With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)wi...With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)will coexist.In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system,we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference(FVD)model.Subsequently,adopting the linear stability theory,we propose the linear stability condition for the model through using the small perturbation method,and the validity of the heterogeneous model is verified by comparing with the FVD model.Through nonlinear theoretical analysis,we further derive the KdV-Burgers equation,which captures the propagation characteristics of traffic density waves.Finally,by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow,the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated.Subsequent analysis reveals emergent traffic phenomena.The experimental findings demonstrate that as CAV permeability increases,the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation,leading to enhanced stability of the traffic system.Furthermore,higher initial traffic density renders the traffic system more susceptible to congestion,resulting in local clustering effect and stop-and-go traffic phenomenon.Remarkably,the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing.Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow.This research contributes to providing theoretical guidance for future CAV applications,aiming to enhance urban road traffic effi展开更多
Predicting traffic flow is a crucial component of an intelligent transportation system.Precisely monitoring and predicting traffic flow remains a challenging endeavor.However,existingmethods for predicting traffic flo...Predicting traffic flow is a crucial component of an intelligent transportation system.Precisely monitoring and predicting traffic flow remains a challenging endeavor.However,existingmethods for predicting traffic flow do not incorporate various external factors or consider the spatiotemporal correlation between spatially adjacent nodes,resulting in the loss of essential information and lower forecast performance.On the other hand,the availability of spatiotemporal data is limited.This research offers alternative spatiotemporal data with three specific features as input,vehicle type(5 types),holidays(3 types),and weather(10 conditions).In this study,the proposed model combines the advantages of the capability of convolutional(CNN)layers to extract valuable information and learn the internal representation of time-series data that can be interpreted as an image,as well as the efficiency of long short-term memory(LSTM)layers for identifying short-term and long-term dependencies.Our approach may utilize the heterogeneous spatiotemporal correlation features of the traffic flowdataset to deliver better performance traffic flow prediction than existing deep learning models.The research findings show that adding spatiotemporal feature data increases the forecast’s performance;weather by 25.85%,vehicle type by 23.70%,and holiday by 14.02%.展开更多
The traffic with tidal phenomenon in Heterogeneous Wireless Networks(HWNs)has radically increased the complexity of radio resource management and its performance analysis.In this paper,a Simplified Dynamic Hierarchy R...The traffic with tidal phenomenon in Heterogeneous Wireless Networks(HWNs)has radically increased the complexity of radio resource management and its performance analysis.In this paper,a Simplified Dynamic Hierarchy Resource Management(SDHRM)algorithm exploiting the resources dynamically and intelligently is proposed with the consideration of tidal traffic.In network-level resource allocation,the proposed algorithm first adopts wavelet neural network to forecast the traffic of each sub-area and then allocates the resources to those sub-areas to maximise the network utility.In connection-level network selection,based on the above resource allocation and the pre-defined QoS requirement,three typical network selection policies are provided to assign traffic flow to the most appropriate network.Furthermore,based on multidimensional Markov model,we analyse the performance of SDHRM in HWNs with heavy tailed traffic.Numerical results show that our theoretical values coincide with the simulation results and the SDHRM can improve the resource utilization.展开更多
The modeling of an efficient classifier is a fundamental issue in automatic training involving a large volume of representative data.Hence,automatic classification is a major task that entails the use of training meth...The modeling of an efficient classifier is a fundamental issue in automatic training involving a large volume of representative data.Hence,automatic classification is a major task that entails the use of training methods capable of assigning classes to data objects by using the input activities presented to learn classes.The recognition of new elements is possible based on predefined classes.Intrusion detection systems suffer from numerous vulnerabilities during analysis and classification of data activities.To overcome this problem,new analysis methods should be derived so as to implement a relevant system to monitor circulated traffic.The main objective of this study is to model and validate a heterogeneous traffic classifier capable of categorizing collected events within networks.The new model is based on a proposed machine learning algorithm that comprises an input layer,a hidden layer,and an output layer.A reliable training algorithm is proposed to optimize the weights,and a recognition algorithm is used to validate the model.Preprocessing is applied to the collected traffic prior to the analysis step.This work aims to describe the mathematical validation of a new machine learning classifier for heterogeneous traffic and anomaly detection.展开更多
