Road safety has long been considered as one of the most important issues.Numerous studies have been conducted to investigate crashes with significant progress,whereas most of the work concentrates on the lifespan peri...Road safety has long been considered as one of the most important issues.Numerous studies have been conducted to investigate crashes with significant progress,whereas most of the work concentrates on the lifespan period of roadways and safety influencing factors.This paper undertakes a systematic literature review from the crash procedure to identify the state-of-the-art knowledge,advantages and disadvantages of crash risk,crash prediction,crash prevention and safety of connected and autonomous vehicles(CAVs).As a result of this literature review,substantive issues in general,data source and modeling selection are discussed,and the outcome of this study aims to provide the summary of crash knowledge with potential insight into both traditional and emerging aspects,and guide the future research direction in safety.展开更多
Traffic signal control(TSC)systems are one essential component in intelligent transport systems.However,relevant studies are usually independent of the urban traffic simulation environment,collaborative TSC algorithms...Traffic signal control(TSC)systems are one essential component in intelligent transport systems.However,relevant studies are usually independent of the urban traffic simulation environment,collaborative TSC algorithms and traffic signal communication.In this paper,we propose(1)an integrated and cooperative Internet-of-Things architecture,namely General City Traffic Computing System(GCTCS),which simultaneously leverages an urban traffic simulation environment,TSC algorithms,and traffic signal communication;and(2)a general multi-agent reinforcement learning algorithm,namely General-MARL,considering cooperation and communication between traffic lights for multi-intersection TSC.In experiments,we demonstrate that the integrated and cooperative architecture of GCTCS is much closer to the real-life traffic environment.The General-MARL increases the average movement speed of vehicles in traffic by 23.2%while decreases the network latency by 11.7%.展开更多
As the advancement of driverless technology,together with information and communication technology moved at a fast pace,autonomous vehicles have attracted great attention from both industries and academic sectors duri...As the advancement of driverless technology,together with information and communication technology moved at a fast pace,autonomous vehicles have attracted great attention from both industries and academic sectors during the past decades.It is evident that this emerging technology has great potential to improve the pedestrian safety on roads,mitigate traffic congestion,increase fuel efficiency,and reduce greenhouse gas emissions.However,there is limited systematic research into the applications and public perceptions of autonomous vehicles in road transportation.The purpose of this systematic literature review is to synthesise and analyse existing research on the applications,implications,and public perceptions of autonomous vehicles in road transportation system.It is found that autonomous vehicles are the future of road transportation and that the negative perception of humans is rapidly changing towards autonomous vehicles.Moreover,to fully deploy autonomous vehicles in a road transportation system,the existing road transportation infrastructure needs significant improvement.This systematic literature review contributes to the comprehensive knowledge of autonomous vehicles and will assist transportation researchers and urban planners to understand the fundamental and conceptual framework of autonomous vehicle technologies in road transportation systems.展开更多
Natural hazards pose significant threats to different communities and various places around the world.Failing to identify and support the most vulnerable communities is a recipe for disaster. Many studies have propose...Natural hazards pose significant threats to different communities and various places around the world.Failing to identify and support the most vulnerable communities is a recipe for disaster. Many studies have proposed social vulnerability indices for measuring both the sensitivity of a population to natural hazards and its ability to respond and recover from them. Existing techniques,however, have not accounted for the unique strengths that exist within different communities to help minimize disaster loss. This study proposes a more balanced approach referred to as the strength-based social vulnerability index(SSVI). The proposed SSVI technique, which is built on sound sociopsychological theories of how people act during disasters and emergencies, is applied to assess comparatively the social vulnerability of different suburbs in the Wollongong area of New South Wales, Australia. The results highlight suburbs that are highly vulnerable, and demonstrates the usefulness of the technique in improving understanding of hotspots where limited resources should be judiciously allocated to help communities improve preparedness, response, and recovery from natural hazards.展开更多
