Journal of Advanced Transportation最新文献

{"title":"Long-Distance Route Planning for Electric Vehicles Considering Range Anxiety and Fatigue Constraints","authors":"Hongfei Jia,&nbsp;Xinlin Zhou,&nbsp;Chao Liu","doi":"10.1155/atr/8207083","DOIUrl":"https://doi.org/10.1155/atr/8207083","url":null,"abstract":"<p>This research proposes to tackle two important issues in long-distance travel for electric vehicles (EVs): range anxiety and driver fatigue. A new route planning approach integrating the perception of psychological and physiological states is presented. This model quantifies range anxiety by integrating conditions such as battery depletion, charging station coverage, and traffic congestion and explicitly considers the evolution of driver fatigue influenced by continuous driving behavior and circadian rhythms, as well as a time-overlapping strategy for simultaneously arranging charging and rest tasks. To efficiently solve the route planning problem for large-scale road networks, this research develops an optimization approach using an A^∗-guided adaptive genetic algorithm (A^∗-AGA), which integrates heuristic search and evolutionary optimization. Simulation experiments on typical long-distance routes demonstrate that the approach is highly effective in reducing driver anxiety and fatigue, optimizing the total travel time, ensuring the route′s feasibility, and greatly improving the long-distance EV driving experience.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/8207083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148077362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement Learning–Based Timetabling of Bus Routes Subject to Oversaturation","authors":"Zongjie Pan,&nbsp;Wenbo Fan,&nbsp;Zhiyuan Zheng,&nbsp;Xiaotian Yan,&nbsp;Yu Mei","doi":"10.1155/atr/5954156","DOIUrl":"https://doi.org/10.1155/atr/5954156","url":null,"abstract":"<p>Timetables are crucial for the efficient operation of public transportation. This paper tackles the timetabling problem of a regular bus line facing oversaturation due to stochastic fluctuation in demand and a lack of fleet, making it more complex than traditional undersaturated scenarios. By oversaturation, we mean some passengers may not board the first-arrival bus and must wait for subsequent ones. We propose a reinforcement learning (RL) model based on the Markov decision process (MDP) to optimize the timetable. The objective is to minimize the expected total system costs, involving the costs of passengers and the operator. Constraints concerning vehicle capacity and fleet size are respected. The dynamics of passengers are captured in the state transition process. The proposed model is applied to the No. 8 bus route in Sanya, China. Specifically, the proximal policy optimization (PPO) algorithm is employed. Under deterministic demand, we compare the optimized timetables against the results obtained by classic models. The results show that the proposed model outperforms classical models in terms of both solution quality and computational efficiency. Under stochastic demand, as variance in demand increases, the benefit of the proposed model in the system cost and passenger waiting time becomes more significant. In addition, we optimize the fleet size, accounting for the acquisition cost of vehicles.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/5954156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148069222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward Class Imbalance in Facial Pain Recognition for Transportation Safety: An Enhanced ResNet–BiLSTM Framework","authors":"Zhicui Lu,&nbsp;Aisyah Mat Jasin,&nbsp;Farizuwana Akma Zulkifle,&nbsp;Mohd Razif Shamsuddin,&nbsp;Ahmad Zia Ul-Saufie,&nbsp;Mingyuan Fu,&nbsp;Jiaqi Zhao","doi":"10.1155/atr/2975355","DOIUrl":"https://doi.org/10.1155/atr/2975355","url":null,"abstract":"<p>Pain is a complex experience involving both sensations and emotions, and its accurate assessment is essential in healthcare and intelligent transportation. Where diverse traffic environments can heighten driver discomfort, leading to fatigue, reduced focus, and increased accident risk. In recent years, extensive studies have demonstrated the potential of deep learning techniques for automatically predicting pain intensity by analyzing pain-related facial expressions. However, two fundamental challenges remain in real-world applications: the uncertainties arising from nonstandardized data collection and the class imbalance problem in pain assessment datasets. Addressing this issue is essential to improving recognition accuracy and robustness. This study proposes a novel pain level recognition framework based on a ResNet–BiLSTM architecture. First, the Squeeze-and-Excitation attention mechanism is integrated to enhance the model’s ability to capture basic pain-related facial features, thereby increasing robustness against data uncertainties. Additionally, the I-center loss function is introduced during model optimization, enhancing intraclass compactness and interclass separability to improve class-imbalanced model learning. Experimental results on the BioVid Heat Pain Database demonstrate that this approach outperforms state-of-the-art baselines, achieving 99.67% accuracy. Furthermore, ablation studies validate the effectiveness of the proposed components in our framework.