A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments

Hu, Jiahuan; Yi, Ding; Bisnath, Sunil

doi:10.3390/s23031631

Cited by 9 publications

(1 citation statement)

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“…There are various reasons for GNSS measurement discrepancies, for example, unreachable GNSS satellites (Grabs et al., 2008), atmospheric disturbances or space weather phenomenons (Kos et al., 2013), multipath inference, for example, due to signal reflections on large buildings (Marais et al., 2005). In particular, the use of smartphones as GNSS receivers is known for lower measurement accuracy (Hu et al., 2023), especially between different devices (CartONG, 2019). Moreover, it is important to know that GNSS measurements tend to overshoot the actual moved distances, and they are spatially and temporally autocorrelated (Ranacher et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches

Wöltche

2023

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Map matching is a widely used technology for mapping tracks to road networks. Typically, tracks are recorded using publicly available Global Navigation Satellite Systems, and road networks are derived from the publicly available OpenStreetMap project. The challenge lies in resolving the discrepancies between the spatial location of the tracks and the underlying road network of the map. Map matching is a combination of defined models, algorithms, and metrics for resolving these differences that result from measurement and map errors. The goal is to find routes within the road network that best represent the given tracks. These matches allow further analysis since they are freed from the noise of the original track, they accurately overlap with the road network, and they are corrected for impossible detours and gaps that were present in the original track. Given the ongoing need for map matching in mobility research, in this work, we present a novel map matching method based on Markov decision processes with Reinforcement Learning algorithms. We introduce the new Candidate Adoption feature, which allows our model to dynamically resolve outliers and noise clusters. We also incorporate an improved Trajectory Simplification preprocessing algorithm for further improving our performance. In addition, we introduce a new map matching metric that evaluates direction changes in the routes, which effectively reduces detours and round trips in the results. We provide our map matching implementation as Open Source Software (OSS) and compare our new approach with multiple existing OSS solutions on several public data sets. Our novel method is more robust to noise and outliers than existing methods and it outperforms them in terms of accuracy and computational speed.

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Section: Introductionmentioning

confidence: 99%