Evaluating Map Matching Algorithms for Smartphone GNSS Data: Matching Vehicle Trajectories to an Urban Road Network
Bibliographic record
Abstract
ABSTRACT: Data from vehicles tracked using Global Navigation Satellite Systems (GNSS) can be used to monitor driving behaviour and road safety. In usage-based insurance programs, driver insurance premiums are tailored according to individual driving behaviour, often using data collected from user-owned smartphones. Due to positional noise caused, map matching algorithms must be used to spatially link GNSS observations to the road network. The purpose of this study is to evaluate the performance of several algorithms to process smartphone GNSS data for vehicular trips in urban road networks. This study evaluated five implementations, namely one topological (TMM), two probabilistic (PMM) using Hidden Markov Models (HMM), one fuzzy (FMM), and one hybrid map matching algorithm (HyMM) in terms of match accuracy and run time. Data was collected using ten smartphone devices and three applications across 12 trip scenarios in Montreal, Canada targeting the downtown, old city, highways, bridges, and tunnels. Results were compared with a series of ANOVA tests. Accuracy was not significantly different for the best performing algorithms (the Fast HMM and TMM) followed by the HyMM, with the Standard HMM and FMM algorithms performing significantly worse. Only the FMM algorithm was significantly slower than the others in terms of run time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".