Pub Date : 2023-11-06DOI: 10.1080/19439962.2023.2273547
Teun Uijtdewilligen, Mehmet Baran Ulak, Gert Jan Wijlhuizen, Frits Bijleveld, Karst T. Geurs, Atze Dijkstra
Cycling levels in cities keep increasing, which is accompanied with more cyclists being involved in serious road crashes. This paper aims to contribute to safer urban cycling by examining risk factors associated with cycling in the four largest Dutch cities, incorporating spatial and temporal variations in bicycle crash risk. For this purpose, the crashes and exposure metrics are analysed on an hourly temporal resolution. The results reveal that utilising an hourly temporal resolution in the exposure metrics and bicycle crash risk gives more detailed results compared to daily averages of these metrics. Moreover, the exposure to cyclists and motorised vehicles both have a significant impact on bicycle crash risk. The results also imply that separating cyclists from high-speed motorised vehicles might be more important than implementing a lower speed limit to curb the increasing severity of crashes. Despite some local differences, the overall results of the risk factors are remarkably similar across the cities, providing increased generalisability and transferability of the study. The findings indicate that concerns about the effects of increasing bicycle use and large flows of motorised vehicles on bicycle crash risk are valid, showing the importance of efforts towards improving bicycle safety in cities.
{"title":"Examining the crash risk factors associated with cycling by considering spatial and temporal disaggregation of exposure: Findings from four Dutch cities","authors":"Teun Uijtdewilligen, Mehmet Baran Ulak, Gert Jan Wijlhuizen, Frits Bijleveld, Karst T. Geurs, Atze Dijkstra","doi":"10.1080/19439962.2023.2273547","DOIUrl":"https://doi.org/10.1080/19439962.2023.2273547","url":null,"abstract":"Cycling levels in cities keep increasing, which is accompanied with more cyclists being involved in serious road crashes. This paper aims to contribute to safer urban cycling by examining risk factors associated with cycling in the four largest Dutch cities, incorporating spatial and temporal variations in bicycle crash risk. For this purpose, the crashes and exposure metrics are analysed on an hourly temporal resolution. The results reveal that utilising an hourly temporal resolution in the exposure metrics and bicycle crash risk gives more detailed results compared to daily averages of these metrics. Moreover, the exposure to cyclists and motorised vehicles both have a significant impact on bicycle crash risk. The results also imply that separating cyclists from high-speed motorised vehicles might be more important than implementing a lower speed limit to curb the increasing severity of crashes. Despite some local differences, the overall results of the risk factors are remarkably similar across the cities, providing increased generalisability and transferability of the study. The findings indicate that concerns about the effects of increasing bicycle use and large flows of motorised vehicles on bicycle crash risk are valid, showing the importance of efforts towards improving bicycle safety in cities.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"45 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135679267","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 : 2023-11-03DOI: 10.1080/19439962.2023.2276195
Iman Mahdinia, Nastaran Moradloo, Amin Mohammadnazar, Asad Khattak
AbstractBicyclists are recognized as vulnerable road users, with the escalating fatalities posing a safety concern. While fatal crashes involving bicyclists are often assumed to be similar, there is a crucial distinction between instant death and death occurring several days later, with the former being substantially more severe. This study delves into the analysis of bicyclists’ time-to-death, spanning from immediate fatalities to deaths within 30 days, using data from the Fatality Analysis Reporting System from 2015 to 2019. Employing the Haddon Matrix approach, the variables are categorized into pre-crash, during-crash, and postcrash phases. This study considers crash notification time as the key postcrash measure. An explainable XGBoost model is developed using the SHAP technique to investigate the associations between variables and bicyclist time-to-death. The results show that substantial delays in crash notification time considerably reduce bicyclists’ time-to-death and increase the likelihood of early death. Specifically, hit-and-run crashes, crashes in rural areas, and crashes during late-night hours exhibit notably longer crash notification times compared to non-hit-and-run crashes, urban areas, and other hours, respectively. In such cases, when no witnesses or survivors can notify emergency responders, on-road vehicle technologies like the advanced automatic collision notification system can promptly inform responders, reducing notification delays.Keywords: bicyclist time-to-deathcrash notification timeexplainable machine learningXGBoostSHAP value AcknowledgementsThe authors express their gratitude for the financial support that enabled the research, authorship, and publication of this article. Specifically, this project received partial funding from the Tennessee Department of Transportation and the US Department of Transportation through the Collaborative Sciences Center for Road Safety (Grant No. 69A3551747113). It is important to note that the views presented in this paper are solely those of the authors, who bear responsibility for the content of this publication.Disclosure statementThe authors report no declarations of interest.Data availability statementThe data that support the findings of this study are openly available in FARS at https://www.nhtsa.gov/research-data/fatality-analysis-reporting-system-fars.
