Pub Date : 2025-09-12DOI: 10.1016/j.iatssr.2025.09.003
Mohamed Ahmed Al-Awad , Mohamed Kharbeche , Faris Tarlochan
The global disparity in Child Restraint System (CRS) adoption contributes directly to variations in road traffic mortality and morbidity among children. Despite the proven effectiveness of CRS in reducing harm, its adoption remains inconsistent across regions due to socio-economic, legislative, and cultural factors. This review evaluates global CRS adoption rates, identifies barriers to effective utilization, and explores interventions to enhance usage and legislative compliance, ultimately proposing strategies to improve child passenger safety.
A total of 93 articles published between 2013 and 2024 were reviewed, with a focus on CRS usage and intervention patterns, the effectiveness of interventions, and legislative impact across high, middle, and low-income countries.
The review highlights a significant gap in CRS usage between high-income and low to middle-income countries, with affordability, lack of awareness, and inadequate legislation as primary barriers. High-income regions showed better adherence but struggled with proper installation and misuse. Intervention strategies, including legislation, public education, and economic incentives, showed varying success in improving CRS adoption.
Enhancing global CRS usage requires stringent legislation, comprehensive campaigns, economic support, and innovative technological solutions. Tailored strategies that account for regional socio-economic and cultural norms are essential to achieve widespread adoption and proper CRS use, ultimately reducing child passenger fatalities and injuries.
{"title":"Evaluating child restraint system (CRS) adoption and policy interventions worldwide: a review","authors":"Mohamed Ahmed Al-Awad , Mohamed Kharbeche , Faris Tarlochan","doi":"10.1016/j.iatssr.2025.09.003","DOIUrl":"10.1016/j.iatssr.2025.09.003","url":null,"abstract":"<div><div>The global disparity in Child Restraint System (CRS) adoption contributes directly to variations in road traffic mortality and morbidity among children. Despite the proven effectiveness of CRS in reducing harm, its adoption remains inconsistent across regions due to socio-economic, legislative, and cultural factors. This review evaluates global CRS adoption rates, identifies barriers to effective utilization, and explores interventions to enhance usage and legislative compliance, ultimately proposing strategies to improve child passenger safety.</div><div>A total of 93 articles published between 2013 and 2024 were reviewed, with a focus on CRS usage and intervention patterns, the effectiveness of interventions, and legislative impact across high, middle, and low-income countries.</div><div>The review highlights a significant gap in CRS usage between high-income and low to middle-income countries, with affordability, lack of awareness, and inadequate legislation as primary barriers. High-income regions showed better adherence but struggled with proper installation and misuse. Intervention strategies, including legislation, public education, and economic incentives, showed varying success in improving CRS adoption.</div><div>Enhancing global CRS usage requires stringent legislation, comprehensive campaigns, economic support, and innovative technological solutions. Tailored strategies that account for regional socio-economic and cultural norms are essential to achieve widespread adoption and proper CRS use, ultimately reducing child passenger fatalities and injuries.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 3","pages":"Pages 362-373"},"PeriodicalIF":3.3,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050150","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}
Driving comfort assessment is a prerequisite to improve the journey experience for the drivers as well as the passengers. In this work, we proposed an advanced approach for the measurement of driving comfort in real-time. Different types of environmental features are considered along with the traditionally used Comfort Index (CI), and an Environment-specific Comfort Index (EsCI) is proposed. EsCI is also inversely proportional to the drivers' comfort level, just like CI. We also developed an android application named QDCL (Quantification of Driver Comfort Level) for overall data collection and computation of EsCI from the same. A series of driving experiments at different times of the day and different traffic conditions have been performed in Indian urban road scenarios to assess the performance of QDCL and the relevance of EsCI. We extended the work by studying the effects of different external stimuli on the computed driving comfort level. The performance of EsCI is observed to outperform the traditionally used CI (Comfort Index) in terms of accuracy for the quantification of overall driving comfort.
