A significant number of traffic crashes are reported at unsignalized intersections. However, in developing countries, challenges such as underreporting and limited crash data hinder the direct correlation of traffic conflicts with reported crashes for effective safety analysis. To address this, the study introduces Critical Conflict Probability (CCP) as a novel metric to quantify the intensity of conflict risk at unsignalized intersections. Higher CCP values indicate a greater likelihood of crash risk. CCP is derived from Post-Encroachment Time (PET) using the Generalized Extreme Value (GEV)-based extreme value theory (EVT) modeling framework. The CCP values are modeled as a function of traffic flow and driving behavior variables using three approaches: fixed parameters, random intercept, and grouped random parameters Beta regression models. The results revealed grouped random parameters Beta regression model as the best fit, highlighting the importance of accounting for spatial unobserved heterogeneity. As a practical outcome, the study develops a CCP-based intersection prioritization framework to rank and identify critical intersections within a traffic network, enabling traffic planners to improve safety management in data-scarce environments.
{"title":"Critical conflict probability: A novel risk measure for quantifying intensity of crash risk at unsignalized intersections","authors":"Aninda Bijoy Paul Ph.D. , Ninad Gore Ph.D. , Shriniwas Arkatkar Ph.D. , Gaurang Joshi Ph.D. , Md Mazharul Haque Ph.D.","doi":"10.1016/j.iatssr.2025.01.001","DOIUrl":"10.1016/j.iatssr.2025.01.001","url":null,"abstract":"<div><div>A significant number of traffic crashes are reported at unsignalized intersections. However, in developing countries, challenges such as underreporting and limited crash data hinder the direct correlation of traffic conflicts with reported crashes for effective safety analysis. To address this, the study introduces Critical Conflict Probability (CCP) as a novel metric to quantify the intensity of conflict risk at unsignalized intersections. Higher CCP values indicate a greater likelihood of crash risk. CCP is derived from Post-Encroachment Time (PET) using the Generalized Extreme Value (GEV)-based extreme value theory (EVT) modeling framework. The CCP values are modeled as a function of traffic flow and driving behavior variables using three approaches: fixed parameters, random intercept, and grouped random parameters Beta regression models. The results revealed grouped random parameters Beta regression model as the best fit, highlighting the importance of accounting for spatial unobserved heterogeneity. As a practical outcome, the study develops a CCP-based intersection prioritization framework to rank and identify critical intersections within a traffic network, enabling traffic planners to improve safety management in data-scarce environments.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 1","pages":"Pages 49-59"},"PeriodicalIF":3.2,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In Japan more than half of all traffic accidents occur at or near intersections and many at small intersections where only minor roads cross. A database of all intersections in the built-up area of Kyoto, Japan was created using Open Street Map data, including spatial characteristics such as the presence and types of surrounding facilities. This data was used as explanatory variables to analyze the relation to traffic accidents reported over a period of three years. Presence of traffic signals, pedestrian infrastructure and traffic flow was used as control variable. The results of the analysis suggest that traffic accidents are less likely to occur at intersections where parks are nearby. More accidents occur at medium and small intersections where facilities such as restaurants, supermarkets and convenience stores are nearby. We discuss that the results suggest that visibility but also attention when “briefly hopping into a store” as well as general business of junctions are determinants of accident risks. These results highlight that to reduce the occurrence of traffic accidents at intersections a broader understanding of who passes the junction at what times and the wider land-use characteristics of the vicinity is important.
