Providing Reliable Route Guidance: A Case Study Using Chicago Data

Reliable route guidance can be generated from solving the reliable a priori shortest path problem, which finds paths that maximize the probability of arriving on time. This paper aims to demonstrate the usefulness and feasibility of such route guidance using a case study. A hybrid discretization approach is first developed to improve the efficiency in computing convolution integral, which is an important and time-consuming component of the reliable routing algorithm. Methods to construct link travel time distributions are discussed and implemented with the data from the case study. Particularly, the travel time distributions on arterial streets are estimated from linear regression models calibrated from freeway data. Numerical experiments demonstrate that optimal paths are substantially affected by the reliability requirement in rush hours, and that reliable route guidance could generate up to 10 20 % of travel time savings. The study also verifies that existing algorithms can solve large-scale problems with modest computational resources.