![]() However, the characteristics of probe data, including the lack of reliability, low sampling frequency, and the randomness of its spatiotemporal coverage, make it insufficient for fully estimating traffic states for large transportation networks. As such, there is considerable research interest in exploring GPS probes for conducting various traffic related applications. ![]() Recently, GPS based probe vehicle data have become a significant data source available for the arterials and highways not covered by dedicated sensing infrastructure. Due to the high cost of deploying and maintaining such devices, their spatialtemporal coverage is usually very limited. ![]() KeywordsĬonventional traffic monitoring methods rely on road sensor data collected from various sensors such as loop detectors, surveillance cameras, and radars. The experimental results demonstrate the superior performance of the model by comparison with previous methods. We evaluate the proposed model on the arterial network of downtown Chicago. To address the computational challenge, a sequential importance sampling based EM algorithm is also introduced. Next we propose an extended Coupled Hidden Markov Model which can effectively integrate GPS probe readings and traffic related tweets to more accurately estimate traffic conditions of an arterial network. Motivated by the increasing amount of traffic information available in Twitter, we first extensively collect tweets that report various traffic events such as congestion, accident, and road construction. For the first time this paper studies how to explore social media as an auxiliary data source and incorporate it with GPS probe data to enhance traffic congestion estimation. However, limited by the lack of reliability and low sampling frequency of GPS probes, probe data are usually not sufficient for fully estimating traffic conditions of a large arterial network. With the increasing availability of GPS equipments installed in various vehicles, GPS probe data is currently becoming a significant data source for traffic monitoring. Estimating traffic conditions in arterial networks with GPS probe data is a practically important while substantially challenging problem.
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