{"title":"Analysis of land-use and POIs contributing to traffic accidents around intersections","authors":"Satoshi Nakao , Koshi Sawada , Andreas Keler , Jan-Dirk Schmöcker","doi":"10.1016/j.iatssr.2024.12.004","DOIUrl":"10.1016/j.iatssr.2024.12.004","url":null,"abstract":"<div><div>In Japan more than half of all traffic accidents occur at or near intersections and many at small intersections where only minor roads cross. A database of all intersections in the built-up area of Kyoto, Japan was created using Open Street Map data, including spatial characteristics such as the presence and types of surrounding facilities. This data was used as explanatory variables to analyze the relation to traffic accidents reported over a period of three years. Presence of traffic signals, pedestrian infrastructure and traffic flow was used as control variable. The results of the analysis suggest that traffic accidents are less likely to occur at intersections where parks are nearby. More accidents occur at medium and small intersections where facilities such as restaurants, supermarkets and convenience stores are nearby. We discuss that the results suggest that visibility but also attention when “briefly hopping into a store” as well as general business of junctions are determinants of accident risks. These results highlight that to reduce the occurrence of traffic accidents at intersections a broader understanding of who passes the junction at what times and the wider land-use characteristics of the vicinity is important.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 1","pages":"Pages 42-48"},"PeriodicalIF":3.2,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intersection-related crashes on Thailand's highways pose a significant risk to road users, particularly motorcyclists. This study develops customized Convolutional Neural Network (CNN) models to classify the severity of intersection crashes and utilizes SHapley Additive exPlanations (SHAP) to interpret the models. The methodology involves using three years of crash data from Thailand's highways, covering the period from 2018 to 2020. Additionally, three CNN model variations were developed: a basic CNN, a CNN with dropout (CNN-D), and a CNN with both dropout and L2 regularization (CNN-DR). The results demonstrate the superior performance of the CNN-DR model in classifying crash severity for both motorcycle-related and nonmotorcycle-related intersection crashes. SHAP analysis reveals key factors influencing crash severity, including the year of the crash, with a clear distinction between pre-COVID-19 years (2018–2019) and the pandemic year (2020). Crash mechanisms, such as impacts with vehicles from adjacent approaches and rear-end collisions, are significant factors that increase the likelihood of serious crashes. The study also identifies the type of intersection, specifically curved intersections, T-intersections, and Y-intersections, as major determinants of crash severity, particularly for motorcycle-related crashes. Time-of-day analysis reveals early morning hours (00:00 to 5:59) as high-risk periods for nonmotorcycle-related crashes. Furthermore, the influence of highway types and vehicle involvement, such as regional secondary highways and the presence of trucks, is linked to the increased severity of motorcycle-related crashes. The insights derived from this study can guide road safety managers in implementing targeted interventions to reduce intersection crash severity on Thailand's highways.
{"title":"SHAP-based convolutional neural network modeling for intersection crash severity on Thailand's highways","authors":"Jirapon Sunkpho , Chamroeun Se , Warit Wipulanusat , Vatanavongs Ratanavaraha","doi":"10.1016/j.iatssr.2024.12.003","DOIUrl":"10.1016/j.iatssr.2024.12.003","url":null,"abstract":"<div><div>Intersection-related crashes on Thailand's highways pose a significant risk to road users, particularly motorcyclists. This study develops customized Convolutional Neural Network (CNN) models to classify the severity of intersection crashes and utilizes SHapley Additive exPlanations (SHAP) to interpret the models. The methodology involves using three years of crash data from Thailand's highways, covering the period from 2018 to 2020. Additionally, three CNN model variations were developed: a basic CNN, a CNN with dropout (CNN-D), and a CNN with both dropout and L2 regularization (CNN-DR). The results demonstrate the superior performance of the CNN-DR model in classifying crash severity for both motorcycle-related and nonmotorcycle-related intersection crashes. SHAP analysis reveals key factors influencing crash severity, including the year of the crash, with a clear distinction between pre-COVID-19 years (2018–2019) and the pandemic year (2020). Crash mechanisms, such as impacts with vehicles from adjacent approaches and rear-end collisions, are significant factors that increase the likelihood of serious crashes. The study also identifies the type of intersection, specifically curved intersections, T-intersections, and Y-intersections, as major determinants of crash severity, particularly for motorcycle-related crashes. Time-of-day analysis reveals early morning hours (00:00 to 5:59) as high-risk periods for nonmotorcycle-related crashes. Furthermore, the influence of highway types and vehicle involvement, such as regional secondary highways and the presence of trucks, is linked to the increased severity of motorcycle-related crashes. The insights derived from this study can guide road safety managers in implementing targeted interventions to reduce intersection crash severity on Thailand's highways.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 1","pages":"Pages 27-41"},"PeriodicalIF":3.2,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-26DOI: 10.1016/j.iatssr.2024.12.001
Parveen Kumar , Geetam Tiwari , Sourabh Bikas Paul