In order to analyze and learn the difference in car-following behavior between normal and rainy days, we first collect car-following trajectory data of an urban elevated road on normal and rainy days by microwave rada...In order to analyze and learn the difference in car-following behavior between normal and rainy days, we first collect car-following trajectory data of an urban elevated road on normal and rainy days by microwave radar and analyze the differences in speed, relative speed, acceleration, space headway, and time headway among data through statistics. Secondly, owing to the time-series characteristics of car-following data, we use the long short-term memory(LSTM) neural network optimized by attention mechanism(AM) and sparrow search algorithm(SSA) to learn the different car-following behaviors under different weather conditions and build corresponding models(ASL-Normal, ASL-Rain, where ASL stands for AM-SSA-LSTM), respectively. Finally, the simulation test shows that the mean square error(MSE) and reciprocal of time-to-collision(RTTC) of the ASL model are better than those of LSTM and intelligent diver model(IDM), which is closer to the real data. The ASL model can better learn different driving behaviors on normal and rainy days. However,it has a higher sensitivity to weather conditions from cross test on normal and rainy data-sets which need classification training or sample diversification processing. In the car-following platoon simulation, the stability performances of two models are excellent, which can describe the basic characteristics of traffic flow on normal and rainy days. Comparing with ASL-Rain model, the convergence time of ASL-Normal is shorter, reflecting that cautious driving behavior on rainy days will reduce traffic efficiency to a certain extent. However, ASL-Normal model produces a more severe and frequent traffic oscillation within a shorter period because of aggressive driving behavior on normal days.展开更多
This study is an attempt to establish a suitable speed–density functional relationship for heterogeneous traffic on urban arterials. The model must reproduce the traffic behaviour on traffic stream and satisfy all st...This study is an attempt to establish a suitable speed–density functional relationship for heterogeneous traffic on urban arterials. The model must reproduce the traffic behaviour on traffic stream and satisfy all static and dynamic properties of speed–flow–density relationships. As a first attempt for Indian traffic condition, two behavioural parameters, namely the kinematic wave speed at jam(Cj) and a proposed saturation flow(k), are estimated using empirical observations. The parameter Cjis estimated by developing a relationship between driver reaction time and vehicle position in the queue at the signalised intersection. Functional parameters are estimated using Levenberg–Marquardt algorithm implemented in the R statistical software.Numerical measures such as root mean squared error, average relative error and cumulative residual plots are used for assessing models fitness. We set out several static and dynamic properties of the flow–speed–density relationships to evaluate the models, and these properties equally hold good for both homogenous and heterogeneous traffic states.From the numerical analysis, it is found that very few models replicate empirical speed–density data traffic behaviour.However, none of the existing functional forms satisfy all the properties. To overcome the shortcomings, we proposed two new speed–density functional forms. The uniqueness of these models is that they satisfy both numerical accuracy and the properties of fundamental diagram. These new forms would certainly improve the modelling accuracy, especially in dynamic traffic studies when coupling with dynamic speed equations.展开更多
A method is proposed to find key components of traffic networks with homogenous and heterogeneous topologies, in which heavier traffic flow is transported. One component, called the skeleton, is the minimum spanning t...A method is proposed to find key components of traffic networks with homogenous and heterogeneous topologies, in which heavier traffic flow is transported. One component, called the skeleton, is the minimum spanning tree (MST) based on the zero flow cost (ZCMST). The other component is the infinite incipient percolation cluster (IIC) which represents the spine of the traffic network. Then, a new method to analysis the property of the bottleneck in a large scale traffic network is given from a macroscopic and statistical viewpoint. Moreover, three effective strategies are proposed to alleviate traffic congestion. The significance of the findings is that one can significantly improve the global transport by enhancing the capacity in the ZCMST with a few links, while for improving the local traffic property, improving a tiny fraction of the traffic network in the IIC is effective. The result can be used to help traffic managers prevent and alleviate traffic congestion in time, guard against the formation of congestion bottleneck, and make appropriate policies for traffic demand management. Meanwhile, the method has very important theoretical significance and practical worthiness in optimizing traffic organization, traffic control, and disposal of emergency.展开更多
