Pub Date : 2021-11-16DOI: 10.1080/19439962.2021.2002991
Milhan Moomen, Mahdi Rezapour, K. Ksaibati
Abstract Crash rates from national and state sources conclusively show that vehicles of all types are prone to crashes on Wyoming downgrades. Crashes on steep downgrades are exacerbated by difficult terrain and an increase in the driving task required to safely navigate such landscape. An important step to evaluate safety on downgrades is to analyze the effects of driver action due to its influence on vehicle operation and crash outcomes. This analysis is critical due to the difference in vehicle dynamics on downgrades compared to level sections. However, most studies on driver action have been disparate and not focused on downgrades. This has led do a dearth of literature on the subject. This paper developed mixed (random parameter) logit models to evaluate factors impacting driver action on downgrades for single- and multiple-vehicle crashes. The approach accounts for unobserved heterogeneity potentially related to crash characteristics, driver factors, and road surface condition. The results were mostly consistent with previous studies, but some unexpected results were highlighted and explained in the light of published literature and engineering intuition.
{"title":"An analysis of factors influencing driver action on downgrade crashes using the mixed logit analysis","authors":"Milhan Moomen, Mahdi Rezapour, K. Ksaibati","doi":"10.1080/19439962.2021.2002991","DOIUrl":"https://doi.org/10.1080/19439962.2021.2002991","url":null,"abstract":"Abstract Crash rates from national and state sources conclusively show that vehicles of all types are prone to crashes on Wyoming downgrades. Crashes on steep downgrades are exacerbated by difficult terrain and an increase in the driving task required to safely navigate such landscape. An important step to evaluate safety on downgrades is to analyze the effects of driver action due to its influence on vehicle operation and crash outcomes. This analysis is critical due to the difference in vehicle dynamics on downgrades compared to level sections. However, most studies on driver action have been disparate and not focused on downgrades. This has led do a dearth of literature on the subject. This paper developed mixed (random parameter) logit models to evaluate factors impacting driver action on downgrades for single- and multiple-vehicle crashes. The approach accounts for unobserved heterogeneity potentially related to crash characteristics, driver factors, and road surface condition. The results were mostly consistent with previous studies, but some unexpected results were highlighted and explained in the light of published literature and engineering intuition.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"83 1","pages":"2111 - 2136"},"PeriodicalIF":2.6,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90145149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-08DOI: 10.1080/19439962.2021.1998939
D. Kang, Zhexian Li, M. Levin
Abstract Autonomous intersection management (AIM) has been widely researched, but previous studies assume that vehicles will follow assigned trajectories precisely. The purpose of this paper is to investigate the safety buffers needed between intersecting vehicles to avoid a collision if a vehicle malfunctions. We optimize vehicle trajectories by deciding the arrival times at each conflict point (point of possible intersection with other vehicles) along each vehicle’s trajectory. Because intersecting vehicles rely on the intersection manager (IM) to detect and communicate malfunctions, the reaction time from the IM determines the minimum safety buffer needed. Although a smaller reaction time reduces the safety buffer, it increases the probability that the IM falsely detects a malfunction, instructing vehicles to stop and creating unnecessary delays. This paper develops a mathematical safety buffer for intersecting vehicles, linearizes this time separation, and constructs a combined mixed-integer linear program. A complete protocol is presented and simulated for normal circumstances, emergency circumstances, and recovery circumstances. Sensitivity analyses on various reaction times show the tradeoff between low reaction times (more false positives) and high reaction times (greater safety buffer).
