Journal of Transportation Safety & Security最新文献_第6页

Zone-level traffic crash analysis with incorporated multi-sourced traffic exposure variables using Bayesian spatial model 基于贝叶斯空间模型的融合多源交通暴露变量的区域级交通事故分析

Journal of Transportation Safety & Security Pub Date : 2023-01-09 DOI: 10.1080/19439962.2022.2164815

Hao Zhang, Jie Bao, Qiong Hong, Lv Chang, Wei Yin

{"title":"Zone-level traffic crash analysis with incorporated multi-sourced traffic exposure variables using Bayesian spatial model","authors":"Hao Zhang, Jie Bao, Qiong Hong, Lv Chang, Wei Yin","doi":"10.1080/19439962.2022.2164815","DOIUrl":"https://doi.org/10.1080/19439962.2022.2164815","url":null,"abstract":"Abstract The primary objective of this study is to discover traffic exposure variables from some new data sources and explore how these new data sources and their combination affects the performance of zone-level crash models. Seven types of check-in activities and five types of taxi trips are inferred from Twitter and taxi GPS records, respectively. Then, Bayesian spatial models are employed to conduct zone-level traffic crash analysis. The results suggest that some specific check-in activities and inferred taxi trips are closely related with zone-level crash counts, and thereby confirms the benefits of incorporating new data sources into zone-level crash models. The comparative analyses further indicate that twitter check-in activities perform better than inferred taxi trips as a proxy for traffic exposures on spatial analyses of traffic crashes, and detailed trip purpose information hidden in new data sources greatly benefit zone-level crash models than simply aggregating location points in each zone. The results of this study reveal that each big data source has its prominent coverage of user groups and spatial areas, and their combination can serve as effective supplementary information to traditional exposure variables to improve the performance of zone-level crash models and better reveal the spatial impacts of human activities on traffic crashes. The findings of this study can help transportation authority develop more targeted traffic demand adjustment strategies to effectively reduce zone-level crash risks.","PeriodicalId":205624,"journal":{"name":"Journal of Transportation Safety & Security","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129726617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Combining emerging hotspots analysis with XGBoost for modeling pedestrian injuries in pedestrian-vehicle crashes: a case study of North Carolina 结合新兴热点分析与XGBoost对行人-车辆碰撞中行人伤害建模:以北卡罗来纳州为例

Journal of Transportation Safety & Security Pub Date : 2023-01-09 DOI: 10.1080/19439962.2022.2164814

Yang Li, W. Fan, Li Song, Shaojie Liu

{"title":"Combining emerging hotspots analysis with XGBoost for modeling pedestrian injuries in pedestrian-vehicle crashes: a case study of North Carolina","authors":"Yang Li, W. Fan, Li Song, Shaojie Liu","doi":"10.1080/19439962.2022.2164814","DOIUrl":"https://doi.org/10.1080/19439962.2022.2164814","url":null,"abstract":"Abstract Pedestrians might face more dangers and sustain severer injuries in crashes than others. Also, the crash data has inherent patterns related to both space and time. Crashes that happened in locations with highly aggregated uptrend patterns should be worth exploring to examine the most recently deteriorative factors affecting pedestrian-injury severities in crashes. Therefore, applying proper modeling approaches is needed to identify the causes of pedestrian-vehicle crashes to improve pedestrian safety. In this study, an emerging hotspot analysis is firstly utilized to identify the most targeted hotspots, followed by a proposed XGBoost model that analyzes the most recently deteriorative factors affecting pedestrian injury severities. The overall accuracy of the best model on the hotspot dataset is 94.49%, which shows a relatively high performance compared to conventional models. Seven factors are identified to increase the likelihood of fatal injury, including “land development: farm, wood and pasture” (FWP), “interstate”, “US route”, “hit and run”, “alcohol-impaired driver” (AID), “urban”, and “alcohol-impaired-pedestrian”. While for incapacitating injury, there are five significant factors including “work zone”, “interstate”, “US route”, “curved roadway” and “alcohol-impaired-pedestrian”. The results of this research could give a solid reference for the identification of contributing factors affecting pedestrian-injury severities to policymakers and researchers.","PeriodicalId":205624,"journal":{"name":"Journal of Transportation Safety & Security","volume":"573 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132074014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Structural equation modeling approach for investigating drivers’ risky behavior in clear and adverse weather using SHRP2 naturalistic driving data 基于SHRP2自然驾驶数据的晴空和恶劣天气下驾驶员危险行为研究的结构方程建模方法

