Journal of Advanced Transportation最新文献

Mode choice behavior directly affects the layout of the urban transportation system and serves as the foundation for the development of policies about the planning and administration of urban transportation. This study focused on the city’s various transportation options, aiming to pinpoint and investigate several factors influencing transportation mode selection. The data included the traffic survey; traveler interviews were gathered using a questionnaire survey, structured interviews, and secondary documents. The collected data from the questionnaire survey were then analyzed using a multinomial logit (MNL) model to assess the relationships between different parameters and mode choice. The investigation considered several concerns, including travel distance, travel time, travel cost, safety, environmental impact, health benefits, and comfort. Both qualitative and quantitative methods of sustainability have been integrated into this study. The MNL model’s pseudo-R-squared value illustrates the apparent correlation between the independent and dependent variables. The multilayer perceptron (MLP) model was used as a comparison model. The results show that MLP has higher predictive performance than the MNL model in assessing transport mode choice in the city. The study reveals that travel distance, time, availability, health benefits, comfort, safety, cost, and environmental impact significantly influence mode choice for work trips. Public services are safer, less environmentally impactful, and more accessible, while walking is safest, offers health benefits, and is more environmentally friendly but is preferred by the youngest. Private vehicle users offer more safety but are less cost-effective. Minibus users provide better cost-benefit and safety but take longer travel times. Overall, the study was used to understand passenger preferences and critical factors in transport options, thereby aiding policymakers in making informed decisions and suggestions for improving the transport system in similar cities.

In order to better understand the factors that affect Hangzhou residents’ acceptance of the license plate–based restriction (LPR) policy, new factors such as fairness, family life cycle factors, and preacceptance of alternative measures were added to explore new interactions between different factors. A questionnaire survey was completed among 958 residents of Hangzhou City, and a partial least squares structural equation model (PLS-SEM) was established to analyze the factors that affect the acceptance of the LPR policy. An analysis of socioeconomic attributes is conducted to explore the impact of education, age, and family life cycle factors on the acceptance of the LPR policy. The results indicate that perceived cost-effectiveness, social norms, policy cognition, fairness, important goals, and preacceptance of alternative measures have significant direct effects on the postacceptance of the LPR policy, while fairness and important goals have indirect effects through social norms. Regarding postacceptance, perceived effectiveness can only indirectly affect postacceptance of the LPR policy through policy cognition and perceived cost-effectiveness. Responsibility attribution can only indirectly affect postacceptance through important goals. As the education level and age increase, residents’ acceptance of the LPR policy will decrease; young families without children and families with minor children have lower acceptance of the LPR policy than families with all adult members and elder families without children.

Vehicle forward collision warning based on machine vision can help to reduce the incidence of traffic accidents. Many researchers have studied this topic in recent years. However, most of the existing studies only focus on one stage of the process such as vehicle detection and distance measurement. It will face many issues in practical application. To solve these problems, we propose a framework for forward collision warning. This study applies the YOLO algorithm to detect the vehicle and uses the Kalman filter to track the vehicle. The monocular vision distance measuring method is used to estimate the distance and travel speed. Finally, we adopt the time to collision (TTC) to decide whether to trigger the warning process. In the speed measurement stage, we design an appropriate time interval to calculate the relative speed of the front vehicle. In the collision warning segment, a TTC threshold is set by considering not only vehicle safety guarantees but also avoiding hard barking that would make drivers uncomfortable. Furthermore, we set a warning area to filter the false warning when the car overtakes and meets. Experiments with real traffic scenes demonstrate that the performance of the proposed model is good to make accurate collision prediction and warning.

The customized bus in operation faces numerous random factors that affect the service level and attractiveness to passengers. Therefore, this paper investigates the optimization problem of customized bus routes considering random vehicle travel times and the capability to respond to dynamic requests in real time. We developed a stochastic programming model that minimizes total cost and passenger travel time. The innovation lies in the model’s ability to respond to requests made by passengers during service and to model the randomness of vehicle travel times using a known distribution. Furthermore, we propose a heuristic algorithm combining the nondominated sorting genetic algorithm II (NSGA-II) and a variable neighborhood search operator. This algorithm starts by generating an optimized initial path based on initial reservation demands and then employs a dynamic adjustment mechanism to respond to real-time requests. The effectiveness and superiority of our algorithm are validated through an illustrative example. Finally, numerical experiments using taxi trajectory data demonstrate that considering both randomness and real-time aspects can significantly reduce the total cost and penalties for early and late arrivals and improve the bus service level.

