Journal of Advanced Transportation最新文献

{"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}

{"title":"Introducing an Experimental Model of Asphalt Shear Strength Using Designed Jaws and Presentation of Shear Strength Prediction Model by Genetic Programming Method","authors":"Morteza Modarresi,&nbsp;Hassan Divandari,&nbsp;Mohsen Amouzadeh Omrani,&nbsp;Mojtaba Esmaeilnia Amiri","doi":"10.1155/atr/2270042","DOIUrl":"https://doi.org/10.1155/atr/2270042","url":null,"abstract":"<div>\u0000 <p>The main material used in the construction of roads is asphalt. Therefore, the recognition of asphalt’s mechanical aspects is very important. One of the important features of asphalt is its shear strength, which should be measured accurately. However, the methods that have been presented to measure this important factor of asphalt always encounter weaknesses. So, it is necessary to find a suitable method to determine the shear strength of asphalt with more accurate results and high compatibility with reality. In this regard, the purpose of the present research was to design jaws in order to measure the shear strength in the direction and opposite direction of the traffic path and provide a model to predict shear strength using Marshall stability resulting from invented jaws. In order to examine the accuracy of the designed jaw in this study, two different types of asphalt, Binder 0–25 and Topeka 0–19 grading, were used. For this purpose, Marshall stability and shear strength tests in the direction and opposite direction of the Marshall were conducted with 12 repetitions on these samples. Also, the genetic programming (GP) evolutionary algorithm was applied in this study to provide a prediction model of shear strength. The results of this study indicated that there was a significant relationship between the Marshall stability and the shear strength in the direction and opposite direction of the Marshall applying the invented jaws in both asphalt types, and the coefficient of determination (<i>R</i>²) for the Binder and Topeka were 0.93 and 0.97 in the Marshall’s direction and 0.96 and 0.95 for the Marshall’s opposite direction, respectively. Also, the results of the GP method indicated that the relationships between predicted and actual values of shear strength for Binder and Topeka asphalt types were appropriately described by <i>R</i>² of 99.47% and 99.21% with RMSE of 8.0177 and 5.0143 in the traffic direction, and <i>R</i>² of 97.45% and 98.08% with RMSE of 1.2684 and 0.7035 in the traffic opposite direction, respectively. Therefore, GP provided a more suitable fit of all experimental data for both Binder and Topeka asphalts, and it can be said that with the help of new designed jaws, the shear strength in the direction and opposite direction of the Marshall can be estimated with high accuracy.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/2270042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862054","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 Incident Duration Prediction: A Systematic Review of Techniques","authors":"Artur Grigorev,&nbsp;Adriana-Simona Mihaita,&nbsp;Fang Chen","doi":"10.1155/atr/3748345","DOIUrl":"https://doi.org/10.1155/atr/3748345","url":null,"abstract":"<div>\u0000 <p>This systematic literature review investigates the application of machine learning (ML) techniques for predicting traffic incident durations, a crucial component of intelligent transportation systems (ITSs) aimed at mitigating congestion and enhancing environmental sustainability. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, we systematically analyze literature that overviews models for incident duration prediction. Our review identifies that while traditional ML models like XGBoost and Random Forest are prevalent, significant potential exists for advanced methodologies such as bilevel and hybrid frameworks. Key challenges identified include the following: data quality issues, model interpretability, and the complexities associated with high-dimensional datasets. Future research directions proposed include the following: (1) development of data fusion models that integrate heterogeneous datasets of incident reports for enhanced predictive modeling; (2) utilization of natural language processing (NLP) to extract contextual information from textual incident reports; and (3) implementation of advanced ML pipelines that incorporate anomaly detection, hyperparameter optimization, and sophisticated feature selection techniques. The findings underscore the transformative potential of advanced ML methodologies in traffic incident management, contributing to the development of safer, more efficient, and environmentally sustainable transportation systems.