Abstract
With the development of intelligent transportation system (ITS) and vehicle to X (V2X), the connected vehicle is capable of sensing a great deal of useful traffic information, such as queue length at intersections. Aiming to solve the problem of existing models’ complexity and information redundancy, this paper proposes a queue length sensing model based on V2X technology, which consists of two sub-models based on shockwave sensing and back propagation (BP) neural network sensing. First, the model obtains state information of the connected vehicles and analyzes the formation process of the queue, and then it calculates the velocity of the shockwave to predict the queue length of the subsequent unconnected vehicles. Then, the neural network is trained with historical connected vehicle data, and a sub-model based on the BP neural network is established to predict the real-time queue length. Finally, the final queue length at the intersection is determined by combining the sub-models by variable weight. Simulation results show that the sensing accuracy of the combined model is proportional to the penetration rate of connected vehicles, and sensing of queue length can be achieved even in low penetration rate environments. In mixed traffic environments of connected vehicles and unconnected vehicles, the queuing length sensing model proposed in this paper has higher performance than the probability distribution (PD) model when the penetration rate is low, and it has an almost equivalent performance with higher penetration rate while the penetration rate is not needed. The proposed sensing model is more applicable for mixed traffic scenarios with much looser conditions.
Highlights
With the increasing traffic congestion problem, the role of adaptive traffic control systems (ATCS)has become more and more important
L is the final queue length, l1 is the predicted value based on the shockwave, l2 is the predicted value based on the back propagation (BP) neural network, α is the weight of the shockwave model, and t where is the stop time of the last connected vehicle
The accuracy of the combined sensing model is tested by two error indices, namely absolute where L is the final queue length, l1 is the predicted value based on the shockwave, l2 is the predicted value based on the BP neural network, α is the weight of the shockwave model, and t is the stop time of the last connected vehicle
Summary
With the increasing traffic congestion problem, the role of adaptive traffic control systems (ATCS). The signal controller at the intersection can use V2I technology to obtain queue information of the entrance lanes and adjust green time in real-time. How to use the information provided by a small number of connected vehicles to accurately predict the queue length becomes the key to adaptive signal timing optimization. V2X technology and the neural network, the sensing model of queue length is established, which will further improve the sensing accuracy and provide convenience for signal control at intersections. The first model uses real-time queue information of connected vehicles to determine the speed of shockwave and predict the length of subsequent unconnected vehicles. The second model analyzes the historical queue length and connected vehicles distribution information and uses the nonlinear mapping characteristics of the BP neural network to establish a functional model.
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