Network-level traffic flow prediction: Functional time series vs. functional neural network approach

Abstract

Traffic state prediction is an essential component and an underlying backbone of intelligent transportation systems, especially in the context of smart city framework. Its significance is mainly twofold in modern transportation systems: supporting advanced traffic operations and management for highways and urban road networks to mitigate traffic congestion and enabling individual drivers with connected vehicles in the traffic system to dynamically optimize their routes to improve travel time. Traffic state prediction with interval-based pointwise methods at 15-minute or hourly intervals is common in traffic literature. However, because traffic dynamics are a continuous process over time, the discrete-time pointwise methods for traffic prediction at a fixed time interval hardly meet the advanced demands of continuous prediction in modern transportation systems. To close the gap, we propose functional approaches to intraday and day-by-day continuous-time prediction for traffic volume. This research focuses on network-level traffic flow predictions concurrently for all locations of interest. Two functional approaches are introduced, namely, the network-integrated functional time-series model and the functional neural network model. With functional approaches a 24-hour intraday traffic profile is modeled as a functional curve over time, and sequences of historical traffic curves are used to predict traffic curves for near future days in a row and multiple locations of interest. We also include the functional varying coefficient model, Sparse VAR and traditional AR models in the comparative study; empirical results show that the network-integrated functional time-series model outperforms other approaches in terms of the accuracy of predictions at network-scale.