When the drivers approaching signalized intersections(onset of yellow signal),the drivers would enter into a zone,where they will be in uncertain mode assessing their capabilities to stop or cross the intersection.The...When the drivers approaching signalized intersections(onset of yellow signal),the drivers would enter into a zone,where they will be in uncertain mode assessing their capabilities to stop or cross the intersection.Therefore,any improper decision might lead to a right-angle or back-end crash.To avoid a right-angle collision,drivers apply the harsh brakes to stop just before the signalized intersection.But this may lead to a back-end crash when the following driver encounters the former's sudden stopping decision.This situation gets multifaceted when the traffic is heterogeneous,containing various types of vehicles.In order to reduce this issue,this study's primary objective is to identify the driving behaviour at signalized intersections based on the driving features(parameters).The secondary objective is to classify the outcome of driving behaviour(safe stopping and unsafe stopping)at the signalized intersection using a support vector machine(SVM)technique.Turning moments are used to identify the zones and label them accordingly for further classification.The classification of 50 instances is identified for training and testing using a 70%-30% rule resulted in an accuracy of 85% and 86%,respectively.Classification performance is further verified by random sampling using five cross-validation and 30 iterations,which gave an accuracy of 97% and 100% for training and testing.These results demonstrate that the proposed approach can help develop a pre-warning system to alert the drivers approaching signalized intersections,thus reducing back-end crash and accidents.展开更多
This study aimed to explore traffic safety climate by quantifying driving conditions and driving behaviour.To achieve the objective,the random parameter structural equation model was proposed so that driver action and...This study aimed to explore traffic safety climate by quantifying driving conditions and driving behaviour.To achieve the objective,the random parameter structural equation model was proposed so that driver action and driving condition can address the safety climate by integrating crash features,vehicle profiles,roadway conditions and environment conditions.The geo-localized crash open data of Las Vegas metropolitan area were collected from 2014 to 2016,including 27 arterials with 16827 injury samples.By quantifying the driving conditions and driving actions,the random parameter structural equation model was built up with measurement variables and latent variables.Results revealed that the random parameter structural equation model can address traffic safety climate quantitatively,while driving conditions and driving actions were quantified and reflected by vehicles,road environment and crash features correspondingly.The findings provide potential insights for practitioners and policy makers to improve the driving environment and traffic safety culture.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
We propose a model structure with a double-layer hidden Markov model (HMM) to recognise driving intention and predict driving behaviour. The upper-layer multi-dimensional discrete HMM (MDHMM) in the double-layer HMM r...We propose a model structure with a double-layer hidden Markov model (HMM) to recognise driving intention and predict driving behaviour. The upper-layer multi-dimensional discrete HMM (MDHMM) in the double-layer HMM represents driving intention in a combined working case, constructed according to the driving behaviours in certain single working cases in the lower-layer multi-dimensional Gaussian HMM (MGHMM). The driving behaviours are recognised by manoeuvring the signals of the driver and vehicle state information, and the recognised results are sent to the upper-layer HMM to recognise driving intentions. Also, driving behaviours in the near future are predicted using the likelihood-maximum method. A real-time driving simulator test on the combined working cases showed that the double-layer HMM can recognise driving intention and predict driving behaviour accurately and efficiently. As a result, the model provides the basis for pre-warning and intervention of danger and improving comfort performance.展开更多
Mixed traffic conditions are often prevalent in developing economies such as India, China, Bangladesh, etc. and are characterised by the presence of multiple vehicle types. The presence of multiple vehicle types with ...Mixed traffic conditions are often prevalent in developing economies such as India, China, Bangladesh, etc. and are characterised by the presence of multiple vehicle types. The presence of multiple vehicle types with varying dynamic and static characteristics results in vehicle-type dependent driving behaviours. For instance, drivers of small sized vehicles such as motorbikes accelerate and decelerate at will, maintain shorter safe gaps with the lead vehicles, and accept smaller lateral clearances to make lateral movements within and across lanes breaking the lane discipline. On the other hand, drivers of heavy vehicles such as trucks have less flexibility in performing the acceleration/deceleration and lateral movement operations. Thus, the representation of mixed traffic systems requires model- ling vehicle-type dependent driving behaviours. This paper first establishes the effect