Journal of Advanced Transportation最新文献

Shared micromobility services are experiencing rapid growth, particularly in addressing last-mile transportation needs. The most crucial questions focus on identifying the determinants of user behavior and the factors driving demand for micromobility vehicles. Investigating this topic is thus essential for meeting the demand of micromobility vehicles, ensuring their dynamic and flexible deployment, and optimizing overall system planning. In this study, demand forecasting was performed using a shared electric scooter (e-scooter) dataset and by comparing 19 distinct machine learning (ML) and deep learning (DL) algorithms, including traditional ML algorithms, neural network–based (NN) models , ANN and metaheuristic hybrid models, and ensemble models. Algorithm performance, evaluated using R² and RMSE metrics, shows that boosting and hybrid models significantly outperform traditional algorithms. In this study, the algorithms were compared not only with RMSE and R² but also with their running times. Our analysis reveals that GRU, ANN–Grid–Search, ANN–Bayesian, ANN–Randomize–Search, ANN-PSO, and ANN-GA models achieve the highest performance, though this performance is inversely related to their computational cost. When the running time is included in the analysis, the GRU algorithm ranks best (RMSE: 0.945248, R²: 0.174226, runtime: 6.1), followed by ANN-GA and ANN-PSO models. These findings will help e-scooter providers plan effectively and make informed investment decisions.

As an important part of modern transportation infrastructure, high-speed rail (HSR) networks not only reduce the spatiotemporal distance between regions but also generate widespread spillover effects through mechanisms such as population mobility, technological innovation, and market expansion. Based on the city-level panel data from 2008 to 2021, this paper uses a spatial econometric model and a generalized structural equation model (GSEM) to study the spatial spillover effects of HSR networks on the three industrial agglomerations and tests the impact mechanism of HSR networks on industrial agglomeration. We find that HSR networks significantly inhibit the agglomeration of primary and secondary industries while significantly promoting that of the tertiary industry. Regional heterogeneity analysis shows that HSR networks have a negative impact on the secondary industry agglomeration in the eastern region but obviously promote the tertiary industry agglomeration, and their promotion effect on the tertiary industry is also significant in the central and western regions. The results of the mechanism test show that HSR networks significantly affect the agglomeration of the three industries through the path of population mobility, technological innovation, and market scale.

Traffic delays are a common challenge for drivers in large cities worldwide. During these delays, drivers must maintain a safe distance from nearby vehicles while avoiding collisions with pedestrians and motorcyclists. This needs frequently alternate pressing and releasing the brake and accelerator pedals, preserving low speeds. Research indicates that this repetitive action can contribute to driver’s fatigue, which is worse for manual vehicles. Other factors, such as inadequate sleep, prolonged driving, monotonous driving conditions, and heavy workloads, may also induce fatigue, further leading to ignorance of the correct seating posture, which can exacerbate the issue. Studies on driver fatigue and its prevention have been widely conducted by scholars and automotive-based industries, focusing on two subject matters: (i) driver fatigue detection systems using various technologies and (ii) fatigue prevention techniques incorporating autonomous braking systems for high-speed and long-distance driving. This paper focuses on extensively reviewing both subject matters, leading to the best proposed solution that can prevent fatigue from happening during road traffic delays at low-speed driving, as limited studies were found that can suit the traffic and social environment in developing countries, i.e., Kuala Lumpur, Malaysia. Clearly, the latter subject area focused on incorporating autonomous braking systems in the electronic control unit (ECU) of vehicles, applicable only for high-end vehicles, thus limiting accessibility. This technology can either reduce the physical effort of pedal pressing or take over the task altogether. The review will examine various causes of fatigue and the existing detection methods, compare the automatic braking solutions’ features, and propose a suitable mechanism that could benefit drivers of all types of vehicles, especially from low- to middle-end vehicles, which addresses the real needs among the affected populations with regard to road traffic delay. The outcome of this review comes in the form of a proposal for mitigating the fatigue issue from happening using a unique technique based on the research gap that is adapted to the targeted environment.

