A novel map-matching procedure for low-sampling GPS data with applications to traffic flow analysis
A novel map-matching procedure for low-sampling GPS data with applications to traffic flow analysis
复制标题
一种新颖的低采样 GPS 数据地图匹配程序及其在交通流分析中的应用
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
L. Giovannini
中科院分区：
文献类型：
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作者：
L. Giovannini
意大利大量的私家车样本（2%）配备了GPS设备，定期测量其位置和动态状态，以确保保险。获得这种类型的数据可以开发非常有趣的理论和实际应用：实时重建某一区域的交通状态，开发精确的车辆动力学模型，研究驾驶员的认知动力学。
An extensive sample (2%) of private vehicles in Italy are equipped with a GPS device that periodically measures their position and dynamical state for insurance purposes. Having access to this type of data allows to develop theoretical and practical applications of great interest: the real-time reconstruction of traffic state in a certain region, the development of accurate models of vehicle dynamics, the study of the cognitive dynamics of drivers.
In order for these applications to be possible, we first need to develop the ability to reconstruct the paths taken by vehicles on the road network from the raw GPS data. In fact, these data are affected by positioning errors and they are often very distanced from each other (~2 Km). For these reasons, the task of path identification is not straightforward.
This thesis describes the approach we followed to reliably identify vehicle paths from this kind of low-sampling data.
The problem of matching data with roads is solved with a bayesian approach of maximum likelihood. While the identification of the path taken between two consecutive GPS measures is performed with a specifically developed optimal routing algorithm, based on A* algorithm. The procedure was applied on an off-line urban data sample and proved to be robust and accurate. Future developments will extend the procedure to real-time execution and nation-wide coverage.