Traffic Flow Prediction using Long Short-Term Memory Network and Optimized Spatial Temporal Dependencies

Abstract

Accurate traffic flow prediction is required in traffic management and optimal route selection. Traffic data patterns are influenced by factors such as road characteristics, time of the day, day of the week, weather conditions, and special events. The accuracy of traffic flow prediction depends on the spatial and temporal extent and diversity of the available data, along with the prediction algorithm. This paper presents a long short-term memory (LSTM) network for traffic prediction which is boosted by optimizing the spatial and temporal extent of the features that are used as input. A grid search method is employed to choose the spatial and temporal window sizes that result in the highest generalization accuracy. The optimal temporal window size is the number of time steps for input features and the spatial window size refers to the size of the geographical neighborhood, where features are used as input. Our experiments with traffic prediction in the State of California showed that optimizing the spatial and temporal window sizes in an LSTM network could improve the RMSE and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> by up to 40.02% and 25.21%.