In the context of increasingly complex and diverse traffic environments, where intelligent and connected vehicles (ICVs) coexist with conventional human-driven vehicles, maintaining reliable navigation under global navigation satellite system (GNSS) outages is crucial for supporting adaptive driving strategies and ensuring operational safety. Low-cost inertial measurement units (IMUs) offer promising solutions due to their low computational load and high self-sufficiency, yet error accumulation remains a persistent challenge, particularly in real-world mixed traffic scenarios. This study introduces a dilated convolutional neural network (DCN)–driven framework to directly estimate vehicle forward velocity and IMU error parameters from raw IMU measurements, addressing the reliance on hardware-based odometry by extending nonholonomic constraints (NHC) into three-dimensional velocity constraints. By dynamically optimizing IMU error parameters through integration with an error model, the proposed method mitigates the adverse effects of inherent noise in low-cost IMUs, enabling robust navigation in GNSS-denied environments. Validation using a GNSS/INS dataset demonstrates that the approach accurately estimates vehicle position while significantly suppressing error accumulation, which is pivotal for maintaining reliable navigation in heterogeneous traffic flows where autonomous and human-driven vehicles coexist. This contributes to the development of robust vehicle autonomy and enhanced safety in mixed-traffic ecosystems, enabling more adaptive and resilient driving strategies.

Traffic accidents are one of the leading causes of death and disability, as well as a significant source of economic losses for society. However, the nonlinear and heterogeneous relationships between environmental factors and traffic accidents are complex and difficult to comprehend. This study constructs an explainable spatial machine learning framework using a geographically weighted support vector machine (GW-SVM) model to address issues of nonlinearity, spatial heterogeneity, and interpretability. Based on a large-scale traffic accident dataset and multisource big data, this study provides both global and local explanations for the nonlinear relationships in California, USA. The study finds that (1) humidity plays a more important role in the relationship between environmental factors and traffic accident severity; (2) all environmental variables, including both natural and socioeconomic variables, exhibit nonlinear and threshold effects on traffic accidents; and (3) compared to the existing models, the GW-SVM model performs better in predicting the severity of traffic accidents on urban roads. The results of this study are significant for reducing traffic accident risks.

Owing to undersea-tunnel constraints concentrating off-ramp maneuvers in confined zones, this study optimizes graded lane-changing strategies to mitigate collision risks. Using the Jiaozhou-Bay Undersea Tunnel case, we propose an innovative exit diversion area graded lane-changing strategy comprising Transition Section I, Transition Section II, gradient section, and auxiliary lane. Six schemes were simulated via UC-WinRoad, with driver physiological stress quantified through Tobii eye-tracking as a novel application of pupil dynamics. Four indicators—lane-change position, lane-change rate, pupil diameter, and speed change—were weighted by the integrated analytic hierarchy process and entropy weight method (AHP–EWM) methodology and evaluated via the set pair analysis with the technique for order preference by similarity to ideal solution (SPA-TOPSIS) theory model. Optimal Scheme E (290-m transition I, 210-m transition II, 120-m gradient, and 140-m auxiliary lane) achieved γ = 0.968, significantly reducing pupil fluctuation by 32% compared with the shortest design (Scheme A) while ensuring smoothest speed control. This demonstrates effective conflict distribution in high-risk undersea environments, providing universally applicable design benchmarks for tunnel safety enhancement.

Urban expressways serve as the main arteries of urban transportation. Congestion and traffic disruptions on expressways can easily lead to the paralysis of the entire regional transportation system. Accurately understanding the patterns of congestion formation and influencing factors on expressways is beneficial for improving traffic efficiency and reducing travel costs. This study takes typical sections of expressway diverging and exit sections as examples, introducing traffic data provided by navigation systems to explore the potential influencing factors and formation processes of urban expressway traffic congestion. This study explores the effects of operating conditions, control facilities, road properties, weather, and other factors on the smoothness of exit sections based on navigation and field survey data. The traffic congestion index is used as an indicator of congestion degree to evaluate the smoothness of typical area of urban expressways exit sections and the overall safety of urban roads. A structural equation model is used to construct a traffic congestion impact model. The results show that traffic facilities (β = 0.462, p < 0.001), road conditions (β = 0.177, p < 0.001), road location (β = 0.129, p < 0.001), spatiotemporal characteristics (time of day: β = 0.295, p < 0.001; day of week: β = −0.105, p < 0.001), environment (β = 0.021, p < 0.001), and driving behavior (β = 0.326, p < 0.001) have a significant impact on traffic congestion. And driving behavior can be used as an intermediate variable to affect the relationship between transportation facilities, road conditions, road location, spatiotemporal characteristics, environment, and traffic congestion. The research contributes to a precise understanding of the formation patterns and influencing factors of urban expressway traffic congestion, laying the groundwork for the adoption of targeted traffic management measures to improve traffic flow efficiency and reduce accident occurrences.

