A Map Matching Algorithm for Noisy, Low Frequent Public Transportation GPS Data

Sudeepa Nadeeshan, A. Perera
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引用次数: 1

Abstract

Identifying the traveled road segments from raw GPS trajectories on a digital road network is known as the Map Matching. Map Matching becomes a challenging problem when the sparse geo-temporal data set is noisy (e.g., 10 meters away from the actual location) and has a low sampling rate (e.g., one data point per 3 minutes). The public transportation domain (e.g., buses) differs from the generic transportation (e.g., taxis) as it follows a predefined route, and that helps to build the ground truth trajectories. Ground truth trajectories are essential to validate the map-matching algorithms. There are many advanced map matching algorithms, but they are focused on the generic map matching problem. We propose an improvement to the existing Hidden Markov Model (HMM) map matching methodology to find the most likely road route considering the probability of the bus being on the predefined route. The proposed algorithm is validated using simulated GPS data in a dense road network with different noises and sample rates. Finally, the results are compared with the existing HMM solution using Route Mismatched Fraction (RMF).
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噪声低频次公共交通GPS数据的地图匹配算法
从数字路网上的原始GPS轨迹中识别行车路段被称为地图匹配。当稀疏的地时数据集有噪声(例如,距离实际位置10米)并且采样率低(例如,每3分钟一个数据点)时,地图匹配成为一个具有挑战性的问题。公共交通领域(如公共汽车)不同于一般交通(如出租车),因为它遵循预定义的路线,这有助于建立地面真实轨迹。地面真实轨迹对于验证地图匹配算法至关重要。虽然有许多先进的地图匹配算法,但它们都集中在通用地图匹配问题上。我们提出了对现有隐马尔可夫模型(HMM)地图匹配方法的改进,以考虑公共汽车在预定义路线上的概率,找到最可能的道路路线。采用不同噪声和采样率的密集路网模拟GPS数据对算法进行了验证。最后,将结果与使用路由不匹配分数(RMF)的现有HMM解决方案进行比较。
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