Journal of Advanced Transportation最新文献

{"title":"Nonlinear and Interactive Effects of the Built Environment on Low-Carbon Travel Intentions: Evidence From Large-Scale Map Usage Data in Beijing","authors":"Liyang Hu,&nbsp;Jianke Cheng,&nbsp;Weijie Chen,&nbsp;Hui Bi,&nbsp;Zhirui Ye","doi":"10.1155/atr/1084122","DOIUrl":"https://doi.org/10.1155/atr/1084122","url":null,"abstract":"<p>Understanding the relationship between travel behavior and modifiable built environment attributes is essential for promoting low-carbon urban mobility, particularly under emerging carbon peaking and neutrality targets. While previous studies have explored this relationship, limited attention has been paid to residents’ intentions for low-carbon travel modes. To address this gap, this study employs large-scale, anonymized map usage data from Beijing and applies a gradient boosting decision trees (GBDT) model to examine the nonlinear and interaction effects of built environment attributes on behavioral intentions at both trip origins and destinations. The results indicate that destination road density exerts the strongest influence on low-carbon mode choices, whereas factors such as scenery density and residential density display notable threshold effects. Furthermore, strong interaction effects between residential density and living service density highlight the importance of integrated urban planning to facilitate sustainable mobility. Model validation demonstrates that the GBDT approach outperforms both random forest and multinomial logit models, achieving superior predictive accuracy (85.7%) and effectively capturing complex nonlinear relationships. These findings offer actionable insights for policymakers: interventions should prioritize enhancing road network density up to 18.5 km/km², fostering medium-density residential areas (10–35 units/km²), and integrating comprehensive living services within neighborhoods. Overall, this study contributes a reliable, data-driven evidence base to inform targeted urban transport planning and land-use management for advancing low-carbon urban development.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/1084122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147320826","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":"Evaluating Ticketing Strategies for Parking Compliance on University Campuses","authors":"Atusa Javaheri,&nbsp;Sai Sneha Channamallu,&nbsp;Sharareh Kermanshachi,&nbsp;Jay Michael Rosenberger,&nbsp;Apurva Pamidimukkala,&nbsp;Chen Kan,&nbsp;Greg Hladik","doi":"10.1155/atr/9910359","DOIUrl":"https://doi.org/10.1155/atr/9910359","url":null,"abstract":"<p>Universities play a crucial role in alleviating students’ financial burdens to ensure that the cost of education remains manageable. Parking fines, though often overlooked, contribute to these ancillary costs. While existing literature explores the monetary effects and compliance rates of digital and physical ticketing systems, a significant gap remains in understanding how these methods specifically affect university settings. This study aims to fill that gap by assessing the effectiveness of ticketing practices in reducing parking violations on university campuses, with a focus on the role of warning tickets in promoting compliance and the financial implications of transitioning from digital to physical ticketing methods. The methodology involved comprehensively analyzing 5 years of parking violation data collected from a university campus, applying chi-square and two-sample <i>z</i>-tests, and developing a random forest model. The results show that warning tickets significantly reduce the incidence of repeat violations, making them an effective nonpunitive strategy. Additionally, the transition from digital to physical ticketing methods led to a reduction in multiple violations and a decrease in the average cost per violator by $25. Physical tickets were found to have a stronger deterrent effect due to their immediacy and visibility. The study provides an evidence-based decision framework to help universities calibrate enforcement design choices under budget and equity constraints.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/9910359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146217527","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":"Multiagent Path Planning With Neural Obstacle Avoidance for Autonomous Heavy Trucks","authors":"Yihan Liu,&nbsp;Rauno Heikkilä","doi":"10.1155/atr/3196768","DOIUrl":"https://doi.org/10.1155/atr/3196768","url":null,"abstract":"<p>Autonomous trucks in busy port terminals must navigate narrow aisles, tight corners, and frequent interactions with multiple vehicles while maintaining both safety and efficiency. This paper presents a hierarchical multiagent navigation framework that integrates an enhanced grid-based Theta^∗ global planner with obstacle inflation and clearance-aware costs, an artificial potential field (APF)–based local controller augmented by lightweight neural correction, and a simple coordination protocol for resolving intertruck conflicts. We evaluate the approach in a high-fidelity Unity digital twin of the Port of Oulu using two traffic scenes with three trucks executing simultaneous tasks. Experiments