Traffic Volume Estimation in Multimodal Urban Networks Using Cell Phone Location Data

This paper proposes a method to estimate multimodal traffic volume on urban road networks using cellphone location data. The study considers the fact that there can be more than one phone user in each driving vehicle. Firstly, a temporal and spatial method is used to distinguish whether the cellphone signals are sent by cellphones in running vehicles on urban roads. Secondly, a minimum spanning tree clustering method is proposed to calculate the number of commuters in each vehicle. Based on the proposed method, the various travel modes, including drive alone, carpooling, and bus, can be identified while their hourly traffic volume can be estimated. Finally, the predicted traffic volumes are compared with the actual values obtained from License Plate Recognition system. The experiment result shows that the proposed method can accurately estimate the hourly traffic volumes of different travel modes and the estimation errors of this method are within a reasonable range.