Traditionally, road safety studies have been conducted independently, either at microscopic or macroscopic levels. This study synthesizes existing literature on road safety research conducted at microscopic, macroscopic, and mesoscopic levels using a Systematic Literature Review (SLR). The objective of this research is to examine the advancement in crash prediction methodologies, crash analysis, and the integration of microscopic, macroscopic, and mesoscopic studies over the past two decades to understand the multiscale dynamics of crash occurrence. In addition, bibliometric analysis helps to map social, conceptual, and intellectual collaborations among sources, authors, and institutions. The comprehensive review of the existing literature shows that some analytical advancements in statistical approaches, as well as Machine Learning (ML) and Deep Learning (DL) approaches, have facilitated them to address data complexity issues. In the latter decade, researchers have started to integrate microscopic and macroscopic approaches to have a nuanced and cohesive understanding of the intrinsic relationships among crash contributing factors and to assess the impact of an integrated approach on the model's predictive performance. The bibliometric analysis of published literature revealed distinct clusters, each providing a unique perspective on road safety. The major gaps observed in the systematic review of studies are the lack of consideration of behavioural aspects of road users, the transferability of models between two independent frameworks, as well as across the integrated modelling methodologies. Another significant gap is the lack of a scale of adjacent street networks in mesoscopic studies. Overall, this review provided critical insights into safety studies that focus on distinct resolutions, analytical advancements in modelling methodologies, mapping of scientific collaborations and identifications of research gaps.
{"title":"Road safety studies at micro, meso, and macroscopic levels: A systematic review","authors":"Parveen Kumar , Geetam Tiwari , Sourabh Bikas Paul","doi":"10.1016/j.iatssr.2024.12.001","DOIUrl":"10.1016/j.iatssr.2024.12.001","url":null,"abstract":"<div><div>Traditionally, road safety studies have been conducted independently, either at microscopic or macroscopic levels. This study synthesizes existing literature on road safety research conducted at microscopic, macroscopic, and mesoscopic levels using a Systematic Literature Review (SLR). The objective of this research is to examine the advancement in crash prediction methodologies, crash analysis, and the integration of microscopic, macroscopic, and mesoscopic studies over the past two decades to understand the multiscale dynamics of crash occurrence. In addition, bibliometric analysis helps to map social, conceptual, and intellectual collaborations among sources, authors, and institutions. The comprehensive review of the existing literature shows that some analytical advancements in statistical approaches, as well as Machine Learning (ML) and Deep Learning (DL) approaches, have facilitated them to address data complexity issues. In the latter decade, researchers have started to integrate microscopic and macroscopic approaches to have a nuanced and cohesive understanding of the intrinsic relationships among crash contributing factors and to assess the impact of an integrated approach on the model's predictive performance. The bibliometric analysis of published literature revealed distinct clusters, each providing a unique perspective on road safety. The major gaps observed in the systematic review of studies are the lack of consideration of behavioural aspects of road users, the transferability of models between two independent frameworks, as well as across the integrated modelling methodologies. Another significant gap is the lack of a scale of adjacent street networks in mesoscopic studies. Overall, this review provided critical insights into safety studies that focus on distinct resolutions, analytical advancements in modelling methodologies, mapping of scientific collaborations and identifications of research gaps.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 1","pages":"Pages 10-26"},"PeriodicalIF":3.2,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-20DOI: 10.1016/j.iatssr.2024.12.002
Qinaat Hussain, Wael K.M. Alhajyaseen
Traffic congestion, especially at signalized intersections along urban arterials, poses a global challenge affecting travel efficiency, mental health, and air quality. This study investigates the effectiveness of innovative forewarning systems in reducing start-up lost time at signalized intersections. In this regard, four different forewarning systems were tested and compared with the untreated control condition in a driving simulator experiment inviting 61 participants with a valid driving license. All the tested conditions were tested for two different waiting times, i.e., 20 s and 60 s to evaluate their impact on reaction times, start-up lost time, and early start-up behaviors. The results of the study demonstrated that among the tested conditions, the clock-based VMS with a 2 s (VMS_2s) warning proved to be the most effective in significantly reducing start-up delays by 21.2 %. In addition, VMS_2s and R_yellow; the condition where the yellow signal was displayed simultaneously with the red signal during the last two seconds, effectively reduced drivers' reaction times at the onset of green signal by 53.5 % and 62.4 %, respectively. Moreover, the results did not reveal any instance of risky early start-up behaviors, such as red light running violations. Based on the study findings, the VMS_2s and R_yellow conditions are suggested for further evaluation and potential real-world implementation to improve drivers' start-up behavior at signalized intersections.
