Journal of Advanced Transportation最新文献

{"title":"Investigating Contributors to Hit-and-Run Violations in Urban River-Crossing Road Tunnels: A Random Parameter Logit Model With Heterogeneity in Means","authors":"Dengzhong Wang,&nbsp;Jiayu Zhou,&nbsp;Gen Li,&nbsp;Haigen Min,&nbsp;Chenming Jiang,&nbsp;Linjun Lu","doi":"10.1155/atr/5635494","DOIUrl":"https://doi.org/10.1155/atr/5635494","url":null,"abstract":"<div>\u0000 <p>The hit-and-run caused a delay in medical assistance to the victim and posed a significant threat to the safety of drivers in road tunnels. This study investigates the potential factors contributing to drivers’ hit-and-run violations in river-crossing tunnels. This paper built three models (the logit model, the random parameter logit model, and the random parameter logit model with heterogeneity in means) based on a dataset consisting of crashes reported in thirteen river-crossing tunnels in Shanghai, China. Potential contributors from five aspects (offending drivers, vehicle conditions, tunnel characteristics, environmental conditions, and crash information) were explored. Results showed that the random parameter logit model with heterogeneity in means produced the highest fitting accuracy among the three models. Eight important variables (nighttime, single-vehicle, multi-vehicle, two-wheeled vehicle, passenger car, heavy goods vehicle, rear-end, and short tunnel) were found to affect hit-and-run violations significantly. The research has highlighted that nighttime and short tunnel increase the likelihood of hit-and-run and other variables are the opposite. The results of this study could provide useful information for the development of interventions to improve the level of safety in tunnels and reduce the rate of hit-and-run offenses.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/5635494","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112412","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":"Improved Asphalt Pavement Crack Detection Model Based on Shuffle Attention and Feature Fusion","authors":"Tursun Mamat,&nbsp;Abdukeram Dolkun,&nbsp;Runchang He,&nbsp;Yonghui Zhang,&nbsp;Zulipapar Nigat,&nbsp;Hanchen Du","doi":"10.1155/atr/7427074","DOIUrl":"https://doi.org/10.1155/atr/7427074","url":null,"abstract":"<div>\u0000 <p>Pavement distress is one of the most serious and prevalent diseases in pavement road detection. However, traditional methods for crack detection often suffer from low efficiency and limited accuracy, necessitating improvements in the accuracy of existing crack detection algorithms. Consequently, we propose the shuffle attention for you only look once version eight (SA-YOLOv8) model, which is based on an enhanced framework. Initially, we establish the required dataset and classify images proportionally based on their states. Subsequently, we conduct comparative testing against the results of the original model, analyzing issues such as the oversight of shallow and small cracks, truncation in the recognition of single-instance long cracks, and imprecise detection. We devise an improved detection approach based on YOLOv8. This method incorporates a small target detection layer to optimize the receptive field range, aiming to focus on identifying shallow and small cracks. Simultaneously, the Shuffle Attention mechanism and the transplanted spatial pyramid pooling-fast (SPP-F) reuse structure are introduced in the feature extraction network to enhance the model’s attention to detection targets. This augmentation improves the fusion of features for shallow small targets and overall and partial features of long cracks, thereby alleviating the precision of the model in crack detection. The experimental results demonstrate a stepwise improvement in the model’s mean average precision (mAP) with each enhancement to the original network. Initially, adding a small object detection layer increased the mAP by 3.4 percentage points, raising it to 68.2%. Subsequently, incorporating the spatial attention (SA) module resulted in a more substantial improvement, boosting the mAP by 8.7 percentage points to 73.5%. Finally, the addition of the transplanted SPP-F module further enhanced accuracy, increasing the mAP by 0.7 percentage points from the previous stage, thus achieving a final mAP of 74.2%. Overall, these modifications resulted in a total improvement of 9.4 percentage points in mAP compared to the original model. In conclusion, the proposed SA-YOLOv8s model effectively supports the automated recognition of asphalt road surface cracks, demonstrating applicability in practical scenarios. The recognition performance is notably favorable, demonstrating robustness in complex environments.