{"title":"Evasion planning for autonomous intersection control based on an optimized conflict point control formulation","authors":"D. Kang, Zhexian Li, M. Levin","doi":"10.1080/19439962.2021.1998939","DOIUrl":"https://doi.org/10.1080/19439962.2021.1998939","url":null,"abstract":"Abstract Autonomous intersection management (AIM) has been widely researched, but previous studies assume that vehicles will follow assigned trajectories precisely. The purpose of this paper is to investigate the safety buffers needed between intersecting vehicles to avoid a collision if a vehicle malfunctions. We optimize vehicle trajectories by deciding the arrival times at each conflict point (point of possible intersection with other vehicles) along each vehicle’s trajectory. Because intersecting vehicles rely on the intersection manager (IM) to detect and communicate malfunctions, the reaction time from the IM determines the minimum safety buffer needed. Although a smaller reaction time reduces the safety buffer, it increases the probability that the IM falsely detects a malfunction, instructing vehicles to stop and creating unnecessary delays. This paper develops a mathematical safety buffer for intersecting vehicles, linearizes this time separation, and constructs a combined mixed-integer linear program. A complete protocol is presented and simulated for normal circumstances, emergency circumstances, and recovery circumstances. Sensitivity analyses on various reaction times show the tradeoff between low reaction times (more false positives) and high reaction times (greater safety buffer).","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"21 1","pages":"2074 - 2110"},"PeriodicalIF":2.6,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81232780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1080/19439962.2021.1994683
Qiangqiang Shangguan, Junhua Wang, Ting Fu, S. Fang
Abstract In the cut-in scenario, drivers are forced to experience a smaller headway distance, which may easily lead to rear-end crashes and reduced road traffic efficiency. Quantitatively evaluating cut-in risks and considering the heterogeneity of driving maneuvers to explore the influencing factors of cut-in risks using microscopic driving behavior data are still limited. In this study, a cut-in risk index (CIRI) was proposed to evaluate the cut-in risk based on fault tree analysis (FTA). To consider the heterogeneity of driving maneuvers, a random parameter ordered probit (RPOP) model was employed to recognize the key determinants of risky cut-in maneuvers. The results obtained in this study show that during the cut-in process, the cut-in vehicle has the highest crash risk with the preceding vehicle in the current lane compared to other surrounding vehicles. The proposed surrogate measure can objectively quantify cut-in risk. The present study suggests that the driver not only needs to pay attention to the following vehicle in the target lane, but also pay more attention to the preceding vehicle in the current lane during cut-in. Quantifying cut-in risks and exploring its influencing factors are essential for road traffic control, thereby improving driving safety and traffic efficiency.
{"title":"Quantification of cut-in risk and analysis of its influencing factors: a study using random parameters ordered probit model","authors":"Qiangqiang Shangguan, Junhua Wang, Ting Fu, S. Fang","doi":"10.1080/19439962.2021.1994683","DOIUrl":"https://doi.org/10.1080/19439962.2021.1994683","url":null,"abstract":"Abstract In the cut-in scenario, drivers are forced to experience a smaller headway distance, which may easily lead to rear-end crashes and reduced road traffic efficiency. Quantitatively evaluating cut-in risks and considering the heterogeneity of driving maneuvers to explore the influencing factors of cut-in risks using microscopic driving behavior data are still limited. In this study, a cut-in risk index (CIRI) was proposed to evaluate the cut-in risk based on fault tree analysis (FTA). To consider the heterogeneity of driving maneuvers, a random parameter ordered probit (RPOP) model was employed to recognize the key determinants of risky cut-in maneuvers. The results obtained in this study show that during the cut-in process, the cut-in vehicle has the highest crash risk with the preceding vehicle in the current lane compared to other surrounding vehicles. The proposed surrogate measure can objectively quantify cut-in risk. The present study suggests that the driver not only needs to pay attention to the following vehicle in the target lane, but also pay more attention to the preceding vehicle in the current lane during cut-in. Quantifying cut-in risks and exploring its influencing factors are essential for road traffic control, thereby improving driving safety and traffic efficiency.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"86 1","pages":"2029 - 2054"},"PeriodicalIF":2.6,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79844155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-27DOI: 10.1080/19439962.2021.1994681
Yuxuan Wang, Ruoxin Xiong, Hao Yu, Jie Bao, Zhao Yang
Abstract This study introduces a hybrid Latent Dirichlet Allocation (LDA) model to excavate hidden crash patterns from the large-scale crash dataset. External semantic descriptions have been attached to raw GPS coordinates of crash events. The K-means clustering algorithm is first applied to determine land use characteristics of crash points by grouping surrounding Points of Interests (POIs). Then, each crash record is transformed into a formalized label consisting of land use, Annual Average Daily Traffic (AADT), and time stamps, allowing the analysis of massive traffic crash data as document corpora. Finally, a data-driven modeling approach based on the LDA is conducted to discover hidden crash patterns from traffic crash records combining the external semantic information. The approach is verified using motor vehicle crash data in Manhattan County of New York City. The novel semantic analysis of crash records provides an effective method to investigate the hidden information in traffic crashes. Identifying spatial-temporal patterns on motor vehicle crashes would provide insights into underlying traffic behaviors for intelligent policy-making and resource allocation.
