The path inference filter: model-based low-latency map matching of probe vehicle data

Details The path inference filter: model-based low-latency map matching of probe vehicle data The path inference filter: model-based low-latency map matching of probe vehicle data

2011-09-09

arxiv.org/abs/1109.1966v1

Computer Science

Artificial Intelligence

Scientific paper

Traffic congestion has a significant impact around the world. Building reliable and cost effective traffic monitoring systems is a prerequisite to addressing this phenomenon. Historically, traffic estimation has been limited to highways, and has relied on a static, dedicated sensing infrastructure such as loop detectors or cameras. In the case of city roads, this estimation problem is rather involved. This situation can be partly attributed to the lack of effective sensing in an urban setting. In this context, the most promising source of data is the GPS receiver in personal smartphones and commercial fleet vehicles. In this article, we present some algorithms that leverage this trend to produce some streaming data compatible with current state-of-the-art traffic estimation algorithms. These algorithms, which we will refer altogether as the path inference algorithm, have been implemented and deployed inside the Mobile Millennium system at Berkeley.

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