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/2975355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148065576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on Quantum Approximation Optimization Algorithm in Airport Cargo Transportation Problem","authors":"Xudong Zhao,&nbsp;Jianping Chen,&nbsp;Lan Liu,&nbsp;Leilei Kang","doi":"10.1155/atr/4046578","DOIUrl":"https://doi.org/10.1155/atr/4046578","url":null,"abstract":"<p>The vehicle routing problem (VRP) is a core NP-hard combinatorial optimization problem in logistics and supply chain management. Quantum computing, particularly the Quantum Approximate Optimization Algorithm (QAOA), is being explored as a promising heuristic tool for tackling such problems. Therefore, this study employs the QAOA approach to solve the airport cargo VRP. Subsequently, through a concrete airport cargo case study, the paper analyzes the process of solving single-vehicle TSP and multivehicle VRP problems using QAOA. It compares the performance of different classical optimizers and discusses the impact of parameter settings (e.g., penalty factors, QAOA depth, and number of iterations) on the solution quality and feasibility, especially comparing the convergence speed of dynamic and fixed penalty coefficients. The paper also describes the design principles of QAOA quantum circuits for such VRP problems. Furthermore, the study presents a preliminary benchmark on a real quantum computer, demonstrating that its computation time for the problem’s QUBO matrix (14 ms) is approximately comparable to that of classical algorithms. Finally, it summarizes the current scalability challenges and limitations faced by QAOA in VRP applications concerning qubit count, circuit depth, and classical optimizer performance, and offers an outlook on future development directions. The research indicates that although QAOA demonstrates solution capabilities on specific small-scale VRP instances, the complexity of parameter tuning and sensitivity to the spatial distribution of aircraft stands are critical considerations before practical application.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/4046578","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148095965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling Short-Term Highway Traffic Conflict Frequency: Integrating Interaction Effects With Stochastic Components","authors":"Hongxian Sun,&nbsp;Xingcai Zheng,&nbsp;Xiaoan Wang,&nbsp;Ruiqi Yin,&nbsp;Min Xu,&nbsp;Dan Liu,&nbsp;Jing Cai,&nbsp;Ran Zhang","doi":"10.1155/atr/1996113","DOIUrl":"https://doi.org/10.1155/atr/1996113","url":null,"abstract":"<p>The application of traffic conflicts in road safety assessment is increasingly favored, owing to its critical importance in conducting real-time safety analyses and formulating proactive safety management strategies. The study aims to construct an accurate model to predict the frequency and occurrence of traffic conflicts on highways within a short time frame. Utilizing the highD dataset, we construct traffic characteristic indicators as covariates, including traffic volume (TV), speed variation coefficient (SVC), the proportion of large vehicles (PLV), lane-to-lane average speed difference (LASD), and front-to-rear vehicle average speed difference (FASD). Traffic conflict frequency, measured by the time-to-collision threshold of fewer than 4 s, serves as the dependent variable. The study employs zero-inflated Poisson, zero-inflated negative binomial, hurdle Poisson, and hurdle negative binomial models to fit conflict frequency, accounting for interaction effects, random effects, and random parameters. The findings indicate that models incorporating interaction effects yield a better fit than those not considering interaction effects. Furthermore, models considering interaction effects, random effects, and random parameters show superior fit compared to models only considering interaction effects, albeit with increased complexity under the hurdle Poisson and hurdle negative binomial distributions. These results offer valuable insights for managing and controlling highway traffic safety.