Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic si...Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic since high speeds on intersection approaches are present which can exacerbate the impact of a crash. Additionally, rural areas are often underserved with EMS services which can further contribute to negative crash outcomes. This paper describes an analysis of driver stopping behavior at rural T-intersections using the SHRP 2 Naturalistic Driving Study data. Type of stop was used as a safety surrogate measure using full/rolling stops compared to non-stops. Time series traces were obtained for 157 drivers at 87 unique intersections resulting in 1277 samples at the stop controlled approach for T-intersections. Roadway (i.e. number of lanes, presence of skew, speed limit, presence of stop bar or other traffic control devices), driver (age, gender, speeding), and environmental characteristics (time of day, presence of rain) were reduced and included as independent variables. Results of a logistic regression model indicated drivers were less likely to stop during the nighttime. However presence of intersection lighting increased the likelihood of full/rolling stops. Presence of intersection skew was shown to negatively impact stopping behavior. Additionally drivers who were traveling over the posted speed limit upstream of the intersection approach were less likely to stop at the approach stop sign.展开更多
Purpose–Feature selection is crucial for machine learning to recognize lane-change(LC)maneuver as there exist a large number of feature candidates.Blindly using feature could take up large storage and excessive compu...Purpose–Feature selection is crucial for machine learning to recognize lane-change(LC)maneuver as there exist a large number of feature candidates.Blindly using feature could take up large storage and excessive computation time,while insufficient feature selection would cause poor performance.Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition.This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data.Design/methodology/approach–In total,1,375 LC cases are analyzed.To comprehensively select features,the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid.Then the effect size(Cohen’s d)and p-value of every feature are computed to assess their contribution for each scenario.Findings–It has been found that the common lateral features,e.g.yaw rate,lateral acceleration and time-to-lane crossing,are not strong features for recognition of LC maneuver as empirical knowledge.Finally,cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic.Experimental results show that the selected features can achieve better recognition performance than using all the features without purification.Originality/value–In this paper,the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data.The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.展开更多
Traffic accidents are one of the most serious problems worldwide,being one of the leading causes of death and economic loss in the world.Low-and middle-income countries,mainly their medium-sized cities,are among the m...Traffic accidents are one of the most serious problems worldwide,being one of the leading causes of death and economic loss in the world.Low-and middle-income countries,mainly their medium-sized cities,are among the most affected by this problem.93%of traffic accidents occur in low and middle-income countries,even though these countries have approximately 60%of the world’s vehicles.This occurs mainly because in these types of countries,especially in medium-sized cities(target context),there are no ideal conditions for driving,such as adequate road infrastructure,good condition of vehicles,and rigorous safety policies.Advanced data analysis techniques including machine learning(ML)have increasingly been used to solve this problem.Naturalistic driving(ND)can be applied as a data collection method that provides information on traffic accidents.ND commonly uses a vehicle’s kinematic data to detect high-risk driving behaviors that could cause an accident.The objectives of this document are to present a review of different alternatives that help in data collection and creation of intelligent solutions related to detection of possible traffic accidents,principally using ND;and to propose an intelligent collision risk detection system(ICRDS)for identification of areas with a high probability of TA in the target context.Through the review,it was possible to analyze and evaluate the devices,variables and algorithms that help characterize a risk event in driving,considering the target context.The development of a prototype of an ICRDS for a medium-sized city in a developing country is considered viable,considering the identified components,with the aim of identifying risk events in driving,and areas of high probability of accidents in the city.展开更多
The contradiction between increasing traffic and the relatively poor roundabout infrastructure is getting stronger.The control and optimization of the macroscopic traffic flow needs to be improved to resolve congestio...The contradiction between increasing traffic and the relatively poor roundabout infrastructure is getting stronger.The control and optimization of the macroscopic traffic flow needs to be improved to resolve congestion and safety problems at roundabouts and the connected road network.In order to better understand the gaps and trends in this field,we have systematically reviewed the main research and developments in traffic phenomena,driving behaviour,autonomous vehicles(AVs),intelligent connected vehicles and real vehicle trajectory data sets at roundabouts.The study is based on 388 papers about roundabouts,selected through a comprehensive literature search.The review demonstrates that based on a microscopic perspective,sensing,prediction,decision-making,planning and control aspects of AVs and intelligent connected vehicles can be designed and optimized to fundamentally and significantly improve traffic capacity and driving safety at roundabouts.However,the generation mechanism of traffic conflicts among traffic participants at roundabouts is complex,which is a tremendous challenge for the systematic design of AVs.Therefore,based on naturalistic driving data and machine learning theory,it is an important research direction to build driver models by learning and imitating human driver decision-making and driving behaviours.展开更多
文摘Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic since high speeds on intersection approaches are present which can exacerbate the impact of a crash. Additionally, rural areas are often underserved with EMS services which can further contribute to negative crash outcomes. This paper describes an analysis of driver stopping behavior at rural T-intersections using the SHRP 2 Naturalistic Driving Study data. Type of stop was used as a safety surrogate measure using full/rolling stops compared to non-stops. Time series traces were obtained for 157 drivers at 87 unique intersections resulting in 1277 samples at the stop controlled approach for T-intersections. Roadway (i.e. number of lanes, presence of skew, speed limit, presence of stop bar or other traffic control devices), driver (age, gender, speeding), and environmental characteristics (time of day, presence of rain) were reduced and included as independent variables. Results of a logistic regression model indicated drivers were less likely to stop during the nighttime. However presence of intersection lighting increased the likelihood of full/rolling stops. Presence of intersection skew was shown to negatively impact stopping behavior. Additionally drivers who were traveling over the posted speed limit upstream of the intersection approach were less likely to stop at the approach stop sign.