{"title":"A semantic embedding methodology for motor vehicle crash records: A case study of traffic safety in Manhattan Borough of New York City","authors":"Yuxuan Wang, Ruoxin Xiong, Hao Yu, Jie Bao, Zhao Yang","doi":"10.1080/19439962.2021.1994681","DOIUrl":"https://doi.org/10.1080/19439962.2021.1994681","url":null,"abstract":"Abstract This study introduces a hybrid Latent Dirichlet Allocation (LDA) model to excavate hidden crash patterns from the large-scale crash dataset. External semantic descriptions have been attached to raw GPS coordinates of crash events. The K-means clustering algorithm is first applied to determine land use characteristics of crash points by grouping surrounding Points of Interests (POIs). Then, each crash record is transformed into a formalized label consisting of land use, Annual Average Daily Traffic (AADT), and time stamps, allowing the analysis of massive traffic crash data as document corpora. Finally, a data-driven modeling approach based on the LDA is conducted to discover hidden crash patterns from traffic crash records combining the external semantic information. The approach is verified using motor vehicle crash data in Manhattan County of New York City. The novel semantic analysis of crash records provides an effective method to investigate the hidden information in traffic crashes. Identifying spatial-temporal patterns on motor vehicle crashes would provide insights into underlying traffic behaviors for intelligent policy-making and resource allocation.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"8 1","pages":"1913 - 1933"},"PeriodicalIF":2.6,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90219951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1080/19439962.2021.1992552
Amr Shalkamy, K. El-Basyouny, Yong Li
Abstract The majority of previous studies on reliability-based highway design focussed on assessing the risk associated with only one mode of non-compliance (i.e. insufficient sight distance on horizontal curves using 2 D sight distance calculations). Only a handful number of studies established a link between risk levels and collisions. This paper calibrates safety-based design charts for horizontal curves considering a system reliability analysis (i.e., multi-mode) where the non-compliance could result from limited sight distance and vehicle skidding. The paper first utilised LiDAR data to collect curve attributes and assess the Available Sight Distance in a 3 D environment on 244 horizontal curves in Alberta, Canada. Monte Carlo Simulation was then used to calculate the associated risk levels, and full-Bayes multivariate Poisson lognormal regression was utilised to develop statistically significant safety performance functions that relate risk levels to collisions. Safety-based design charts were calibrated to relate curve attributes to risk levels and collisions. The calibrated charts showed the importance of using multi-mode reliability analysis. An example of using the calibrated charts in estimating the expected safety benefits of geometric improvements was introduced. The developed charts can offer designers a tool to estimate the safety consequences of design alternatives and aid the decision-making process of rehabilitation projects.
{"title":"Calibrating safety-based design charts for horizontal curves using system reliability analysis and multivariate models","authors":"Amr Shalkamy, K. El-Basyouny, Yong Li","doi":"10.1080/19439962.2021.1992552","DOIUrl":"https://doi.org/10.1080/19439962.2021.1992552","url":null,"abstract":"Abstract The majority of previous studies on reliability-based highway design focussed on assessing the risk associated with only one mode of non-compliance (i.e. insufficient sight distance on horizontal curves using 2 D sight distance calculations). Only a handful number of studies established a link between risk levels and collisions. This paper calibrates safety-based design charts for horizontal curves considering a system reliability analysis (i.e., multi-mode) where the non-compliance could result from limited sight distance and vehicle skidding. The paper first utilised LiDAR data to collect curve attributes and assess the Available Sight Distance in a 3 D environment on 244 horizontal curves in Alberta, Canada. Monte Carlo Simulation was then used to calculate the associated risk levels, and full-Bayes multivariate Poisson lognormal regression was utilised to develop statistically significant safety performance functions that relate risk levels to collisions. Safety-based design charts were calibrated to relate curve attributes to risk levels and collisions. The calibrated charts showed the importance of using multi-mode reliability analysis. An example of using the calibrated charts in estimating the expected safety benefits of geometric improvements was introduced. The developed charts can offer designers a tool to estimate the safety consequences of design alternatives and aid the decision-making process of rehabilitation projects.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"5 Suppl 1 1","pages":"1997 - 2028"},"PeriodicalIF":2.6,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85034219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-25DOI: 10.1080/19439962.2021.1995800
Subasish Das, Anandi Dutta, M. Rahman
Abstract In the era of food delivery and grocery delivery startups, traffic crashes associated with light delivery vehicles have increased significantly. Since the number of these crashes is increasing, it is important to investigate light vehicle crashes to gain insights into potential contributing factors. This study collected seven years (2010-2016) of data from traffic crash narrative reports and structured traffic crash data from Louisiana. Using text search options and manual exploration, a database of 1,623 light delivery-related crashes was examined with a comparatively robust clustering method known as cluster correspondence analysis. The findings identified six clusters with specific traits. The key clusters are fatigue, alcohol impairment, young drivers on low to moderate speed roadways, open country and moderate speed state/U.S. highways, and interstate-related crashes due to inattention. Policymakers can use the findings of the current study to perform data-driven policy development and promote safety for delivery-related travels.