驾驶舒适性评估是提高驾驶员和乘客出行体验的前提。在这项工作中，我们提出了一种先进的实时驾驶舒适性测量方法。考虑了不同类型的环境特征以及传统使用的舒适度指数（CI），并提出了环境特异性舒适度指数（EsCI）。与CI一样，EsCI也与驾驶员的舒适度成反比。我们还开发了一个名为QDCL （Quantification of Driver Comfort Level）的android应用程序，用于收集驾驶员舒适度的整体数据并计算驾驶员舒适度。在印度城市道路场景中，在一天中不同时间和不同交通条件下进行了一系列驾驶实验，以评估QDCL的性能和EsCI的相关性。我们通过研究不同外部刺激对计算的驾驶舒适度的影响来扩展工作。EsCI的性能被观察到优于传统使用的CI（舒适指数）在准确性量化整体驾驶舒适性。
{"title":"A new paradigm in driving comfort measurement: Environment-specific comfort index and its real-time application in Indian context","authors":"Ishita Sar , Soumitra Kundu , Aurobinda Routray , Biswajit Mahanty","doi":"10.1016/j.iatssr.2025.09.002","DOIUrl":"10.1016/j.iatssr.2025.09.002","url":null,"abstract":"<div><div>Driving comfort assessment is a prerequisite to improve the journey experience for the drivers as well as the passengers. In this work, we proposed an advanced approach for the measurement of driving comfort in real-time. Different types of environmental features are considered along with the traditionally used Comfort Index (CI), and an Environment-specific Comfort Index (EsCI) is proposed. EsCI is also inversely proportional to the drivers' comfort level, just like CI. We also developed an android application named QDCL (Quantification of Driver Comfort Level) for overall data collection and computation of EsCI from the same. A series of driving experiments at different times of the day and different traffic conditions have been performed in Indian urban road scenarios to assess the performance of QDCL and the relevance of EsCI. We extended the work by studying the effects of different external stimuli on the computed driving comfort level. The performance of EsCI is observed to outperform the traditionally used CI (Comfort Index) in terms of accuracy for the quantification of overall driving comfort.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 3","pages":"Pages 353-361"},"PeriodicalIF":3.3,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050149","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}
Pub Date : 2025-09-02DOI: 10.1016/j.iatssr.2025.09.001
Debashis Ray Sarkar , K. Ramachandra Rao , Niladri Chatterjee
Crash prediction models (CPMs) typically use statistical or data-driven approaches derived from observed crash data, but these can be limited by unreliable historical data. Near-crash-based CPMs provide a proactive alternative, predicting crash frequencies before actual crashes occur. Surrogate Safety Measures (SSMs) examine potentially hazardous traffic events to improve the understanding of traffic safety dynamics. These events serve as proxies for crashes, enabling proactive and timely safety assessments. This study proposes a methodological framework for evaluating crash risk at unsignalized intersections using UAV-acquired vehicle trajectory data and applies Extreme Value Theory (EVT) to statistically model the tail behavior of a time-based SSM—Post Encroachment Time (PET). High-resolution (4 K) video data were acquired at six different unsignalized intersections to capture morning rush hour traffic (8 to 9 a.m.). Vehicle trajectories and surrogate measures such as Post Encroachment Time (PET) were extracted using advanced AI-driven video analysis via the DataFromSky (DFS) platform. The analysis employed the Peak Over Threshold (POT) method. The threshold was determined to be −1.25 s using the Mean Residual Life (MRL) plot, as well as the scale and shape parameter stability plots of the Generalized Pareto Distribution (GPD). The results show that traffic volume and crash frequency have a significant impact on collision risk. As traffic volume increases, PET decreases, leading to a higher likelihood of conflicts and crashes. Additionally, mean speed shows an inverse relationship with both crash frequency and collision risk. Overall, traffic volume and conflict frequency emerge as key predictors of crash risk occurrences. This study establishes a foundation for leveraging UAV-based vehicle trajectory data in conducting proactive safety assessments at unsignalized intersections.
