Enhancing Traffic Congestion Estimation with Social Media by Coupled Hidden Markov Model

Abstract

Estimating traffic conditions in arterial networks with GPS probe data is a practically important while substantially challenging problem. With the increasing availability of GPS equipments installed in various vehicles, GPS probe data is currently becoming a significant data source for traffic monitoring. 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. 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. 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. 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. To address the computational challenge, a sequential importance sampling based EM algorithm is also introduced. We evaluate the proposed model on the arterial network of downtown Chicago. The experimental results demonstrate the superior performance of the model by comparison with previous methods.