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 （417）

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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 （239）

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PDF(English version) （3938KB）（8）

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 （266）

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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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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 （316）

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PDF(English version) （1195KB）（2）

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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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 （211）

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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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