During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under hetero...During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under heterogeneous traffic where on-street bicyclists encounter a complex interaction with various types of vehicles and show divergent operational characteristics. Thus, the present study proposes an initial model suitable for urban road segments in mid-sized cities under such complex situations. For analysis purpose, various operational and physical factors along with user perception data sets （13,624 effective ratings in total） were collected from 74 road segments. Eight important road attributes affecting the bicycle service quality were identified using the most recent and most promising machine learning technique namely, random forest. The identified variables are namely, effective width of outside through lane, pavement condition index, traffic volume, traffic speed, roadside commercial activities, interruptions by unauthorized stoppages of intermittent public transits, vehicular ingress-egress to on-street parking area, and frequency of driveways carrying a high volume of traffic. Service prediction models were developed using ordered probit and ordered logit modeling structures which meet a confidence level of 95%. Prediction performances of developed models were assessed in terms of several statistical parameters and the ordered probit model outperformed the ordered logit model. Incorporating outputs of the probit model, a pre- dictive equation is presented that can identify under what level a segment is offering services for bicycle use. The service levels offered by roadways were classified into six categories varying from ＇excellent＇ to ＇worst＇ （A-F）.展开更多
The main objective of intersection design is to facilitate the convenience, comfort, and safety of people traversing the intersection by enhancing the efficient movement of road users. The intersections on urban roads...The main objective of intersection design is to facilitate the convenience, comfort, and safety of people traversing the intersection by enhancing the efficient movement of road users. The intersections on urban roads in India generally cater to heterogeneous motorized traffic, along with slow-moving traffic including pedestrians. It is therefore necessary to consider saturation flow for mixed traffic conditions to evaluate the overall operation of signalized intersections. A proper traffic model must consider varying characteristics of all the road users to effectively design and efficiently manage signalized intersections. This paper presents the results of the study on analyses of saturation flow rate conducted at signalized intersections with mixed traffic conditions in the city of Bangalore, India. Studies were carried out at 15 signalized intersections in the city of Bangalore with varying geometric factors such as width of road (w), gradient of the road (g), and turning radius (r) for right turning vehicles. Saturation flow rate computed as per Highway Capacity manual (HCM: 2000), Indonesian highway capacity manual (IHCM), and IRC SP: 41-1994 was compared with the field observations. The geometric factors, which affect the saturation flow, have been considered in this study and accordingly a new model has been proposed for determining saturation flow. It has been shown that by the introduction of the suggested adjustment factors in this paper, the saturation flow rate can give better picture of the field conditions, especially under heterogeneous traffic conditions of an urban area.展开更多
The safety of heterogeneous traffic is a vital topic in the oncoming era of autonomous vehicles(AVs).The cooperative vehicle infrastructure system(CVIS)is considered to improve heterogeneous traffic safety by connecti...The safety of heterogeneous traffic is a vital topic in the oncoming era of autonomous vehicles(AVs).The cooperative vehicle infrastructure system(CVIS)is considered to improve heterogeneous traffic safety by connecting and controlling AVs cooperatively,and the connected AVs are so-called connected and automated vehicles(CAVs).However,the safety impact of cooperative control strategy on the heterogeneous traffic with CAVs and human-driving vehicles(HVs)has not been well investigated.In this paper,based on the traffic simulator SUMO,we designed a typical highway scenario of on-ramp merging and adopted a cooperative control method for CAVs.We then compared the safety performance for two different heterogeneous traffic systems,i.e.AV and HV,CAV and HV,respectively,to illustrate the safety benefits of the cooperative control strategy.We found that the safety performance of the CAV and HV traffic system does not always outperform that of AV and HV.With random departSpeed and higher arrival rate,the proposed cooperative control method would decrease the conflicts significantly whereas the penetration rate is over 80%.We further investigated the conflicts in terms of the leading and following vehicle types,and found that the risk of a AV/CAV followed by a HV is twice that of a HV followed by another HV.We also considered the safety effect of communication failure,and found that there is no significant impact until the packet loss probability is greater than 30%,while communication delay’s impact on safety can be ignored according to our experiments.展开更多
基金Project supported by the Fundamental Research Funds for Central Universities,China(Grant No.2022YJS065)the National Natural Science Foundation of China(Grant Nos.72288101 and 72371019).
