Stochastic Dynamic Networks Loading for Travel Time Variability Due to Incidents

Abstract

This chapter presents a new method that allows fast Monte Carlo simulation of incidents on a road network. The marginal incident computation (MIC) model presented in this chapter applies similar link and node models as the link transmission model (LTM), a multi-commodity dynamic network loading (DNL) model that combines realistic queue propagation and congestion spillback (consistent with first order kinematic wave theory) with high computational efficiency. The MIC algorithm determines the congestion effects caused by an incident in an approximate way, superimposing these effects onto a single base simulation run with the LTM or an other existing DNL model. The base cumulative vehicle number are altered, according to the congesting arising from the incident. Calculations are only carried out for the affected links, not for the entire network. A significant computation advantage is achieved compared to full explicit simulation, where identical traffic flows are recalculated for different Monte Carlo samples. In large networks, the computation can be reduced to less than 0.1 percent of explicit simulation.