Aiming to address the issue of highway traffic safety under crosswind conditions, this study utilizes the CarSim/TruckSim simulation platform to systematically analyze the effects of crosswind speed and direction on the driving stability of cars and trucks. A safety speed model is developed for different road adhesion coefficients, and a highway crosswind warning system is designed. Through 625 simulation experiments, the study reveals that lateral offset, lateral acceleration, and lateral load transfer rate are significantly influenced by vehicle speed, wind speed, wind direction, and road adhesion coefficient, with the road adhesion coefficient identified as the key factor. Separate safety speed models for cars and trucks under various road and crosswind conditions are established. The findings are as follows: for cars, crosswind speed and direction impact safe driving speed only when the road adhesion coefficient is 0.1. Overall, for constant wind direction, safe driving speed decreases as wind speed increases; at a constant wind speed, safe driving speed gradually decreases as wind direction shifts from 45° to 135°. For trucks, when the road adhesion coefficient ranges from 0.1 to 0.9, the relationship between safe driving speed, wind speed, and wind direction mirrors that of small cars. However, the critical safety speed for trucks is 40% lower than that for cars under identical crosswind conditions when the road adhesion coefficient is 0.1. Based on the Visual FoxPro platform, which enables real-time early warning decision-making through the integration of the safety speed model, the highway driving stability early warning system (comprising information collection, processing, and release modules) is applied to the Zhengzhou Taohuayu Yellow River Highway Bridge case. The system is verified to significantly enhance highway driving safety and provides technical support for dynamic safety management and control of highways under crosswind conditions.

The burgeoning urbanization and construction activities pose significant challenges to the structural integrity and safety of the existing metro tunnels. This study introduces a hybrid spatial–temporal deep learning model, integrating graph convolutional network (GCN) and long short-term memory (LSTM) networks, to predict metro tunnel displacements under the imperatives of “dual carbon” goals. The model leverages the strengths of GCNs in capturing spatial correlations and LSTM networks in processing temporal dynamics, offering a robust framework for accurate displacement prediction. The methodology encompasses data preprocessing, including outlier removal and missing value imputation, followed by feature extraction and normalization. The proposed GCN-LSTM model is trained on historical displacement data, employing a robotic total station (RTS) for high-precision monitoring. The model’s performance is evaluated using metrics such as root mean square error (RMSE), mean absolute error (MAE), and weighted mean absolute percentage error (WMAPE) and is compared against other models including LSTM, recurrent neural network (RNN), gated recurrent unit (GRU), residual LSTM (ResLSTM), and a variant of GCN-LSTM. The results indicate that the GCN-LSTM model outperforms comparative models across various sliding window sizes, demonstrating lower error metrics and higher stability. The model’s efficacy is further corroborated through a case study on the Jinan Metro Line 2, where it provides reliable predictions crucial for proactive maintenance and sustainable urban development. The study contributes to the field of metro tunnel displacement prediction and supports the advancement of intelligent monitoring systems for urban infrastructure.

This paper presents an insightful examination of the modeling and efficient solution algorithm for the link capacitated nonadditive traffic assignment problem (CNaTAP) to provide highly accurate flow solutions for large-scale networks. Despite the increasing significance of the CNaTAP, the ability to efficiently solve it for satisfactory accuracy in practical applications remains inadequate. Given that existing CNaTAP models and algorithms are typically limited to small experimental networks, the CNaTAP model is formulated as a variational inequality (VI) problem in this paper. This formulation is decomposed into two VI subproblems that involve equilibrium and capacity constraints, utilizing the Karush–Kuhn–Tucker (KKT) conditions. The Lagrangian multipliers for the capacity constraints are treated as fixed costs for the links in the equilibrium subproblem, ensuring the stability of the Cartesian product structure within the feasible set. This approach facilitates the decomposition of OD pairs, enabling the efficient solution of CNaTAP in large-scale networks. In addition, an algorithmic framework is developed that incorporates high-frequency updates of these Lagrangian multipliers, along with an adaptive Barzilai–Borwein (ABB) step-size calculation method applied to expedite convergence in the equilibrium subproblem. Extensive numerical experiments confirm the efficacy of the proposed algorithm in efficiently solving large-scale networks with high convergence accuracy.

