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Title: Online map-matching of noisy and sparse location data with hidden Markov and route choice models
Authors: Jagadeesh, George Rosario
Srikanthan, Thambipillai
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Jagadeesh, G. R., & Srikanthan, T. (2017). Online map-matching of noisy and sparse location data with hidden Markov and route choice models. IEEE Transactions on Intelligent Transportation Systems, 18(9), 2423-2434. doi:10.1109/TITS.2017.2647967
Journal: IEEE Transactions on Intelligent Transportation Systems
Abstract: With the growing use of crowdsourced location data from smartphones for transportation applications, the task of map-matching raw location sequence data to travel paths in the road network becomes more important. High-frequency sampling of smartphone locations using accurate but power-hungry positioning technologies is not practically feasible as it consumes an undue amount of the smartphone’s bandwidth and battery power. Hence, there exists a need to develop robust algorithms for map matching inaccurate and sparse location data in an accurate and timely manner. This paper addresses the above need by presenting a novel map matching solution that combines the widely-used approach based on a Hidden Markov Model (HMM) with the concept of drivers’ route choice. Our algorithm uses a HMM tailored for noisy and sparse data to generate partial map-matched paths in an online manner. We use a route choice model, estimated from real drive data, to reassess each HMM-generated partial path along with a set of feasible alternative paths. We evaluated the proposed algorithm with real-world as well as synthetic location data under varying levels of measurement noise and temporal sparsity. The results show that the map-matching accuracy of our algorithm is significantly higher than that of the state of the art, especially at high levels of noise.
ISSN: 1524-9050
DOI: 10.1109/TITS.2017.2647967
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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