{"title":"Crash risk assessment at unsignalized intersections using vehicle trajectory data","authors":"Debashis Ray Sarkar , K. Ramachandra Rao , Niladri Chatterjee","doi":"10.1016/j.iatssr.2025.09.001","DOIUrl":"10.1016/j.iatssr.2025.09.001","url":null,"abstract":"<div><div>Crash prediction models (CPMs) typically use statistical or data-driven approaches derived from observed crash data, but these can be limited by unreliable historical data. Near-crash-based CPMs provide a proactive alternative, predicting crash frequencies before actual crashes occur. Surrogate Safety Measures (SSMs) examine potentially hazardous traffic events to improve the understanding of traffic safety dynamics. These events serve as proxies for crashes, enabling proactive and timely safety assessments. This study proposes a methodological framework for evaluating crash risk at unsignalized intersections using UAV-acquired vehicle trajectory data and applies Extreme Value Theory (EVT) to statistically model the tail behavior of a time-based SSM—Post Encroachment Time (PET). High-resolution (4 K) video data were acquired at six different unsignalized intersections to capture morning rush hour traffic (8 to 9 a.m.). Vehicle trajectories and surrogate measures such as Post Encroachment Time (PET) were extracted using advanced AI-driven video analysis via the DataFromSky (DFS) platform. The analysis employed the Peak Over Threshold (POT) method. The threshold was determined to be −1.25 s using the Mean Residual Life (MRL) plot, as well as the scale and shape parameter stability plots of the Generalized Pareto Distribution (GPD). The results show that traffic volume and crash frequency have a significant impact on collision risk. As traffic volume increases, PET decreases, leading to a higher likelihood of conflicts and crashes. Additionally, mean speed shows an inverse relationship with both crash frequency and collision risk. Overall, traffic volume and conflict frequency emerge as key predictors of crash risk occurrences. This study establishes a foundation for leveraging UAV-based vehicle trajectory data in conducting proactive safety assessments at unsignalized intersections.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 4","pages":"Pages 459-469"},"PeriodicalIF":3.3,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145326258","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}
Unsignalized intersections require more diverse and appropriate physical functions than signalized intersections because traffic is not controlled, and more objects and broader areas must be checked. Elderly drivers may lack adequate physical functions, leading to inadequate spatial exploration and missed recognition, resulting in accidents. This study aims to clarify the spatial exploration behavior of elderly drivers when passing through unsignalized intersections, considering road conditions, traffic conditions, and drivers' physical functions. Participants (n = 62) whose physical function was measured were asked to watch an entry video of 10 intersections using a head-mounted display with eye-tracking function, and the average angular velocity during the viewing was measured. Factorial analysis on spatial exploration showed that the elderly group did not exhibit greater spatial exploration at intersections with acute angles compared to other age groups, even considering physical function effects and traffic conditions through the linear mixed model.
{"title":"A study on spatial exploration of elderly drivers at unsignalized intersections considering road and traffic conditions and driver's physical function","authors":"Yasuhiro Mimura , Keiichi Higuchi , Misako Yamagishi , Ryo Ito","doi":"10.1016/j.iatssr.2025.07.003","DOIUrl":"10.1016/j.iatssr.2025.07.003","url":null,"abstract":"<div><div>Unsignalized intersections require more diverse and appropriate physical functions than signalized intersections because traffic is not controlled, and more objects and broader areas must be checked. Elderly drivers may lack adequate physical functions, leading to inadequate spatial exploration and missed recognition, resulting in accidents. This study aims to clarify the spatial exploration behavior of elderly drivers when passing through unsignalized intersections, considering road conditions, traffic conditions, and drivers' physical functions. Participants (<em>n</em> = 62) whose physical function was measured were asked to watch an entry video of 10 intersections using a head-mounted display with eye-tracking function, and the average angular velocity during the viewing was measured. Factorial analysis on spatial exploration showed that the elderly group did not exhibit greater spatial exploration at intersections with acute angles compared to other age groups, even considering physical function effects and traffic conditions through the linear mixed model.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 3","pages":"Pages 314-323"},"PeriodicalIF":3.3,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005198","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}
Monitoring of road safety performance is essential to effectively address the global road safety problem. Consistent and accurate monitoring allows policymakers to assess the effectiveness of current safety measures, identify emerging risk factors, and develop targeted interventions. Different key performance indicators can be used to monitor road safety performance. In addition to the traditional road safety indicators based on the number of fatalities or injured people in road traffic crashes, complementary road safety performance indicators can be used in relation to vehicles, infrastructure or road users' behaviour.