{"title":"Enhancing bicyclist survival time in fatal crashes: Investigating the impact of faster crash notification time through explainable machine learning","authors":"Iman Mahdinia, Nastaran Moradloo, Amin Mohammadnazar, Asad Khattak","doi":"10.1080/19439962.2023.2276195","DOIUrl":"https://doi.org/10.1080/19439962.2023.2276195","url":null,"abstract":"AbstractBicyclists are recognized as vulnerable road users, with the escalating fatalities posing a safety concern. While fatal crashes involving bicyclists are often assumed to be similar, there is a crucial distinction between instant death and death occurring several days later, with the former being substantially more severe. This study delves into the analysis of bicyclists’ time-to-death, spanning from immediate fatalities to deaths within 30 days, using data from the Fatality Analysis Reporting System from 2015 to 2019. Employing the Haddon Matrix approach, the variables are categorized into pre-crash, during-crash, and postcrash phases. This study considers crash notification time as the key postcrash measure. An explainable XGBoost model is developed using the SHAP technique to investigate the associations between variables and bicyclist time-to-death. The results show that substantial delays in crash notification time considerably reduce bicyclists’ time-to-death and increase the likelihood of early death. Specifically, hit-and-run crashes, crashes in rural areas, and crashes during late-night hours exhibit notably longer crash notification times compared to non-hit-and-run crashes, urban areas, and other hours, respectively. In such cases, when no witnesses or survivors can notify emergency responders, on-road vehicle technologies like the advanced automatic collision notification system can promptly inform responders, reducing notification delays.Keywords: bicyclist time-to-deathcrash notification timeexplainable machine learningXGBoostSHAP value AcknowledgementsThe authors express their gratitude for the financial support that enabled the research, authorship, and publication of this article. Specifically, this project received partial funding from the Tennessee Department of Transportation and the US Department of Transportation through the Collaborative Sciences Center for Road Safety (Grant No. 69A3551747113). It is important to note that the views presented in this paper are solely those of the authors, who bear responsibility for the content of this publication.Disclosure statementThe authors report no declarations of interest.Data availability statementThe data that support the findings of this study are openly available in FARS at https://www.nhtsa.gov/research-data/fatality-analysis-reporting-system-fars.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"41 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135819603","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 : 2023-11-03DOI: 10.1080/19439962.2023.2273545
Adekunle Adebisi, John E. Ash
AbstractWith the increased adoption of connected vehicle (CV) technologies, safety information is becoming increasingly available to drivers. This study investigates three main questions (1) Do CV-based traffic management applications improve safety on roadways with existing infrastructure-based traffic management systems? (2) Can combining two CV technologies have a greater impact on safety than a single CV technology? and (3) Do geometric and traffic composition factors impact the efficiency of CV technologies? We applied a rarely-used CV dataset and conducted a comprehensive simulation analysis of varying conditions and CV penetration rates that studies have not considered. Two CV applications (queue warning and speed harmonization) implemented in the Intelligent Network Flow Optimization experiment in Seattle, WA were evaluated. Results showed that driver safety performance, based on speed metrics (standard deviation and percentage of extreme values) improved under the CV driving conditions. Combining conventional variable speed limit systems with queue warnings also improved safety for CV drivers. Furthermore, the implementation of a single CV application (queue warning) showed positive changes in the aforementioned speed metrics, congestion mitigation, and reduced conflicts. With the two CV applications combined, no significant differences were observed. Additional tests investigated the impacts of lane changes and roadway attributes on safety in the CV environment.Keywords: connected vehiclesdriving informationtraffic safetytraffic simulation Author contributionsAdekunle Adebisi: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Visualization, Writing - original draft, Writing - review & editing. John Ash: Conceptualization, Supervision, Methodology, Validation, Writing - review & editing.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research was not part of any funded project.