{"title":"Nudging drivers: The influence of innovative green-phase forewarning systems on drivers' start-up behavior at signalized intersections","authors":"Qinaat Hussain, Wael K.M. Alhajyaseen","doi":"10.1016/j.iatssr.2024.12.002","DOIUrl":"10.1016/j.iatssr.2024.12.002","url":null,"abstract":"<div><div>Traffic congestion, especially at signalized intersections along urban arterials, poses a global challenge affecting travel efficiency, mental health, and air quality. This study investigates the effectiveness of innovative forewarning systems in reducing start-up lost time at signalized intersections. In this regard, four different forewarning systems were tested and compared with the untreated control condition in a driving simulator experiment inviting 61 participants with a valid driving license. All the tested conditions were tested for two different waiting times, i.e., 20 s and 60 s to evaluate their impact on reaction times, start-up lost time, and early start-up behaviors. The results of the study demonstrated that among the tested conditions, the clock-based VMS with a 2 s (VMS_2s) warning proved to be the most effective in significantly reducing start-up delays by 21.2 %. In addition, VMS_2s and R_yellow; the condition where the yellow signal was displayed simultaneously with the red signal during the last two seconds, effectively reduced drivers' reaction times at the onset of green signal by 53.5 % and 62.4 %, respectively. Moreover, the results did not reveal any instance of risky early start-up behaviors, such as red light running violations. Based on the study findings, the VMS_2s and R_yellow conditions are suggested for further evaluation and potential real-world implementation to improve drivers' start-up behavior at signalized intersections.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"49 1","pages":"Pages 1-9"},"PeriodicalIF":3.2,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.1016/j.iatssr.2024.11.002
John McCombs, Haitham Al-Deek, Adrian Sandt
In this paper, a corridor-level approach is used to network screen and analyze pedestrian and bicyclist crashes. This approach uses less data than site-level analyses while also considering the relationship between intersections and roadway segments. 548 roadway corridors covering over 1000 centerline miles (1609 km) were identified on urban and suburban arterial roads in seven Florida counties based on context classification and lane count. From 2017 to 2021, these corridors experienced 3773 pedestrian crashes and 2599 bicyclist crashes, with about 88 % of these crashes resulting in fatalities or injuries. Three negative binomial regression models were developed to predict pedestrian crashes only, bicyclist crashes only, and both pedestrian and bicyclist crashes together (combined crashes model). Significant predictors from the models included traffic volume, speed limit, area type, intersection-related variables, and modality-related variables. Using the combined crashes model, a 0.75-mile (1.21-km) corridor was identified as the corridor with highest potential for crash frequency reduction. Examination of this corridor suggested that bicycle lanes, improved lighting, and midblock crossings could be effective countermeasures to reduce pedestrian and bicyclist crashes. Based on several performance metrics, the developed approach provided an accurate and statistically reliable way to model crashes in corridors. This corridor-level approach can help agencies expedite network screening and identify locations where many pedestrian and bicyclist crashes are likely to occur so they can take proactive actions to prevent these crashes and help keep these vulnerable road users safe.