The significance of intelligent transportation systems and artificial intelligence in road transportation networks has made the prediction of traffic flow a subject of discussion among transportation engineers,urban p...The significance of intelligent transportation systems and artificial intelligence in road transportation networks has made the prediction of traffic flow a subject of discussion among transportation engineers,urban planners,and researchers in the last decade.However,limited research has been done on traffic flow modelling of long and short trucks considering that they are among the major causes of traffic congestions and traffic-related accidents on freeways,especially freeway collisions between them and passengers’vehicles.This study focused on the traffic flow of long and short trucks on the N1 freeway in South Africa due to its high traffic volume and persistent traffic congestions caused by trucks.We obtained traffic data from this freeway using inductive loop detectors and video cameras.Traffic flow variables such as speed,time,traffic density,and traffic volume were identified,and the traffic datasets comprising 920 datasets were divided into 70%for training and 30%for testing.A hybrid ANN~PSO model was used in modelling the truck traffic flow due to its ability to converge to optimization quickly.The PSO’s features(accelerating factors and number of neurons)assist in evaluating traffic flow conditions(traffic flow,traffic density,and vehicular speed).Also,PSO algorithms are simple and require few adjustment parameters.The results suggest that the ANN-PSO model can model long and short trucks traffic flow with a R2 training and testing of 0:9990 and 0:9930.This is the first study to undertake a longitudinal analysis of traffic flow modelling of long and short trucks on a freeway using a metaheuristic algorithm(ANN-PSO).The results of this study will provide knowledgeable insights(division of traffic flow variables and analysing of traffic flow data)to transportation planners and researchers when it comes to minimizing truck-related accidents and traffic congestions on freeways.展开更多
With advanced artificial intelligence and deep learning techniques, a growing number of data sources are playing more and more critical roles in planning and operating transportation services. The General Transit Feed...With advanced artificial intelligence and deep learning techniques, a growing number of data sources are playing more and more critical roles in planning and operating transportation services. The General Transit Feed Specification (GTFS), with standard open-source data in both static and real-time formats, is being widely used in public transport planning and operation management. However, compared to other extensively studied data sources such as smart card data and GPS trajectory data, the GTFS data lacks proper investigation yet. Utilization of the GTFS data is challenging for both transport planners and researchers due to its difficulty and complexity of understanding, processing, and leveraging the raw data. In this paper, a GTFS data acquisition and processing framework is proposed to offer an efficient and effective benchmark tool for converting and fusing the GTFS data to a ready-to-use format. To validate and test the proposed framework, a multivariate multistep Long Short-Term Memory is developed to predict train delay with minor anomaly in Sydney as a case study. The contribution of this new framework will render great potential for broader applications and deeper research.展开更多
基金supported by National Natural Science Foundation of China(No:72131008)National Key Research and Development Program(No:2022YFC3800103-03).
文摘Road safety has long been considered as one of the most important issues.Numerous studies have been conducted to investigate crashes with significant progress,whereas most of the work concentrates on the lifespan period of roadways and safety influencing factors.This paper undertakes a systematic literature review from the crash procedure to identify the state-of-the-art knowledge,advantages and disadvantages of crash risk,crash prediction,crash prevention and safety of connected and autonomous vehicles(CAVs).As a result of this literature review,substantive issues in general,data source and modeling selection are discussed,and the outcome of this study aims to provide the summary of crash knowledge with potential insight into both traditional and emerging aspects,and guide the future research direction in safety.
基金supported by the National Natural Science Foundation of China(Grant Nos.61673150,11622538).
文摘Traffic signal control(TSC)systems are one essential component in intelligent transport systems.However,relevant studies are usually independent of the urban traffic simulation environment,collaborative TSC algorithms and traffic signal communication.In this paper,we propose(1)an integrated and cooperative Internet-of-Things architecture,namely General City Traffic Computing System(GCTCS),which simultaneously leverages an urban traffic simulation environment,TSC algorithms,and traffic signal communication;and(2)a general multi-agent reinforcement learning algorithm,namely General-MARL,considering cooperation and communication between traffic lights for multi-intersection TSC.In experiments,we demonstrate that the integrated and cooperative architecture of GCTCS is much closer to the real-life traffic environment.The General-MARL increases the average movement speed of vehicles in traffic by 23.2%while decreases the network latency by 11.7%.