{"title":"Traffic safety performance evaluation in a connected vehicle environment with queue warning and speed harmonization applications","authors":"Adekunle Adebisi, John E. Ash","doi":"10.1080/19439962.2023.2273545","DOIUrl":"https://doi.org/10.1080/19439962.2023.2273545","url":null,"abstract":"AbstractWith the increased adoption of connected vehicle (CV) technologies, safety information is becoming increasingly available to drivers. This study investigates three main questions (1) Do CV-based traffic management applications improve safety on roadways with existing infrastructure-based traffic management systems? (2) Can combining two CV technologies have a greater impact on safety than a single CV technology? and (3) Do geometric and traffic composition factors impact the efficiency of CV technologies? We applied a rarely-used CV dataset and conducted a comprehensive simulation analysis of varying conditions and CV penetration rates that studies have not considered. Two CV applications (queue warning and speed harmonization) implemented in the Intelligent Network Flow Optimization experiment in Seattle, WA were evaluated. Results showed that driver safety performance, based on speed metrics (standard deviation and percentage of extreme values) improved under the CV driving conditions. Combining conventional variable speed limit systems with queue warnings also improved safety for CV drivers. Furthermore, the implementation of a single CV application (queue warning) showed positive changes in the aforementioned speed metrics, congestion mitigation, and reduced conflicts. With the two CV applications combined, no significant differences were observed. Additional tests investigated the impacts of lane changes and roadway attributes on safety in the CV environment.Keywords: connected vehiclesdriving informationtraffic safetytraffic simulation Author contributionsAdekunle Adebisi: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Validation, Visualization, Writing - original draft, Writing - review & editing. John Ash: Conceptualization, Supervision, Methodology, Validation, Writing - review & editing.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research was not part of any funded project.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"41 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135819601","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 : 2023-11-03DOI: 10.1080/19439962.2023.2276197
Huijie Ouyang, Yin Han, Pengfei Liu, Jing Zhao
AbstractThere has been a concerning rise in pedestrian fatalities resulting from traffic accidents. This study conducts an in-depth analysis of the factors that contribute to the injury severity of pedestrians considering human, vehicle, roadway, and environmental characteristics of the crashes. A novel approach is proposed by combing a data mining technique and mixed logit models. First, the Apriori algorithm is employed to uncover patterns between fatal and incapacitating injury outcomes and their influencing factors. Then, mixed logit models are developed to investigate heterogeneity across all observations under two different lighting conditions. It is found that eight variables show heterogeneity affecting the injury severity of crash outcomes. Results also indicate that drivers older than 65 years old will increase the probability of pedestrian injury severity at dark-unlighted roads. Additionally, the present of signal and double yellow line has could increase the injury severity. Creating optimal lighting conditions at pedestrian crossings during nighttime hours and enhancing safety education initiatives for pedestrians are critical factors to improve pedestrian safety. The finding of this study will benefit policymakers and road safety professionals develop more effective strategies for preventing pedestrian injuries in vehicle crashes.Keywords: pedestrian safetyinjury severityApriori algorithmmixed logit model Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China under Grants 52122215, Shanghai Shu Guang Program under Grant 22SG45, and Shanghai Pu Jiang Program under Grant 21PJC085.
