Vehicular traffic density forecasting through the eyes of traffic cameras; a spatio-temporal machine learning study

Abstract

Forecasting vehicular mobility and density is essential to a wide array of mobile applications, including VANETs, crowd-sourcing, participatory sensing, network provisioning, and shared transportation. Forecasting is intrinsically complex and scarcity and lack-of-scale of vehicular mobility data is adding to the challenge. In this paper, relying on traffic cameras as the main data acquisition tool and the traffic densities extracted from the images, we explore trends pertaining to density data for the purposes of temporal and spatial forecasting. We investigate the promise of deep learning by conducting a comparative analysis of conventional (seasonal) models, and multiple variants of recurrent neural models, based on 40 day-long traffic density data from 58 cameras in London. Our findings show a dramatic reduction in forecast error using deep learning, where the best seasonal model gets 0.0176 mean squared error, and our proposed neural model achieves 0.0067 (62% less error). This is 10.5% in percentage error, down from 19.3%. We also design an end-to-end multivariate architecture that forecasts all the cameras which achieves 0.0125 error (14.5% in percentage error), but is trained in half the time needed to train 58 cameras individually. Finally, to forecast locations without explicit monitoring, we build on these insights and investigate spatial relationships between cameras. We introduce a spatial forecast model similar to the multivariate model. This results in an average reconstruction error of 0.0169 when every camera is reconstructed based on only one camera, and goes down to 0.0125 when 8 cameras are used to predict others (on par with that of the multivariate model with all 58 cameras as input). Moreover, a set of 23 cameras is found that can forecast the other cameras with an error of 0.0086. These results provide great promise for prediction in future vehicular-based networks and services.