are repeated under identical initial conditions with independent random perturbations to capture run-to-run variability, and results are reported as the mean ± standard deviation. We compare the proposed Theta^∗-based planner with a standard grid-based A^∗ baseline and an 8-neighborhood A^∗ variant under the same occupancy grid, obstacle inflation, and curvature constraints to isolate the impact of expanded action sets within the A^∗ framework. A greedy heuristic baseline is also included in the simpler scene, where it can complete scheduling. Across trucks, Theta^∗ achieves 43.0% lower travel time and 39.4% fewer avoidance events than A^∗ in the dense-yard scene and 59.5% lower travel time and 91.4% fewer avoidance events in the gate–yard scene, while also improving a combined tracking-accuracy index by 22.1% and 12.7%, respectively. Path-tracking evaluation shows stable mean errors (average mean lateral deviation ≈ 0.40 m and mean heading error ≈ 1.69° across trucks), with transient peaks mainly occurring at high-curvature segments, narrow-clearance passages, and interaction-driven maneuvers. We further include a time-bounded scalability study by increasing the local fleet size to assess the coordination overhead under denser intertruck interactions. These results indicate that clearance-aware any-angle planning, together with neural-tuned local avoidance and lightweight coordination, can improve both efficiency and execution quality for port–yard truck autonomy.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/3196768","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146217528","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":"Trajectory Planning and Tracking With Multiobjective Optimization for Connected and Automated Vehicles at Expressway On-Ramps","authors":"Zhibo Gao,&nbsp;Lan Yao,&nbsp;Jin Li,&nbsp;Yanduo Yin,&nbsp;Jian Xiang,&nbsp;Kejun Long","doi":"10.1155/atr/9412778","DOIUrl":"https://doi.org/10.1155/atr/9412778","url":null,"abstract":"<p>On-ramp merging is a common expressway maneuver for connected and automated vehicles (CAVs), where trajectory planning and tracking control are central to avoiding collisions. However, existing studies rarely optimize the selection of merge start and end points and give limited attention to constraints from acceleration-lane length. This study proposes a structured trajectory planning and tracking method with multiobjective optimization under the CAV’s environment. First, by sampling the starting and ending points of the merging process, the quintic polynomial is used to plan the initial trajectory of the merging vehicles, and trajectory safety is checked with a collision-avoidance algorithm based on rectangular vehicle geometry. Then, a multiobjective optimization model selects the on-ramp trajectory by balancing merging urgency, driving safety, traffic efficiency, and comfort. Finally, an integrated tracking strategy combines lateral and longitudinal control: a feedforward LQR for lateral motion and a PID-based longitudinal controller. To further improve the tracking accuracy, the particle swarm algorithm tunes key parameters of the lateral LQR controller. The numerical result demonstrates that the planner can generate smooth and stable trajectories that could be selected as an optimal reference for the tracking controller. The simulation results show that when the initial speed of the on-ramp vehicle is 68 km/h, the maximum tracking errors of lateral and longitudinal displacements are less than 0.02 and 0.2 m, respectively.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/9412778","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147315460","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":"Analysis of the Performances of Electric Vehicle Batteries: A Systematic Literature Review","authors":"Antonio Comi,&nbsp;Ippolita Idone","doi":"10.1155/atr/5546290","DOIUrl":"https://doi.org/10.1155/atr/5546290","url":null,"abstract":"<p>Electric vehicles (EVs) are increasingly becoming the main mobility option, with global sales in 2023 driven predominantly by China (60%), Europe (25%) and the United States (10%). This widespread adoption has intensified demand for EV batteries, which exceeded 750 GWh in 2023, marking a 40% year-on-year increase. As EVs expand in number, battery performance and reuse emerge as pivotal challenges in the transition to sustainable mobility. This study presents a systematic literature review focussing on the performance evaluation and life prediction of EV batteries, with a primary emphasis on lithium-ion technology, which is the more common technologies. Using a bibliometric clustering approach by CiteSpace©, the review identifies key research domains and highlights critical gaps, especially the underrepresentation of real-world usage conditions in performance models. This review integrates findings across diagnostic, modelling and field-performance studies to propose a unified framework for evaluating EV battery performance under dynamic operating conditions. Findings reveal that while numerous diagnostic tools and modelling techniques exist, their applicability to dynamic operational contexts remains limited. The results aim to support future innovation in battery management systems (BMSs), second-life strategies and potentially circular economy applications, providing both a scientific foundation and practical guidance for advancing sustainable energy solutions.