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/7427074","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143110936","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":"Traffic Signal Setting at Urban Junctions and Fundamental Diagram: A Before–After Study","authors":"Borja Alonso,&nbsp;Salvatore Del Giudice,&nbsp;Giuseppe Musolino,&nbsp;Antonino Vitetta","doi":"10.1155/atr/3475935","DOIUrl":"https://doi.org/10.1155/atr/3475935","url":null,"abstract":"<div>\u0000 <p>The paper analyses the effects of modifying traffic light regulations at urban road junctions, focussing on the ratio between green time and cycle time, as a function of vehicular traffic variables (flows, density and speed) on the links. The analyses are conducted in an urban setting using a before-and-after approach, employing traffic data detected by loop detectors and traffic light control parameters (the ratio between green time and cycle time) that were actually implemented. The data pertain to a central street in the city of Santander (Spain), collected during several significant weeks in different periods corresponding to varying demands for mobility. In the context of existing studies on the flow–density diagram, a function is estimated that considers the ratio between green time and cycle time as the independent variable and link capacity as the dependent variable. The analysis at the link level may be extended in the future to the network level by incorporating the network fundamental diagram into the traffic signal setting design problem.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/3475935","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143110958","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":"Multiobjective Optimization of Port Collecting and Distributing Network Considering the Balance Among Efficiency, Environmental Performance, and Disruption to Urban Traffic","authors":"Yi Yang,&nbsp;Bochi Liu,&nbsp;Dongan Chen,&nbsp;Xinglu Xu,&nbsp;Wenyuan Wang","doi":"10.1155/atr/6851139","DOIUrl":"https://doi.org/10.1155/atr/6851139","url":null,"abstract":"<div>\u0000 <p>Port collecting and distributing network (PCDN) carries both freight traffic flow associated with the port and urban traffic flow, serving as the only channel between the port and the hinterland. The congestion caused by increasing freight traffic seriously disrupts urban traffic and leads to environmental issues such as increased carbon emissions. To address this issue, this study proposes a multiobjective optimization approach for the route selection of freight traffic flow within the PCDN, considering the balance among efficiency, environmental performance, and disruption to urban traffic. First, the generation mechanism and characteristics of freight traffic flow in the PCDN are analyzed, followed by the development of a mathematical model based on the static traffic flow distribution theory and the multiobjective optimization theory. Then, a solution framework with NSGA-III as the core is developed, and an improved Dial algorithm is utilized to allocate traffic flow. Finally, taking a large-scale container port as the case study, the solution framework is implemented to address the multiobjective optimization model and obtain the optimal route for freight traffic. The results show that there are significant negative correlations between distributing efficiency and disruption to urban traffic, as well as between distributing efficiency and carbon emissions. Decision-makers can choose the optimal route according to different preferences or adopt the compromise solution by referencing the Pareto front obtained by the solution framework. The proposed method provides theoretical support for designing the PCDN scientifically.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/6851139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120959","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":"Managing Air Traffic Flow With Link Flow Rate Control: A Pure Integer Programming Model With More Accurate Link Connection Modeling","authors":"Han Zhong,&nbsp;Lai Wei,&nbsp;Wei Guan,&nbsp;Wenyi Zhang","doi":"10.1155/atr/6116015","DOIUrl":"https://doi.org/10.1155/atr/6116015","url":null,"abstract":"<div>\u0000 <p>This paper develops a mathematical optimization approach for link-level air traffic flow management. First, a pure integer programming model is established for the problem on a link level. The model seeks to minimize the weighted summation of ground delay and airborne delay, and offers a more precise modeling for the capacity and traffic dynamics at the merge, diverge, and intersection nodes. The optimization problem is computationally NP-hard, and a modified genetic algorithm is then presented to solve it. Besides a new chromosome coding rule for converting binary decision into time series vectors, the algorithm uses a taboo mutation operator and an adaptive mutation probability selection operator. Finally, the effectiveness and superiority of the model and algorithm are demonstrated through a real-world case study in Dalian, China, and some managerial insights are presented.