Multi-Modal Design of an Intelligent Transportation System

Abstract

This paper proposes a novel intelligent transportation system (ITS) using the cellular network, GPS probes, and limited ITS infrastructure for edge-level speed estimation under heterogeneous traffic condition. The erroneous vehicle position data taken from cellular network are processed in real time to compute edge level vehicle flow, space occupancy, and congestion with a mean error of less than 10%. For edge-level speed estimation, two models of ITS infrastructure deployment are proposed: the COngestion COverage MOdel (COCOMO) and the Edge COverage MOdel (ECOMO). The GPS Probes’ speed data are used to extrapolate speed estimations from an infrastructure edge to the associated infrastructureless edge(s). The infrastructure requirement of COCOMO is constant, whereas that of ECOMO depends upon diversity in the congestion profile of edges. The COCOMO and ECOMO permit edge-level speed estimation with the 90 percentile error of 10%–22% and 10%–13%, respectively. The communication and storage requirement of the proposed ITS and the utility of generated traffic information are analyzed.