{"title":"Factors affecting pedestrian injury severity in pedestrian-vehicle crashes: Insights from a data mining and mixed logit model approach","authors":"Huijie Ouyang, Yin Han, Pengfei Liu, Jing Zhao","doi":"10.1080/19439962.2023.2276197","DOIUrl":"https://doi.org/10.1080/19439962.2023.2276197","url":null,"abstract":"AbstractThere has been a concerning rise in pedestrian fatalities resulting from traffic accidents. This study conducts an in-depth analysis of the factors that contribute to the injury severity of pedestrians considering human, vehicle, roadway, and environmental characteristics of the crashes. A novel approach is proposed by combing a data mining technique and mixed logit models. First, the Apriori algorithm is employed to uncover patterns between fatal and incapacitating injury outcomes and their influencing factors. Then, mixed logit models are developed to investigate heterogeneity across all observations under two different lighting conditions. It is found that eight variables show heterogeneity affecting the injury severity of crash outcomes. Results also indicate that drivers older than 65 years old will increase the probability of pedestrian injury severity at dark-unlighted roads. Additionally, the present of signal and double yellow line has could increase the injury severity. Creating optimal lighting conditions at pedestrian crossings during nighttime hours and enhancing safety education initiatives for pedestrians are critical factors to improve pedestrian safety. The finding of this study will benefit policymakers and road safety professionals develop more effective strategies for preventing pedestrian injuries in vehicle crashes.Keywords: pedestrian safetyinjury severityApriori algorithmmixed logit model Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China under Grants 52122215, Shanghai Shu Guang Program under Grant 22SG45, and Shanghai Pu Jiang Program under Grant 21PJC085.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"42 13","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135820026","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 : 2023-10-11DOI: 10.1080/19439962.2023.2253759
Eunsol Cho, Yunjong Kim, Seolyoung Lee, Cheol Oh
AbstractIdentification of driving behavior is a fundamental to developing effective treatments to address various traffic-related problems. In particular, the driving behavior of city bus drivers is of great interest because the crash severity can become much higher than any other vehicle types due to the larger number of passengers on board. However, there is a lack of effective policy preparation to prevent crashes because of limitations associated with identifying intrinsic factors underlying the cause of traffic crashes based on driving behavior analysis. This study aims to develop a methodology to predict high-risk bus drivers, which can be a baseline in establishing effective bus safety policies. An in-depth questionnaire survey was conducted to collect wellness data to represent intrinsic characteristics used for inputs of the proposed prediction methodology in addition to the aggressive driving behavior data obtained from in-vehicle data recorders. Bus drivers were classified into two groups, normal drivers and risky drivers, based on aggressive driving behavior. The priority of intrinsic factors was determined by a gradient boosting method and further utilized to derive input features of the proposed method. Deep-learning-based neural network models were evaluated to predict risky bus drivers in this study. A model with variables up to 11th priority as inputs was selected as the best model. A classification accuracy of 85% was achievable with the proposed model. The outcome of this study would be valuable in supporting policymaking activities to prevent aggressive driving behavior.Keywords: aggressive driving behaviorartificial neural networkbus driver wellnessgradient boosting methodtraffic safety Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis research was supported by a grant from Transportation and Logistics Research Program funded by Ministry of Land, Infrastructure and Transport of the Korean government (21TLRP-B148683-04).
{"title":"Prediction of high-risk bus drivers characterized by aggressive driving behavior","authors":"Eunsol Cho, Yunjong Kim, Seolyoung Lee, Cheol Oh","doi":"10.1080/19439962.2023.2253759","DOIUrl":"https://doi.org/10.1080/19439962.2023.2253759","url":null,"abstract":"AbstractIdentification of driving behavior is a fundamental to developing effective treatments to address various traffic-related problems. In particular, the driving behavior of city bus drivers is of great interest because the crash severity can become much higher than any other vehicle types due to the larger number of passengers on board. However, there is a lack of effective policy preparation to prevent crashes because of limitations associated with identifying intrinsic factors underlying the cause of traffic crashes based on driving behavior analysis. This study aims to develop a methodology to predict high-risk bus drivers, which can be a baseline in establishing effective bus safety policies. An in-depth questionnaire survey was conducted to collect wellness data to represent intrinsic characteristics used for inputs of the proposed prediction methodology in addition to the aggressive driving behavior data obtained from in-vehicle data recorders. Bus drivers were classified into two groups, normal drivers and risky drivers, based on aggressive driving behavior. The priority of intrinsic factors was determined by a gradient boosting method and further utilized to derive input features of the proposed method. Deep-learning-based neural network models were evaluated to predict risky bus drivers in this study. A model with variables up to 11th priority as inputs was selected as the best model. A classification accuracy of 85% was achievable with the proposed model. The outcome of this study would be valuable in supporting policymaking activities to prevent aggressive driving behavior.Keywords: aggressive driving behaviorartificial neural networkbus driver wellnessgradient boosting methodtraffic safety Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis research was supported by a grant from Transportation and Logistics Research Program funded by Ministry of Land, Infrastructure and Transport of the Korean government (21TLRP-B148683-04).","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136097925","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 : 2023-10-09DOI: 10.1080/19439962.2023.2265312