{"title":"Pattern recognition from light delivery vehicle crash characteristics","authors":"Subasish Das, Anandi Dutta, M. Rahman","doi":"10.1080/19439962.2021.1995800","DOIUrl":"https://doi.org/10.1080/19439962.2021.1995800","url":null,"abstract":"Abstract In the era of food delivery and grocery delivery startups, traffic crashes associated with light delivery vehicles have increased significantly. Since the number of these crashes is increasing, it is important to investigate light vehicle crashes to gain insights into potential contributing factors. This study collected seven years (2010-2016) of data from traffic crash narrative reports and structured traffic crash data from Louisiana. Using text search options and manual exploration, a database of 1,623 light delivery-related crashes was examined with a comparatively robust clustering method known as cluster correspondence analysis. The findings identified six clusters with specific traits. The key clusters are fatigue, alcohol impairment, young drivers on low to moderate speed roadways, open country and moderate speed state/U.S. highways, and interstate-related crashes due to inattention. Policymakers can use the findings of the current study to perform data-driven policy development and promote safety for delivery-related travels.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"10 1","pages":"2055 - 2073"},"PeriodicalIF":2.6,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85250931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-12DOI: 10.1080/19439962.2021.1988788
Hatem Abou-Senna, E. Radwan, H. Abdelwahab
Abstract This research investigates the characteristics and contributing causes of pedestrian crashes that occurred in Central Florida over a 5 year-period at intersections and mid-block crossings along roadway segments. The factors contributing to pedestrian crashes were classified into four main categories: location characteristics, pedestrian factors, driver/vehicle characteristics, and environmental-related factors along with their corresponding crash characteristics. Categorical Principal Components Analysis (CATPCA) was applied to understand the structure of a set of variables and to reduce the dimensionality of the dataset to a predefined number of dimensions and components. CATPCA analysis revealed that four dimensions accounted for almost 50% of the model indicating strong positive relationships between datasets with driver and pedestrian characteristics along with their corresponding crash characteristics relatively significant than the location and the environmental characteristics. The analysis showed that majority of the intersection crashes were during nighttime with pedestrians under influence and failing to yield to the right of way (ROW). They included mainly left-turn and right-turn crashes. In addition, drivers were also found at fault due to vision issues resulting from absence of lighting at intersections and categorized as failure to yield to the ROW. At midblock locations, major crash types were through moving vehicles hitting pedestrians crossing and walking along the roadway especially during nighttime conditions. However, majority of the crashes were at locations away from the designated crossings likely due to the long distances between legal crossing locations and pedestrian’s failure to utilize them. The findings of this research and examining the factors affecting pedestrians’ crash likelihood and injury severity can lead to better crash mitigation strategies, countermeasures and policies that would alleviate this growing problem in Central Florida.
