RCIVMM: A Route Choice-Based Interactive Voting Map Matching Approach for Complex Urban Road Networks

Abstract

With the enhancement of location-acquisition technologies, GPS trajectories play an essential role in data-driven intelligent transportation applications, which requires an accurate approach to match raw GPS trajectories to road segments on a digital map. However, for complex urban roads containing elevated roads and surface roads, map matching for low-frequency GPS data is still challenging. This article aims to address the biases and instability problem in existing approaches. To this end, we combine the spatial-temporal characteristics of GPS data in complex roads with driving behaviours and present a novel global map matching method including truncated density clustering algorithm, statistic features based spatial-temporal analysis, and driving-behaviour-based track modification. Additionally, a weighted-matrix based interactive voting algorithm is proposed to select the best results from a global perspective. The experiments are conducted with two real GPS trajectory datasets under three road conditions. The result shows that our approach outperforms state-of-art approaches for urban complex road networks in both accuracy and efficiency.