Journal of Transportation Safety & Security最新文献_第5页

Contributing factors on the level of delay caused by crashes: a hybrid method of latent class analysis and XGBoost based SHAP algorithm 影响崩溃造成的延迟水平的因素:潜在类分析和基于XGBoost的SHAP算法的混合方法

Journal of Transportation Safety & Security Pub Date : 2023-03-16 DOI: 10.1080/19439962.2023.2189339

Zehao Wang, Pengpeng Jiao, Jianyu Wang, Wei Luo, H. Lu

引用次数: 1

Investigating the impacts of driver’s risky driving behavior on traffic crash risk detection model 研究驾驶员危险驾驶行为对交通碰撞风险检测模型的影响

Journal of Transportation Safety & Security Pub Date : 2023-03-15 DOI: 10.1080/19439962.2023.2189340

Miao Guo, Xiaohua Zhao, Ying Yao, Haiyi Yang, Jianyu Qi, Yuelong Su

引用次数: 0

Tempo-categorization of road accident hotspots to enhance the problem diagnosis process and detect hidden hazardous locations 对道路交通事故热点进行时序分类，提高问题诊断流程，发现隐患

Journal of Transportation Safety & Security Pub Date : 2023-03-15 DOI: 10.1080/19439962.2023.2169800

Zaniar Babaei, Mehmet Metin Kunt

{"title":"Tempo-categorization of road accident hotspots to enhance the problem diagnosis process and detect hidden hazardous locations","authors":"Zaniar Babaei, Mehmet Metin Kunt","doi":"10.1080/19439962.2023.2169800","DOIUrl":"https://doi.org/10.1080/19439962.2023.2169800","url":null,"abstract":"Abstract Identifying roads’ hazardous locations and solving their problems are key measures in traffic safety management. However, since the traditional hotspot identification (HSID) rests on the yearly-aggregated crashes, two problems appear: locations that become unsafe at specific short periods may remain unidentified as they may not show noticeable crash counts, and results of the problem diagnosis analysis on hotspots’ crashes potentially contain a great amount of uncertainty. Even though researchers have recently added the dimension of time and analyzed accidents spatio-temporally to obtain more insights, the mentioned problems have not been addressed fully. Hence, this paper first suggests a new linear DBSCAN-based HSID method and demonstrates its acceptable performance by comparison with KDE+, the well-known clustering technique; Second, employing the proposed technique, the paper presents an algorithm for the spatial analysis of accidents through diverse time dimensions, which categorizes the risky locations based on their periodic reappearance. The tempo-categorization purpose is to enhance diagnosing causative risks by understanding their arising periods. The algorithm is tested using Allegheny highways crash data from 2014 to 2019. Results illustrate the contribution of the suggested method to problem diagnosis and detecting hidden unsafe points.","PeriodicalId":205624,"journal":{"name":"Journal of Transportation Safety & Security","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131753499","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

Exploring the heterogeneities in vehicle-involved traffic violations at intersections using latent class clustering and partial proportional odds models 利用潜在类聚类和部分比例几率模型探讨交叉口车辆交通违规的异质性

Journal of Transportation Safety & Security Pub Date : 2023-03-13 DOI: 10.1080/19439962.2023.2185845

Ya Sun, Jianfeng Lu, Gang Ren, Jingfeng Ma

引用次数: 0

Normalizing crash risk of partially automated vehicles under sparse data 稀疏数据下部分自动驾驶车辆碰撞风险归一化

Journal of Transportation Safety & Security Pub Date : 2023-03-01 DOI: 10.1080/19439962.2023.2178566

Noah Goodall

引用次数: 2

Investigating the safety impacts of discontinuities in cycle network: A case study of London 循环网络中不连续性对安全的影响:以伦敦为例

Journal of Transportation Safety & Security Pub Date : 2023-02-26 DOI: 10.1080/19439962.2023.2180561

Haojie Li, Ziqian Zhang, Huitao Lv, Gang Ren

引用次数: 1

A methodology for prioritizing safety indicators using individual vehicle trajectory data 使用单个车辆轨迹数据确定安全指标优先级的方法

