Dynamic path selection model based on logistic regression for the shunt point of highway

In the operation management and real-time monitoring of the highway, we always want to know the current position of all vehicles in real time, so that we can find the congestion and accident section timely and make effective treatment. But in reality, only a small part of the highway vehicles equipped with a global positioning system, and can access to the location information in real-time, for the most of the vehicles, we can only access the position point when they are in and out the highway by the toll data, and cannot get access to their specific routing when they are on the highway. Especially when the vehicle is moving to the shunt point, during the current state we cannot know exactly which direction the vehicle will choose next, which leads to the result that we cannot estimate the correct position of vehicles. In order to accurately identify the direction of vehicles in the shunt point, this paper proposes a framework that based on the highway toll data, the A* algorithm and logistic regression were used to predict the direction choose of vehicles on the shunt point of highway. The framework takes the historical toll data as a sample to train the feature weight in logistic regression model, and takes the actual direction of vehicles on the shunt point as a test set to evaluate the effectiveness of the method proposed by this paper.