The E-Survey of Road Users' Attitudes (ESRA) is an online survey that aims to collect and analyse comparable data on road safety performance and traffic safety culture across the world. In its three editions (from 2015 to 2023) ESRA has included data from more than 120,000 road users from a total of 68 different countries. This paper focuses on data from the third edition of the ESRA survey (ESRA3), which was conducted in 2023 across 39 countries and includes answers from over 37,000 road users. The objectives are to provide an overview of the ESRA3 survey methodology and to present results related to several road safety topics, such as drink-driving, speeding, or distraction, across different types of road users: car drivers, pedestrians, cyclists, and moped riders/motorcyclists. It examines multiple dimensions of risky behaviours in traffic, including self-declared behaviours, personal acceptability of unsafe behaviours, and support for policy measures.
Results show low acceptability of unsafe traffic behaviours like speeding, drink-driving, fatigued driving or using a mobile phone while driving a car – less than 5 % of respondents considered these behaviours acceptable. Notwithstanding the low acceptability, a high percentage of car drivers declared engaging in risky behaviours in traffic: speeding within built-up areas was declared by 37 % to 47 % of car drivers, using a mobile phone by 22 % to 32 %, fatigued driving by 18 % to 20 %, and driving under the influence of alcohol by 10 % to 14 %. As for vulnerable road users, distraction (reading messages/checking social media or listening to music through headphones) was the most declared risky behaviour by pedestrians, the non-use of helmet the most declared by cyclists, and speeding the most declared by moped riders and motorcyclists. Most respondents support policy measures to restrict risky behaviour.
The ESRA survey offers a unique database and provides policy makers and researchers with valuable insights into public perception of road safety.
{"title":"Measuring road safety performance and culture: A comparative study of 39 countries","authors":"Carlos Pires , Uta Meesmann , Alain Areal , Naomi Wardenier , Marie-Axelle Granié , Gerald Furian , Dimitrios Nikolaou , Dagmara Jankowska-Karpa , Craig Lyon , Mette Møller , Fabian Surges , Hideki Nakamura , Agnieszka Stelling","doi":"10.1016/j.iatssr.2025.07.001","DOIUrl":"10.1016/j.iatssr.2025.07.001","url":null,"abstract":"<div><div>Monitoring of road safety performance is essential to effectively address the global road safety problem. Consistent and accurate monitoring allows policymakers to assess the effectiveness of current safety measures, identify emerging risk factors, and develop targeted interventions. Different key performance indicators can be used to monitor road safety performance. In addition to the traditional road safety indicators based on the number of fatalities or injured people in road traffic crashes, complementary road safety performance indicators can be used in relation to vehicles, infrastructure or road users' behaviour.</div><div>The <em>E</em>-Survey of Road Users' Attitudes (ESRA) is an online survey that aims to collect and analyse comparable data on road safety performance and traffic safety culture across the world. In its three editions (from 2015 to 2023) ESRA has included data from more than 120,000 road users from a total of 68 different countries. This paper focuses on data from the third edition of the ESRA survey (ESRA3), which was conducted in 2023 across 39 countries and includes answers from over 37,000 road users. The objectives are to provide an overview of the ESRA3 survey methodology and to present results related to several road safety topics, such as drink-driving, speeding, or distraction, across different types of road users: car drivers, pedestrians, cyclists, and moped riders/motorcyclists. It examines multiple dimensions of risky behaviours in traffic, including self-declared behaviours, personal acceptability of unsafe behaviours, and support for policy measures.</div><div>Results show low acceptability of unsafe traffic behaviours like speeding, drink-driving, fatigued driving or using a mobile phone while driving a car – less than 5 % of respondents considered these behaviours acceptable. Notwithstanding the low acceptability, a high percentage of car drivers declared engaging in risky behaviours in traffic: speeding within built-up areas was declared by 37 % to 47 % of car drivers, using a mobile phone by 22 % to 32 %, fatigued driving by 18 % to 20 %, and driving under the influence of alcohol by 10 % to 14 %. As for vulnerable road users, distraction (reading messages/checking social media or listening to music through headphones) was the most declared risky behaviour by pedestrians, the non-use of helmet the most declared by cyclists, and speeding the most declared by moped riders and motorcyclists. Most respondents support policy measures to restrict risky behaviour.</div><div>The ESRA survey offers a unique database and provides policy makers and researchers with valuable insights into public perception of road safety.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 3","pages":"Pages 335-352"},"PeriodicalIF":3.3,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005200","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}