Magnetic sensor-based vehicle detection is a crucial approach for traffic information collection. However, existing methods that rely on individual magnetic sensors—typically installed at the lane center or roadside—struggle in multilane scenarios due to weak data correlation across sensors and limited accuracy from the isolated sensor. To address these challenges, this paper proposes a novel method that integrates a distributed wireless magnetic sensor network with a temporal-spatial correlation algorithm to associate vehicle signals from multiple sensors. Compared with traditional single-sensor methods, the proposed approach significantly enhances detection reliability by enabling cross-lane vehicle signal fusion. A vehicle position localization technique is introduced to identify detection events, achieving a detection rate of approximately 90%. Experimental results show that while common errors include lane positioning, duplication, omission, and interference, these tend to counteract each other, resulting in a traffic volume detection accuracy of 99.6%. Furthermore, a cellular automaton-based trajectory tracking model is proposed to connect vehicle positions into continuous trajectories, yielding an 89.0% trajectory accuracy and further reducing detection errors. The construction of vehicle trajectories also lays a foundation for future applications such as vehicle speed estimation and vehicle type classification.

Automated warehousing and distribution has been an innovation approach to reduce costs and increase efficiency in logistics industry. Taking the intelligent demonstration warehouse in a commercial logistics park in Shandong, China as the background, this paper constructs a platform resource scheduling model under the Automatic Guided Vehicle (AGV) sharing mode to solve the problems of platform allocation and equipment scheduling, and solves it using the simulated annealing algorithm. This paper designs First-Come, First-Served (FCFS) rule, platform resource scheduling rules when AGVs are used separately, and platform resource scheduling rules when AGVs are shared, outputting platform operation scheduling schemes. Meanwhile, different numbers of AGVs are scheduled under the AGV sharing mode to validate the model and algorithm. The results show that the platform resource scheduling model proposed in this paper improves the platform utilization rate by 4.4% compared to the traditional FCFS rule, and the latest departure event is advanced by 95 min. The AGV sharing mode can complete vehicle loading tasks in a shorter time and with faster operational efficiency.

This comprehensive review examines various controller architectures for autonomous driving systems, from rule-based approaches to advanced deep learning methods. Research trends reveal a significant shift toward deep learning approaches (65.6%) compared to rule-based methods (34.4%), reflecting the growing dominance of data-driven techniques in autonomous vehicle research. Performance analysis of transformer-based models demonstrates exceptional accuracy, with ViT-SAC achieving 100% success rate in low-density traffic scenarios and DRLNDT reaching 99.9% success rate in navigation tasks. Temporal reasoning capabilities assessment shows BEVWorld excelling in context maintenance and historical data integration (both 95/100), while Holistic Transformer demonstrates superior noise robustness (95/100). Computational efficiency varies significantly, with VCNN (38.50 FPS) and DSCNN Transformer (34.07 FPS) exceeding real-time thresholds, while complex BEV architectures like BEVSegformer (3.97 FPS) require further optimization. Simulation platform comparison identifies CARLA as the most comprehensive environment, supporting five of seven key testing features, though no single platform provides complete coverage of all requirements. Technical challenges assessment quantifies real-time processing requirements as the most critical challenge (90/100), followed by generalization limitations (85/100). These suggest that while rule-based approaches offer computational efficiency and interpretability, deep learning methods demonstrate superior perception and decision-making capabilities. A balanced combination of learning-based, rule-based, and simulation-based validation approaches, with particular emphasis on addressing real-time performance and generalization capabilities, will likely be necessary to achieve reliable autonomous driving systems capable of navigating complex and dynamic environments.