Journal of Transportation Safety & Security Pub Date : 2023-02-20 DOI: 10.1080/19439962.2023.2178567

Yunjong Kim, Kawon Kang, Juneyoung Park, C. Oh

引用次数: 0

A novel deep learning framework for detecting seafarer’s unsafe behavior 一种检测海员不安全行为的新型深度学习框架

Journal of Transportation Safety & Security Pub Date : 2023-02-06 DOI: 10.1080/19439962.2023.2169801

Haifeng Ding, Jinxian Weng, Bing Han

引用次数: 0

Investigating the application of deep learning to identify pedestrian collision-prone zones 研究深度学习在识别行人碰撞易发区域中的应用

Journal of Transportation Safety & Security Pub Date : 2023-01-20 DOI: 10.1080/19439962.2022.2164636

Haniyeh Ghomi, Mohamed Hussein

{"title":"Investigating the application of deep learning to identify pedestrian collision-prone zones","authors":"Haniyeh Ghomi, Mohamed Hussein","doi":"10.1080/19439962.2022.2164636","DOIUrl":"https://doi.org/10.1080/19439962.2022.2164636","url":null,"abstract":"Abstract The main objective of this study is to understand the factors that contribute to the frequency of both the total pedestrian-vehicle collisions and collisions that involve pedestrian violations and identify collision-prone areas. The two Full Bayes (FB) macro-level models were applied to historical collision records of the City of Hamilton to identify the collision-prone zones and the key factors that contribute to collision occurrence in TAZs. Finally, a self-organizing map (SOM) deep learning model was developed to identify collision-prone zones for the two collision classes. The results showed that the SOM model identified collision-prone zones with a high accuracy that exceeded the traditional Bayesian approach, based on the developed consistency test. As for the total collisions, the SOM model revealed that intersection density is the most important factor in distinguishing between collision-prone and non-collision-prone zones, followed by the pedestrian network directness and the proportion of residential land uses. As for the collisions that involved pedestrian violations, intersection density was also found to be the most important factor, followed by the density of bike-share stations and parking lots in a TAZ. The results of this study could aid planners in designing pedestrian-friendly networks and develop specific recommendations to enhance safety in unsafe zones.","PeriodicalId":205624,"journal":{"name":"Journal of Transportation Safety & Security","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126267580","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

Gate violation prediction at highway-rail grade crossings using tree-based ensemble techniques 基于树系集成技术的公路铁路平交道口闸门违章预测

Journal of Transportation Safety & Security Pub Date : 2023-01-16 DOI: 10.1080/19439962.2022.2164819

Li Zhao, L. Rilett, Chenhui Liu

{"title":"Gate violation prediction at highway-rail grade crossings using tree-based ensemble techniques","authors":"Li Zhao, L. Rilett, Chenhui Liu","doi":"10.1080/19439962.2022.2164819","DOIUrl":"https://doi.org/10.1080/19439962.2022.2164819","url":null,"abstract":"Abstract Highway-rail grade crossing (HRGC) safety is one of the priority areas in the United States transportation system that requires for greater research efforts not just limited to crash analysis, but also to gain a deeper understanding of surrogate safety measures such as driver behavior-based traffic violations at HRGCs. This paper uses vehicle profile data to identify the key variables and develop prediction models for gate violations and examine the relationship between model accuracy and the key input variables. A data set of 256 vehicle-train events was collected at two HRGC testbeds in Lincoln, Nebraska. Among them, 76 events are gate violations, and 180 events are non-violations. Two tree-based ensemble techniques, the bootstrap forest and the boosted tree, were applied to the data set. It was found that once a vehicle is within 190 feet from the HRGC stop line, the model was approximately 80 percent accurate in predicting a gate violation. It was also found that as vehicles came closer to the HRGC, the prediction error decreased. With the advent of vehicle profile data collection, tree-based ensemble techniques are ideal for safety studies as they can utilize the highly non-linear vehicle profiles and relate these to safety surrogate metrics.","PeriodicalId":205624,"journal":{"name":"Journal of Transportation Safety & Security","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126818668","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

Journal of Transportation Safety &amp; Security最新文献

Journal of Transportation Safety & Security最新文献