Optimizing the number and locations of freeway roadside equipment units for travel time estimation in a connected vehicle environment

This article introduces a methodology for determining the optimal number and locations of roadside equipment (RSE) units for travel time estimation in vehicle-to-infrastructure and vehicle-to-vehicle communication environments. The developed approach is a novel technique for modeling RSE placement to optimize the number and positions of RSE units while minimizing the travel time estimation error rate. A non-dominated sorting genetic algorithm (NSGA-II) was used to optimize this multi-objective problem. A microsimulation model of the highway 401 network in Toronto, Canada, was used as a testbed to evaluate the proposed approach. The NSGA-II approach produces an optimal Pareto front that minimizes the number, and hence cost, of RSE units while maximizing travel time estimation accuracy. Points on the Pareto front are equally optimal, dominate over all other points in the cost-accuracy search space, and offer the option to optimize the trade-off between infrastructure cost and estimation accuracy. This empirical study illustrates the impact of RSE placement on travel time accuracy in a connected vehicle environment. The optimization results indicate that the actual locations of the RSE units have a greater influence on the quality of the estimates than the number of RSE units. Thus, the accuracy of travel time estimates depends primarily on the locations of the RSE units and less on the total RSE density. Expanding RSE deployment might improve the accuracy of estimation; however, the associated costs will simultaneously increase.