文摘With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)will coexist.In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system,we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference(FVD)model.Subsequently,adopting the linear stability theory,we propose the linear stability condition for the model through using the small perturbation method,and the validity of the heterogeneous model is verified by comparing with the FVD model.Through nonlinear theoretical analysis,we further derive the KdV-Burgers equation,which captures the propagation characteristics of traffic density waves.Finally,by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow,the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated.Subsequent analysis reveals emergent traffic phenomena.The experimental findings demonstrate that as CAV permeability increases,the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation,leading to enhanced stability of the traffic system.Furthermore,higher initial traffic density renders the traffic system more susceptible to congestion,resulting in local clustering effect and stop-and-go traffic phenomenon.Remarkably,the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing.Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow.This research contributes to providing theoretical guidance for future CAV applications,aiming to enhance urban road traffic effi
基金Supported by Universitas Muhammadiyah Yogyakarta,Indonesia and Asia University,Taiwan.
文摘Predicting traffic flow is a crucial component of an intelligent transportation system.Precisely monitoring and predicting traffic flow remains a challenging endeavor.However,existingmethods for predicting traffic flow do not incorporate various external factors or consider the spatiotemporal correlation between spatially adjacent nodes,resulting in the loss of essential information and lower forecast performance.On the other hand,the availability of spatiotemporal data is limited.This research offers alternative spatiotemporal data with three specific features as input,vehicle type(5 types),holidays(3 types),and weather(10 conditions).In this study,the proposed model combines the advantages of the capability of convolutional(CNN)layers to extract valuable information and learn the internal representation of time-series data that can be interpreted as an image,as well as the efficiency of long short-term memory(LSTM)layers for identifying short-term and long-term dependencies.Our approach may utilize the heterogeneous spatiotemporal correlation features of the traffic flowdataset to deliver better performance traffic flow prediction than existing deep learning models.The research findings show that adding spatiotemporal feature data increases the forecast’s performance;weather by 25.85%,vehicle type by 23.70%,and holiday by 14.02%.
基金ACKNOWLEDGEMENT This work was supported by the National Na- tural Science Foundation of China under Gra- nts No. 61172079, 61231008, No. 61201141, No. 61301176 the National Basic Research Program of China （973 Program） under Grant No. 2009CB320404+2 种基金 the 111 Project under Gr- ant No. B08038 the National Science and Tec- hnology Major Project under Grant No. 2012- ZX03002009-003, No. 2012ZX03004002-003 and the Shaanxi Province Science and Techno- logy Research and Development Program un- der Grant No. 2011KJXX-40.
文摘The traffic with tidal phenomenon in Heterogeneous Wireless Networks(HWNs)has radically increased the complexity of radio resource management and its performance analysis.In this paper,a Simplified Dynamic Hierarchy Resource Management(SDHRM)algorithm exploiting the resources dynamically and intelligently is proposed with the consideration of tidal traffic.In network-level resource allocation,the proposed algorithm first adopts wavelet neural network to forecast the traffic of each sub-area and then allocates the resources to those sub-areas to maximise the network utility.In connection-level network selection,based on the above resource allocation and the pre-defined QoS requirement,three typical network selection policies are provided to assign traffic flow to the most appropriate network.Furthermore,based on multidimensional Markov model,we analyse the performance of SDHRM in HWNs with heavy tailed traffic.Numerical results show that our theoretical values coincide with the simulation results and the SDHRM can improve the resource utilization.
文摘The modeling of an efficient classifier is a fundamental issue in automatic training involving a large volume of representative data.Hence,automatic classification is a major task that entails the use of training methods capable of assigning classes to data objects by using the input activities presented to learn classes.The recognition of new elements is possible based on predefined classes.Intrusion detection systems suffer from numerous vulnerabilities during analysis and classification of data activities.To overcome this problem,new analysis methods should be derived so as to implement a relevant system to monitor circulated traffic.The main objective of this study is to model and validate a heterogeneous traffic classifier capable of categorizing collected events within networks.The new model is based on a proposed machine learning algorithm that comprises an input layer,a hidden layer,and an output layer.A reliable training algorithm is proposed to optimize the weights,and a recognition algorithm is used to validate the model.Preprocessing is applied to the collected traffic prior to the analysis step.This work aims to describe the mathematical validation of a new machine learning classifier for heterogeneous traffic and anomaly detection.
