Joint Flow and Density Reconstruction in Large Traffic Networks Using Partial Turning Ratio Information

Abstract

We address the recent problem of state reconstruction in large scale traffic networks using heterogeneous sensor data. First, we deal with the conditions imposed on the number and location of fixed sensors such that all flows in the network can be uniquely reconstructed. We determine the minimum number of sensors needed to solve the problem given partial information of turning ratios, and then we propose a linear time algorithm for their allocation in a network. Using these results in addition to floating car data, we propose a method to reconstruct all traffic density and flow. Finally, the algorithms are tested in a simulated Manhattan-like network.