{"title":"Categorical principal component analysis (CATPCA) of pedestrian crashes in Central Florida","authors":"Hatem Abou-Senna, E. Radwan, H. Abdelwahab","doi":"10.1080/19439962.2021.1988788","DOIUrl":"https://doi.org/10.1080/19439962.2021.1988788","url":null,"abstract":"Abstract This research investigates the characteristics and contributing causes of pedestrian crashes that occurred in Central Florida over a 5 year-period at intersections and mid-block crossings along roadway segments. The factors contributing to pedestrian crashes were classified into four main categories: location characteristics, pedestrian factors, driver/vehicle characteristics, and environmental-related factors along with their corresponding crash characteristics. Categorical Principal Components Analysis (CATPCA) was applied to understand the structure of a set of variables and to reduce the dimensionality of the dataset to a predefined number of dimensions and components. CATPCA analysis revealed that four dimensions accounted for almost 50% of the model indicating strong positive relationships between datasets with driver and pedestrian characteristics along with their corresponding crash characteristics relatively significant than the location and the environmental characteristics. The analysis showed that majority of the intersection crashes were during nighttime with pedestrians under influence and failing to yield to the right of way (ROW). They included mainly left-turn and right-turn crashes. In addition, drivers were also found at fault due to vision issues resulting from absence of lighting at intersections and categorized as failure to yield to the ROW. At midblock locations, major crash types were through moving vehicles hitting pedestrians crossing and walking along the roadway especially during nighttime conditions. However, majority of the crashes were at locations away from the designated crossings likely due to the long distances between legal crossing locations and pedestrian’s failure to utilize them. The findings of this research and examining the factors affecting pedestrians’ crash likelihood and injury severity can lead to better crash mitigation strategies, countermeasures and policies that would alleviate this growing problem in Central Florida.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"26 1","pages":"1890 - 1912"},"PeriodicalIF":2.6,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78595397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-08DOI: 10.1080/19439962.2019.1616020
Yuan Zhang, Yong Qin, Y. Du, Lei Zhu, Xiukun Wei
Abstract A risk monitoring method based on normal region estimation (NRE) is systematically proposed for the actual situation of the lack of fault data in the condition identification and monitoring of railway vehicle bearings. First, the basic concept of normal domain theory is expounded, and the formal expression of normal domain is given. Secondly, the academic thoughts and implementation steps of risk monitoring based on NRE are summarized. Then, two algorithms based on convex hull and support vector data description (SVDD) are proposed respectively to solve the core problem of boundary estimation. Finally, the rolling-bearing vibration acceleration data was used for the experiment, and the performance of the two algorithms is compared. The results show that both algorithms are effective. In contrast, the convex hull algorithm is faster, and the SVDD algorithm is smoother and more flexible. In practical applications, the two algorithms can be selected according to different requirements of real time and accuracy.
{"title":"Railway vehicle bearings risk monitoring based on normal region estimation for no-fault data situations","authors":"Yuan Zhang, Yong Qin, Y. Du, Lei Zhu, Xiukun Wei","doi":"10.1080/19439962.2019.1616020","DOIUrl":"https://doi.org/10.1080/19439962.2019.1616020","url":null,"abstract":"Abstract A risk monitoring method based on normal region estimation (NRE) is systematically proposed for the actual situation of the lack of fault data in the condition identification and monitoring of railway vehicle bearings. First, the basic concept of normal domain theory is expounded, and the formal expression of normal domain is given. Secondly, the academic thoughts and implementation steps of risk monitoring based on NRE are summarized. Then, two algorithms based on convex hull and support vector data description (SVDD) are proposed respectively to solve the core problem of boundary estimation. Finally, the rolling-bearing vibration acceleration data was used for the experiment, and the performance of the two algorithms is compared. The results show that both algorithms are effective. In contrast, the convex hull algorithm is faster, and the SVDD algorithm is smoother and more flexible. In practical applications, the two algorithms can be selected according to different requirements of real time and accuracy.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"126 1 1","pages":"1047 - 1065"},"PeriodicalIF":2.6,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77439784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-08DOI: 10.1080/19439962.2021.1988787
Haniyeh Ghomi, Mohamed Hussein
Abstract This study aims at developing a solid understanding of the contributing factors to pedestrian fatal and injury collisions at highway-railway grade crossings (HRGC), along with the impact of different warning devices that are commonly used at HRGCs. The study utilized integrated Machine Learning and Bayesian models to analyze the United States HRGC collision using the Federal Railroad Administration database between 2009 and 2018. The results demonstrate the association between different factors and the collision severity in each cluster and attempt to explain the inconsistency associated with the impact of some factors, such as weather conditions and pedestrian traits, on collision severity. The results also highlighted the conditions at which the different types of countermeasures and warning devices are most effective and the circumstances that limit their benefits. The results confirmed the benefits of the proposed analysis approach, in which collision data are classified into a group of clusters first before investigating the impact of the different factors on collision severity. The results wills support engineers and planners to develop specific policies and designs that aim at mitigating severe collisions at HRGCs and enhance pedestrian safety.