Journal of Transportation Safety & Security Pub Date : 2022-12-22 DOI: 10.1080/19439962.2022.2155744

Anik Das, Mohamed M. Ahmed

{"title":"Structural equation modeling approach for investigating drivers’ risky behavior in clear and adverse weather using SHRP2 naturalistic driving data","authors":"Anik Das, Mohamed M. Ahmed","doi":"10.1080/19439962.2022.2155744","DOIUrl":"https://doi.org/10.1080/19439962.2022.2155744","url":null,"abstract":"Abstract This study presented an extensive assessment of risky driving behavior through Structural Equation Modeling (SEM) technique and explored the applicability of this method in identifying contributing factors influencing drivers’ risk-taking behavior in clear and adverse weather. Drivers’ questionnaire responses as well as vehicle trajectories of their completed trips in clear and adverse weather were utilized from the SHRP2 Naturalistic Driving Study (NDS). Factor analyses were conducted to identify the number of unobserved “latent” variables. Subsequently, two SEM models in clear and adverse weather were developed to attain the relationships between the observed and the latent variables. “Human Factors” and “Driving Skills” were determined as exogenous latent variables in both models to investigate their impacts on an endogenous latent variable (i.e., risky driving). The results suggested that “Human Factors” was the most significant latent variable affecting drivers’ risk-taking behavior in clear and adverse weather conditions. Moreover, speeding was found to have a significant impact on risky behavior in adverse weather conditions. The findings could help safety practitioners with better understanding of the influencing factors affecting risky driving to improve safety through proper enforcement and necessary training programs, particularly targeting young and inexperienced drivers.","PeriodicalId":205624,"journal":{"name":"Journal of Transportation Safety & Security","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128970606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Near misses and split routes: Comparing rider behavior, driver interaction, and route choice for cyclists 差之毫厘和分离路线:比较骑车人的行为，司机的互动，以及骑车者的路线选择

Journal of Transportation Safety & Security Pub Date : 2022-12-13 DOI: 10.1080/19439962.2022.2155745

J. Iuliano, L. Keith

{"title":"Near misses and split routes: Comparing rider behavior, driver interaction, and route choice for cyclists","authors":"J. Iuliano, L. Keith","doi":"10.1080/19439962.2022.2155745","DOIUrl":"https://doi.org/10.1080/19439962.2022.2155745","url":null,"abstract":"Abstract The built environment, cyclist behavior, and driver interactions can influence route choice and, ultimately, cyclist safety. Recent studies use crowdsourced data, such as Strava, to document route selection; however, aggregated data may not fully explain the factors underpinning route selection. Utilizing naturalistic methods, we analyze videos of recorded rides and GPS data from six cyclists representing three types of riders—commuters, recreational, and athletes—to explore route choices, behavior, and driver interactions in Tucson, Arizona. Our analysis of three route selection cases highlights how intersection design, driver interactions, pavement conditions, and type of riding lead cyclists to modify behaviors and select longer detours to avoid unsafe intersections. Additionally, our study combines Strava heatmaps and physical bicycle counts to explore the number of cyclists potentially facing similar factors influencing route choice. By studying cyclists with different riding aims and utilizing both Strava heatmaps and video recordings, researchers can determine the underlying conditions, identify route locations in need of improvements, and collaborate with practitioners to implement changes to increase cyclist safety through appropriate solutions. This analysis can help ensure that designs meet user expectations.","PeriodicalId":205624,"journal":{"name":"Journal of Transportation Safety & Security","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133885291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Takeover behavior patterns for autonomous driving in crash scenarios 碰撞场景下自动驾驶的接管行为模式

Journal of Transportation Safety & Security Pub Date : 2022-12-05 DOI: 10.1080/19439962.2022.2153954

Haijian Li, Haina Zhao, Chong Li, Qiuhong Wang, Xiao-juan Zhao

引用次数: 0

Journal of Transportation Safety &amp; Security最新文献

Journal of Transportation Safety & Security最新文献