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/1996113","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148095968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Evolution of Multilevel Rail Transit Destination Choice Behavior Based on Disaggregate–Reinforcement Learning","authors":"Wei Gao,&nbsp;Enjian Yao,&nbsp;Yongsheng Zhang,&nbsp;Keyan Bai","doi":"10.1155/atr/2377931","DOIUrl":"https://doi.org/10.1155/atr/2377931","url":null,"abstract":"<p>To address the distribution of origin–destination (OD) passenger flow in multilevel rail transit complex network, we propose a simulation method based on travel behavior and reinforcement learning for travelers’ multilevel rail transit network destination choice behavior. First, we extended the node–place (NP) model into node–place–ridership (NPR) model as the ridership and the level of rail transit are introduced into the indicators. The place value is calculated based on the difference in the catchment area of stations at each level to construct an indicator system for stations. From the perspective of traveler behavior, a destination choice model is developed using the station’s NPR variables. Then, the Bush–Mosteller (BM) reinforcement learning model is employed to simulate the evolutionary mechanism of travelers’ destination choice behavior under the influence of group decision-making characteristics, thereby designing an OD matrix convergence algorithm. The methodology’s validity is demonstrated through a case study involving a multilevel rail transit network in Guangzhou and Foshan, Guangdong Province. Results indicate that the approach effectively captures the choice behavior mechanisms of travelers within incomplete information contexts. The evolution of OD passenger flows between intracity, suburban city, and intercity stations exhibits distinct patterns; however, OD volumes between stations spanning different hierarchical levels remain consistently low (lower than 300) and stable. During the propagation and diffusion of travel-related information throughout the network, the positive effect exerted by dominant stations demonstrates significantly greater transmission intensity than the negative effect associated with nondominant stations. The estimated result of the same-level dummy in the destination choice model (0.527–0.825) indicates that the interchange barrier between interlevel OD is a major factor affecting the travel of multilevel rail users.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/2377931","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148095964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Operational Safety of New Energy Truck Platoons Under Information Uncertainty and Longitudinal Slope","authors":"Liangfeng Xie,&nbsp;Guizhen Yu,&nbsp;Junjie Zhang,&nbsp;Bo Yang,&nbsp;Xiangyu Shao,&nbsp;Bin Zhou","doi":"10.1155/atr/7257516","DOIUrl":"https://doi.org/10.1155/atr/7257516","url":null,"abstract":"<p>Autonomous truck platooning represents a transformative advancement in modern highway logistics, offering substantial benefits in fuel efficiency and traffic throughput. However, the practical deployment of such systems is frequently compromised by sensing limitations and environmental factors, particularly localization uncertainty and varying longitudinal road slopes. In this study, the desired safety margin (DSM) model is employed to systematically investigate how these variables jointly affect platoon-level string stability and the safety performance of car-following maneuvers. Through rigorous linear stability analysis, we derived a novel stability criterion that explicitly accounts for stochastic localization errors and road geometry parameters. The findings reveal a critical trade-off: a negative uncertainty value, indicating a conservative distance estimation, significantly expands the stable region, thereby reinforcing platoon stability and mitigating the likelihood of rear-end collisions. Conversely, with positive uncertainty, the stability region is reduced, while dynamic performance is improved by shortening the system’s response time to lead-vehicle maneuvers. Furthermore, although the longitudinal slope has a negligible impact on fundamental stability characteristics, it exerts a decisive influence on operational safety due to the gravity-induced longitudinal acceleration component. Consequently, the results indicate that constraining randomly varying uncertainty within a calibrated range, together with slope-aware control, can optimize the balance between stability and responsiveness, offering valuable theoretical guidance for robust control design in next-generation autonomous heavy-duty vehicles.