2025 Selected Papers in English

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Effectiveness of New Energy Vehicle Incentive Strategies Considering Urban and Population Heterogeneity

WENG Jiancheng, ZHOU Huiyuan, ZHANG Mengyuan, YU Jiangbo

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (1): 2-14. DOI: 10.16097/j.cnki.1009-6744.2025.01.001

Abstract （977）

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Formulating policies tailored to urban low-carbon development phases and resident characteristics is essential for optimizing incentive structures and promoting green mobility. This study evaluates new energy vehicle (NEV) incentive strategies across four city categories, considering factors such as air quality, NEV penetration, and charging infrastructure maturity. It analyzes social media data using the Latent Dirichlet Allocation (LDA) model and designs user surveys. A Latent Class Ordered Logit Model (LCOL) is employed to assess different urban populations' preferences for vehicle electrification incentives, identifying key impacted groups. The results indicate that immediate incentives, such as driving ban exemptions and significant fiscal subsidies, effectively enhance the purchasing intent of NEVs among less receptive residents. Conversely, more receptive residents respond better to regular, smaller subsidies. Cities with low NEV penetration exhibit a higher probability of purchasing under incentives, highlighting greater potential for improvement. Enhancing charging infrastructure significantly boosts purchasing intentions in infrastructure-deficient cities, with a 1% increase in likelihood for every minute reduction in charging time. However, this effect diminishes in cities with extensive charging networks. In metropolises with vehicle access restrictions, exempting NEVs from these increases purchasing probabilities by 3.5%. These insights guide NEV promotional strategy development in diverse urban settings.

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Key Node Identification of Rail Transit Network Based on Gravity Influence Model

ZUO Zhongyi, LIU Zeyu, YANG Guangchuan

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (1): 102-112. DOI: 10.16097/j.cnki.1009-6744.2025.01.011

Abstract （747）

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The identification of key nodes in a rail transit network is critical to evaluate the network robustness and develop risk resistant plans and therefore ensure efficient operation of the transit network. This paper considers the mutual influence between nodes in the rail transit network and selects the Degree Centrality (DC), Betweenness Centrality (BC) and Closeness Centrality (CC) as comprehensive measurement indicators of node importance. The real rail transit network is converted as the corresponding topological network. The key nodes of the rail transit network are identified through the gravitational influence model, and the differences in network performance under different influencing factors are analyzed to obtain the optimal gravitational influence radius and attack strategy. The study assesses the robustness of the rail transit network from a gravitational perspective, and proposes relevant improvement recommendations. The results indicate that the importance of nodes is composed of the gravitational attraction generated by the target node and other nodes. When the gravitational influence model has a gravitational radius R=8 and a dynamic attack strategy is selected, the relative size decrease rate of the largest connected subgraph is respectively 13.25% and 10.39% higher than that when R=7 and R=9. The relative size decrease rate of network passenger flow efficiency is respectively 5.12% and 6.71% higher than that when R=7 and R=9 . Compared with the FGM, GC, KSGC, CI recognition models, the gravitational influence model has obvious advantages in identifying key nodes in rail transit networks. In addition, after attacking the top 30 nodes, the relative size of the largest connected subgraph in Beijing's subway network decreases by 91.68%, and the relative size of network passenger flow efficiency decreases by 86.17%. The results show that the gravitational influence model is applicable and effective in Beijing's subway network. The proposed method provides a new perspective for analyzing network robustness and provides an effective basis for decision makers to create network risk prevention plans.

