Estimating and predicting traffic conditions in arterial networks using probe data has proven to be a substantial challenge. In the United States, sparse probe data represents the vast majority of the data available on arterial roads in most major urban environments. This article proposes a probabilistic modeling framework for estimating and predicting arterial travel time distributions using sparsely observed probe vehicles. We evaluate our model using data from a fleet of 500 taxis in San Francisco, CA, which send GPS data to our server every minute. The sampling rate does not provide detailed information about where vehicles encountered delay or the reason for any delay (i.e. signal delay, congestion delay, etc.). Our model provides an increase in estimation accuracy of 35% when compared to a baseline approach for processing probe vehicle data.
Herring et al. REF applied coupled HMMs to taxi probe data on arterial roads to estimate traffic conditions.
Estimating arterial traffic conditions using sparse probe data
13th International IEEE Conference on Intelligent Transportation Systems
13th International IEEE Conference on Intelligent Transportation Systems
Engineering Computer Science