文摘Purpose–Feature selection is crucial for machine learning to recognize lane-change(LC)maneuver as there exist a large number of feature candidates.Blindly using feature could take up large storage and excessive computation time,while insufficient feature selection would cause poor performance.Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition.This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data.Design/methodology/approach–In total,1,375 LC cases are analyzed.To comprehensively select features,the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid.Then the effect size(Cohen’s d)and p-value of every feature are computed to assess their contribution for each scenario.Findings–It has been found that the common lateral features,e.g.yaw rate,lateral acceleration and time-to-lane crossing,are not strong features for recognition of LC maneuver as empirical knowledge.Finally,cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic.Experimental results show that the selected features can achieve better recognition performance than using all the features without purification.Originality/value–In this paper,the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data.The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.
基金Universidad del Cauca(Colombia)Universidad Icesi(Colombia)for supporting this research。
文摘Traffic accidents are one of the most serious problems worldwide,being one of the leading causes of death and economic loss in the world.Low-and middle-income countries,mainly their medium-sized cities,are among the most affected by this problem.93%of traffic accidents occur in low and middle-income countries,even though these countries have approximately 60%of the world’s vehicles.This occurs mainly because in these types of countries,especially in medium-sized cities(target context),there are no ideal conditions for driving,such as adequate road infrastructure,good condition of vehicles,and rigorous safety policies.Advanced data analysis techniques including machine learning(ML)have increasingly been used to solve this problem.Naturalistic driving(ND)can be applied as a data collection method that provides information on traffic accidents.ND commonly uses a vehicle’s kinematic data to detect high-risk driving behaviors that could cause an accident.The objectives of this document are to present a review of different alternatives that help in data collection and creation of intelligent solutions related to detection of possible traffic accidents,principally using ND;and to propose an intelligent collision risk detection system(ICRDS)for identification of areas with a high probability of TA in the target context.Through the review,it was possible to analyze and evaluate the devices,variables and algorithms that help characterize a risk event in driving,considering the target context.The development of a prototype of an ICRDS for a medium-sized city in a developing country is considered viable,considering the identified components,with the aim of identifying risk events in driving,and areas of high probability of accidents in the city.
基金partly supported by the National Natural Science Foundation of China(Grant No.52202414)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX22_3618).
文摘The contradiction between increasing traffic and the relatively poor roundabout infrastructure is getting stronger.The control and optimization of the macroscopic traffic flow needs to be improved to resolve congestion and safety problems at roundabouts and the connected road network.In order to better understand the gaps and trends in this field,we have systematically reviewed the main research and developments in traffic phenomena,driving behaviour,autonomous vehicles(AVs),intelligent connected vehicles and real vehicle trajectory data sets at roundabouts.The study is based on 388 papers about roundabouts,selected through a comprehensive literature search.The review demonstrates that based on a microscopic perspective,sensing,prediction,decision-making,planning and control aspects of AVs and intelligent connected vehicles can be designed and optimized to fundamentally and significantly improve traffic capacity and driving safety at roundabouts.However,the generation mechanism of traffic conflicts among traffic participants at roundabouts is complex,which is a tremendous challenge for the systematic design of AVs.Therefore,based on naturalistic driving data and machine learning theory,it is an important research direction to build driver models by learning and imitating human driver decision-making and driving behaviours.
