Regional Network Complexity Reduction for Estimating Diversion Effects from Large-Scale Work Zones – Motivation and Lessons Learned

The U.S. Interstate highway system is vital to the economic prosperity of the country and requires regular upkeep and maintenance. Such construction projects require detailed planning and analysis, supported by various modeling tools. Regional network modeling helps transportation agencies in estimating diversions caused by work zones and identifying key alternate routes to mitigate congestion. Dynamic traffic assignment (DTA)-based mesoscopic models have been used to estimate effects of large scale work zones in an urban network. However, highly detailed regional network models fail to model driver route choice accurately, since they divert traffic through local roads, contradicting reality. In this paper, a procedure is developed to reduce the complexity and areal extent of a large regional network, using macroscopic modeling to simulate effects of a long-term work zone. The reduced network, after removal of lower category roads, is then simulated in a DTA-based mesoscopic model, to estimate driver route choice and diversion rates. The methodology is also explained with an illustrative case study of an on-going large scale work zone close to a metropolitan area. Results from the macroscopic model helped in identification of key alternate routes and found 20% more diversions within a 2 mile radius of the work zone. In the reduced mesoscopic model, almost 10% more traffic was diverted through freeways than in the detailed network model. However, validation of this model showed that the removal of some important local roads led to problems in convergence of the model and enforces the need for traffic counts on arterial streets for calibration.