{"title":"An integrated clustering and Bayesian approach to investigate the severity of pedestrian collisions at highway-railway grade crossings collisions","authors":"Haniyeh Ghomi, Mohamed Hussein","doi":"10.1080/19439962.2021.1988787","DOIUrl":"https://doi.org/10.1080/19439962.2021.1988787","url":null,"abstract":"Abstract This study aims at developing a solid understanding of the contributing factors to pedestrian fatal and injury collisions at highway-railway grade crossings (HRGC), along with the impact of different warning devices that are commonly used at HRGCs. The study utilized integrated Machine Learning and Bayesian models to analyze the United States HRGC collision using the Federal Railroad Administration database between 2009 and 2018. The results demonstrate the association between different factors and the collision severity in each cluster and attempt to explain the inconsistency associated with the impact of some factors, such as weather conditions and pedestrian traits, on collision severity. The results also highlighted the conditions at which the different types of countermeasures and warning devices are most effective and the circumstances that limit their benefits. The results confirmed the benefits of the proposed analysis approach, in which collision data are classified into a group of clusters first before investigating the impact of the different factors on collision severity. The results wills support engineers and planners to develop specific policies and designs that aim at mitigating severe collisions at HRGCs and enhance pedestrian safety.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"49 1","pages":"1865 - 1889"},"PeriodicalIF":2.6,"publicationDate":"2021-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90661421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Bicycle–motor vehicle (BMV) crashes have been identified as a major type of traffic accident affecting transportation safety. In order to determine the characteristics of BMV crashes in cold regions, this study presents an analysis using police-reported data from 2015 to 2017 on BMV crashes in Shenyang, China. A two-stage approach integrating latent class analysis (LCA) and the random parameter logit (RP-logit) model is proposed to identify specific crash groups and explore their contributing factors. First, LCA was used to classify data into several homogenous clusters, and then the RP-logit model was established to identify significant factors in the whole data model and the cluster-based model from LCA. The proposed two-stage approach can maximize the heterogeneity effects both among clusters and within clusters. Results show that three significant factors in the cluster-based model are obscured by the whole data model in which male cyclists are associated with a higher risk of fatality, especially in the winter. Additionally, differences exist in the exploration of factors due to the characteristics of clusters; thus, countermeasures for specific crash groups should be implemented. This research can provide references for regulators to develop targeted policies and reduce injury severity in BMV crashes in cold regions.
{"title":"Exploring injury severity of bicycle-motor vehicle crashes: A two-stage approach integrating latent class analysis and random parameter logit model","authors":"Zhiyuan Sun, Yuxuan Xing, Jianyu Wang, Xin Gu, Huapu Lu, Yanyan Chen","doi":"10.1080/19439962.2021.1971814","DOIUrl":"https://doi.org/10.1080/19439962.2021.1971814","url":null,"abstract":"Abstract Bicycle–motor vehicle (BMV) crashes have been identified as a major type of traffic accident affecting transportation safety. In order to determine the characteristics of BMV crashes in cold regions, this study presents an analysis using police-reported data from 2015 to 2017 on BMV crashes in Shenyang, China. A two-stage approach integrating latent class analysis (LCA) and the random parameter logit (RP-logit) model is proposed to identify specific crash groups and explore their contributing factors. First, LCA was used to classify data into several homogenous clusters, and then the RP-logit model was established to identify significant factors in the whole data model and the cluster-based model from LCA. The proposed two-stage approach can maximize the heterogeneity effects both among clusters and within clusters. Results show that three significant factors in the cluster-based model are obscured by the whole data model in which male cyclists are associated with a higher risk of fatality, especially in the winter. Additionally, differences exist in the exploration of factors due to the characteristics of clusters; thus, countermeasures for specific crash groups should be implemented. This research can provide references for regulators to develop targeted policies and reduce injury severity in BMV crashes in cold regions.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"40 1","pages":"1838 - 1864"},"PeriodicalIF":2.6,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85946718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：