Probabilistic map matching of sparse and noisy smartphone location data

• Main Authors: Jagadeesh, George Rosario, Srikanthan, Thambipillai
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; Hidden Markov Models; Runtime
• Online Access: https://hdl.handle.net/10356/145769
• Institution: Nanyang Technological University

There is an immense amount of location data being collected today from smartphone users by various service providers. Due to bandwidth and battery-life considerations, smartphone locations are generally sampled at sparse intervals using energy-efficient, but inaccurate, alternatives to the power-hungry Global Positioning System (GPS). If sparse sequences of coarse location data obtained from mobile users can be accurately map-matched to travel paths on the road network, then this data can be effectively used for several traffic-related applications. Unlike most other map-matching methods in the literature, we, in this paper, focus on the problem of map-matching sparse and noisy non-GPS smartphone location data. We adopt the widely-followed Hidden Markov Model (HMM) approach and propose new probabilistic models for the observation and transition probabilities tailored towards the type of data being considered. Our map-matching method has been evaluated using ground-truth labelled non-GPS location data collected from real drives. Tests show that the accuracy of the proposed method is about 12% more than that of a comparable HMM-based method from the literature. Our results also show that the runtime and latency of the proposed method can be kept within reasonable bounds using simple techniques.