Accelerated Map Matching for GPS Trajectories
| Autor: | Marko Dogramadzi, Aftab Khan |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
050210 logistics & transportation
Heuristic (computer science) business.industry Computer science Mechanical Engineering 05 social sciences Map matching computer.software_genre Bottleneck Computer Science Applications Reduction (complexity) 0502 economics and business Automotive Engineering Global Positioning System Trajectory Segmentation Data mining business Hidden Markov model computer |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems. 23:4593-4602 |
| ISSN: | 1558-0016 1524-9050 |
| Popis: | The processing and analysis of large-scale journey trajectory data is becoming increasingly important as vehicles become ever more prevalent and interconnected. Mapping these trajectories onto a road network is a complex task, largely due to the inevitable measurement error generated by GPS sensors. Past approaches have had varying degrees of success, but achieving high accuracy has come at the expense of performance, memory usage, or both.In this paper, we solve these issues by proposing a map matching algorithm based on Hidden Markov Models (HMM). The proposed method is shown to be more efficient when compared against a traditional HMM based map matching method, whilst maintaining high accuracy and eschewing any requirements for CPU-intensive and memory-expensive pre-processing. The proposed algorithm offers a method for significantly accelerating transition-probability calculations using instances of high data-availability, which have previously been a large bottleneck in map matching algorithm performance. It is shown that this can be accomplished with the application of road-network segmentation combined with a spatially-aware heuristic. Experiments are performed using two different datasets, with over 9 hours of GPS samples. We show that the proposed framework is able to offer a reduction in run-time of over 90% with no significant effect on the algorithm's accuracy when compared against the traditional HMM approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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