2026 Selected Papers in English

Select

Identification of Key Nodes and Invulnerability Analysis of Northeast Sea-Land Corridor Transportation Network

WU Nuan, LIN Ting, WANG Wanxiang

Journal of Transportation Systems Engineering and Information Technology 2026, 26 (1): 2-10. DOI: 10.16097/j.cnki.1009-6744.2026.01.001

Abstract （559）

PDF （1968KB）（501）

PDF(English version) （489KB）（7）

The Northeast Sea-Land Corridor involves a large number of transportation nodes. Various risk events will not only directly affect the normal operation of these nodes, but also trigger cascading failures in the network through the transfer of freights flow between nodes, which undermine the efficient operation of the corridor. Therefore, the invulnerability of the Northeast Sea Land Corridor transportation network was studied based on a complex network theory. First, a multi-layer composite network was constructed based on the diverse transportation modes of the Northeast Sea-Land Corridor transportation network, and its topological structure was analyzed. Second, metrics, such as degree centrality, closeness centrality, betweenness centrality, eigenvector centrality, and clustering coefficient were selected to establish a node importance evaluation system. The improved correlation CRITIC-TOPSIS, incorporating information entropy, was used to quantify the node importance. Finally, a load-capacity cascading failure model was constructed based on node importance. The impact of different parameters and attack modes on network invulnerability was simulated and analyzed. The findings indicate that when the top 10 nodes by node importance fail, the global efficiency of the Northeast Sea-Land Corridor transportation network decreases by 58%, and the network connectivity drops by 30%. When the node load parameter α=2, node capacity coefficient β=1.4, and γ=1.6, the network exhibits strong resilience against various attack modes.

Select

Fleet Route Optimization of Shipping Companies Under Spatiotemporal Variations in Strait Traffic Risk

WANG Jie, WANG Xinghao, LIANG Jinpeng, GAO Jun

Journal of Transportation Systems Engineering and Information Technology 2026, 26 (1): 11-23. DOI: 10.16097/j.cnki.1009-6744.2026.01.002

Abstract （409）

PDF （3596KB）（304）

PDF(English version) （1623KB）（5）

Maritime cargo transportation through strait waterways faces elevated accident risks, with incidents exhibiting distinct spatiotemporal clustering according to the statistics from the International Maritime Organization. This study addresses spatiotemporal risk heterogeneity by developing an integrated assessment and optimization framework. Based on the data from the Automatic Identification System (AIS), this paper quantifies the dynamic risk profiles across strait waterways at various temporal scales with grid-based analysis and matter-element extension theory. A mixed-integer programming model was formulated with the aim of maximizing fleet profitability subject to aggregate risk constraints. The model simultaneously optimizes vessel speeds, route selection, and precise navigation paths through straits, and uses the Gurobi solver to solve efficiently. Empirical validation using the crude oil transportation data from a major shipping company reveals the key insights as follows: dynamic speed adjustment and adaptive routing effectively mitigate exposure to high-risk zones; fleet revenue increases monotonically with the relaxation of risk threshold; delay penalties exhibit non-linear impacts, with initial implementation (0~5%) reducing revenue by 7.04%, while subsequent increases (5%~30%) yield only 3.54% additional reduction; lower risk thresholds demonstrate reduced sensitivity to demand volatility; and price elasticity shows near-proportional relationships, with 5% price increases generating approximately 5.5% revenue growth. The findings provide some optimization recommendations for shipping companies based on their risk preferences and practical demands for navigating through straits.

Select

Integrated Location-Routing Optimization for Two-echelon Logistics Network with Heterogeneous UAVs

GENG Shaoqing, ZHAI Yibing, CAO Yunchun

Journal of Transportation Systems Engineering and Information Technology 2026, 26 (1): 34-44. DOI: 10.16097/j.cnki.1009-6744.2026.01.004

