Eddy: an error-bounded delay-bounded real-time map matching algorithm using HMM and online Viterbi decoder

Guanfeng Wang, Roger Zimmermann

Abstract: Real-time map matching is a fundamental but challenging problem with various applications in Geographic Information Systems (GIS), Intelligent Transportation Systems (ITS) and beyond. It aims to align a sequence of measured latitude/longitude positions with the road network on a digital map in real-time. There exist a number of statistical matching approaches that unfortunately either process trajectory data offline or provide an online solution without an infimum analysis. Here we propose a novel statistics-based online map matching algorithm called Eddy with a solid <u>e</u>rror-and <u>d</u>elay-bound analysis. More specifically, Eddy employs a Hidden Markov Model (HMM) to represent the spatio-temporal data as state chains, which elucidates the road network's topology, observation noises and their underlying relations. After modeling, we shape the decoding phase as a ski-rental problem, and an improved online-version Viterbi decoding algorithm is proposed to find the most likely sequence of hidden states (road routes) in real-time. We reduce the candidate routes search range during the decoding for efficiency reasons. Moreover, our deterministic decoder trades off latency for expected accuracy dynamically, without having to choose a fixed window size beforehand. We also provide the competitive analysis and the proof that our online algorithm is error-bounded (with a competitive ratio of 2) and latency-bounded. Our experimental results show that the proposed algorithm outperforms widely used existing approaches on both accuracy and latency.