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/6116015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119626","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 Nonrecurrent Congestion Event Detection and Tracking Method With DBSCAN on Speed Watersheds","authors":"Jing Jin,&nbsp;Yizhou Wang,&nbsp;Anjiang Chen,&nbsp;Branislav Dimitrijevic,&nbsp;Joyoung Lee","doi":"10.1155/atr/8404251","DOIUrl":"https://doi.org/10.1155/atr/8404251","url":null,"abstract":"<div>\u0000 <p>Nonrecurrent congestion (NRC) events caused by incidents bring unexpected delays and affect normal traffic operations. Imprecise NRC event detection methods can trigger false alarms and repetitive incident alerts for the same congestion event. The speed watershed from the historical profile based on DBSCAN can provide a reference for identifying NRC. This paper proposes a DBSCAN-based dynamic NRC tracking (DyNRTrac) algorithm to detect and track NRC events. By comparing real-time spatial–temporal patterns of the speed contour diagram against the historical speed contour diagram along a corridor, this method effectively distinguishes NRC events from regular traffic patterns. The proposed algorithm applies the Rauch–Tung–Striebel smoother for speed noise reduction and establishes a historical congestion profile for each recurrent congestion event within a corridor by each day of the week and season. A new event-profile–based 3D speed volume comparison method is proposed to detect NRC events that do not significantly overlap with recurrent congestions in the historical profile. Finally, a bilevel congestion confirmation process is introduced for NRC persistency checking and filtering. The proposed algorithm was evaluated by using field travel time data and with the New Jersey Department of Transportation OpenReach incident database. Overall, the proposed model shows up to 88.3% detection rate for NRC that can match the incident in the database, and it shows superior detection rates on NRC events at a similar false alarm rate level when compared with three prior models over the same datasets. Furthermore, a detailed spatial–temporal map analysis is provided to show the capability of the proposed method in distinguishing NRC and RC and identifying nonaccidental NRC events, providing its potential for traffic operation management systems to assist traffic operators to be alerted about NRC events.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/8404251","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119625","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":"Urban Traffic Flow Forecasting Based on Graph Structure Learning","authors":"Guangyu Huo,&nbsp;Yong Zhang,&nbsp;Yimei Lv,&nbsp;Hao Ren,&nbsp;Baocai Yin","doi":"10.1155/atr/7878081","DOIUrl":"https://doi.org/10.1155/atr/7878081","url":null,"abstract":"<div>\u0000 <p>The transportation system is a complex dynamic giant system which integrates and intertwines the elements of people, vehicles, roads, and the environment. The city-level traffic flow forecasting can effectively reflect the flow changes of the traffic system and provide practical guidance for the formulation of traffic rules. Recent city-level traffic flow forecasting works rely on accurate prior knowledge of graphs (i.e., the spatial relationships between roads), which hinders their effectiveness and application in the real world. We propose a novel framework for urban traffic flow forecasting, which simultaneously infers and utilizes the relationship between time series. In our model, the graph structure learning module dynamically captures the correlation and causation between the different time series and infers a potentially fully connected graph. At the same time, the temporal convolution network captures the temporal correlation between a single time series. The graph neural network uses the graph for forecasting. Our model no longer relies on accurate graph priors and achieves better forecasting results than previous work. Experiments on two public datasets verify that the proposed model is very competitive.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/7878081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119489","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":"Understanding the Spatial Variation of Integrated Use of Ride-Hailing Services With the Metro","authors":"Mingyang Du,&nbsp;Zhicheng Li,&nbsp;Xuefeng Li,&nbsp;Jiacong Xu,&nbsp;Dong Liu,&nbsp;Mei-Po Kwan","doi":"10.1155/atr/9210901","DOIUrl":"https://doi.org/10.1155/atr/9210901","url":null,"abstract":"<div>\u0000 <p>This study aims to explore the spatial heterogeneity of influential factors of integrated use of ride-hailing service with the metro. Using the operation data of ride-hailing services in Chengdu, China, first, an identification method of integrated ride-hailing trips is proposed. Then, the ordinary least squares (OLS) and geographically weighted regression (GWR) models are established to discern the factors that affect access-integrated ride-hailing use and egress-integrated ride-hailing use on weekdays and weekends. The model results demonstrate that the fitting effect of GWR models is superior to that of OLS models, and the coefficient estimates of each explanatory variable vary across regions. Accommodation facilities promote the access-integrated trips in the eastern area, and this positive impact for egress-integrated trips extends to the northeastern area. Tourist attractions have a positive impact on the integrated trips in the central and western regions, while they have a negative impact in the northwest and southeast regions. The research results can provide the theoretical support for the seamless connection and coordinated development of these two services.