A novel traffic information estimation method based on mobile network signaling

Abstract

Two commonly used methods for traffic information rely on Vehicle Detector (VD) and Global Positioning System-Based Vehicle Probe (GVP); however, they have some confinements, such as high cost for construction and maintenance, and limited coverage. For the sake of overcoming dilemmas happened on VD and GVP, Cellular-Based Vehicle Probe (CVP) comes into being. However, the current applications for CVP mainly focus on arteries or freeways, where traffic information for longer distance is derived from two Inter-Visitor Location Register Location Area Update (Inter-VLR LAU) events with various Location Area Code (LAC) borders, and the one for shorter distance is from two consecutive handover events. The perplexity of available CVP techniques comes about is that there are no two Inter-VLR LAU events with various LAC borders and few handover events in scenic spots. In order to expand the applications for CVP to scenic spots, a cost-effective and flexible method utilizing mobile network signalling called Enhanced CVP (ECVP) is proposed. The key concept for ECVP is that we adopt Inter-VLR LAU events at the origin and all kinds of communication events at the destination to retrieve traffic information. The inaccuracy of ECVP consists in the uncertainty of event occurred time at the destination. Therefore, with a view to acquiring more accurate traffic information, three novel Reinforced CVP (RCVP) algorithms, inclusive of Fixed r percent samples CVP (F-RCVP), Dynamic r percent samples (D-RCVP), and Dynamic r percent samples with Discarding former samples (DD-RCVP), are presented. Numerical results show that F-RCVP is suitable for scenic spots that the LAC border only contain samples resulting from cars. By contrast, if the samples consist of both cars and motorcycles, it is recommended that D-RCVP and DD-RCVP are introduced.