of vehicle type on the longitudinal and lateral movement behaviours of drivers using the trajectory data collected in India and subsequently presents the proposed vehicle-type dependent driver behavioural models based on the same dataset. The efficiency of the proposed models is tested by implementing them in a simulation framework compatible with non-lane-based movements of vehicles and cross validating with the field data. The results indicate better predictability of the driver behaviour and thus more realistic rep- resentation in the mixed traffic systems. Moreover, the simulator hinged upon the pro- posed behavioural models will be useful in evaluating alternate traffic improvement initiatives and help the transport planners to design the transport systems of developing countries in an efficient and sustainable manner.展开更多
With the continuous development of information technology,the information environment while driving is constantly being enriched,and driver information processing and application are also dynamically evolving.Analysin...With the continuous development of information technology,the information environment while driving is constantly being enriched,and driver information processing and application are also dynamically evolving.Analysing information processing and application can better provide information services and is particularly important for traffic safety.Based on VOSviewer bibliometric software,this paper explores the research hotspots and future development trends of the driver information processing and application fields using the Web of Science(WoS)core collection as the data source.The results show that the field has a long history and has grown steadily in recent years.The United States,China and Germany are the top three countries in terms of the number of published articles.“Situational awareness and visual load”,“route selection under variable information signs”,“en-route information and behaviour”and“new information technology attitudes”are important knowledge bases for driver information processing and application.En-route information sources,human-computer interaction,and autonomous vehicle information are the research trends of the driver information processing and application field.The results of this research can help people comprehensively and systematically understand the current situation of driver information processing and application research,provide directions for future driver information processing and application research,and promote the engineering application of such research.展开更多
基金supported by Universiti Brunei Darussalam under the University Bursary ScholarshipUniversiti Brunei Darussalam's Research Grants(Nos,UBD/PNC2/2/RG/1(311)and UBD/RSCH/1.11/FICBF/2018/002)。
文摘When the drivers approaching signalized intersections(onset of yellow signal),the drivers would enter into a zone,where they will be in uncertain mode assessing their capabilities to stop or cross the intersection.Therefore,any improper decision might lead to a right-angle or back-end crash.To avoid a right-angle collision,drivers apply the harsh brakes to stop just before the signalized intersection.But this may lead to a back-end crash when the following driver encounters the former's sudden stopping decision.This situation gets multifaceted when the traffic is heterogeneous,containing various types of vehicles.In order to reduce this issue,this study's primary objective is to identify the driving behaviour at signalized intersections based on the driving features(parameters).The secondary objective is to classify the outcome of driving behaviour(safe stopping and unsafe stopping)at the signalized intersection using a support vector machine(SVM)technique.Turning moments are used to identify the zones and label them accordingly for further classification.The classification of 50 instances is identified for training and testing using a 70%-30% rule resulted in an accuracy of 85% and 86%,respectively.Classification performance is further verified by random sampling using five cross-validation and 30 iterations,which gave an accuracy of 97% and 100% for training and testing.These results demonstrate that the proposed approach can help develop a pre-warning system to alert the drivers approaching signalized intersections,thus reducing back-end crash and accidents.
基金supported by National Natural Science Foundation of China(No.52072214).
文摘This study aimed to explore traffic safety climate by quantifying driving conditions and driving behaviour.To achieve the objective,the random parameter structural equation model was proposed so that driver action and driving condition can address the safety climate by integrating crash features,vehicle profiles,roadway conditions and environment conditions.The geo-localized crash open data of Las Vegas metropolitan area were collected from 2014 to 2016,including 27 arterials with 16827 injury samples.By quantifying the driving conditions and driving actions,the random parameter structural equation model was built up with measurement variables and latent variables.Results revealed that the random parameter structural equation model can address traffic safety climate quantitatively,while driving conditions and driving actions were quantified and reflected by vehicles,road environment and crash features correspondingly.The findings provide potential insights for practitioners and policy makers to improve the driving environment and traffic safety culture.