Gender is an acknowledge factor in road traffic fatalities, with men far more likely to die in road traffic crashes than women. This study aims to determine whether gender differences in drivers' self-reported risk behaviours and psychosocial factors vary by country's gender equality policies. Using the third edition of the E-Survey of Road users' Attitudes database, we analysed gender differences in the behaviour and attitudes of 16,031 frequent drivers (54.46 % men) surveyed via an online questionnaire in 39 countries. We characterised these countries according to their level on the World Economic Forum's Global Gender Gap Index and its four sub-indices (economic, education, health and political). We analysed differences by driver gender, levels of the gender gap indices and their interactions, on reported behaviour, social and personal acceptability, self-efficacy, risk perception, support for road safety policies and perceived deterrence, controlling for driver age and country income level. The results show that men drivers tend to have riskier behaviours, attitudes and perceptions than women drivers, regardless of the level of gender equality in the countries studied. They also show that gender equality policies seem to increase the risky behaviours and attitudes of men and women drivers in the sample. Furthermore, greater gender policies do not appear to reduce gender differences in these psychological constructs. In particular, high levels of equality in the economic, educational and health seems to reinforce gender differences in attitudes and perceptions related to risky driving behaviour. We discuss this gender paradox as a result of the essentialisation of gender stereotypes and the impact of safety culture on men and women. These findings may be useful, particularly in a safe system approach to road safety, for better framing road safety campaigns and education.
{"title":"Gender differences in drivers' road risks and gender equality policies: A gender paradox? Comparative analysis of 39 countries","authors":"Marie-Axelle Granié , Julie Devif , Nathalie Moreau , Shirley Delannoy","doi":"10.1016/j.iatssr.2025.06.004","DOIUrl":"10.1016/j.iatssr.2025.06.004","url":null,"abstract":"<div><div>Gender is an acknowledge factor in road traffic fatalities, with men far more likely to die in road traffic crashes than women. This study aims to determine whether gender differences in drivers' self-reported risk behaviours and psychosocial factors vary by country's gender equality policies. Using the third edition of the <em>E</em>-Survey of Road users' Attitudes database, we analysed gender differences in the behaviour and attitudes of 16,031 frequent drivers (54.46 % men) surveyed via an online questionnaire in 39 countries. We characterised these countries according to their level on the World Economic Forum's Global Gender Gap Index and its four sub-indices (economic, education, health and political). We analysed differences by driver gender, levels of the gender gap indices and their interactions, on reported behaviour, social and personal acceptability, self-efficacy, risk perception, support for road safety policies and perceived deterrence, controlling for driver age and country income level. The results show that men drivers tend to have riskier behaviours, attitudes and perceptions than women drivers, regardless of the level of gender equality in the countries studied. They also show that gender equality policies seem to increase the risky behaviours and attitudes of men and women drivers in the sample. Furthermore, greater gender policies do not appear to reduce gender differences in these psychological constructs. In particular, high levels of equality in the economic, educational and health seems to reinforce gender differences in attitudes and perceptions related to risky driving behaviour. We discuss this gender paradox as a result of the essentialisation of gender stereotypes and the impact of safety culture on men and women. These findings may be useful, particularly in a safe system approach to road safety, for better framing road safety campaigns and education.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 3","pages":"Pages 291-304"},"PeriodicalIF":3.3,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005196","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}
Road transportation plays an indispensable role in people's lives as it forms the backbone of regions and towns, shapes the environment and landscape, and supports daily life and economic activities. However, it also constantly carries the risk of traffic crashes, making it an ongoing social issue amidst the development of road transportation. While there has been a decreasing trend in the number of traffic crashes and fatalities in recent years, many traffic crashes still occur, with over half of them happening at intersections or nearby. Past research on traffic crashes around intersections has predominantly focused on large-scale intersections, lacking sufficient analysis on small-scale intersections in residential areas due to data limitations. Therefore, in this study, we aggregated OpenData-based intersection representations for obtaining the number of corner cuts (so called “sumikiri” in Japan) via a computational-geometric approach for relating them to nearby-situated facilities and finding relationships between visibility characteristics and the number of traffic crashes, particularly focusing on small-scale intersections. The multiple linear regression analysis revealed that while the number of corner cuts did not show a significant impact on the number of traffic crashes at large-scale intersections, they had a statistically significant negative impact on traffic crashes at medium to small-scale intersections. This implies that intersection size influences the impact of corner cuts (i.e., visibility) on traffic crash occurrence. The results of this study suggest a potential relationship between the corner cut and the occurrence of traffic crashes at medium to small-scale intersections, providing insights that could contribute to future traffic crash prevention strategies.