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/5546290","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146216988","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":"Improvement and Calibration of Driving Safety Field Model: Resolving Risk Characterization Mismatches","authors":"Yanting Hu,&nbsp;Shifeng Niu,&nbsp;Chenhao Zhao,&nbsp;Jianyu Song,&nbsp;Min Li","doi":"10.1155/atr/5573870","DOIUrl":"https://doi.org/10.1155/atr/5573870","url":null,"abstract":"<p>The driving safety field (DSF) model, which comprehensively evaluates driving risks in complex environments by integrating human–vehicle–road factors, serves as a quantitative methodology for assessing dynamic traffic risks. However, it exhibits limitations in certain scenarios where its risk characterization deviates from actual risk variations. To address the challenges, an improved driving safety field (IDSF) model is proposed. The new framework redesigns the calculation of virtual mass, field force, and driving safety index. Parameters in the model were calibrated using accident data and driving simulator experiments. Results demonstrate that the IDSF outperforms conventional time-to-collision (TTC) inverse (TTCi) and DSF models. Specifically, in car-following scenarios, IDSF demonstrates higher correlation (<i>r</i>≈0.9) with the TTCi model. In complex environments with high vehicular heterogeneity, compared with the DSF model, the IDSF model exhibits greater stability (80% lower coefficient of variation) and fewer extreme deviations (38% reduction). This study provides a novel theoretical framework for automotive intelligent safety technologies and offers valuable insights for designing more reasonable driving safety algorithms.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/5573870","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146224082","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 and Predicting the Spatiotemporal Dynamics of Construction Waste Hauling Trucks Using an Input–Output Hidden Markov Approach","authors":"Xiang Liu,&nbsp;Boyi Lei,&nbsp;Hongtai Yang,&nbsp;Ke Han,&nbsp;Lee D. Han","doi":"10.1155/atr/8896444","DOIUrl":"https://doi.org/10.1155/atr/8896444","url":null,"abstract":"<p>Construction waste hauling (CWH) trucks are a significant source of air pollution and particulate emissions in urban environments, prompting strict regulatory controls and monitoring. Accurate prediction of their transportation activities, including destinations and arrival times, is critical for improving environmental management and regulatory enforcement. In this study, we present a probabilistic approach that captures the complex spatiotemporal dynamics inherent in the transportation behavior of CWH trucks using the input–output hidden Markov model (IOHMM). This model leverages contextual factors such as historical trajectories, weather conditions, and time-based patterns to make real-time predictions of transportation activities with high accuracy. The model is applied to a dataset of 1000 CWH trucks collected over a 5-month period in Chengdu, China. The model’s performance was evaluated against several baseline methods, including traditional Markov chains, long short-term memory (LSTM) networks, and DeepMove, an attention-based deep learning model. Results demonstrated that the IOHMM outperforms these models in both prediction accuracy and interpretability. Specifically, the IOHMM achieved an average destination prediction accuracy of 51.2%, compared to 47.9% for DeepMove, 43.1% for LSTM, and 39.4% for Markov chains. In terms of arrival time prediction, the IOHMM obtained an accuracy of 38.8%, outperforming all other models, with DeepMove at 36.8%, LSTM at 35.6%, and Markov chains at 27.5%. These findings highlight the IOHMM’s ability to effectively incorporate both spatial and temporal factors in predicting transportation dynamics, providing a powerful tool for regulatory agencies to improve real-time interventions and environmental management of heavy-duty vehicles.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/8896444","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146217047","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":"Temporal Analysis of Nonmandatory Trip Frequency Using an Adaptive Multivariate Ordered Probit Model: Empirical Investigation in Shanghai, China","authors":"Ying Liu,&nbsp;Xin Ye,&nbsp;Kun Huang","doi":"10.1155/atr/3469033","DOIUrl":"https://doi.org/10.1155/atr/3469033","url":null,"abstract":"<p>This study investigates the temporal evolution of nonmandatory trip frequencies in Shanghai over a decade using a temporally adaptive multivariate ordered probit (MOP) model. Two large-scale travel surveys are pooled, and temporal changes are captured through year dummy interaction terms, year-specific threshold shifts, and a year-specific correlation structure. Parameters are estimated using full-information maximum likelihood estimation with an analytic approximation of multivariate normal cumulative distribution. The findings reveal substantial