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/3748345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851458","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":"Energy Consumption Prediction Model for Electric Buses Considering Actual Quantifiable Features","authors":"Guowei Zhu,&nbsp;Miao Shi,&nbsp;Jia He","doi":"10.1155/atr/3058575","DOIUrl":"https://doi.org/10.1155/atr/3058575","url":null,"abstract":"<div>\u0000 <p>Accurate prediction of electric bus energy consumption is a key step to realize the orderly planned charging of electric buses. Meanwhile, to address the problem that the current electric bus energy consumption prediction model is not conducive to realistic application, this paper proposes an energy consumption prediction model that considers actual electric bus operation data to predict trip energy consumption. First, based on the operation data of six routes in Beijing, the influencing factors of electric bus energy consumption are summarized, including route name, travel direction, weekday and nonweekday, operation time, vehicle number, and driver’s name. Secondly, the energy consumption influencing factors were used to extract trip energy consumption features, including departure moment features, vehicle performance features, and driver attribute features. A new simple method is proposed to deal with un-ordered characteristic data to solve the problem of quantifying the influencing factors. The energy consumption prediction model considering actual quantifiable features utilizes the concept of distance to identify several historical trips that have characteristics most similar to the predicted trip in terms of energy consumption. The new prediction model is essentially a machine learning model based on <i>k</i>-means clustering algorithm, which leverages feature extraction and data analysis to make predictions. Finally, the real data are used to predict the energy consumption of different routes and different driving directions on weekdays, respectively. The energy consumption prediction error is as low as 7.112%, and the prediction results are compared with other traditional prediction models, and the model accuracy is high.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/3058575","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851459","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":"Indicators for Active Transportation in Tier II Indian Cities: A Case of Bhopal, India","authors":"Shumaila Saleem,&nbsp;Anuj Jaiswal","doi":"10.1155/atr/2175645","DOIUrl":"https://doi.org/10.1155/atr/2175645","url":null,"abstract":"<div>\u0000 <p>For a developing country to flourish sustainably, the transport sector needs to be balanced yet compete with its peers to support the growth of diverse sectors of the urban economy. Encouraging active mobility is one of the vital steps for the development of sustainable urban transportation. It indicates any mode of transport that involves physical activity, for example, cycling, walking, skateboarding and skiing. This paper is an attempt to identify the performance indicators that majorly affect the walkability and cyclability of people in cities capable of promoting active mobility. The objective is to corroborate the presence of qualitative and quantitative indicators in various sustainable transportation practices. Based on analytical hierarchy process, modified Delphi approach and user perception survey were utilised for the identification of performance indicators for Bhopal city. The indicators were segregated using exploratory factor analysis into five dimensions to categorise the performance indicators: sociodemographic, socioeconomic, physical and built environment and safety. It was found that supportive facilities were crucial for developing existing land use, physical and built environment and safety for users in a beginner city wanting to encourage users to switch to active modes choices. It was also found that urban design and built environment were the most influential factors which affect the various performance indicators for the establishment of active mobility modes for sustainable urban transportation.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/2175645","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142862055","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 Model for Predicting Short-Term Operating Speeds of Compact Passenger Vehicles on Interchange Ramps Within Urban Expressway Networks","authors":"Tingyu Liu,&nbsp;Lanfang Zhang,&nbsp;Genze Li,&nbsp;Yating Wu,&nbsp;Zhenyu Zhao","doi":"10.1155/atr/5788307","DOIUrl":"https://doi.org/10.1155/atr/5788307","url":null,"abstract":"<div>\u0000 <p>The prediction of operating speed plays a crucial role in road design and safety assessment, especially on complex urban expressway interchange ramps. This task is challenging due to various influences like road conditions, traffic dynamics, and driver behavior. This study aims to identify the optimal model configuration for predicting operating speeds on urban expressway interchange ramps. Three models are established: a short-term operating speed model based on a generalized linear model (GLM), a GLM incorporating for spatial correlation (GLMS), and a deep neural network model considering spatial correlation (DNNS). Each model incorporates considerations for the impact of the plan, profile, and other facets of the interchange ramp in urban expressways. Naturalistic driving experiments are conducted in Shanghai, 70% for model calibration and 30% for validation. Comparative analysis shows that the DNNS model outperforms the others, effectively capturing speed fluctuations along the interchange ramp, demonstrating its robustness and generalization capabilities.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2024 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/5788307","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142851417","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}