{"title":"Analysis of traffic crashes considering the field of view","authors":"Andreas Keler , Daijiro Maeda , Satoshi Nakao , Kei Yasuda , Jan-Dirk Schmöcker","doi":"10.1016/j.iatssr.2025.07.002","DOIUrl":"10.1016/j.iatssr.2025.07.002","url":null,"abstract":"<div><div>Road transportation plays an indispensable role in people's lives as it forms the backbone of regions and towns, shapes the environment and landscape, and supports daily life and economic activities. However, it also constantly carries the risk of traffic crashes, making it an ongoing social issue amidst the development of road transportation. While there has been a decreasing trend in the number of traffic crashes and fatalities in recent years, many traffic crashes still occur, with over half of them happening at intersections or nearby. Past research on traffic crashes around intersections has predominantly focused on large-scale intersections, lacking sufficient analysis on small-scale intersections in residential areas due to data limitations. Therefore, in this study, we aggregated OpenData-based intersection representations for obtaining the number of corner cuts (so called “sumikiri” in Japan) via a computational-geometric approach for relating them to nearby-situated facilities and finding relationships between visibility characteristics and the number of traffic crashes, particularly focusing on small-scale intersections. The multiple linear regression analysis revealed that while the number of corner cuts did not show a significant impact on the number of traffic crashes at large-scale intersections, they had a statistically significant negative impact on traffic crashes at medium to small-scale intersections. This implies that intersection size influences the impact of corner cuts (i.e., visibility) on traffic crash occurrence. The results of this study suggest a potential relationship between the corner cut and the occurrence of traffic crashes at medium to small-scale intersections, providing insights that could contribute to future traffic crash prevention strategies.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 3","pages":"Pages 305-313"},"PeriodicalIF":3.3,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005197","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}
Traffic violations can pose significant challenges to public safety and road infrastructure. The consequences of such violations may be managed based on the insights from their observed trends. Following the COVID-19 outbreak and changes in driving behavior, the violation patterns were affected. This study examines traffic violations in the Isfahan province of Iran between 2016 and 2022, focusing on seat belt and speeding violations. Two analytical approaches, time series analysis and count data modeling, were employed to explore various aspects of these violations. Time series analysis involved analyzing aggregated monthly violation records to forecast trends before and during the pandemic. A comparison of projected and observed patterns revealed remarkable shifts in traffic violations, especially after the start of the vaccination campaign in February 2021. This study also found that recording speeding violations was influenced by the maintenance of the speed-control cameras. The second approach focused on police-issued violation records across three periods: two pre-pandemic phases (Pre1 and Pre2) and a pandemic phase (Pand). A set of zero-truncated Poisson models assessed individual and environmental factors in Pre1 and Pre2, such as car type, license plate, driver characteristics, time of day, day type, road hierarchy, and season. The results showed that these factors significantly impacted violation probabilities. To analyze the effects of COVID-19 on these influential factors, another zero-truncated Poisson model was applied to the Pand phase, along with t-tests comparing the coefficients across the three phases. The findings revealed statistically significant changes in how these factors influenced seat belt and speeding violations. Notably, driver characteristics, day type, and season became more determinant for seat belt violations in the Pand phase, while the importance of license plate type decreased.
