Journal of Advanced Transportation最新文献

{"title":"A Study of Mixed Platooning Considering Driver Perceptual Uncertainty","authors":"Junfeng Jiang,&nbsp;Yikang Rui,&nbsp;Bin Ran","doi":"10.1155/atr/5366331","DOIUrl":"https://doi.org/10.1155/atr/5366331","url":null,"abstract":"<div>\u0000 <p>Human-driven vehicles (HDVs) are the most critical element in mixed platoon research for their uncertainty. This paper presents a novel car-following model that considers the driver’s perceived uncertainty. A mixed platoon model of HDVs and connected and autonomous vehicles (CAVs) is established. Through data analysis, the stability of this model is validated. Additionally, a meticulous comparison and analysis regarding the platoon convergence ability and stable state under various platoon grouping forms and running speeds are carried out. Further, this paper introduces a virtual spring strategy to describe the car-following relationship between mixed platoon vehicles. Numerical simulations are then employed to explore the anti-interference capabilities of different mixed platoon modes and lengths. The results indicate that CAVs can effectively attenuate the randomness of HDVs. The platoon formation and operating speed impact the stability of mixed vehicle platoons. The platooning configuration “1 + <i>n</i> + 1” as the smallest platooning unit can help mixed vehicle platoons achieve a better stable state more quickly, with the optimal platoon length being four vehicles. However, as the platoon combinations grow more complex, the optimal platooning unit tends to shorten.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/5366331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786791","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 Lane Changes at Freeway On-Ramps With a Novel Car-Following Model Based on Desired Time Headways","authors":"Moritz Berghaus,&nbsp;Markus Oeser","doi":"10.1155/atr/9971254","DOIUrl":"https://doi.org/10.1155/atr/9971254","url":null,"abstract":"<div>\u0000 <p>The traffic flow at freeway on-ramps is influenced not only by the lane changes made by merging vehicles but also by the longitudinal behavior of the merging vehicles and vehicles in the main lane. Existing car-following models are not suitable to represent the longitudinal behavior during merging because they are based on the idea that vehicles intend to reach a steady state, that is, constant time headway and zero speed difference, as soon as possible. At on-ramps, however, merging vehicles have time to reach this steady state until they reach the end of the on-ramp. We therefore derive a novel car-following model based on desired time headways that is able to represent this continuous adaptation toward a steady state. From this car-following model, we derive a lane change model for freeway on-ramps with seven parameters. The lane change model includes a leader selection algorithm, which enables merging vehicles to pass or be passed by vehicles in the main lane. The model also includes components to predict the lane change start time based on surrogate safety measures and to describe the lateral behavior during the lane change. Due to the resemblance to car-following models, the methodology to calibrate the lane change model at the microscopic scale can be adopted from car-following models. To validate the model, we conduct traffic simulations and compare the simulated traffic flow with trajectory data from two German freeway on-ramps. The results show that the model accurately represents the longitudinal driving behavior of merging vehicles and their followers, although it slightly overestimates the number of merging vehicles passing a vehicle in the main lane under congested traffic conditions. The simulations yield accurate headway distributions, except in cases of very risky driver behavior, and realistically capture the macroscopic speed-density relationship at the on-ramp.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/9971254","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143762050","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":"Ethiopian Traffic Sign Recognition Using Customized Convolutional Neural Networks and Transfer Learning","authors":"Amlakie Aschale Alemu,&nbsp;Misganaw Aguate Widneh","doi":"10.1155/atr/9971499","DOIUrl":"https://doi.org/10.1155/atr/9971499","url":null,"abstract":"<div>\u0000 <p>Intelligent transportation systems rely greatly on their capacity to identify and recognize traffic signs. Traffic signs are important for modern transportation systems because they keep roads safe and help drivers, especially in areas like Ethiopia where sign designs are unique and diversified. In this study, we presented a convolutional neural network (CNN)–based model for Ethiopian traffic sign recognition (ETSR) purposes. We applied the transfer learning technique to fine-tune the pretrained models, namely, MobileNet, VGG16, and ResNet50. Both training and model hyperparameters are fine-tuned, and the 11,000 Ethiopian traffic sign images, which have 156 unique signs, are leveraged to build the new models. Optimizer, batch size, learning rate, and epoch are among the tuned training hyperparameters. All convolutional bases (learning layers) are trained using new weights. We built the fully connected layer of each model from two batch normalization layers and two dense layers. The output layer of the models has 156 units (neurons) with a softmax activation layer. The performances of newly developed models are rigorously compared with those of the base (pretrained) models. The best model was also selected after rigorous experiments. Based on the experiment, we achieved testing accuracy of 97.91%, 93.45%, and 80.18% for fine-tuned VGG16, MobileNet, and ResNet50, respectively.