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Influence Mechanisms and Identification of Cognitive Distraction of Car-following on Expressways

PENG Jinshuan, ZHANG Lingjun, ZHOU Lei, YUAN Hao, REN Chaoyu, XU Lei

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (1): 221-230. DOI: 10.16097/j.cnki.1009-6744.2025.01.021

Abstract （829）

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To investigate the impact of cognitive distraction on drivers' car-following behavior on expressways, this study conducted driving simulation experiment with various distraction tasks. The study dynamically collected vehicle kinematics characteristics, driver manipulation, and eye movement parameters, and analyzed the influence mechanism of the secondary task state and speed interval on car-following performance. A set of cognitive distraction state representation parameters was developed for car-following behavior in different speed intervals. Methods such as the Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were introduced to identify drivers' cognitive distraction states in real-time. The findings indicated that immersive computing imposed a higher cognitive load on drivers compared to conversational secondary tasks. Cognitive distraction reduced drivers' control over the steering wheel and throttle pedal, more focused gaze on the road ahead, and suppressed visual transfer. The cognitive distraction representation parameters varied across different speed intervals. The XGBoost model outperformed both the SVM and RF. By calibrating the optimal sliding window width and step size under different speed intervals, the XGBoost model achieved recognition accuracies of respectively 85.98%, 87.98%, 88.45%, and 92.21% for the overall interval and the speed intervals of I ([60, 80) km·h-1), II [80, 100) km·h-1), and III [100, 120] km·h-1). Up to the risk threshold moment, the recognition rate for cognitive distraction samples reached a maximum of 90%. The findings provide references for recognizing cognitive distraction and optimizing early warning systems on expressways.

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Research Progress and Challenges on Equity in Flight Slot Allocation

HU Rong, ZHANG Yutong, DING Jiahao, WANG Yiren, ZHANG Junfeng

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (2): 1-15. DOI: 10.16097/j.cnki.1009-6744.2025.02.001

Abstract （931）

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To further improve the feasibility of slot allocation results and reduce the unfairness among the participants in slot allocation, much literature has been studied on the fair allocation of flight schedules. By searching relevant databases at home and abroad, this paper systematically sorts out the individual fairness indicators and overall fairness goals in the optimization of existing slot allocations. Firstly, the development process and metrics of the concept of "fairness" are summarized, and the connotation of fairness in slot allocation is analyzed from three perspectives: horizontal/vertical, individual/overall and absolute/ relative. Secondly, the individual fairness index of each participant in the slot allocation are sorted out and compared based on the two dimensions of the number of slot adjustment and slot displacement. Then, from the perspectives of absolute fairness, relative fairness and Gini index, the overall fairness optimization objectives of the slot allocation model are summarized. The results show that the current fairness indicators are mainly constructed based on the principle of proportionality, while the weighted construction method is limited due to the difficulty of data acquisition and strong subjectivity. The research on the fairness goals has been relatively well-developed, and the Gini index has been widely used because of its global characteristics. Based on the content of the literature review, this paper further analyzes the shortcomings of existing studies and provides suggestions for future research. The study concludes that, in-depth research should be carried out in four aspects in the future: quantitative calculation of flight value, expansion of fairness research objects, construction of environmental fairness indicators and evaluation of the impact of dynamic parameters, to help the healthy and sustainable development of the civil aviation industry.

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Comparison on Influence of Job-housing and Commuting Status on Travel Mode Choice in Multiple Types of Cities

ZHOU Yuyang, ZHAO Congying, LI Jingkun, CHEN Yanyan, LIU Di, WANG Shuling

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (2): 26-35. DOI: 10.16097/j.cnki.1009-6744.2025.02.003

Abstract （1048）

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PDF(English version) （1195KB）（19）

Establishing a green and efficient travel service system is an important part of China's Green Travel Action Plan. It is necessary to consider the heterogeneity of job-housing status and commuting mode in different levels of cities. Based on 1788 valid questionnaires collected from three types of cities, the SEM-MNL model is constructed to quantitatively analyze the comprehensive impact of job-housing status, commuting attributes and personal economic characteristics on the choice of commuting modes in various types of cities. The findings reveal that the latent variable commuting attribute is the key factor affecting the travel mode, and the restrictive effect is more prominent in ordinary cities than in first-tier and new first-tier cities. Job-housing status indirectly affects commuting mode choice through commuting attributes. The path coefficients of three classes of cities are 0.83, 0.89, and 0.93, respectively. The effects of residential type on commuting distance and mode choice show an opposite trend in first-tier cities and ordinary cities. Highly educated travelers in first-tier cities prefer green travel modes, while in non-first-tier cities, the result is reversed. In new first-tier cities, residents with short commute distances have the highest proportion of renting, nearly half of them choose slow-speed transportation. Adjusting the job-housing distribution to increase the proportion of short-distance commuting can raise the share of green travel mode. As the city level declines, the feedback sensitivity of regulation increases. The research results provide differentiated policy recommendations for job-housing balance and transportation infrastructure planning in multiple types of cities. The results are conducive to promoting the low-carbon travel and contribute to the balance of urban transportation supply and demand and thus sustainable development.