decade-long transformations in discretionary mobility. Gender differences narrowed or reversed across several activities; the impact of aging was apparent; occupational constraints persisted; the influence of central-area residence intensified, reflecting uneven spatial development; and weekend effects weakened, indicating increasingly blurred boundaries between weekday and weekend activity patterns. Correlation patterns across activities also shifted, suggesting changes in trip chaining and time allocation. By developing a unified, temporally adaptive MOP framework capable of jointly capturing structural stability and temporal change, this study provides new empirical evidence on how nonmandatory trip adapts to rapid sociodemographic, economic, and spatial transformations. It offers rare evidence from a major megacity of developing country where activity-based modeling applications remain limited. These insights support the design of context-sensitive transportation and land-use policies.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/3469033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146223967","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":"Optimal Quota for Trip Reservation at Key Corridors of Urban Road Networks","authors":"Shumin Yang,&nbsp;Meiping Yun,&nbsp;Junjun Zhan","doi":"10.1155/atr/9180797","DOIUrl":"https://doi.org/10.1155/atr/9180797","url":null,"abstract":"<p>To alleviate expressway congestion caused by excessive private vehicle use, trip reservation has emerged as a proactive traffic management strategy. However, when too many vehicles are admitted within the same time window, the travel efficiency of reservation users deteriorates, compromising the strategy’s effectiveness. Conversely, admitting too few vehicles leads to underutilization of road resources and degrades the operational performance of adjacent roads. This study addresses this challenge by identifying the optimal reservation quota. A reservation-based travel strategy is proposed for key corridors in urban road networks, comprising expressway segments and their parallel surface streets. The initial quota is determined through a dual-threshold bottleneck breakdown analysis, which estimates the capacity of reservation segments under varying service levels. A bilevel programming model is subsequently developed to allocate traffic flow across the network based on the initial quota. Simulation results reveal that the reservation quota significantly affects the performance of the network. The optimal quota lies between 70% of the theoretical maximum capacity and the prebreakdown threshold, within which the key corridor network maintains moderate traffic conditions. Compared to the no-reservation scenario, the average travel speed of reservation vehicles more than doubles (from 25.86 km/h to above 52.12 km/h), while the average travel delay is reduced by over 77% (from 774.77 s to below 179.01 s). The service level of reservation segments improves to Level C. Moreover, the strategy imposes minimal adverse effects on parallel surface streets, where average speeds decrease by less than 31% but remain above 22 km/h. These findings validate the effectiveness of the key corridors trip reservation system and confirm the optimal reservation quota range.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/9180797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146224030","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":"Risk Analysis of Accident Severities on Freeway Based on Copula Bayesian Network","authors":"Jun Jing,&nbsp;Xizhi Ding,&nbsp;Wenke Liu,&nbsp;Zhongyi Han,&nbsp;Delan Kong,&nbsp;Runze Liu","doi":"10.1155/atr/9731282","DOIUrl":"https://doi.org/10.1155/atr/9731282","url":null,"abstract":"<p>Preventing severe injuries in crashes has emerged as a central concern in freeway traffic safety research. To mitigate severe injuries, it is essential that the influential factors affecting accident severity be identified. In this research, accident data were collected from Los Angeles County, California, USA, freeways in the years 2016–2019, aggregating five influencing factors from five perspectives, including temporal factors, environmental factors, accident factors, accident participant factors, and traffic factors. A copula Bayesian network modeling approach was developed which combines a Bayesian network with a copula function to depict the interrelationships among crash severity outcomes and various influencing factors. The approach has the following advantages: (1) It has a more reasonable and interpretable structure. (2) It makes up for the limitation of traditional Bayesian networks that can only analyze discrete features by enabling the handling of both discrete and continuous variables. The copula Bayesian network reasoning analysis further demonstrates that various interconnections exist among different factors, and that accident type, lighting conditions, alcohol involvement, and average occupancy are the most critical contributors to fatal or severe injury accidents.</p>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/9731282","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146216840","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}