基金Project supported by the National Natural Science Foundation of China (Grant No. 52072108)the Natural Science Foundation of Anhui Province, China (Grant No. 2208085ME148)the Open Fund for State Key Laboratory of Cognitive Intelligence, China (Grant No. W2022JSKF0504)。
文摘In order to analyze and learn the difference in car-following behavior between normal and rainy days, we first collect car-following trajectory data of an urban elevated road on normal and rainy days by microwave radar and analyze the differences in speed, relative speed, acceleration, space headway, and time headway among data through statistics. Secondly, owing to the time-series characteristics of car-following data, we use the long short-term memory(LSTM) neural network optimized by attention mechanism(AM) and sparrow search algorithm(SSA) to learn the different car-following behaviors under different weather conditions and build corresponding models(ASL-Normal, ASL-Rain, where ASL stands for AM-SSA-LSTM), respectively. Finally, the simulation test shows that the mean square error(MSE) and reciprocal of time-to-collision(RTTC) of the ASL model are better than those of LSTM and intelligent diver model(IDM), which is closer to the real data. The ASL model can better learn different driving behaviors on normal and rainy days. However,it has a higher sensitivity to weather conditions from cross test on normal and rainy data-sets which need classification training or sample diversification processing. In the car-following platoon simulation, the stability performances of two models are excellent, which can describe the basic characteristics of traffic flow on normal and rainy days. Comparing with ASL-Rain model, the convergence time of ASL-Normal is shorter, reflecting that cautious driving behavior on rainy days will reduce traffic efficiency to a certain extent. However, ASL-Normal model produces a more severe and frequent traffic oscillation within a shorter period because of aggressive driving behavior on normal days.
文摘This study is an attempt to establish a suitable speed–density functional relationship for heterogeneous traffic on urban arterials. The model must reproduce the traffic behaviour on traffic stream and satisfy all static and dynamic properties of speed–flow–density relationships. As a first attempt for Indian traffic condition, two behavioural parameters, namely the kinematic wave speed at jam(Cj) and a proposed saturation flow(k), are estimated using empirical observations. The parameter Cjis estimated by developing a relationship between driver reaction time and vehicle position in the queue at the signalised intersection. Functional parameters are estimated using Levenberg–Marquardt algorithm implemented in the R statistical software.Numerical measures such as root mean squared error, average relative error and cumulative residual plots are used for assessing models fitness. We set out several static and dynamic properties of the flow–speed–density relationships to evaluate the models, and these properties equally hold good for both homogenous and heterogeneous traffic states.From the numerical analysis, it is found that very few models replicate empirical speed–density data traffic behaviour.However, none of the existing functional forms satisfy all the properties. To overcome the shortcomings, we proposed two new speed–density functional forms. The uniqueness of these models is that they satisfy both numerical accuracy and the properties of fundamental diagram. These new forms would certainly improve the modelling accuracy, especially in dynamic traffic studies when coupling with dynamic speed equations.
基金Supported by the National Basic Research Program of China ("973" Project) (Grant No. 2006CB705500)the FANEDD (Grant No. 200763)+1 种基金the National Natural Science Foundation of China (Grant Nos. 70801005, 70871099)the Foundation of State Key Laboratory of Rail Control and Safety (Grant No. RCS2008ZZ001)
文摘A method is proposed to find key components of traffic networks with homogenous and heterogeneous topologies, in which heavier traffic flow is transported. One component, called the skeleton, is the minimum spanning tree (MST) based on the zero flow cost (ZCMST). The other component is the infinite incipient percolation cluster (IIC) which represents the spine of the traffic network. Then, a new method to analysis the property of the bottleneck in a large scale traffic network is given from a macroscopic and statistical viewpoint. Moreover, three effective strategies are proposed to alleviate traffic congestion. The significance of the findings is that one can significantly improve the global transport by enhancing the capacity in the ZCMST with a few links, while for improving the local traffic property, improving a tiny fraction of the traffic network in the IIC is effective. The result can be used to help traffic managers prevent and alleviate traffic congestion in time, guard against the formation of congestion bottleneck, and make appropriate policies for traffic demand management. Meanwhile, the method has very important theoretical significance and practical worthiness in optimizing traffic organization, traffic control, and disposal of emergency.