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Heterogeneous Effects of Built Environment on Ridership of Integrated Use of Bus and Metro

XU Qi, QIN Beining, REN Peng, CHEN Yue, LAI Jinxuan

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (2): 352-363. DOI: 10.16097/j.cnki.1009-6744.2025.02.032

Abstract （324）

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PDF(English version) （1375KB）（7）

Metro and buses both play important roles in urban public transportation system. It is of great significance to investigate the bus and metro connections and the influencing mechanism to enhance an integrated public transit system. This paper uses the smart card data of Beijing to analyze four types of bus connecting metro ridership during morning and evening peak hours. Based on the 5D principle, the built environment index system is constructed to describe the characteristics of metro stations. The Multiscale Geographically Weighted Regression (MGWR) model is used to compare and analyze the differences in the impact of the built environment. The results show that the MGWR model can well reflect the impact of the built environment on different connection conditions. The distance from the city center has the greatest impact on the total number of connections. The residential point of interest (POI) density and land use mixed entropy are sensitive to time, and the effect of the two is more significant in the evening peak period. The number of bus stops is sensitive to the connection mode. The public transport accessibility and closeness centrality are sensitive to time and connection mode. Both show a restraining effect on the sensitive ridership in the central area of the city. In the peripheral area of the city, there is a promoting effect. Therefore, when considering the optimization of the connection target of the bus and metro system, it is necessary to fully consider the heterogeneous effects of the built environment on spatial, temporal and connection mode, and formulate strategies according to local conditions and time to promote the integrated development of public transportation.

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Review of Literature on Air-rail Intermodality Focusing on Passenger Travel Behaviour

ZHANG Xiaoqiang, ZHOU Huixuan, WU Xiaoyu

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (3): 5-21. DOI: 10.16097/j.cnki.1009-6744.2025.03.002

Abstract （845）

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PDF(English version) （734KB）（14）

Comprehensive transportation is the development direction of intelligent, green and safe transportation in recent years. Through comprehensive development and utilization of aviation, railway, highway, waterway and pipeline transportation modes, it builds a transportation system with advanced transportation technology, reasonable layout and structure. Air-rail intermodality is one type of comprehensive transportation, which not only reduces the waste of resources through the reasonable use of existing transportation infrastructure, but also improves transportation accessibility through low cost of transportation mode shift. Air-rail intermodality is an effective strategy to ease airport congestion, expand the scope of airport radiation, reduce carbon emissions and meet the diversity of travel demand. In recent years, related research on air-rail intermodality has included research on competition and cooperation of air-rail intermodality, air-rail intermodality network, and evaluation methods of air-rail intermodality at macro level, then research on of air-rail intermodality at the middle level, and research on passenger travel behavior of air-rail intermodality at the micro level. This paper summarizes and the representative studies in domestic and international level of air-rail intermodality from 2000 to 2024, and sorts out the research results under different operation backgrounds and different transportation network scales. It is found that domestic scholars are more concerned on transport passenger travel behavior, emphasize passenger travel demand, and scholars in other countries tend to focus on air-rail intermodality social benefits and environmental benefits, emphasize the sustainable development of air-rail intermodality. The reasons of the research differences at home and abroad are analyzed from the social background, technical background and policy support. In the future, to explore practical collaborative research, including conflicts of interest between operators and between operators and passengers, the conflict between social and environmental benefits is a valuable research direction. Systematic research on the construction of air rail intermodal network, the location of transit nodes to network optimization, construct the evaluation method system of air-rail intermodality, it has guiding significance to the development of air-rail intermodality. Expanding the study area for air-rail intermodality with the considerations of the diverse needs of travelers, coping strategies that incorporate uncertainty factors, such as providing flight delay insurance, is a very meaningful research topic. It is a practical work to deeply integrate the theoretical research and practical application of passenger travel behavior of air-rail intermodality. Atlast, the future development of air-rail intermodality should use advanced science and technology to reduce the data acquisition cost of the whole travel chain of air-rail intermodality passengers and build a travel service platform of air-rail intermodality including travel path planning, one-stop ticket purchase, connection process visualization, connection time prediction and other services.

