Link Travel Time Estimation Model Fusing Data from Mobile and Stationary Detector Based on BP Neural Network

Abstract

Real-time transportation information is the foundation and ensure of dynamic route guidance system. Stationary sensors' detecting precision is high. But because stationary sensors only can detect point information of links, stationary sensors' maturity degree is bad. On account of mobile sensors' detecting links' livelong transportation information, mobile sensors' maturity degree is high. However because of GPS data's errors and probe vehicles' randomness mobile sensors' detecting precision is bad. Considering colligating the mobile and stationary sensors' advantage, this paper proposes a new mobile and stationary sensor fusion model based on BP neural network to improve the accuracy and maturity degree of estimating travel time. The model consists of three modules: (1) mobile detecting module which measure first part, second part and third part travel time over a link using taxis equipped with differential global positioning system receivers; (2) loop detecting module which measure travel time using fixed detectors fixed in roads and traffic signal timing parameters; and (3) data fusion module which uses a neural network to combine outputs from the above two modules to improve the travel time estimation accuracy. This model's inputs respectively are: travel time detected by mobile sensors, travel time detected by stationary sensors, mobile sensors' density in the link and stationary sensors' density in the link. This model's output is the link's travel time. To validate the validity of this model, this paper presents the test of this model using a great deal of real data in Guangzhou city. The result indicates that this model is valid