Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150415
Title: Incremental route inference from low-sampling GPS data : an opportunistic approach to online map matching
Authors: Luo, Linbo
Hou, Xiangting
Cai, Wentong
Guo, Bin
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Luo, L., Hou, X., Cai, W. & Guo, B. (2019). Incremental route inference from low-sampling GPS data : an opportunistic approach to online map matching. Information Sciences, 512, 1407-1423. https://dx.doi.org/10.1016/j.ins.2019.10.060
Journal: Information Sciences
Abstract: With the surging of smart device sensing and mobile networking, GPS data has been widely available for identifying vehicle position and route on the road map. For many real-time applications, such as traffic sensing and route recommendation, it is critical to immediately infer travelling route with incoming GPS data. In this paper, an opportunistic approach to online map matching is proposed to incrementally infer routes from low-sampling GPS data with low output latency. Unlike the hidden Markov model (HMM)-based approach, which often experiences certain delay between the GPS observation and inference, our algorithm can produce immediate inference when a new GPS point becomes available. Furthermore, a rollback mechanism is provided to correct the already inferred route when some abnormal situations are detected during the opportunistic inference process. We evaluate the proposed algorithm using real dataset of GPS trajectories over 100 cities around the world. Experimental results show that our algorithm is better than, or at least comparable to the state-of-the-art algorithms in terms of inference accuracy. More importantly, our algorithm can yield much shorter output latency and require less execution time, which is critical for many real-time navigation applications and location-based services.
URI: https://hdl.handle.net/10356/150415
ISSN: 0020-0255
DOI: 10.1016/j.ins.2019.10.060
Rights: © 2019 Elsevier Inc. All rights reserved. This paper was published in Information Sciences and is made available with permission of Elsevier Inc.
Fulltext Permission: embargo_20220307
Fulltext Availability: With Fulltext
Appears in Collections:IGS Journal Articles
SCSE Journal Articles

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