Andreas Psarras, Theodore Panagiotidis, Andreas Andronikidis
AbstractThe COVID-19 pandemic has resulted in the implementation of traffic and movement restrictions as governments were trying to limit the spread of the virus. Tourism has been affected by these travel restrictions. We examine the impact of curfews and the re-opening of borders on road traffic accidents. We investigate the effects of lockdown on motor vehicle collisions by analyzing recorded car accidents in 58 districts in Greece. We employ a difference-in-differences approach to compare motor vehicle collisions in 2020 with the previous five years. We reveal a decline in road traffic collisions during the curfew period (with 1617 fewer collisions). This is followed by an increase after the re-opening of borders (168 more vehicle collisions in tourist-popular areas despite the decline in tourist arrivals), compared to what would have been expected in the absence of the pandemic restrictions.Keywords: COVID-19vehicle collisionstourismdifference-in-differencesJEL CLASSIFICATION: R41Z32I18 Disclosure statementThe authors report there are no competing interests to declare.Notes1 See Adanu et al. (Citation2021), Barnes et al. (Citation2020), Brodeur et al. (Citation2021b), Doucette et al. (Citation2021), Liao and Lowry (Citation2021a), Lin et al. (Citation2020), Rudisill (Citation2021) and Qureshi et al. (Citation2020).2 See for instance Oguzoglu (Citation2020), Sekadakis et al. (Citation2021), Vandoros and Papailias (Citation2021) and Vandoros (Citation2022).3 Studies that employ DiD: Barnes et al. (Citation2020), Brodeur et al. (Citation2021b), Liao and Lowry (Citation2021b), Lin et al. (Citation2020), Oguzoglu (Citation2020) and Vandoros and Papailias (Citation2021). Studies that employ interrupted time series: Doucette et al. (Citation2021), Qureshi et al. (Citation2020), Vandoros (Citation2022).4 Section 5 provides graphic evidence of mobility reduction in Greece (see Figure 9).5 Data were sent via email from the Traffic Police on 07/02/2022.6 http://www.astynomia.gr7 http://archive.data.gov.gr/dataset/statistikh-epethrida8 https://covid19.apple.com/mobility
{"title":"COVID-19, tourism and road traffic accidents: Evidence from Greece","authors":"Andreas Psarras, Theodore Panagiotidis, Andreas Andronikidis","doi":"10.1080/19439962.2023.2265312","DOIUrl":"https://doi.org/10.1080/19439962.2023.2265312","url":null,"abstract":"AbstractThe COVID-19 pandemic has resulted in the implementation of traffic and movement restrictions as governments were trying to limit the spread of the virus. Tourism has been affected by these travel restrictions. We examine the impact of curfews and the re-opening of borders on road traffic accidents. We investigate the effects of lockdown on motor vehicle collisions by analyzing recorded car accidents in 58 districts in Greece. We employ a difference-in-differences approach to compare motor vehicle collisions in 2020 with the previous five years. We reveal a decline in road traffic collisions during the curfew period (with 1617 fewer collisions). This is followed by an increase after the re-opening of borders (168 more vehicle collisions in tourist-popular areas despite the decline in tourist arrivals), compared to what would have been expected in the absence of the pandemic restrictions.Keywords: COVID-19vehicle collisionstourismdifference-in-differencesJEL CLASSIFICATION: R41Z32I18 Disclosure statementThe authors report there are no competing interests to declare.Notes1 See Adanu et al. (Citation2021), Barnes et al. (Citation2020), Brodeur et al. (Citation2021b), Doucette et al. (Citation2021), Liao and Lowry (Citation2021a), Lin et al. (Citation2020), Rudisill (Citation2021) and Qureshi et al. (Citation2020).2 See for instance Oguzoglu (Citation2020), Sekadakis et al. (Citation2021), Vandoros and Papailias (Citation2021) and Vandoros (Citation2022).3 Studies that employ DiD: Barnes et al. (Citation2020), Brodeur et al. (Citation2021b), Liao and Lowry (Citation2021b), Lin et al. (Citation2020), Oguzoglu (Citation2020) and Vandoros and Papailias (Citation2021). Studies that employ interrupted time series: Doucette et al. (Citation2021), Qureshi et al. (Citation2020), Vandoros (Citation2022).4 Section 5 provides graphic evidence of mobility reduction in Greece (see Figure 9).5 Data were sent via email from the Traffic Police on 07/02/2022.6 http://www.astynomia.gr7 http://archive.data.gov.gr/dataset/statistikh-epethrida8 https://covid19.apple.com/mobility","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135094013","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 : 2023-09-26DOI: 10.1080/19439962.2023.2250307
Pengfei Cui, Xiaobao Yang, Lu Ma, Chaoxu Mu