{"title":"An analysis of COVID-19 effects on the trends of traffic violations","authors":"Masoud Foroutan Shad , Mahmoud Mesbah , Mahdie Asl-Javadian","doi":"10.1016/j.iatssr.2025.06.005","DOIUrl":"10.1016/j.iatssr.2025.06.005","url":null,"abstract":"<div><div>Traffic violations can pose significant challenges to public safety and road infrastructure. The consequences of such violations may be managed based on the insights from their observed trends. Following the COVID-19 outbreak and changes in driving behavior, the violation patterns were affected. This study examines traffic violations in the Isfahan province of Iran between 2016 and 2022, focusing on seat belt and speeding violations. Two analytical approaches, time series analysis and count data modeling, were employed to explore various aspects of these violations. Time series analysis involved analyzing aggregated monthly violation records to forecast trends before and during the pandemic. A comparison of projected and observed patterns revealed remarkable shifts in traffic violations, especially after the start of the vaccination campaign in February 2021. This study also found that recording speeding violations was influenced by the maintenance of the speed-control cameras. The second approach focused on police-issued violation records across three periods: two pre-pandemic phases (Pre1 and Pre2) and a pandemic phase (Pand). A set of zero-truncated Poisson models assessed individual and environmental factors in Pre1 and Pre2, such as car type, license plate, driver characteristics, time of day, day type, road hierarchy, and season. The results showed that these factors significantly impacted violation probabilities. To analyze the effects of COVID-19 on these influential factors, another zero-truncated Poisson model was applied to the Pand phase, along with <em>t</em>-tests comparing the coefficients across the three phases. The findings revealed statistically significant changes in how these factors influenced seat belt and speeding violations. Notably, driver characteristics, day type, and season became more determinant for seat belt violations in the Pand phase, while the importance of license plate type decreased.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 3","pages":"Pages 324-334"},"PeriodicalIF":3.3,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145005199","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}
The unprecedented COVID-19 pandemic massively affected the long-distance trips all over the world. Like other countries worldwide, inter-regional mobility restrictions with the capital city were also imposed in Bangladesh to control the spread of coronavirus. Therefore, it is important to examine the changes in long-distance travel behavior to understand people's mobility needs and responses during travel restrictions, as well as the influences of individuals' socio-economic conditions and the country's COVID severity on their travel decisions. Data for this research were collected from 402 respondents in Dhaka using online questionnaires. Voluntary response and convenience sampling techniques were followed in this study. Moreover, district-wise COVID data was obtained from the dashboard of Directorate General of Health Services (DGHS). Descriptive statistics and spatial analyses were employed in this study. In addition, binary logistic regression model and mixed-effect logistic regression model were developed to understand the underlying factors behind the changes in long-distance travel behavior during the pandemic. The findings reveal that the majority of the respondents decreased their long-distance trips during the first pandemic wave. A notable percentage of trip makers' long-distance trip patterns and mode use remained the same as their pre-pandemic situation. Access to private cars was a positive determinant for long-distance trips during the pandemic; hence, the excess cost of private transportation compelled people to use risky public transportation. The presence of elderly individuals and children in households reduced the likelihood of traveling longer distances during the pandemic. Hygiene and safety from COVID-19 contamination were the main concerns for respondents while choosing long-distance travel modes. Individuals' high-risk perception regarding COVID-19 decreased the probability of traveling longer-distance during the pandemic. In general, travelers relatively less preferred COVID hotspots as their long-distance trip destinations during the first pandemic wave. This study's recommendations will assist planners and policymakers in designing a safe and affordable long-distance transport corridor during future pandemics.