Abstract （703）

PDF （2066KB）（348）

PDF(English version) （1118KB）（13）

To address the bottlenecks faced by UAVs in conducting trunk and last-mile delivery operations across complex terrains, this paper investigates the location-routing problem for a two-echelon logistics network composed of regional hubs and local distribution centers. A critical gap in existing research is the tendency to overlook the functional and cost heterogeneity among UAVs operating at different echelons. To fill this gap, we formulate a mixed-integer programming model with the objective of minimizing the total system cost, which comprises the facility construction, differentiated two-echelon transportation, and time penalty costs. The model is designed to jointly optimize the distribution of two-echelon facilities, UAV delivery routes, and timeliness of customer service. To solve this problem, a hybrid algorithm is designed, in which a Genetic Algorithm is employed for global facility location and customer allocation, while a Variable Neighborhood Tabu Search is utilized for local route optimization. A case study of Yunlong County, Yunnan, demonstrates that, compared to a sequential decision-making approach, the proposed joint optimization method reduces the total cost of system by 88.3%. Furthermore, in contrast to a single-echelon direct delivery network, the on-time delivery rate is enhanced to 91.9%, achieving an effective balance between cost and service quality. This research provides an effective decision-making model and methodology for the planning and operation of distributed UAV logistics networks.

Select

Heterogeneity Analysis of Human-Factor Traffic Accidents on National and Provincial Arterial Roads Considering Chain-based Causal Inference

YAO Liang, ZHANG Wengui, WU Li, LIU Zunqing, CHEN Yile

Journal of Transportation Systems Engineering and Information Technology 2026, 26 (1): 360-370. DOI: 10.16097/j.cnki.1009-6744.2026.01.033

Abstract （291）

PDF （2493KB）（291）

PDF(English version) （1184KB）（5）

To investigate the heterogeneity characteristics of human-caused traffic accidents on national and provincial arterial roads, this paper proposes a chain causal inference method with the synchronous analysis to analyze the causal mechanism of traffic accidents and quantify the interaction effects. First, the recent traffic accident information was collected from 11 arterial roads in Xinjiang province, and the severity of the accidents was classified into three categories using the K-prototype clustering algorithm. Based on the structural causal model, causal forest, and the SHAP (SHapley Additive exPlanation) algorithm, the study proposes a chain causal inference model to infer the key causal chains of the accidents, the interaction effects of multi-dimensional causes, and the predicted values of the accident categories. The chain transmission characteristics and heterogeneity of "scene combination-human factors-accident category" were also analyzed. Then, the contribution of multi-dimensional factors was evaluated based on the SHAP value. The behavioral causes were classified according to the strength of the causal effect to identify the key human causes of the accidents and propose targeted accident prevention and control strategies. The results show that:(1) the weighted average F1 score and macro average AUC (Area Under Curve) value of the proposed method are 0.86 and 0.82, which are relatively higher than those of commonly used machine learning algorithms. The proposed method overcomes the limitations of traditional association models in characterizing and quantifying the interaction mechanism of multiple factors, making it suitable for the analysis of the heterogeneous mechanism of human-caused accidents. (2) From the analysis of the causal chain, it can be seen that human factors are the main causes of accidents, and environmental factors such as weather and time periods have significant impacts on the consequences of accidents. The combined effect of adverse environments and dangerous behaviors has a nonlinear impact on the escalation of accident severity. (3) Speeding, fatigued driving, following too closely, and poor observation are the critical behavioral causes. For commercial vehicles, these driving behaviors normally cause higher severity of the accidents(i.e., the proportion of severe accidents exceeding 50%), which indicates an important area for accident monitoring and prevention.

Select

Resilience Evolution and Improvement Methods of Regional Road Traffic Networks Under Uncertain Disturbances

WANG Xiaorong, LI Yinzhen, SUN Yingjie, XIAN Yong, LI Wen, JU Yuxiang

Journal of Transportation Systems Engineering and Information Technology 2026, 26 (2): 47-59. DOI: 10.16097/j.cnki.1009-6744.2026.02.005

Abstract （346）

PDF （3240KB）（350）

PDF(English version) （5392KB）（6）

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

Select

Traffic Flow Characteristics of Large-flow Expressway Based on Electronic Toll Collection Data

XU Jin, ZHANG Gaofeng, JIN Yong, WANG Tao

Journal of Transportation Systems Engineering and Information Technology 2026, 26 (2): 328-341. DOI: 10.16097/j.cnki.1009-6744.2026.02.031

Abstract （340）

PDF （4534KB）（363）

PDF(English version) （2943KB）（3）

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

Topics