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Route Choice of China-Europe Intermodal Transport Considering Northeast China Sea-Land Transport Corridor

GUO Shujuan, XU Xiao, LIU Zhi, DONG Yanlu, HUA Mengying, PENG Kangzhen

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (3): 32-43. DOI: 10.16097/j.cnki.1009-6744.2025.03.004

Abstract （355）

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The Northeast China Sea-Land Transport Corridor emerges as a novel international intermodal transport route, seamlessly integrating maritime and China Railway Express railway systems. This paper addresses the intermodal route choice problem for intermodal transport operators between China and Europe, specifically focusing on the Northeast China Sea-Land Transport Corridor. First, accounting for intermodal transport risks stemming from regional conflict events, an evaluation framework for intermodal transport risk indicators is developed from the perspectives of general risk and regional conflict risk. It incorporates the probability of risk occurrence and the severity level of risks. A multi-objective model for the intermodal transport route choice of China-Europe containers is constructed, aiming to minimize both total transport costs and transport risks. A NSGA II algorithm, based on topological sorting path chromosome coding, is devised to identify Pareto-optimal intermodal transport route choice schemes that align with the requirements of intermodal transport operators. Ultimately, three risk scenarios were established for numerical experiments based on the different stages of the regional conflict and its scope of influence. The findings indicate that the Northeast China Sea-Land Transport Corridor outperforms traditional corridors in complex and dynamic risk environments, demonstrating competitiveness for both high-value, time-sensitive, and time-insensitive cargoes. The competitiveness of Northeast China Sea-Land Transport Corridor increases and then stabilizes as the delivery time limit extends. It demonstrates strong competitiveness for goods with delivery time requirements within the medium range.

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Vehicle Trajectory Prediction Method Considering Dynamic Coupling of Spatial-temporal Features

GAO Yuan, FU Jinlong, FENG Wenwen

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (3): 107-116. DOI: 10.16097/j.cnki.1009-6744.2025.03.010

Abstract （538）

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In complex multi-vehicle dynamic interaction scenarios, intelligent vehicles need to accurately perceive and predict the driving trajectories of surrounding vehicles to ensure safe and efficient driving. To address the issue that existing models fail to fully consider the dynamic coupling relationships among multi-dimensional features, this paper proposes a vehicle trajectory prediction method based on a spatio-temporal cross-attention mechanism. First, a spatial attention module is adopted to extract the dynamic interaction features between the target vehicle and surrounding vehicles from their historical trajectory data. Then, the obtained dynamic interaction feature parameters are input into a long short-term memory (LSTM) neural network encoder to capture cross-time dependencies from the time domain perspective. Subsequently, the hidden state of the encoder is input into a cross-attention module that combines Fourier transform and a learnable router to capture cross-time dependencies in the frequency domain and further extract the coupling features among multi-dimensional features. At last, the future trajectory of the target vehicle is generated through an LSTM neural network decoder. The model is trained, validated, and tested using the next generation simulation (NGSIM) dataset. The results show that the model has a root mean square error of 0.74 meters in the 5-second prediction time domain, which is a 10% improvement in accuracy compared to the best results of other prediction models (0.82 meters).

