REGRESSION-BASED URBAN ARTERIAL ROAD DELAY MODELS

During the last two decades, emphasis in transportation planning has shifted from long-term capital-intensive construction projects to short- and medium-term, relatively low capital cost, projects aimed at using existing transportation facilities more efficiently. This shift caused the integration of transportation system management (TSM) activities into the overall transportation planning process. Presently, the TSM endeavours are regarded as a prerequisite prior to initiating major capacity expansion type of capital works. Although the TSM initiatives are undertaken in response to congestion (delay), energy, environment, and safety concerns, reducing delay is a key measure of effectiveness. Despite the importance of this factor, there is a lack of efficient tools that are capable of estimating delay corresponding to TSM endeavours. Most, if not all, of the existing delay models have been developed for detailed operational analysis. These models are data-intensive and require highly specialized human and machine types of resources. In this research, multi-link streets and networks were synthesized and their performance in delay terms was estimated through the use of a microscopic simulation program, NETSIM. The output Of NETSIM, in conjunction with physical and traffic characteristics of networks, were used to develop the multiple regression type of macro-simulation models of delay. The regression models developed are capable of estimating vehicle delay for urban streets. Field data collected by using the videotape technology and maps were utilized to validate the models.