CLRIC: Collecting Lane-Based Road Information Via Crowdsourcing

Abstract

Lane-based road network information, such as the number and locations of traffic lanes on a road, has played an important role in intelligent transportation systems. In this paper, we propose a Collecting Lane-based Road Information via Crowdsourcing (CLRIC) method, which can automatically extract detailed lane structure of roads by using crowdsourcing data collected by vehicles. First, CLRIC filters the high-precision GPS data from the raw trajectories based on region growing clustering with prior knowledge. Second, CLRIC mines the number and locations of traffic lanes through optimized constrained Gaussian mixture model. Experiments are conducted with taxi GPS trajectories in Wuhan, China, and the results show that CLRIC is quantified and displays detailed road networks with the number and locations of traffic lanes comparing with the satellite image and human-interpreted situation.