NDLI: semMatch: road semantics-based accurate map matching for challenging positioning data

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semMatch: road semantics-based accurate map matching for challenging positioning data

Content Provider	ACM Digital Library
Author	Aly, Heba Youssef, Moustafa
Abstract	Map matching has been used to reduce the noisiness of the location estimates by aligning them to the road network on a digital map. A growing number of applications, e.g. energy-efficient localization and cellular provider side localization, depend on the availability of only sparse and coarse-grained positioning data; leading to a challenging map matching process. In this paper, we present semMatch: a system that can provide accurate HMM-based map matching for challenging positioning traces. semMatch leverages the smartphone's inertial sensors to detect different road semantics; such as speed bumps, tunnels, and turns; and uses them in a mathematically-principled way as hints to overcome the sparse, noisy, and coarse-grained input positioning data, improving the HMM map matching accuracy and efficiency. To do that, semMatch applies a series of preprocessing modules to handle the noisy locations. The filtered location data is then processed by the core of semMatch system using a novel incremental HMM algorithm that combines a semantics-enriched digital map and the car's ambient road semantics in its estimation process. We have evaluated semMatch using traces collected from different cities covering more than 150km under different harsh scenarios including coarse-grained cellular-based positioning data, sparse GPS traces with extremely low sampling rate, and noisy traces with a large number of back-and-force transitions. The results show that semMatch significantly outperforms traditional map matching algorithms under all scenarios, with an enhancement of at least 416% and 894% in precision and recall respectively in the most difficult cases. This highlights its promise as a next generation map matching algorithm for challenging environments.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450339674
DOI	10.1145/2820783.2820824
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-11-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Map matching for challenging positioning data Road semantics-based map matching Hmm-based map matching
Content Type	Text
Resource Type	Article

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