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https://doi.org/10.1145/3003421.3003426
Title: | A General Feature-based Map Matching Framework with Trajectory Simplification | Authors: | Yin, Yifang Shah, Rajiv Ratn Zimmermann, Roger |
Keywords: | Science & Technology Technology Physical Sciences Computer Science, Information Systems Geography, Physical Computer Science Physical Geography GIS worldwide GPS data feature based map matching trajectory simplification |
Issue Date: | 1-Jan-2016 | Publisher: | ASSOC COMPUTING MACHINERY | Citation: | Yin, Yifang, Shah, Rajiv Ratn, Zimmermann, Roger (2016-01-01). A General Feature-based Map Matching Framework with Trajectory Simplification. 7th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS) : 43-52. ScholarBank@NUS Repository. https://doi.org/10.1145/3003421.3003426 | Abstract: | Accurate map matching has been a fundamental but challenging problem that has drawn great research attention in recent years. It aims to reduce the uncertainty in a trajectory by matching the GPS points to the road network on a digital map. Most existing work has focused on estimating the likelihood of a candidate path based on the GPS observations, while neglecting to model the probability of a route choice from the perspective of drivers. Here we propose a novel feature-based map matching algorithm that estimates the cost of a candidate path based on both GPS observations and human factors. To take human factors into consideration is very important especially when dealing with low sampling rate data where most of the movement details are lost. Additionally, we simultaneously analyze a subsequence of coherent GPS points by utilizing a new segment-based probabilistic map matching strategy, which is less susceptible to the noisiness of the positioning data. We have evaluated the proposed approach on a public large-scale GPS dataset, which consists of 100 trajectories distributed all over the world. The experimental results show that our method is robust to sparse data with large sampling intervals (e.g., 60 s ∼ 300 s) and challenging track features (e.g., u-turns and loops). Compared with two state-of-the-art map matching algorithms, our method substantially reduces the route mismatch error by 6.4% ∼ 32.3% and obtains the best map matching results in all the different combinations of sampling rates and challenging features. | Source Title: | 7th ACM SIGSPATIAL International Workshop on GeoStreaming (IWGS) | URI: | https://scholarbank.nus.edu.sg/handle/10635/200731 | ISBN: | 9781450345798 | DOI: | 10.1145/3003421.3003426 |
Appears in Collections: | Staff Publications Elements |
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