文摘During the past two decades, several methodologies are endorsed to assess the compatibility of roadways for bicycle use under homogeneous traffic conditions. However, these methodologies cannot be adopted under heterogeneous traffic where on-street bicyclists encounter a complex interaction with various types of vehicles and show divergent operational characteristics. Thus, the present study proposes an initial model suitable for urban road segments in mid-sized cities under such complex situations. For analysis purpose, various operational and physical factors along with user perception data sets （13,624 effective ratings in total） were collected from 74 road segments. Eight important road attributes affecting the bicycle service quality were identified using the most recent and most promising machine learning technique namely, random forest. The identified variables are namely, effective width of outside through lane, pavement condition index, traffic volume, traffic speed, roadside commercial activities, interruptions by unauthorized stoppages of intermittent public transits, vehicular ingress-egress to on-street parking area, and frequency of driveways carrying a high volume of traffic. Service prediction models were developed using ordered probit and ordered logit modeling structures which meet a confidence level of 95%. Prediction performances of developed models were assessed in terms of several statistical parameters and the ordered probit model outperformed the ordered logit model. Incorporating outputs of the probit model, a pre- dictive equation is presented that can identify under what level a segment is offering services for bicycle use. The service levels offered by roadways were classified into six categories varying from ＇excellent＇ to ＇worst＇ （A-F）.
文摘The main objective of intersection design is to facilitate the convenience, comfort, and safety of people traversing the intersection by enhancing the efficient movement of road users. The intersections on urban roads in India generally cater to heterogeneous motorized traffic, along with slow-moving traffic including pedestrians. It is therefore necessary to consider saturation flow for mixed traffic conditions to evaluate the overall operation of signalized intersections. A proper traffic model must consider varying characteristics of all the road users to effectively design and efficiently manage signalized intersections. This paper presents the results of the study on analyses of saturation flow rate conducted at signalized intersections with mixed traffic conditions in the city of Bangalore, India. Studies were carried out at 15 signalized intersections in the city of Bangalore with varying geometric factors such as width of road (w), gradient of the road (g), and turning radius (r) for right turning vehicles. Saturation flow rate computed as per Highway Capacity manual (HCM: 2000), Indonesian highway capacity manual (IHCM), and IRC SP: 41-1994 was compared with the field observations. The geometric factors, which affect the saturation flow, have been considered in this study and accordingly a new model has been proposed for determining saturation flow. It has been shown that by the introduction of the suggested adjustment factors in this paper, the saturation flow rate can give better picture of the field conditions, especially under heterogeneous traffic conditions of an urban area.
基金the Collaboration Project between China and Sweden regarding Research,Development and Innovation within Life Science and Road Traffic Safety(Grant No.2018YFE0102800)in part by the Key Program of National Natural Science Foundation of China(Grant No.U21B2089)+1 种基金in part by the National Natural Science Foundation of China(Grant No.71671100)in part by the Swedish Innovation Agency Vinnova(Grant No.2018-02891).
文摘The safety of heterogeneous traffic is a vital topic in the oncoming era of autonomous vehicles(AVs).The cooperative vehicle infrastructure system(CVIS)is considered to improve heterogeneous traffic safety by connecting and controlling AVs cooperatively,and the connected AVs are so-called connected and automated vehicles(CAVs).However,the safety impact of cooperative control strategy on the heterogeneous traffic with CAVs and human-driving vehicles(HVs)has not been well investigated.In this paper,based on the traffic simulator SUMO,we designed a typical highway scenario of on-ramp merging and adopted a cooperative control method for CAVs.We then compared the safety performance for two different heterogeneous traffic systems,i.e.AV and HV,CAV and HV,respectively,to illustrate the safety benefits of the cooperative control strategy.We found that the safety performance of the CAV and HV traffic system does not always outperform that of AV and HV.With random departSpeed and higher arrival rate,the proposed cooperative control method would decrease the conflicts significantly whereas the penetration rate is over 80%.We further investigated the conflicts in terms of the leading and following vehicle types,and found that the risk of a AV/CAV followed by a HV is twice that of a HV followed by another HV.We also considered the safety effect of communication failure,and found that there is no significant impact until the packet loss probability is greater than 30%,while communication delay’s impact on safety can be ignored according to our experiments.
