Dynamic traffic demand prediction using conventional and emerging data sources

Abstract

Origin–destination (OD) flow estimation and prediction is an important problem with applications in Dynamic Traffic Management, and traffic estimation and prediction systems. Recent developments in traffic data collection technologies provide data that have not yet been used in OD estimation and prediction. In this paper, a new, flexible, and general methodology for OD estimation and prediction is presented. The methodology can incorporate any available information from conventional and emerging traffic data collection technologies (such as automatic vehicle identification systems and probe vehicles). The application of the methodology is presented through a case study. The results support the importance of incorporating additional data in the OD estimation problem. An overall improvement for estimation and one-step prediction exceeds 45% when point-to-point information is added to the model (over the base case when only point link flows are available), while an improvement of more than 35% is maintained even for four-step prediction (i.e. 1 h into the future).