Abstract
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.
Highlights
Adaptive traffic signal controllers use real-time traffic data to effectively and efficiently process conflicting traffic flows at intersections, aiming to reduce delay and traffic congestion
We propose the idea of ‘‘k-leader connected vehicle (CV)’’ to be able to predict the queue which is propagated after the communication range of dedicated short-range communication
We develop an neural network (NN) model to estimate the queue length at each approach of intersections in a CV system
Summary
Adaptive traffic signal controllers use real-time traffic data to effectively and efficiently process conflicting traffic flows at intersections, aiming to reduce delay and traffic congestion. Most of the previous queue estimation methods highly depend on data such as the arrival pattern of vehicles and the traffic signal control parameters, which are not available. In the context of existing literature, this research proposes a neural network (NN) model to estimate queue length. We, for the first time, introduce the concept of kleader CVs as a new method It is to aggregate data of a range of CVs to leading ones and let the leading CVs communicate with the RSU. If the second fails, the chain of communication will go down to the third, fourth and so on to the kth CV in the queue which is located at the communication range of RSU It is where the name of the kleader CVs has been derived.
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