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/9971499","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726797","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":"Resilience Assessment and Recovery Strategy for High-Speed Railway Networks Considering Spatiotemporal Dynamic Characteristics","authors":"Zhuo Li,&nbsp;Ruichun He,&nbsp;Wenxia Li,&nbsp;Juncheng Bai","doi":"10.1155/atr/9890906","DOIUrl":"https://doi.org/10.1155/atr/9890906","url":null,"abstract":"<div>\u0000 <p>The service link in a high-speed railway (HSR) network has an evident temporal attribute, and conventional research methods ignore the importance of temporal information in resilience assessments. Hence, in this study, an HSR service network model based on a temporal network framework is constructed, and an HSR service network resilience assessment method considering spatiotemporal dynamic characteristics is proposed. Considering the heterogeneity of train flow in different time periods and taking the time cost of the shortest temporal path as the network performance measure, a resilience assessment model is established based on the HSR temporal service network, and an algorithm for solving the network performance is designed. Taking China’s HSR network as a case study, the research results showed that the optimal recovery strategy exhibited a higher resilience value than four other recovery strategies. In different spatiotemporal dimensions, the impact of disturbance events on network resilience is different, and the corresponding optimal restoration sequence is also different, making the resilience of the HSR network to exhibit evident differences in the spatiotemporal distribution. In addition, an increase in the number of repair resources is not proportional to the improvement in network resilience. The railway emergency department should comprehensively consider the spatiotemporal characteristics of the disturbance distribution and reasonably determine the restoration sequence and the number of repair resources.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/9890906","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689739","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":"Optimization of Speed Limit Method for Freeways Considering AVs Under Rainy Weather","authors":"Le Yu,&nbsp;Qin Luo,&nbsp;Jianying He","doi":"10.1155/atr/1720155","DOIUrl":"https://doi.org/10.1155/atr/1720155","url":null,"abstract":"<div>\u0000 <p>Rainy weather significantly affects traffic efficiency and safety on freeways. This study investigates a safe speed limit model for freeways under rainy conditions, considering both human-driven vehicles (HVs) and automatic vehicles (AVs). The relationship between safe speed limits and rainfall intensity is quantified. The intelligent driver model (IDM) was selected as the basis for this study due to its complexity and applicability. By analyzing the effects of rainfall on model parameters, an improved IDM was proposed, optimizing the expected speed. Simulation results demonstrate that the proposed speed limit method effectively reduces collision rates, with AVs showing superior traffic efficiency, stability, and safety compared to HVs.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/1720155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688900","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 Traffic Speed Prediction Accuracy: The Multialgorithmic Ensemble Model With Spatiotemporal Feature Engineering","authors":"Ali Ardestani,&nbsp;Hao Yang,&nbsp;Saiedeh Razavi","doi":"10.1155/atr/9941856","DOIUrl":"https://doi.org/10.1155/atr/9941856","url":null,"abstract":"<div>\u0000 <p>Accurate traffic speed prediction is crucial for efficient traffic management and planning in urban areas. Traditional traffic prediction models often fall short due to their inability to capture the complex and dynamic nature of traffic flow. There is a need for more advanced models that can effectively handle dynamic traffic conditions. This study introduces the multialgorithmic ensemble model (MAEM), a novel framework designed to improve traffic speed prediction accuracy by integrating graph neural networks (GNNs), bidirectional gated recurrent units (Bi-GRUs), and long short-term memory (LSTM) networks, to effectively analyze the spatiotemporal