{"title":"Network screening and analysis of pedestrian and bicyclist crashes on Florida arterials using a corridor-level approach","authors":"John McCombs, Haitham Al-Deek, Adrian Sandt","doi":"10.1016/j.iatssr.2024.11.002","DOIUrl":"10.1016/j.iatssr.2024.11.002","url":null,"abstract":"<div><div>In this paper, a corridor-level approach is used to network screen and analyze pedestrian and bicyclist crashes. This approach uses less data than site-level analyses while also considering the relationship between intersections and roadway segments. 548 roadway corridors covering over 1000 centerline miles (1609 km) were identified on urban and suburban arterial roads in seven Florida counties based on context classification and lane count. From 2017 to 2021, these corridors experienced 3773 pedestrian crashes and 2599 bicyclist crashes, with about 88 % of these crashes resulting in fatalities or injuries. Three negative binomial regression models were developed to predict pedestrian crashes only, bicyclist crashes only, and both pedestrian and bicyclist crashes together (combined crashes model). Significant predictors from the models included traffic volume, speed limit, area type, intersection-related variables, and modality-related variables. Using the combined crashes model, a 0.75-mile (1.21-km) corridor was identified as the corridor with highest potential for crash frequency reduction. Examination of this corridor suggested that bicycle lanes, improved lighting, and midblock crossings could be effective countermeasures to reduce pedestrian and bicyclist crashes. Based on several performance metrics, the developed approach provided an accurate and statistically reliable way to model crashes in corridors. This corridor-level approach can help agencies expedite network screening and identify locations where many pedestrian and bicyclist crashes are likely to occur so they can take proactive actions to prevent these crashes and help keep these vulnerable road users safe.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"48 4","pages":"Pages 574-583"},"PeriodicalIF":3.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142745266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-22DOI: 10.1016/j.iatssr.2024.10.002
Hideyuki Kita
This study proposes a methodology for planning local public transport services as social common capital necessary for people to secure a “healthy and cultural life.” The methodology systematizes the process of setting the level of achievement of the “size of activity opportunities,” which indicates the degree of “healthy and cultural life,” as a consensus among the local community, and socially choosing a planning alternative for public transport services to realize it from the perspective of mutual assistance. Although the individual methods that constitute the methodology are concrete “examples” for understanding the methodology as a whole, the empirical analysis conducted through a web-based survey showed that the social choice process worked as expected and confirmed the practicality of the methodology.
{"title":"Securing opportunities for activities and choices by local communities - A planning methodology for local public transport services as social common capital-","authors":"Hideyuki Kita","doi":"10.1016/j.iatssr.2024.10.002","DOIUrl":"10.1016/j.iatssr.2024.10.002","url":null,"abstract":"<div><div>This study proposes a methodology for planning local public transport services as social common capital necessary for people to secure a “healthy and cultural life.” The methodology systematizes the process of setting the level of achievement of the “size of activity opportunities,” which indicates the degree of “healthy and cultural life,” as a consensus among the local community, and socially choosing a planning alternative for public transport services to realize it from the perspective of mutual assistance. Although the individual methods that constitute the methodology are concrete “examples” for understanding the methodology as a whole, the empirical analysis conducted through a web-based survey showed that the social choice process worked as expected and confirmed the practicality of the methodology.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"48 4","pages":"Pages 560-571"},"PeriodicalIF":3.2,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1016/j.iatssr.2024.11.001
Qing Chang , Yukun Song , Md Mahmud Hossain , Huaguo Zhou
The complex geometric layout of partial cloverleaf (parclo) interchanges elevates the chances of drivers inadvertently choosing the incorrect ramp or direction. Previous research on wrong-way driving (WWD) has emphasized the frequent incidents at the off-ramp terminals of parclo interchanges. Many WWD incidents that did not result in a collision haven't received enough attention from researchers. Geometric features such as ramp design, signage placement, and interchange layout significantly influence driver behavior and decision-making. In this study, a comparative analysis was conducted to explore the correlation between geometric design elements and their impact on WWD incidents at parclo interchange terminals. Over 5000 h of video footage were meticulously reviewed to document instances of WWD at 75 parclo interchange terminals across 13 states. Multiple correspondence analysis was applied to explore the characteristics of the locations with recurring WWD incidents. The results revealed numerous associations among design elements that contributed to the increased risk of WWD incidents, including uncontrolled ramp terminal intersections without street lighting on a two-lane crossroad, poor pavement marking on a wide median between the on- and off-ramps, and so on. The outcomes of this study can be helpful in identifying and improving the critical geometric design criteria in parclo interchanges to minimize WWD incidents.