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Optimization of Train Operation Diagram Considering Maintenance Windows on Railway Lines with Long and Steep Gradients

TIAN Zhiqiang, LIU Lei, SUN Guofeng, ZHANG Junfeng, LIANG Hui

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (3): 288-298. DOI: 10.16097/j.cnki.1009-6744.2025.03.026

Abstract （218）

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In the context of railway lines with long and steep gradients, the train operation process involves traction and braking stages that last for a relatively long time. By fully considering the overlap of train working conditions between the traction stage and the braking stage within the same power supply section, favorable conditions can be provided for the energy-saving operation of trains. Firstly, based on the characteristics of long and steep gradient lines, the impact of train arrival and departure times on the overlap of working conditions is analyzed. Secondly, to avoid power supply conflicts, assuming that the start and end times of maintenance windows in the same power supply section are synchronized, a mixed-integer programming model is constructed, incorporating the constraints of train operation and maintenance window opening comprehensively. The objective of model is to maximize the train working condition overlap time while minimizing the total travel time. Based on the characteristics of the model, the ε-constraint method and GUROBI are used for solution. The effectiveness of the model and algorithm is verified through a case study. The results show that the algorithm can effectively solve the problem, increasing the total overlap time of train working conditions by 24.39% compared to pre-optimization. The research results can provide a reference for the transportation organization decision-making on long and steep gradient lines.

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Methods for Assessing Wider Economic Benefits of Urban Transportation Infrastructure Based on Integrated Modeling

WANG Wanle, ZHONG Ming, HUNT John douglas

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (4): 1-12. DOI: 10.16097/j.cnki.1009-6744.2025.04.001

Abstract （848）

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Transportation infrastructure has a strong driving effect on urban economic development. In particular, the construction of large-scale transportation infrastructure can have a significant impact on land use and spatial form. In response to the need for a comprehensive assessment of the economic benefits of urban transportation infrastructure, this study proposes a framework and method for assessing the Wider Economic Benefits (WEBs) of transportation infrastructure based on the Urban-Integrated Economy, Land Use, and Transport (U-IELUT) modeling. This study extends the traditional "Four-Step" transportation planning model to a "PECAS+(Production, Exchange and Consumption Allocation System)" WEBs assessment model by linking it with an economic and population forecasting, a socio-economic activities allocation, a space development, and a wider economic benefits assessment module. The WEBs assessment model is designed to evaluate both the direct economic benefits and the wider economic benefits, mainly the agglomeration benefits, of urban transportation infrastructure. Taking Wuhan Metro Line 2 as an example, the direct and wider economic benefits were assessed using the "PECAS+" wider economic benefits assessment model. The findings indicate that the direct economic benefits of the Metro Line 2 in 2027 are approximately 1.043 billion yuan. The dynamic agglomeration benefits are about 264 million yuan, which is approximately 25.3% of the direct economic benefits. This demonstrates that the wider economic benefits, especially the agglomeration benefits, should not be overlooked in the economic benefits assessment of transportation infrastructure. Meanwhile, it is also possible to ascertain the impact differences of transportation infrastructure construction on various zones of the study area, that is, the spatial distribution of wider economic benefits, which can provide multidimensional decision-making support for the investment and construction of transportation infrastructure.

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Autonomous Driving Decision-making Method Based on Cooperative Reinforcement Learning of Large Language Model

WANG Xiang, REN Hao, TAN Guozhen, LI Jianping, WANG Jue, WANG Yanli

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (4): 137-146. DOI: 10.16097/j.cnki.1009-6744.2025.04.014