AbstractUnderstanding the relationship between vehicle speed and the risk of sustaining a life-threatening injury has garnered continual attention. This study seeks to gain deep insight into the relationship between two-vehicle speeds and the risk of serious injury at intersections. The 2016–2018 crash data that occurred at intersections from the US Crash Report Sampling System (CRSS) were examined. We present a more general framework that allows the crash risk to be simultaneously linked to a universal two-dimensional variable of two-vehicle speeds, instead of the one-dimensional variable of impact speed calculated according to crash types in the existing literature. The results indicate that the risk of serious injury for head-on crashes in the medium-speed zone is mainly influenced by the faster vehicle although having little relation to the slower vehicle. More importantly, we find that the marginal relationship between the two-vehicle speeds and the crash risk is non-monotonic for angle and rear crashes. Finally, appropriate measures are suggested to reduce the crash risk at intersections, including alerting the driver not to cross intersections at exceedingly low speed, assisting the driver in making an emergency response at a medium speed, and warning the driver not to operate at a very high speed.Keywords: two-vehicle speedsrisk of serious injuryintersection crashtwo-dimensionalnon-parametric effect Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Thin plate splines are a form of smoothing spline that finds utility in visualizing intricate relationships between continuous predictors and response variables (Hutchinson, Citation1998). Their versatile nature makes them particularly suitable for assessing the collective impact of two continuous predictors on a singular outcome, owing to their ability to capture multidimensional patterns (Pedersen et al., Citation2019).Additional informationFundingThis research was supported by the Key Program of the National Natural Science Foundation of China(No. 62333016) and China Scholarship Council (No.202307090082).
{"title":"Modeling non-parametric effects of two-vehicle speed on crash risk at intersections: Leveraging two-dimensional additive logistic regression beyond univariable approach","authors":"Pengfei Cui, Xiaobao Yang, Lu Ma, Chaoxu Mu","doi":"10.1080/19439962.2023.2250307","DOIUrl":"https://doi.org/10.1080/19439962.2023.2250307","url":null,"abstract":"AbstractUnderstanding the relationship between vehicle speed and the risk of sustaining a life-threatening injury has garnered continual attention. This study seeks to gain deep insight into the relationship between two-vehicle speeds and the risk of serious injury at intersections. The 2016–2018 crash data that occurred at intersections from the US Crash Report Sampling System (CRSS) were examined. We present a more general framework that allows the crash risk to be simultaneously linked to a universal two-dimensional variable of two-vehicle speeds, instead of the one-dimensional variable of impact speed calculated according to crash types in the existing literature. The results indicate that the risk of serious injury for head-on crashes in the medium-speed zone is mainly influenced by the faster vehicle although having little relation to the slower vehicle. More importantly, we find that the marginal relationship between the two-vehicle speeds and the crash risk is non-monotonic for angle and rear crashes. Finally, appropriate measures are suggested to reduce the crash risk at intersections, including alerting the driver not to cross intersections at exceedingly low speed, assisting the driver in making an emergency response at a medium speed, and warning the driver not to operate at a very high speed.Keywords: two-vehicle speedsrisk of serious injuryintersection crashtwo-dimensionalnon-parametric effect Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Thin plate splines are a form of smoothing spline that finds utility in visualizing intricate relationships between continuous predictors and response variables (Hutchinson, Citation1998). Their versatile nature makes them particularly suitable for assessing the collective impact of two continuous predictors on a singular outcome, owing to their ability to capture multidimensional patterns (Pedersen et al., Citation2019).Additional informationFundingThis research was supported by the Key Program of the National Natural Science Foundation of China(No. 62333016) and China Scholarship Council (No.202307090082).","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134887219","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}
AbstractRailway accidents caused by gales are indeed influenced by multiple factors, making them a complex process. However, current research and monitoring systems often focus solely on wind speed, overlooking the combined effects of other factors. This paper proposes a data-driven assessment strategy specifically designed for high-speed railways. The disaster-inducing factor, disaster-pregnant environment, disaster-bearing body, and disaster prevention/mitigation capabilities are all taken into consideration. Moreover, it explores the interrelationships between these factors. To validate the proposed mechanism, the spatial-temporal distribution of gale-induced risks along China’s high-speed railways is studied in this paper. By analyzing and interpreting the data, the researchers are able to identify areas and time periods that are particularly prone to gale-induced accidents. These findings are crucial for the development of effective strategies for disaster prevention and mitigation in the context of high-speed railways.Keywords: high-speed railgale disasterrisk assessmentrailway safety Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China (No. 61903023), State Key Laboratory of Rail Traffic Control and Safety (No. RCS2022ZZ002), and the Fundamental Research Funds for the Central Universities (No. 2022JBXT009).