Regional rapid rail transit is an emerging rail transit system in China in recent years, with the same level of service frequency and longer station spacing as metro. The traditional fixed train composition mode has weak adaptability to its unbalanced transport demand in time and space, leading to high rolling stock traveling kilometers and operation costs. As a novel operation strategy, the flexible train composition mode can make up for this shortcoming, but the matched rolling stock circulation planning is a complex optimization problem. This paper proposes an operation mechanism of the rolling stock circulation plan under flexible train composition mode with multiple coupling/decoupling operation sites for regional rapid rail transit, where trains can change compositions at both terminal and intermediate stations. A mixed-integer nonlinear programming (MINLP) model is constructed for rolling stock circulation planning based on the proposed mechanism. The optimization objective is to minimize the total operation costs of train services, depot entry/exit processes, and coupling/decoupling activities at terminal and intermediate stations. The model is then reformulated to an equivalent mixed-integer linear programming (MILP) model, which can be solved by the CPLEX solver. A numerical experiment based on the real-world data from a regional rapid rail transit line in China is designed to verify the effectiveness of the model and solution approaches. The results show that the obtained rolling stock circulation plan effectively reduces the rolling stock traveling kilometers and operation costs with the pregiven timetable. The methods in this paper provide dispatchers with more options to better match the transport demand of regional rapid rail transit.

To enhance the rationality of lane-changing decisions and the adaptability of trajectory planning, this study incorporates short-term driving styles to construct a multiobjective optimized lane-changing trajectory planning model based on naturalistic driving data. First, lane-changing behavior rules were defined to extract lane-changing and lane-keeping data. Essential factors influencing lane-changing behavior were identified using the eXtreme Gradient Boosting (XGBoost) model. Based on the essential factors, drivers were classified into three categories (conservative, moderate, and aggressive) using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which revealed the behavioral difference during lane-changing. Subsequently, an attention-enhanced long short-term memory (LSTM) network was employed to predict surrounding vehicle trajectories, generating dynamically updated environmental parameters. Further considering comfort and safety benefits during lane-changing, a multiobjective trajectory planning model was developed. Reinforcement learning algorithms iteratively optimized the trajectories to derive the optimal trajectory. Finally, the behavioral characteristics of planned trajectories for the three categories of drivers and the deviations between planned and actual trajectories were compared. Results indicate that planned trajectories exhibit shorter lane-changing length and higher efficiency compared with actual trajectories. Planned trajectory can smooth microlevel behavior and improve safety and comfort during lane-changing. For different types of drivers, conservative drivers show the longest lane-changing length but smallest headway space distances, which reflects drivers’ caution during lane-changing. Aggressive drivers mostly focus on speed improvement. The findings can be applied to vehicle trajectory planning in connected environments, which can enhance the lane-changing efficiency while ensuing safety.

High-speed railway (HSR) station area is the key focus of urban construction, and the development of HSR station area has significant regional differences. This study adopts the association rule mining model and ordinary least squares (OLS) regression method to explore the relationships among the development level of the station-setting cities, the development level of the HSR station area, and the urban vitality of the HSR station area. It further investigates how each factor influences the urban vitality in the HSR station area. Findings reveal a prominent multicenter clustering pattern in the urban vitality of the HSR station area. The association rule mining analysis reveals a clear and complex link between the urban vitality in the station area and the influencing factors of the development level of the station-setting cities and the development level of the HSR station area. OLS regression analysis results indicate that the proportion of the tertiary industry in GDP and the intensity of intracity travel are significantly positively correlated with urban vitality in the HSR station area, directly contributing to the growth of the urban vitality. The study’s innovation mainly lies in utilizing multisource data to analyze the spatial pattern characteristics and influencing mechanism of the urban comprehensive vitality in the HSR station area from multiple perspectives, as well as applying association rule mining to explore the correlations between urban vitality in the HSR station area and its determinants. From the perspective of urban vitality, gaining deeper insight into the overall development status of the HSR station area and identifying the factors that affect the urban vitality of the HSR station area can support efforts to enhance the vitality of the broader urban environment.