We develop a Kalman filter for predicting traffic flow at urban arterials based on data obtained from con-nected vehicles. The proposed algorithm is computationally efficient and offers a real-time prediction since it...We develop a Kalman filter for predicting traffic flow at urban arterials based on data obtained from con-nected vehicles. The proposed algorithm is computationally efficient and offers a real-time prediction since it invokes the connected vehicle data just before the prediction period. Moreover, it can predict the traffic flow for various pene-tration rates of connected vehicles (the ratio of the number of connected vehicles to the total number of vehicles). At first, the Kalman filter equations are calibrated using data derived from Vissim traffic simulator for different penetra-tion rates, different fluctuating arrival rates of vehicles and various signal settings. Then the filter is evaluated for a variety of traffic scenarios generated in Vissim simulator. We evaluate the performance of the algorithm for different penetration rates under several traffic situations using some statistical measures. Although many of the previous pre-diction methods depend highly on data from fixed sensors (i.e., loop detectors and video cameras), which are associ-ated with huge installation and maintenance costs, this study provides a low-cost mean for short-term flow prediction only based on the connected vehicle data.展开更多
This paper presents a novel method to estimate queue length at signalised intersections using connected vehicle(CV)data.The proposed queue length estimation method does not depend on any conventional information such ...This paper presents a novel method to estimate queue length at signalised intersections using connected vehicle(CV)data.The proposed queue length estimation method does not depend on any conventional information such as arrival flow rate and parameters pertaining to traffic signal controllers.The model is applicable for real-time applications when there are sufficient training data available to train the estimation model.To this end,we propose the idea of “k-leader CVs” to be able to predict the queue which is propagated after the communication range of dedicated short-range communication(the communication platform used in CV system).The idea of k-leader CVs could reduce the risk of communication failure which is a serious concern in CV ecosystems.Furthermore,a linear regression model is applied to weigh the importance of input variables to be used in a neural network model.Vissim traffic simulator is employed to train and evaluate the effectiveness and robustness of the model under different travel demand conditions,a varying number of CVs(i.e.CVs'market penetration rate)as well as various traffic signal control scenarios.As it is expected,when the market penetration rate increases,the accuracy of the model enhances consequently.In a congested traffic condition(saturated flow),the proposed model is more accurate compared to the undersaturated condition with the same market penetration rates.Although the proposed method does not depend on information of the arrival pattern and traffic signal control parameters,the results of the queue length estimation are still comparable with the results of the methods that highly depend on such information.The proposed algorithm is also tested using large size data from a CV test bed(i.e.Australian Integrated Multimodal Ecosystem)currently underway in Melbourne,Australia.The simulation results show that the model can perform well irrespective of the intersection layouts,traffic signal plans and arrival patterns of vehicles.Based on the numerical results,20%penetration rate of CVs is a critical threshold.For penetration rates below 20%,prediction algorithms fail to produce reliable outcomes.展开更多
基金sponsored by the Australian Integrated Multimodal EcoSystem (AIMES), https://industry.eng. unimelb.edu.au/aimes
文摘We develop a Kalman filter for predicting traffic flow at urban arterials based on data obtained from con-nected vehicles. The proposed algorithm is computationally efficient and offers a real-time prediction since it invokes the connected vehicle data just before the prediction period. Moreover, it can predict the traffic flow for various pene-tration rates of connected vehicles (the ratio of the number of connected vehicles to the total number of vehicles). At first, the Kalman filter equations are calibrated using data derived from Vissim traffic simulator for different penetra-tion rates, different fluctuating arrival rates of vehicles and various signal settings. Then the filter is evaluated for a variety of traffic scenarios generated in Vissim simulator. We evaluate the performance of the algorithm for different penetration rates under several traffic situations using some statistical measures. Although many of the previous pre-diction methods depend highly on data from fixed sensors (i.e., loop detectors and video cameras), which are associ-ated with huge installation and maintenance costs, this study provides a low-cost mean for short-term flow prediction only based on the connected vehicle data.
基金sponsored by the Australian Integrated Multimodal Eco System(AIMES)
文摘This paper presents a novel method to estimate queue length at signalised intersections using connected vehicle(CV)data.The proposed queue length estimation method does not depend on any conventional information such as arrival flow rate and parameters pertaining to traffic signal controllers.The model is applicable for real-time applications when there are sufficient training data available to train the estimation model.To this end,we propose the idea of “k-leader CVs” to be able to predict the queue which is propagated after the communication range of dedicated short-range communication(the communication platform used in CV system).The idea of k-leader CVs could reduce the risk of communication failure which is a serious concern in CV ecosystems.Furthermore,a linear regression model is applied to weigh the importance of input variables to be used in a neural network model.Vissim traffic simulator is employed to train and evaluate the effectiveness and robustness of the model under different travel demand conditions,a varying number of CVs(i.e.CVs'market penetration rate)as well as various traffic signal control scenarios.As it is expected,when the market penetration rate increases,the accuracy of the model enhances consequently.In a congested traffic condition(saturated flow),the proposed model is more accurate compared to the undersaturated condition with the same market penetration rates.Although the proposed method does not depend on information of the arrival pattern and traffic signal control parameters,the results of the queue length estimation are still comparable with the results of the methods that highly depend on such information.The proposed algorithm is also tested using large size data from a CV test bed(i.e.Australian Integrated Multimodal Ecosystem)currently underway in Melbourne,Australia.The simulation results show that the model can perform well irrespective of the intersection layouts,traffic signal plans and arrival patterns of vehicles.Based on the numerical results,20%penetration rate of CVs is a critical threshold.For penetration rates below 20%,prediction algorithms fail to produce reliable outcomes.
