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.
Featured Application: The proposed algorithm and architecture has the capability of estimating the queue length of waiting vehicles at a signalized intersection, where traffic cameras, edge server, and some connected vehicles are available. This method can resolve the queue length estimation problem in a mixed traffic scenario, especially when there are variable types of vehicles. It can provide key information for traffic lights control and improve the traffic efficiency.Abstract: Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by integration between IoT and transport system models (TSMs). The formation process of queue at this intersection is analyzed based on the state information of connected vehicles and the velocity of shockwave is calculated to predict queue length. The computing results can be delivered to the traffic information edge server. However, not all the vehicles are capable of connecting to the network and will affect the queue length estimation accuracy. At the same time, traffic cameras transmit the traffic image to the edge server and a deep neuron network (DNN) is constructed on the edge server to tackle the traffic image. It can recognize and classify the vehicles in the image but takes several seconds to work with the complex DNN. At last, the final queue length is determined according to the weight of the two computing results. The integrated result is delivered to the traffic light controller and traffic monitoring center cloud. It reveals that the estimation from DNN can compensate the estimation from shockwave when the penetration rate of connected vehicles is low. A testbed is built based on VISSIM, and the evaluation results demonstrate the availability and accuracy of the integrated queue length estimation algorithm.
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