文摘As the advancement of driverless technology,together with information and communication technology moved at a fast pace,autonomous vehicles have attracted great attention from both industries and academic sectors during the past decades.It is evident that this emerging technology has great potential to improve the pedestrian safety on roads,mitigate traffic congestion,increase fuel efficiency,and reduce greenhouse gas emissions.However,there is limited systematic research into the applications and public perceptions of autonomous vehicles in road transportation.The purpose of this systematic literature review is to synthesise and analyse existing research on the applications,implications,and public perceptions of autonomous vehicles in road transportation system.It is found that autonomous vehicles are the future of road transportation and that the negative perception of humans is rapidly changing towards autonomous vehicles.Moreover,to fully deploy autonomous vehicles in a road transportation system,the existing road transportation infrastructure needs significant improvement.This systematic literature review contributes to the comprehensive knowledge of autonomous vehicles and will assist transportation researchers and urban planners to understand the fundamental and conceptual framework of autonomous vehicle technologies in road transportation systems.
文摘Natural hazards pose significant threats to different communities and various places around the world.Failing to identify and support the most vulnerable communities is a recipe for disaster. Many studies have proposed social vulnerability indices for measuring both the sensitivity of a population to natural hazards and its ability to respond and recover from them. Existing techniques,however, have not accounted for the unique strengths that exist within different communities to help minimize disaster loss. This study proposes a more balanced approach referred to as the strength-based social vulnerability index(SSVI). The proposed SSVI technique, which is built on sound sociopsychological theories of how people act during disasters and emergencies, is applied to assess comparatively the social vulnerability of different suburbs in the Wollongong area of New South Wales, Australia. The results highlight suburbs that are highly vulnerable, and demonstrates the usefulness of the technique in improving understanding of hotspots where limited resources should be judiciously allocated to help communities improve preparedness, response, and recovery from natural hazards.
文摘The significance of intelligent transportation systems and artificial intelligence in road transportation networks has made the prediction of traffic flow a subject of discussion among transportation engineers,urban planners,and researchers in the last decade.However,limited research has been done on traffic flow modelling of long and short trucks considering that they are among the major causes of traffic congestions and traffic-related accidents on freeways,especially freeway collisions between them and passengers’vehicles.This study focused on the traffic flow of long and short trucks on the N1 freeway in South Africa due to its high traffic volume and persistent traffic congestions caused by trucks.We obtained traffic data from this freeway using inductive loop detectors and video cameras.Traffic flow variables such as speed,time,traffic density,and traffic volume were identified,and the traffic datasets comprising 920 datasets were divided into 70%for training and 30%for testing.A hybrid ANN~PSO model was used in modelling the truck traffic flow due to its ability to converge to optimization quickly.The PSO’s features(accelerating factors and number of neurons)assist in evaluating traffic flow conditions(traffic flow,traffic density,and vehicular speed).Also,PSO algorithms are simple and require few adjustment parameters.The results suggest that the ANN-PSO model can model long and short trucks traffic flow with a R2 training and testing of 0:9990 and 0:9930.This is the first study to undertake a longitudinal analysis of traffic flow modelling of long and short trucks on a freeway using a metaheuristic algorithm(ANN-PSO).The results of this study will provide knowledgeable insights(division of traffic flow variables and analysing of traffic flow data)to transportation planners and researchers when it comes to minimizing truck-related accidents and traffic congestions on freeways.
文摘With advanced artificial intelligence and deep learning techniques, a growing number of data sources are playing more and more critical roles in planning and operating transportation services. The General Transit Feed Specification (GTFS), with standard open-source data in both static and real-time formats, is being widely used in public transport planning and operation management. However, compared to other extensively studied data sources such as smart card data and GPS trajectory data, the GTFS data lacks proper investigation yet. Utilization of the GTFS data is challenging for both transport planners and researchers due to its difficulty and complexity of understanding, processing, and leveraging the raw data. In this paper, a GTFS data acquisition and processing framework is proposed to offer an efficient and effective benchmark tool for converting and fusing the GTFS data to a ready-to-use format. To validate and test the proposed framework, a multivariate multistep Long Short-Term Memory is developed to predict train delay with minor anomaly in Sydney as a case study. The contribution of this new framework will render great potential for broader applications and deeper research.
