Providing Reliable Route Guidance using Chicago Data
Yu Marco Nie, Xing Wu, Peter C. Nelson, John F. Dillenburg · 2009
In this research, the problem of generating reliable route guidance is modeled as the reliable a priori shortest path problem (RASP). The RASP problem aims to find a priori paths that are shortest to ensure a specified probability of on-time arrival. Previous studies have given the mathematical formulation for the RASP problem, examined the analytical properties and designed various solution algorithms. The goal of this research is to resolve the relevant implementation and deployment issues in order to move the techniques one step further to practice, and potentially commercialization. To these ends, three key issues are identified and addressed. The first is acquiring necessary data to prepare inputs for the RASP. Most important of all are road travel time distribution data, which are not directly available from existing traffic data collection and archiving practice and therefore have to be constructed from raw data. Second, the benefits of reliable routing over the conventional routing modes are demonstrated using real data. Finally, through comprehensive numerical experiments, this report verifies the feasibility of existing solution techniques in generating reliable route guidance on very large regional networks.