Measuring Regret in Routing: Assessing the Impact of Increased App Usage

Abstract

This article is focused on measuring the impact of navigational apps on road traffic patterns. We first define the marginal regret, which characterizes the difference between the travel time experienced on the most optimal path and the path of interest between the same origin destination pair. We then introduce a new metric, the average marginal regret, which is the average of marginal regret, taken over all possible OD pairs in the network. We evaluate the average marginal regret in simulations with varying proportions of app and non-app users (information vs. no information) using the microsimulation software Aimsun. We conduct experiments on a benchmark network as well as a calibrated corridor model of the I–210 in Los Angeles for which OD demand data is gathered from several sensing sources as well as actual signal timing plans. In both cases (i.e. the benchmark and I–210) experiments demonstrate that the use of apps leads to a system-wide convergence towards Nash equilibrium.