bike-sharing supply is a key prerequisite for maintaining a high feeder ratio. The results for bus stop density reveal spatial differentiation in the relationship between bus and bike sharing, showing both competition and complementarity: the two modes tend to be complementary in central urban areas but competitive in peripheral areas. In addition, the bike-sharing feeder ratio is not only affected by the built environment but also moderated by station functional attributes. Specifically, residential-oriented stations are more strongly affected by competition from bus, whereas employment-oriented stations are more sensitive to transport location factors.

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Cooperative Control Method for Arterial Signals and Variable Speed Limit Under Mixed Traffic Environment YUE Rui, GE Rongxi, LIU Shijie, YANG Guangchuan, LIN Dongmei, TIAN Zongzhong Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 124-136.   DOI: 10.16097/j.cnki.1009-6744.2026.04.011 Abstract （84）      PDF （3425KB）（70）       This study addresses start-stop delay, queue spillback, and reduced traffic efficiency caused by platoon arrivals at multiple intersections along an arterial under mixed traffic flow with connected and automated vehicles (CAVs). A bi-level cooperative control method integrating signal timing and variable speed limit (VSL) control is proposed. At the upper level, a rolling optimization strategy is adopted. Lane-level queue states and short-term arrival predictions are used to generate dynamic phases, green durations, and lane-level green service windows, while real-time corrections are made through early green onset and delayed green termination. At the lower level, the lane-level green service windows are used as constraints. The speed limit for each controlled CAV is calculated according to its distance to the stop line and the target green window, so that vehicles are guided to arrive at the stop line during the green interval as much as possible. Speed boundaries, additional-delay constraints, speed-limit holding and smoothing, and car-following risk checks are further introduced to reduce unnecessary speed-limit interventions and speed fluctuations. Simulation results show that, under moderate traffic demand, the proposed cooperative control reduces average delay by approximately 51.2% to 65.8% compared with the fixed- time signal scheme, and the delay reduction becomes more significant as the CAV penetration rate increases. Under high demand with a high CAV penetration rate, the average number of stops per vehicle is reduced by about 57%, indicating that the proposed method can effectively suppress stop-and-go oscillations and approach queue accumulation. In addition, under moderate demand, average fuel consumption per vehicle is reduced by approximately 40.9% to 48.6% compared with the fixed-time scheme. The results indicate that the proposed cooperative control method can improve traffic efficiency and vehicle running smoothness while reducing energy consumption under different traffic demand levels.

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Coordinated Optimization of Dry Bulk Fleet Configuration and Operational Scheduling LIANG Jinpeng, ZHAO Xu, SONG Jianxin, WANG Jie Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 289-298.   DOI: 10.16097/j.cnki.1009-6744.2026.04.025 Abstract （53）      PDF （2071KB）（70）       To address the strong coupling among multiple decision variables and the large solution scale in the joint optimization of dry bulk fleet configuration and operational scheduling, this paper develops a mixed-integer linear programming model based on a space-time network, with the objective of maximizing total system profit. A dynamic vessel addition algorithm is proposed to solve the model, which decomposes the multi-vessel joint optimization problem into a sequence of single-vessel subproblems and employs a marginal profit termination criterion to reduce computational complexity effectively. Randomly generated instances and a large-scale realistic case based on iron ore transportation are used to validate the model and algorithm and to conduct sensitivity analyses. Numerical results show that, for most test instances, the objective value deviation between the dynamic vessel addition algorithm and Gurobi is within 5%, while computation time is substantially reduced, with particularly significant advantages in large-scale cases. Fixed costs and fuel prices exhibit a threshold effect on fleet operational decisions: within a certain range, they compress profit margins without altering fleet configuration, whereas beyond the critical threshold they trigger a reduction in the number of deployed vessels and the abandonment of low-profit cargoes. In addition, system net profit is more sensitive to freight rate changes in high-volume backbone cargo categories, while freight rate fluctuations of a small number of high-value long-haul cargoes have a relatively limited impact on overall profitability.

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Park and Ride Behaviors for Non-Local Private Car Travelers in Big Events XIONG Ping Journal of Transportation Systems Engineering and Information Technology    2010, 10 (5): 188-193.   Abstract （4552）      PDF （824KB）（1167）       During big events, non-local private car travelers can be divided into two types which were returning in one day and in a few days. Focusing on the travelers returning in a few days, the paper analyzed the traveling attributes and requirements for P & R. A P & R choice behavior disaggregated model was established and calibrated based on random utility theory. The model concludes three variables, which were in-vehicle time of P & R transit, diversity of parking fee, and comprehensive attractiveness index for suburban satellite towns comparing to inner city. The results revealed that the planning and policy of P & R for travelers returning in a few days should take more consideration on the requirements of the whole traveling chain. The key point is increasing the attractiveness of suburban satellite towns. The suggestion of establishing P & R system combining hotels at suburb has been put forward.

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Traffic Police Air-Ground Collaborative Patrol Routing Optimization Considering Congestion Risk Agglomeration NIU Xuejun, LIU Xiao, ZHANG Ke Journal of Transportation Systems Engineering and Information Technology    2026, 26 (4): 89-100.   DOI: 10.16097/j.cnki.1009-6744.2026.04.008 Abstract （72）      PDF （2662KB）（69）       In the routine patrol operations of urban traffic police, ground patrol vehicles are constrained by the road network topology and traffic congestion, which limits their mobility and makes it difficult to simultaneously achieve full-area patrol visibility and rapid response at congested locations. This paper proposes a bi-objective mixed-integer programming model for air-ground collaborative traffic police patrol routing, with the joint objective to maximize the risk capture of congestion and the scale of covered patrol nodes. The model characterizes the heterogeneous motion constraints of ground patrol vehicles and police UAVs, as well as incorporates a collaborative task logic of "airborne detection- ground response- task reassignment". To address the computational challenges over large-scale road networks, an improved adaptive large neighborhood search algorithm is developed: an improved Shaw removal operator incorporating dual weights of spatial distance and congestion risk difference is proposed to enhance identification of risk-clustered zones; a unified greedy repair operator is designed to enable dynamic air-ground resource allocation; and a local incremental time evaluation formula is derived to reduce computational overhead. Experiments are conducted on a real road network in the cross-river district of Hangzhou. Results show that the proposed algorithm outperforms the genetic algorithm, NSGA- II, and tabu search baselines in terms of captured risk score, number of covered nodes, and composite score. Compared with the tabu search, the proposed algorithm improves the composite score by 19.3% and the number of covered nodes by 24.7%. The ablation study shows that, after incorporating the improved Shaw operator, the proposed algorithm improves the composite score by 4.5%, the captured risk score by 12.5%, and reduces the running time by 21.6% compared with ALNS-G; Pareto frontier analysis shows that the proposed algorithm clearly dominates the tabu search in the bi-objective space of risk capture and node coverage, which provides decision-makers with patrol scheme options under different operational preferences.