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/9210901","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119501","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":"Key Characteristics of Multimodal Public Transport Across the Entire Commuting Process: Quantitative Evidence from Shanghai","authors":"Meiping Yun,&nbsp;Junjun Zhan,&nbsp;Cen Zhang,&nbsp;Shumin Yang","doi":"10.1155/atr/2344587","DOIUrl":"https://doi.org/10.1155/atr/2344587","url":null,"abstract":"<div>\u0000 <p>Determining the critical factors influencing commuters’ choice of public transport is essential for increasing its commuting mode share. This study examines multimodal public transport (metro and bus) across the entire commuting process by utilizing survey data on commuting behavior and Internet-extracted data. The concept of travel time ratio, defined as the ratio of public transport travel time to car travel time for the same origin–destination (OD), is introduced to perform a quantitative analysis. A classification and regression tree (CART) model is then applied to identify and rank the key characteristics affecting public transport selection for commuting, and a marginal utility analysis quantifies their impact on commuting behavior. The results show that the travel time ratio is the most critical variable influencing commuters’ choice of public transport. Under the same commuting task, the average travel time of public transport is 25% longer than that of cars. This figure can reach nearly 70% for buses and 10% for the metro, which is the main reason for the low efficiency of public transport. Service characteristics optimization has a more substantial impact on increasing the commuting mode share for metro services than for buses. For every 0.1 decrease in the travel time ratio, the average commuting mode share for the metro and buses increased by 3.7% and 2.4%, respectively. To attract more commuters to public transport, it is necessary to maintain the travel time ratio within the range of 1–1.5. For bus services, in addition to improving commuting efficiency, it is essential to optimize convenience characteristics, such as transfer times, walking distance, and service frequency. This includes ensuring no transfers and maintaining a walking distance of less than 880 m. If the walking distance exceeds this threshold, the travel time ratio should be reduced below 1.1. Commuters without a private car demonstrate a higher tolerance for bus services, with a travel time ratio threshold of 1.8, provided that the overall service frequency is within 7 min. When fully served by the metro, if its service distance is within 6 km, the travel time should be comparable to that of cars to remain competitive. This study provides a quantitative basis for increasing the commuting mode share of public transport and improving its service quality.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/2344587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117985","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":"Drive Risk Assessment Based on Game Theory Combinatorial Weighting—Unascertained Measure Theory","authors":"Lingyu Zhang,&nbsp;Dehui Sun,&nbsp;Lili Zhang,&nbsp;Li Wang","doi":"10.1155/atr/4659804","DOIUrl":"https://doi.org/10.1155/atr/4659804","url":null,"abstract":"<div>\u0000 <p>The driving risk is assessed using the theory of unascertained measures to determine the presence of a conditional switch in the control system of a human-machine codriving vehicle. Relevant risk indicators for driving are selected, including five driver-related indicators and three vehicle-related indicators. Subsequently, each indicator’s threshold range and associated risk level are analyzed and defined. The methodologies for establishing unascertained measure and their corresponding functions for both single and multiple indicator unascertained measure are then elucidated. A game theory–based weighting method is proposed, employing ordinal relationship analysis (ORA) and entropy weighting (EW) to determine indicator weights while utilizing confidence identification criteria to ascertain risk levels. Finally, experimental analyses are conducted on the driving risk assessment model, and the simulation results demonstrated the model’s ability to distinguish between normal and risky driving. In a continuous driving simulation, the model successfully identified a peak risk period (Level V) and, following a system alert, driving behavior returned to normal risk levels within 5 min. The model demonstrated utility for control switching decisions in human-machine codriving scenarios, identifying instances where driver risk (Level IV) significantly exceeded vehicle risk (Level II), indicating a need to transfer control to the vehicle system. Consequently, the study’s findings can provide theoretical support for control switching mechanisms in human-machine codriving vehicles.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/4659804","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861875","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}