characteristics of the traffic network. The methodology involves constructing a virtual graph based on road segment correlations and applying a combination of spatial and temporal feature extraction techniques. The model is further enhanced with an attention mechanism to focus on critical time intervals. The dataset used for this study consists of one-year aggregated probe vehicle traffic data of 4788 road segments in the City of Hamilton, Ontario. The results demonstrate significant performance, achieving the mean absolute percentage error (MAPE) of 3.5% and root-mean-square error (RMSE) of 2.4 km/h, indicating the potential of the proposed framework to significantly enhance traffic speed prediction accuracy and provide a reliable tool for urban traffic management and planning.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/9941856","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594900","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":"Simulation Optimization of Shovel-Truck System in Open-Pit Mines Considering Rockmass Parameters","authors":"Shafi Muhammad Pathan,&nbsp;Abdul Ghani Pathan,&nbsp;Muhammad Saad Memon","doi":"10.1155/atr/7939037","DOIUrl":"https://doi.org/10.1155/atr/7939037","url":null,"abstract":"<div>\u0000 <p>The shovel-truck system remains a popular method for overburden removal and mineral excavation in open-pit mines, needing rigorous logistical management to achieve required productivity levels and maximize resource utilization. Fixed truck assignment (FTA) models represent a prevalent method for truck allocation in open-pit mining, owing to their simplified fleet operational management. However, existing FTA models often overlook the simultaneous minimization of both trucks’ waiting time and shovels’ idle time. Consequently, these oversights lead to suboptimal allocation of trucks to shovels, resulting in either trucks queuing or shovels idling while awaiting trucks. Such inefficiencies contribute to fleet underutilization and increased fuel costs. To tackle the above issue, this research introduces a novel truck dispatching rule, MFTA, which integrates geotechnical parameters and excavating equipment performance to optimize truck allocation in open-pit mining. Geotechnical parameters across various rock and soil formations reveal significant variability, influencing shovel performance assessed through the total loading time (TLT) indicator. Utilizing TLT and travel times of loaded and empty trucks, the study determines the optimal number of fixed trucks allocated to each shovel by minimizing the total waiting time (TWT). A case study conducted in an open-pit coal mine in Thar, Pakistan, validates the approach, demonstrating that adjusting truck allocations based on TLT significantly reduces operational inefficiencies and enhances productivity. The findings highlight the effectiveness of this method in improving overall operational efficiency and economics in open-pit mining. Integrating real-time data and advanced simulation techniques, this research enhances the competitiveness and sustainability of mining operations. These outcomes are particularly relevant for mining professionals aiming to optimize mining operations for improved efficiency and sustainability.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/7939037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143530517","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":"Familial Aggregation of High-Risk Driving Behaviors in Northwestern Iran: A Cross-Sectional Study","authors":"Elham Davtalab Esmaeili,&nbsp;Leila R. Kalankesh,&nbsp;Alireza Ghaffari,&nbsp;Ali Hossein Zeinalzadeh,&nbsp;Saeed Dastgiri","doi":"10.1155/atr/9969847","DOIUrl":"https://doi.org/10.1155/atr/9969847","url":null,"abstract":"<div>\u0000 <p>Although the association between human factors, such as driving traffic risky behavior (DRB) and road traffic crashes (RTCs), have extensively been studied, there is a gap in understanding the role of familial predisposing factors in DRB occurrence. This study in northwestern Iran aimed to elucidate the sociodemographic profile of drivers and assess the familial aggregation (FA) of DRB in first-degree relatives. This cross-sectional study used stratified random sampling to examine the FA of DRB among 541 individuals in Tabriz, Iran, in 2023. The head of household served as a proband and first-degree relatives were included. Data were collected using two standard self-administered questionnaires. The generalized estimating equations with 95% confidence intervals (CIs) assessed the FA of DRB. The overall prevalence of high-risk driving behavior was 46.02%, with significant FA observed between mothers and offspring (OR: 1.97, 95%CI: 1.05–3.69). Fathers or offspring with violation driving behaviors significantly increased the likelihood of similar violations among their offspring or fathers approximately. Offspring’s slip behaviors were significantly associated with these behaviors in their parents and siblings. Moreover, lapse behaviors showed significant FA among siblings. Our findings showed that FA exists in the DRB, particularly in the slip behavior dimension, with aggregation between fathers-offspring, mothers-offspring, and siblings. No FA of DRB was found between spouses. Regardless of the reason for FA, these imply that the family plays a significant role in DRB occurrence, suggesting the potential effectiveness of a family-based prevention program. Screening programs are recommended to identify DRB in relatives referred to a trauma referral hospital to provide targeted preventive interventions.