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/7257516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148099006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Severity Grading Method for Traffic Interruptions Induced by Geological Hazards: A Case Study of Tibetan Highways","authors":"Jianbei Liu,&nbsp;Zhiqiang Li,&nbsp;Tian Xu,&nbsp;Gandu Chi,&nbsp;Ling Wu,&nbsp;Xianyong Liu,&nbsp;Jian Tian","doi":"10.1155/atr/3628595","DOIUrl":"https://doi.org/10.1155/atr/3628595","url":null,"abstract":"<p>The complex geological environment and frequent natural disasters on the Qinghai–Tibetan plateau pose significant challenges to the reliability of the regional highway transportation network. In this paper, a severity grading method for traffic interruption caused by geological disasters on Tibet highways is proposed, based on principal component analysis (PCA) and spectral clustering (SC). Firstly, the causes of typical highway hazards, such as mudslides, subsidence, flood damage, collapse, and landslides, and their effects on traffic interruption were analyzed, and a classification system for these hazards was constructed. Subsequently, PCA was employed to extract the key factors affecting the severity of traffic interruption from multidimensional disaster data, thereby reducing data dimensionality and improving model interpretability. Based on dimensionally reduced data, the SC algorithm was used to classify the severity of traffic interruption, and a four-level classification system was established. The results show that high traffic volume, large affected areas, steep road gradients, and severe road hazards are the main factors leading to moderate and severe traffic interruption. The classification model proposed in this paper can provide a scientific basis for early warning and monitoring, emergency response, and optimal resource allocation for highway disaster management in Tibetan areas.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/3628595","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148094755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Eco-Cooperative Driving at Back-to-Back Signalized Intersections for Battery-Electric Vehicles During Both Red and Green Phases of the Traffic Light","authors":"Ali Parsi,&nbsp;Ebrahim Farjah,&nbsp;Teymoor Ghanbari","doi":"10.1155/atr/5983597","DOIUrl":"https://doi.org/10.1155/atr/5983597","url":null,"abstract":"<p>Battery electric vehicles (BEVs) are known as a sustainable solution for decarbonizing the transportation system and attaining the objectives of Paris Agreement. The main challenge issues for the widespread adoption of BEVs are battery capacity and driving range limitations. Signalized intersections are one of the places where a considerable amount of the total trip energy is consumed due to their stop-and-go nature. One efficient solution to deal with range limitations in BEVs is eco-driving. In this study, an eco-cooperative driving strategy was developed for BEVs at back-to-back signalized intersections in free-flow traffic. The optimal trip time and acceleration and deceleration levels are calculated to enable the vehicle to pass through signalized intersections without stopping. The proposed eco-cooperative driving optimizes a cost function that considers both energy consumption and travel time. The optimization problem was solved using an approach based on the particle swarm optimization technique. The results show that the proposed eco-cooperative driving strategy could save energy consumption and trip time up to 26.61% and 44.29%, respectively.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/5983597","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148093136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Electric Interterminal Transport: A Truck Decoupling System With Early Information on Arrivals","authors":"Matteo Brunetti,&nbsp;Eduardo Lalla-Ruiz,&nbsp;Martijn Mes","doi":"10.1155/atr/8968454","DOIUrl":"https://doi.org/10.1155/atr/8968454","url":null,"abstract":"<p>We focus on the design of a truck decoupling system employing interterminal transport (ITT) vehicles at a logistics node, such as a port or business park. We assume the node faces a stochastic flow of trucks delivering and retrieving containers from logistics companies (LCs), i.e., warehouses and terminals. During peak hours, trucks may stop at a parking area, where the ITT fleet, composed of manned or automated electric vehicles, takes over container transport between the parking area and the LCs. The decoupling decision determines whether trucks should proceed to their LC, park, or decouple. The decision model is based on several parameters, such as the estimated workload of the ITT fleet over a time window. We assess the decision model using a discrete event simulation model of the Port of Moerdijk, The Netherlands. This allows experimenting with various arrival patterns, earliness of information, the charging infrastructure, and decoupling decision parameters. The simulation model involves realistic traffic behavior, six decoupling scenarios, and more than 130 LCs. Through parameter calibration, the decision model capitalizes on early information to reduce truck turnaround time and truck-driven kilometers by up to 19%, preventing 14.4 metric tons of CO₂eq emissions per day and reducing the share of late containers by up to 10%.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/8968454","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147714954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}