An approach to use cooperative car data in dynamic OD matrix estimation

Abstract

Traffic management applications are supported by dynamic models whose input should be realistic real-time OD demand matrices in order to find efficient network state estimates and fore-cast their short-term evolution. OD matrices have been so far usually estimated from historic and/or real-time data collection and prior matrices. Some of those methods developed by the authors in pre-vious works provide realistic matrices to cope with day-to-day demand variability and real-time traffic conditions. Off-line time-sliced OD matrices estimation based on a simulation-optimization Bilevel-DUE approach has proved to provide appropriate initializations for on-line and real-time dynamic OD estimation methods based on specific versions of Kalman filtering whose input data requirements are traffic counts collected from traffic detection stations and other data supplied by ICT (Information and Communication Technologies) sensors, as for instance travel times between pairs of fixed ICT sensors . In this work, we present a review of the contributions to the on-line/off-line estimation of Dynamic OD matrices and we examine how new data provided by cooperative vehicles as the track-ing along trajectories giving travel times between intermediate points of OD trips can be incorporated into the Kalman filtering equations for on-line dynamic OD estimation. Cooperative vehicles can be considered as mobile sensors, generating data from any point of the network, the computational bur-den of the adapted Kalman approach to cope with tracking data is examined in depth in order to guar-antee the on-line applicability of the proposed approach for a mid-sized urban network.