Strip: A Short-Term Traffic Jam Prediction Based On Logistic Regression

Predicting the traffic jam in urban areas is a challenge, specially when the goal is to perform short-term forecasting. We can find in the literature some advances in algorithms and techniques to handle this issue, but there is still room for innovative solutions. For example, new approaches considering different sources of information about city dynamics and urban social behavior. In fact, one of the goals of this paper is to show the benefits of using this type of data to improve short-term traffic prediction. This paper propose STRIP, a novel short-term traffic prediction model that combines logistic regressions with two urban data sources: historical data of traffic flow obtained from online maps, such as Bing Maps, and users' checkins, shared on participatory sensor networks, which capture the routines of city inhabitants (here known as social sensors). Simulation results show that STRIP improves the accuracy of state of the art studies, specially when using data from social sensors as input.