{"title":"Exploring the impact of design elements on wrong-way driving incidents at partial cloverleaf interchange terminals","authors":"Qing Chang , Yukun Song , Md Mahmud Hossain , Huaguo Zhou","doi":"10.1016/j.iatssr.2024.11.001","DOIUrl":"10.1016/j.iatssr.2024.11.001","url":null,"abstract":"<div><div>The complex geometric layout of partial cloverleaf (parclo) interchanges elevates the chances of drivers inadvertently choosing the incorrect ramp or direction. Previous research on wrong-way driving (WWD) has emphasized the frequent incidents at the off-ramp terminals of parclo interchanges. Many WWD incidents that did not result in a collision haven't received enough attention from researchers. Geometric features such as ramp design, signage placement, and interchange layout significantly influence driver behavior and decision-making. In this study, a comparative analysis was conducted to explore the correlation between geometric design elements and their impact on WWD incidents at parclo interchange terminals. Over 5000 h of video footage were meticulously reviewed to document instances of WWD at 75 parclo interchange terminals across 13 states. Multiple correspondence analysis was applied to explore the characteristics of the locations with recurring WWD incidents. The results revealed numerous associations among design elements that contributed to the increased risk of WWD incidents, including uncontrolled ramp terminal intersections without street lighting on a two-lane crossroad, poor pavement marking on a wide median between the on- and off-ramps, and so on. The outcomes of this study can be helpful in identifying and improving the critical geometric design criteria in parclo interchanges to minimize WWD incidents.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"48 4","pages":"Pages 550-559"},"PeriodicalIF":3.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-08DOI: 10.1016/j.iatssr.2024.10.004
Sina Rejali , Kayvan Aghabayk , Nirajan Shiwakoti
Fully automated vehicles (FAVs) are anticipated to enter the passenger vehicle market soon. Given the uncertainties surrounding user adoption of this emerging technology, research is needed to understand their user acceptance. While most studies on the acceptance of automated vehicles have been conducted in upper-middle-income or high-income developed countries, similar research in middle-income countries is limited. This study aims to evaluate a priori acceptance of FAVs in a middle-income developing country by extending the Technology Acceptance Model (TAM). Trust, subjective norms, perceived safety risk and four decision-making styles (Thoroughness, Hesitancy, Social resistance, and Perfectionism) were included in the extended model. This study aims to evaluate a priori acceptance of FAVs in a middle-income developing country by extending the Technology Acceptance Model (TAM). Trust, subjective norms, perceived safety risk and four decision-making styles (Thoroughness, Hesitancy, Social resistance, and Perfectionism) were included in the extended model. Structural Equation Modeling was applied to confirm model validation by using data from 1026 drivers from different cities in Iran. A multi-group analysis was conducted to assess whether the relationships between model constructs vary across different demographic and background groups. Additionally, an importance-performance analysis was performed to gain a deeper understanding of the factors influencing behavioral intention. The findings of the model highlighted that beyond the original TAM scales, subjective norms were the strongest predictor in explaining drivers' intentions to use FAVs. Initial trust also moderately contributed to explaining user acceptance of FAVs. The results also revealed that among decision-making styles, thoroughness positively affected behavioral intention through trust, while social resistance had an indirect negative effect on intention. The findings showed that effect of the perceived safety risk on behavioral intention through the initial trust was confirmed; however, importance-performance map analysis revealed that a significant improvement was observed in the safety area that could still be obtained. It is suggested that policymakers start promoting the usefulness and ease of use of FAVs through advertisements, social media, public campaigns and autonomous vehicle test ride events to facilitate the adoption of FAVs when available in countries with similar sociocultural contexts.