{"title":"Data-driven gale-induced risk assessment strategy for the high-speed railway system","authors":"Guanyuan Zhao, Xiaoping Ma, Xuying Qiu, Hanqing Zhang, Zhiping Zhang","doi":"10.1080/19439962.2023.2253749","DOIUrl":"https://doi.org/10.1080/19439962.2023.2253749","url":null,"abstract":"AbstractRailway accidents caused by gales are indeed influenced by multiple factors, making them a complex process. However, current research and monitoring systems often focus solely on wind speed, overlooking the combined effects of other factors. This paper proposes a data-driven assessment strategy specifically designed for high-speed railways. The disaster-inducing factor, disaster-pregnant environment, disaster-bearing body, and disaster prevention/mitigation capabilities are all taken into consideration. Moreover, it explores the interrelationships between these factors. To validate the proposed mechanism, the spatial-temporal distribution of gale-induced risks along China’s high-speed railways is studied in this paper. By analyzing and interpreting the data, the researchers are able to identify areas and time periods that are particularly prone to gale-induced accidents. These findings are crucial for the development of effective strategies for disaster prevention and mitigation in the context of high-speed railways.Keywords: high-speed railgale disasterrisk assessmentrailway safety Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China (No. 61903023), State Key Laboratory of Rail Traffic Control and Safety (No. RCS2022ZZ002), and the Fundamental Research Funds for the Central Universities (No. 2022JBXT009).","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134886407","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 : 2023-09-11DOI: 10.1080/19439962.2023.2253750
Chenzhu Wang, Muhammad Ijaz, Fei Chen, Said M. Easa, Yunlong Zhang, Jianchuan Cheng, Muhammad Zahid
This study explores the temporal instability and non-transferability of the determinants affecting injury severities of pedestrians struck by motorcycles and non-motorcycles. Using the pedestrian-vehicle crash data in Rawalpindi, Pakistan, over three years (2017–2019), three possible crash injury severity categories (minor injury, severe injury, and fatal injury) are estimated using alternative models to account for unobserved heterogeneity. These are a random-parameters multinomial logit (RP-ML) model with heterogeneity in means and variances and a latent-class multinomial logit (LC-ML) model with class probability functions. Temporal instability and non-transferability in the effects of explanatory variables are confirmed using a series of likelihood ratio tests based on the two alternative models. Various variables are observed to determine pedestrian-injury severities, and the estimation results show significant temporal instability and non-transferability in both RP-ML and LC-ML models. However, several explanatory variables produce relatively temporally stable and transferable effects, providing valuable insights to implement effective countermeasures from a long-term perspective. Moreover, out-of-sample predictions are simulated to confirm the temporal instability and non-transferability. At the same time, the LC-ML models produce higher differences for temporal instability and lower differences for non-transferability compared to the RP-ML model. Understanding and depth comparing the estimation results, likelihood ratio tests, and out-of-sample predictions using alternative models is a promising direction for future research to explore how the observed and unobserved heterogeneity can be estimated in terms of temporal instability and non-transferability.
