Shockwave Models for Crowdsourcing-based Traffic Information Mining

Abstract

Crowdsourcing is a new trend for pervasively discovering traffic information due to its low deployment and maintenance cost as compared with traditional infrastructure-based approaches, e. g., loop detectors and CCTV. Mining techniques and the penetration rate of participators in the discovery process are two major issues in such approaches. In this work, we first point out the shockwave phenomenon occurring in signalized traffic can be used to discover useful traffic information including traffic light information and vehicle flow information. To reduce the requirement on the penetration rate, a folding heuristic is proposed. The proposed concepts are verified via extensive simulations, especially on the penetration rate issue. Our results show that shockwave models are useful to extract traffic information from crowdsourced data, and the folding technique can effectively reduce the requirement on the penetration rate. It is remarkable that the proposed approach can provide high quality information even at a penetration rate as low as 1.6%.