Multi-step Short Term Traffic Flow Forecasting Using Temporal and Spatial Data

Short term traffic flow forecasting is valuable to both governments for designing intelligent transportation systems and everyday commuters or travelers who are interested in the best routes to their destinations. This work focuses on forecasting traffic flow in major freeways in southern California using large amounts of data collected from the Caltrans Performance Measurement System. Both statistical models and machine learning models are considered. The statistical models include seasonal ARIMA, seasonal VARMA, exponential smoothing and various regression models. The machine learning models include Support Vector Regression, feed forward Neural Networks, and Long Short-Term Memory Neural Networks. Forecasting is performed in both a univariate manner by relying on the historical temporal data of a particular sensor as well as in a multivariate manner by considering a neighborhood of three closely located sensors. Multivariate forecasters generally improve upon their univariate counterparts.