Abstract （405）

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Aiming at the problems that the high-level decision-making of the current autonomous driving system lacks specific execution details and continuous learning ability, this paper focuses on applying the Large Language Model (LLM) in refining the decision-making process of autonomous driving. Based on the powerful reasoning ability of the LLM and the exploration ability of Reinforcement Learning (RL), this paper proposes a method of combining the LLM and RL to refine the vehicle decision-making process. First, based on the high-level actions output of the RL, the reasoning ability of the LLM is used to predict the future trajectory points of the host vehicle. Then, the output of the RL model is combined with the current state information to make a safe, collision-free and interpretable prediction of the next state. At last, the above driving decision-making process is vectorized and stored in the memory module as driving experience, and the driving experience is updated regularly to achieve sustainable learning. The trajectory points predicted by the LLM provide a detailed motion path for the Proportional-Integral-Derivative (PID) controller, providing a basis for adjusting the vehicle's acceleration and speed to ensure that the vehicle travels along the predetermined path. In addition, the trajectory prediction can also evaluate and avoid potential collision risks, and create a safe path by analyzing the traffic state and historical data. The results of the closed-loop experiment show that the proposed decision-making method outperforms other models in all evaluation indicators. Compared to the RL, the decision-making method based solely on the LLM, and the LLM-based car-following model, the driving scores are increased by 35.12, 14.33 and 12.28 respectively. The method with the memory module increases the driving score by 25.59 compared to the method without the memory module.

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Driver's Visual Load Based on Factor Analysis and Entropy Weight Methods for Mountainous Two-lane Road

MENG Yunwei, WANG Lei, LI Zhipeng, LI Binbin, QING Guangyan, LIU Zhongshuai

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (4): 361-372. DOI: 10.16097/j.cnki.1009-6744.2025.04.033

Abstract （255）

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To investigate the influence of the horizontal and vertical alignments of mountainous two-lane highways on drivers' visual workload, this study conducted a naturalistic driving experiment with 28 participants. The experiment utilized the Dikablis eye-tracking glasses and the CTM-8A non-contact multifunctional speedometer to collect visual response data and speed values, which were subsequently validated for accuracy. By analyzing the drivers' pupil area variation rate, fixation duration, and blink frequency across different gradients, horizontal curve radii, and combined curve-slope sections, the study quantitatively assessed the impact of alignment conditions on visual workload. A visual workload model was developed using factor analysis and entropy weight methods, and thresholds for visual workload levels were determined based on clustering algorithms. The results indicate that in sections with gradients ranging from 1.50% to 6.00%, the pupil area variation rate and fixation duration are positively correlated with the gradient, whereas the blink frequency is negatively correlated. Specifically, under the same gradient, the pupil area variation rate is higher in downhill sections compared to uphill sections. Additionally, drivers' visual workload is negatively correlated with the horizontal curve radius. In sections with smaller radii, visual workload is primarily influenced by limited visibility, whereas as the curve radius increases, the pupil area variation rate and fixation duration decrease, while the blink frequency gradually increases. Based on the analysis, visual workload can be classified into three levels: low, medium, and high. It was also found that when the combined curve-slope index is less than 23.4 %·km-1, the visual workload remains at a medium level or below. The study results provide a theoretical foundation for improving the safety and comfort of mountainous highways.

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Review of Connected and Autonomous Vehicle Dedicated Lane Setup

CHENG Guozhu, WANG Wenzhi, YANG Zihan, WANG Guopeng, CHEN Yongsheng, GU Shuang

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (5): 25-39. DOI: 10.16097/j.cnki.1009-6744.2025.05.002

Abstract （962）

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With the advancement of information and communication technologies, autonomous vehicles (AV) and connected and autonomous vehicles have emerged as promising solutions to address traffic congestion, to enhance traffic safety, and to improve overall traffic efficiency. This paper provides a comprehensive review of the methods for setting up dedicated lanes for connected and autonomous vehicle (CAV). It begins by tracing the evolution of CAV dedicated lanes and elaborating on the background and significance of their implementation. Based on relevant literatures, it then delves into the methodologies for calculating road capacity, providing a foundation for predicting the impact of CAV dedicated lanes on traffic operations, evaluating strategies, and making necessary adjustments. Furthermore, the paper conducts an in-depth analysis on the strategies for setting up CAV dedicated lanes, including the conditions based on CAV penetration rates and traffic demand. It also explores the determination of the number and location of lanes, access methods, and lane separation approaches under various influencing factors. Finally, the paper proposes that future research should focus on understanding the changes in influencing factors post-implementation of CAV dedicated lanes and their alignment with real-world traffic conditions. It also emphasizes the need for establishing specific standards for setting up CAV dedicated lanes to ensure their function effectively across different traffic scenarios.