全自动驾驶汽车（FAV）预计将很快进入乘用车市场。鉴于用户采用这一新兴技术的不确定性，需要开展研究以了解用户对其的接受程度。大多数关于自动驾驶汽车接受度的研究都是在中上收入或高收入发达国家进行的，而在中等收入国家进行的类似研究却很有限。本研究旨在通过扩展技术接受模型（TAM），评估中等收入发展中国家对自动驾驶汽车的先验接受度。信任、主观规范、感知安全风险和四种决策风格（彻底性、犹豫不决、社会抵制和完美主义）被纳入扩展模型。本研究旨在通过对技术接受模型（TAM）进行扩展，评估中等收入发展中国家对固定电话和自动交换机的先验接受程度。信任、主观规范、感知安全风险和四种决策风格（彻底性、犹豫不决、社会抵制和完美主义）被纳入扩展模型。利用来自伊朗不同城市的 1026 名驾驶员的数据，采用结构方程模型对模型进行了验证。为了评估模型结构之间的关系在不同人口和背景群体中是否存在差异，我们进行了多群体分析。此外，还进行了重要性表现分析，以深入了解影响行为意向的因素。该模型的研究结果表明，除了原有的 TAM 量表外，主观规范是解释司机使用 FAV 意图的最强预测因素。初始信任也在一定程度上解释了用户对 FAV 的接受程度。结果还显示，在决策风格中，彻底性通过信任对行为意向产生积极影响，而社会阻力则对意向产生间接的负面影响。研究结果表明，感知到的安全风险通过最初的信任对行为意向的影响得到了证实；然而，重要性-绩效图分析表明，在安全领域仍然可以观察到显著的改善。建议政策制定者开始通过广告、社交媒体、公共活动和自动驾驶汽车试乘活动来宣传自动驾驶汽车的实用性和易用性，以促进自动驾驶汽车在具有类似社会文化背景的国家得到采用。
{"title":"Assessing public a priori acceptance of fully automated vehicles using an extended technology acceptance model and importance-performance analysis","authors":"Sina Rejali , Kayvan Aghabayk , Nirajan Shiwakoti","doi":"10.1016/j.iatssr.2024.10.004","DOIUrl":"10.1016/j.iatssr.2024.10.004","url":null,"abstract":"<div><div>Fully automated vehicles (FAVs) are anticipated to enter the passenger vehicle market soon. Given the uncertainties surrounding user adoption of this emerging technology, research is needed to understand their user acceptance. While most studies on the acceptance of automated vehicles have been conducted in upper-middle-income or high-income developed countries, similar research in middle-income countries is limited. This study aims to evaluate a priori acceptance of FAVs in a middle-income developing country by extending the Technology Acceptance Model (TAM). Trust, subjective norms, perceived safety risk and four decision-making styles (Thoroughness, Hesitancy, Social resistance, and Perfectionism) were included in the extended model. This study aims to evaluate a priori acceptance of FAVs in a middle-income developing country by extending the Technology Acceptance Model (TAM). Trust, subjective norms, perceived safety risk and four decision-making styles (Thoroughness, Hesitancy, Social resistance, and Perfectionism) were included in the extended model. Structural Equation Modeling was applied to confirm model validation by using data from 1026 drivers from different cities in Iran. A multi-group analysis was conducted to assess whether the relationships between model constructs vary across different demographic and background groups. Additionally, an importance-performance analysis was performed to gain a deeper understanding of the factors influencing behavioral intention. The findings of the model highlighted that beyond the original TAM scales, subjective norms were the strongest predictor in explaining drivers' intentions to use FAVs. Initial trust also moderately contributed to explaining user acceptance of FAVs. The results also revealed that among decision-making styles, thoroughness positively affected behavioral intention through trust, while social resistance had an indirect negative effect on intention. The findings showed that effect of the perceived safety risk on behavioral intention through the initial trust was confirmed; however, importance-performance map analysis revealed that a significant improvement was observed in the safety area that could still be obtained. It is suggested that policymakers start promoting the usefulness and ease of use of FAVs through advertisements, social media, public campaigns and autonomous vehicle test ride events to facilitate the adoption of FAVs when available in countries with similar sociocultural contexts.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"48 4","pages":"Pages 537-549"},"PeriodicalIF":3.