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/9969847","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513744","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":"Investigating Factors Contributing to Urban Traffic Incident Risk Using High-Resolution Heterogeneous Data","authors":"Dongpeng Zhu,&nbsp;Yuzhi Zhang,&nbsp;Dilin Wen,&nbsp;Linchao Li","doi":"10.1155/atr/5065270","DOIUrl":"https://doi.org/10.1155/atr/5065270","url":null,"abstract":"<div>\u0000 <p>Urban traffic incidents are among the leading causes of death, injury, and traffic congestion in metropolises worldwide. This paper aims to investigate the effects of social demography, road networks, land use, and public transportation facilities on the traffic incident risk in Shenzhen, China. High-resolution heterogeneous data are collected for 4207 grids which are used as the basic geographic units. The traffic incident risks of grids are divided into four levels according to the number of incidents. A generalized ordered logit (GOL) model is developed to explore the contributions and elasticity of the variables. The results show that the effect of the significant variables on various thresholds is different. Employment density has a more significant impact on the risk level than population density. Compared to nonworking days, trips during working days are more relevant to the traffic incident risk. Road network variables such as the length of various road types and intersections are positively correlated with the incident risk. For the land use variables, most of them are significant influencing variables. The findings of the paper can provide some suggestions for safety management policies of arterial intersections, critical infrastructures, and future urban land use planning.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/5065270","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521899","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":"The Analysis of Controlled Flight Into Terrain Incidents From Flight Crew Perspective Using Named Entity Recognition and Bayesian Networks","authors":"Junjie Liu,&nbsp;Wenzheng Yi,&nbsp;Aihua Zhang,&nbsp;Pengcheng Tian","doi":"10.1155/atr/8225597","DOIUrl":"https://doi.org/10.1155/atr/8225597","url":null,"abstract":"<div>\u0000 <p>Controlled flight into terrain (CFIT) can result in significant aircraft damage and human casualties. Analyzing incident factors and their evolutionary relationships in aviation safety reports helps explore the inherent mechanisms of CFIT, thereby potentially reducing their occurrence. This study proposes a methodology combining named entity recognition (NER) and Bayesian network (BN) to address the challenges of efficiently extracting incident factors from textual reports from the crew’s perspective and analyzing the overall evolution process of CFIT incidents to better prevent accidents. First, this study collected 354 CFIT incident reports in the Aviation Safety Reporting System (ASRS) for the period November 2021 to August 2023. Second, important concepts from Threat and Error Management (TEM) were referenced to determine principles for extracting factor types and their evolutionary relationships. Third, NER was applied using the BERT–BiLSTM–MHA–CRF model to extract incident factors, followed by model comparison. Experimental results demonstrated good performance with precision, recall, and <i>F</i>1 score of 0.97, 0.90, and 0.90, respectively. Last, BN was then employed to analyze the CFIT evolution process. Results indicate that if factors such as Terrain (0.04) and Unfamiliarity/Inexperience (0.036) are present, CFIT risk will increase. Conversely, if protective factors such as Perfect Weather/Great Visibility (0.397) and Perform the Escape Maneuver (0.341) are present, CFIT risk will decrease. The analysis reveals that Airline Operational Pressure, Fatigue (57%), Lack of Situational Awareness (21%), Automation Errors (45%), Aircraft Handling Deviations (34%), Aviation System–Based Countermeasures (72%), Perform the Escape Maneuver (75%), and Make a Stabilized Approach (89%) form the highest probability evolution pathway for CFIT incidents. This study concludes that reducing these identified risk factors and increasing protective factors can contribute to reducing CFIT accidents.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/8225597","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521900","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}