{"title":"Temporal assessment of injury severities of two types of pedestrian-vehicle crashes using unobserved-heterogeneity models","authors":"Chenzhu Wang, Muhammad Ijaz, Fei Chen, Said M. Easa, Yunlong Zhang, Jianchuan Cheng, Muhammad Zahid","doi":"10.1080/19439962.2023.2253750","DOIUrl":"https://doi.org/10.1080/19439962.2023.2253750","url":null,"abstract":"This study explores the temporal instability and non-transferability of the determinants affecting injury severities of pedestrians struck by motorcycles and non-motorcycles. Using the pedestrian-vehicle crash data in Rawalpindi, Pakistan, over three years (2017–2019), three possible crash injury severity categories (minor injury, severe injury, and fatal injury) are estimated using alternative models to account for unobserved heterogeneity. These are a random-parameters multinomial logit (RP-ML) model with heterogeneity in means and variances and a latent-class multinomial logit (LC-ML) model with class probability functions. Temporal instability and non-transferability in the effects of explanatory variables are confirmed using a series of likelihood ratio tests based on the two alternative models. Various variables are observed to determine pedestrian-injury severities, and the estimation results show significant temporal instability and non-transferability in both RP-ML and LC-ML models. However, several explanatory variables produce relatively temporally stable and transferable effects, providing valuable insights to implement effective countermeasures from a long-term perspective. Moreover, out-of-sample predictions are simulated to confirm the temporal instability and non-transferability. At the same time, the LC-ML models produce higher differences for temporal instability and lower differences for non-transferability compared to the RP-ML model. Understanding and depth comparing the estimation results, likelihood ratio tests, and out-of-sample predictions using alternative models is a promising direction for future research to explore how the observed and unobserved heterogeneity can be estimated in terms of temporal instability and non-transferability.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135939253","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 : 2023-05-04DOI: 10.1080/19439962.2022.2092571
Taehun Lee, C. Cunningham, N. Rouphail
Abstract Traditional crash frequency models cannot estimate crash frequency for individual traffic movements at an intersection, which precludes the safety evaluation of individual movements and identification of hazardous ones. This paper proposes a movement-based (MB) model that estimates crash frequency for individual movements as well as for the entire intersection. A base model using the safety performance function form in the Highway Safety Manual was also developed for comparison against the MB model. This study used crashes collected for five to eight years at 41 signalized intersections in North Carolina for the model estimation and validation (21 intersections for the estimation and 20 intersections for the validation). The models were validated using cumulative residual plots, test set validation, and in a case study. The test set validation showed that the MB model yielded slight improvements in estimations compared to the base model (1.17%−5.83% reductions in mean absolute error and 3.32%−6.64% reductions in root-mean-square error). The case study showed the MB model correctly identified hazardous traffic movements that had demonstrable safety problems based on observed and estimated crash frequencies. The MB model will enable engineers to identify hazardous movements and approaches to implement safety improvement countermeasures at the deserving locations and movements.
{"title":"Movement-based intersection crash frequency modeling","authors":"Taehun Lee, C. Cunningham, N. Rouphail","doi":"10.1080/19439962.2022.2092571","DOIUrl":"https://doi.org/10.1080/19439962.2022.2092571","url":null,"abstract":"Abstract Traditional crash frequency models cannot estimate crash frequency for individual traffic movements at an intersection, which precludes the safety evaluation of individual movements and identification of hazardous ones. This paper proposes a movement-based (MB) model that estimates crash frequency for individual movements as well as for the entire intersection. A base model using the safety performance function form in the Highway Safety Manual was also developed for comparison against the MB model. This study used crashes collected for five to eight years at 41 signalized intersections in North Carolina for the model estimation and validation (21 intersections for the estimation and 20 intersections for the validation). The models were validated using cumulative residual plots, test set validation, and in a case study. The test set validation showed that the MB model yielded slight improvements in estimations compared to the base model (1.17%−5.83% reductions in mean absolute error and 3.32%−6.64% reductions in root-mean-square error). The case study showed the MB model correctly identified hazardous traffic movements that had demonstrable safety problems based on observed and estimated crash frequencies. The MB model will enable engineers to identify hazardous movements and approaches to implement safety improvement countermeasures at the deserving locations and movements.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"103 1","pages":"493 - 514"},"PeriodicalIF":2.6,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79657039","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}
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