{"title":"Understanding the changes in long-distance travel behavior due to socio-economic and pandemic drivers","authors":"Farzana Faiza Farha, Sadia Afroj, Md. Musleh Uddin Hasan, Effat Farzana","doi":"10.1016/j.iatssr.2025.06.003","DOIUrl":"10.1016/j.iatssr.2025.06.003","url":null,"abstract":"<div><div>The unprecedented COVID-19 pandemic massively affected the long-distance trips all over the world. Like other countries worldwide, inter-regional mobility restrictions with the capital city were also imposed in Bangladesh to control the spread of coronavirus. Therefore, it is important to examine the changes in long-distance travel behavior to understand people's mobility needs and responses during travel restrictions, as well as the influences of individuals' socio-economic conditions and the country's COVID severity on their travel decisions. Data for this research were collected from 402 respondents in Dhaka using online questionnaires. Voluntary response and convenience sampling techniques were followed in this study. Moreover, district-wise COVID data was obtained from the dashboard of Directorate General of Health Services (DGHS). Descriptive statistics and spatial analyses were employed in this study. In addition, binary logistic regression model and mixed-effect logistic regression model were developed to understand the underlying factors behind the changes in long-distance travel behavior during the pandemic. The findings reveal that the majority of the respondents decreased their long-distance trips during the first pandemic wave. A notable percentage of trip makers' long-distance trip patterns and mode use remained the same as their pre-pandemic situation. Access to private cars was a positive determinant for long-distance trips during the pandemic; hence, the excess cost of private transportation compelled people to use risky public transportation. The presence of elderly individuals and children in households reduced the likelihood of traveling longer distances during the pandemic. Hygiene and safety from COVID-19 contamination were the main concerns for respondents while choosing long-distance travel modes. Individuals' high-risk perception regarding COVID-19 decreased the probability of traveling longer-distance during the pandemic. In general, travelers relatively less preferred COVID hotspots as their long-distance trip destinations during the first pandemic wave. This study's recommendations will assist planners and policymakers in designing a safe and affordable long-distance transport corridor during future pandemics.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 2","pages":"Pages 280-289"},"PeriodicalIF":3.2,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491311","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}
Pub Date : 2025-06-23DOI: 10.1016/j.iatssr.2025.06.002
Shengqi Liu, Harry Evdorides
This study investigates the effectiveness of unsignalized crossings to enhance pedestrian safety through a robust data-driven approach utilizing multiple machine learning models, including the statistical classifier Logistic Regression, Decision Tree, Random Forest, and Neural Network Multi-Layer Perceptron (MLP). While numerous studies have applied predictive models to traffic crash data, few have systematically analysed pedestrian crash severity at unsignalized crossings using multiple machine learning algorithms. By leveraging historical crash data from the UK's STATS19 database, key factors influencing pedestrian safety at unsignalized crossings were identified and analysed. The research highlights the superior predictive performance of Random Forest and MLP models, with accuracies of 84 % and 86 %, respectively, underscoring their capability to handle complex, nonlinear relationships in crash data. Feature importance analysis revealed critical determinants of crash severity. The findings emphasize the need for targeted interventions to mitigate crash severity of crash outcomes. Despite challenges like underreporting and data imputation biases, this study provides valuable insights into the role of infrastructure in pedestrian safety, offering a foundation for policy recommendations and future research on improving unsignalized crossing designs.
{"title":"Analysing the effectiveness of unsignalized crossing infrastructure in improving pedestrian safety using multiple data-driven approaches","authors":"Shengqi Liu, Harry Evdorides","doi":"10.1016/j.iatssr.2025.06.002","DOIUrl":"10.1016/j.iatssr.2025.06.002","url":null,"abstract":"<div><div>This study investigates the effectiveness of unsignalized crossings to enhance pedestrian safety through a robust data-driven approach utilizing multiple machine learning models, including the statistical classifier Logistic Regression, Decision Tree, Random Forest, and Neural Network Multi-Layer Perceptron (MLP). While numerous studies have applied predictive models to traffic crash data, few have systematically analysed pedestrian crash severity at unsignalized crossings using multiple machine learning algorithms. By leveraging historical crash data from the UK's STATS19 database, key factors influencing pedestrian safety at unsignalized crossings were identified and analysed. The research highlights the superior predictive performance of Random Forest and MLP models, with accuracies of 84 % and 86 %, respectively, underscoring their capability to handle complex, nonlinear relationships in crash data. Feature importance analysis revealed critical determinants of crash severity. The findings emphasize the need for targeted interventions to mitigate crash severity of crash outcomes. Despite challenges like underreporting and data imputation biases, this study provides valuable insights into the role of infrastructure in pedestrian safety, offering a foundation for policy recommendations and future research on improving unsignalized crossing designs.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 2","pages":"Pages 271-279"},"PeriodicalIF":3.2,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338963","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}
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