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research explores enhancing intersection safety, a critical aspect of urban traffic management, by analyzing the effects of infrastructure modifications and understanding driver behavior. Centered on a critical junction near Sichon Municipality, the study evaluates three proposed redesign scenarios using the VISSIM simulation tool and the Safety Surrogate Assessment Model (SSAM). These scenarios include the implementation of a roundabout with guidance feature (Model 2), the introduction of a dumbbell-shaped roundabout (Model 3), and the construction of a roundabout without turning points (Model 4). Findings suggest that Models 2 and 4, which incorporate roundabouts, can reduce conflict points, potentially decreasing traffic collisions. However, these models also indicate possible increases in travel times and queue lengths, highlighting the trade-offs between enhancing safety and maintaining traffic efficiency. An in-depth analysis of Deltas (ΔS) values through Optimized Hot Spot Analysis reveals areas with high and low collision severity, providing direction for targeted safety measures. The study demonstrates the complex effects of intersection redesigns on safety and traffic flow. For instance, Model 3 shows increased conflict points, emphasizing the need for specific design considerations to counteract potential negative impacts. Conversely, Model 4 achieves streamlined traffic flow but necessitates careful design to prevent new safety risks. This research underscores the need for a comprehensive approach to intersection safety that combines infrastructure improvements with insights into driver behavior. By utilizing advanced simulation tools and analyzing driving behavior, the study contributes valuable insights towards designing and assessing traffic safety interventions, aiming for safer and more efficient urban traffic environments.
{"title":"Improving urban intersection safety insights from simulation analysis","authors":"Chaiwat Yaibok , Piyapong Suwanno , Thaksakorn Pornbunyanon , Chollada Kanjanakul , Paramet Luathep , Atsushi Fukuda","doi":"10.1016/j.iatssr.2024.10.005","DOIUrl":"10.1016/j.iatssr.2024.10.005","url":null,"abstract":"<div><div>This research explores enhancing intersection safety, a critical aspect of urban traffic management, by analyzing the effects of infrastructure modifications and understanding driver behavior. Centered on a critical junction near Sichon Municipality, the study evaluates three proposed redesign scenarios using the VISSIM simulation tool and the Safety Surrogate Assessment Model (SSAM). These scenarios include the implementation of a roundabout with guidance feature (Model 2), the introduction of a dumbbell-shaped roundabout (Model 3), and the construction of a roundabout without turning points (Model 4). Findings suggest that Models 2 and 4, which incorporate roundabouts, can reduce conflict points, potentially decreasing traffic collisions. However, these models also indicate possible increases in travel times and queue lengths, highlighting the trade-offs between enhancing safety and maintaining traffic efficiency. An in-depth analysis of Deltas (ΔS) values through Optimized Hot Spot Analysis reveals areas with high and low collision severity, providing direction for targeted safety measures. The study demonstrates the complex effects of intersection redesigns on safety and traffic flow. For instance, Model 3 shows increased conflict points, emphasizing the need for specific design considerations to counteract potential negative impacts. Conversely, Model 4 achieves streamlined traffic flow but necessitates careful design to prevent new safety risks. This research underscores the need for a comprehensive approach to intersection safety that combines infrastructure improvements with insights into driver behavior. By utilizing advanced simulation tools and analyzing driving behavior, the study contributes valuable insights towards designing and assessing traffic safety interventions, aiming for safer and more efficient urban traffic environments.</div></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":"48 4","pages":"Pages 523-536"},"PeriodicalIF":3.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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