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Cooperative Optimization of Lane Allocation and Vehicle Trajectory at Intersections Under Connected-and-Automated-Vehicle Environment

SONG Lang, HU Xiaowei, YU Shanchuan, AN Shi

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (5): 59-71. DOI: 10.16097/j.cnki.1009-6744.2025.05.005

Abstract （853）

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In the collaborative optimization of intersection signal timing and Connected and Automated Vehicle (CAV) trajectory planning, the CAV exit, left turn, through, and right turn lanes can be assigned dynamically in the operation period. Based on the characteristics of CAV technology, this paper proposes a set of dynamic control rules for lane assignment under CAV, named as "flexible lane strategy". Compared to the existing fixed lane strategy, the proposed strategy can adjust the number of exit lanes and entrance lanes (including left turn, through, right turn) for different directions of traffic flow during operation. Lane assignment, signal timing and CAV trajectory planning are incorporated into a unified optimization framework to build a mixed integer linear programming optimization model. Meanwhile, feasible phase and sequence schemes can be automatically generated according to lane assignment in each direction, and the effectiveness of the model is verified through a case study. The results show that the optimization model can generate the optimal lane assignment scheme according to the traffic demand of each flow direction, especially when the lane assignment of the fixed lane strategy does not match the traffic composition of each flow direction, the flexible lane strategy helps to improve the intersection traffic efficiency. In low flow scenario, the flexible Lane strategy reduces average vehicle delay by 4.08%. In high-traffic scenarios, the fixed lane strategy at the intersection will be in a supersaturated state, while the flexible lane strategy can still meet the demand.

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Intermodal Transportation Network Design Optimization Considering Demand Uncertainty Under "Dual Carbon" Background

HUANG Rui, ZHAO Xu, WANG Jingyun

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (6): 1-12. DOI: 10.16097/j.cnki.1009-6744.2025.06.001

Abstract （1040）

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

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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 （1780）

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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 Operation Plans for Full-length and Short-turn Routings of Urban Rail Transit Based on Flexible Train Composition

LIU Bin, ZHAO Jinhui, TIAN Zhiqiang, MA Chaofan, LIANG Hui, LI Hebi

Journal of Transportation Systems Engineering and Information Technology 2025, 25 (6): 143-152. DOI: 10.16097/j.cnki.1009-6744.2025.06.013

Abstract （773）

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To address capacity redundancy or shortages caused by uneven passenger flow distribution in urban rail transit, this study optimizes train operation plan under flexible train composition during peak hours to achieve precise demand-capacity alignment, enhance cost control, and improve resource utilization efficiency. Focusing on asymmetric passenger flow patterns, this study develops a bi-objective nonlinear integer programming model with the goals of minimizing operator costs and maximizing average load factor and the constraints such as standing-passenger density limits. A hierarchical three-stage algorithm and Technique for Order Preference by Similarity to Ideal Solution are designed for solution prioritization. The model and algorithm are validated through case studies of an operational metro line. Results demonstrate that flexible train composition outperforms fixed-composition strategies (single routing and combined large/small routing). During peak hours, the operating costs decreased by 25.67% compared with the single routing method, and 7.34% compared to the combined large/small routing method. The average load factor improvements are respectively 29.55% and 26.02% compared to the single routing and combined large/small routing. Sensitivity analysis indicates that allowing a moderate increase in standing-passenger density for small routing can further reduce operational costs, though requires a balance between passenger comfort and safety. When the standing-passenger density increases from 7 to 9 persons ⋅ m ^-2, the flexible train composition reduces costs by 15.38%, decreases the load factor ratio 14.83%, and lower the required number of train sets by 15.79%. The flexible train composition effectively adapts to spatiotemporal passenger flow fluctuations, and could achieve synergistic optimization of dynamic capacity allocation and cost control.

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