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.
基金Project (Nos. 50775096 and 51075176) supported by the National Natural Science Foundation of China
文摘We propose a model structure with a double-layer hidden Markov model (HMM) to recognise driving intention and predict driving behaviour. The upper-layer multi-dimensional discrete HMM (MDHMM) in the double-layer HMM represents driving intention in a combined working case, constructed according to the driving behaviours in certain single working cases in the lower-layer multi-dimensional Gaussian HMM (MGHMM). The driving behaviours are recognised by manoeuvring the signals of the driver and vehicle state information, and the recognised results are sent to the upper-layer HMM to recognise driving intentions. Also, driving behaviours in the near future are predicted using the likelihood-maximum method. A real-time driving simulator test on the combined working cases showed that the double-layer HMM can recognise driving intention and predict driving behaviour accurately and efficiently. As a result, the model provides the basis for pre-warning and intervention of danger and improving comfort performance.
文摘Mixed traffic conditions are often prevalent in developing economies such as India, China, Bangladesh, etc. and are characterised by the presence of multiple vehicle types. The presence of multiple vehicle types with varying dynamic and static characteristics results in vehicle-type dependent driving behaviours. For instance, drivers of small sized vehicles such as motorbikes accelerate and decelerate at will, maintain shorter safe gaps with the lead vehicles, and accept smaller lateral clearances to make lateral movements within and across lanes breaking the lane discipline. On the other hand, drivers of heavy vehicles such as trucks have less flexibility in performing the acceleration/deceleration and lateral movement operations. Thus, the representation of mixed traffic systems requires model- ling vehicle-type dependent driving behaviours. This paper first establishes the effect of vehicle type on the longitudinal and lateral movement behaviours of drivers using the trajectory data collected in India and subsequently presents the proposed vehicle-type dependent driver behavioural models based on the same dataset. The efficiency of the proposed models is tested by implementing them in a simulation framework compatible with non-lane-based movements of vehicles and cross validating with the field data. The results indicate better predictability of the driver behaviour and thus more realistic rep- resentation in the mixed traffic systems. Moreover, the simulator hinged upon the pro- posed behavioural models will be useful in evaluating alternate traffic improvement initiatives and help the transport planners to design the transport systems of developing countries in an efficient and sustainable manner.
基金supported by the National Natural Science Foundation of China(Grant No.52272345,71971073,52372326)Anhui Science and Technology Project-Sci-techPolice(2022k07020005)+2 种基金Key Research and Development Projects of Anhui Province(202304a05020050)Science and Technology Plan Project of the Housing Urban and Rural Construction in Anhui Province(2021-YF43,2023-YF095)Postgraduate Academic Innovation Project of Anhui Province(2022xscx020)。
文摘With the continuous development of information technology,the information environment while driving is constantly being enriched,and driver information processing and application are also dynamically evolving.Analysing information processing and application can better provide information services and is particularly important for traffic safety.Based on VOSviewer bibliometric software,this paper explores the research hotspots and future development trends of the driver information processing and application fields using the Web of Science(WoS)core collection as the data source.The results show that the field has a long history and has grown steadily in recent years.The United States,China and Germany are the top three countries in terms of the number of published articles.“Situational awareness and visual load”,“route selection under variable information signs”,“en-route information and behaviour”and“new information technology attitudes”are important knowledge bases for driver information processing and application.En-route information sources,human-computer interaction,and autonomous vehicle information are the research trends of the driver information processing and application field.The results of this research can help people comprehensively and systematically understand the current situation of driver information processing and application research,provide directions for future driver information processing and application research,and promote the engineering application of such research.
