Strategic Route Planning to Manage Transit’s Susceptibility to Disease Transmission

Abstract

Transit agencies have experienced dramatic changes in service and ridership because of the COVID-19 pandemic. As communities transition to a new normal, strategic measures are needed to support continuing disease suppression efforts. This research provides actionable results to transit agencies in the form of improved transit routes. A multi-objective heuristic optimization framework employing the non-dominated sorting genetic algorithm II algorithm generates multiple route solutions that allow transit agencies to balance the utility of service to riders against the susceptibility of routes to enabling the spread of disease in a community. This research uses origin–destination data from a sample population to assess the utility of routes to potential riders, allows vehicle capacity constraints to be varied to support social distancing efforts, and evaluates the resulting transit encounter network produced from the simulated use of transit as a proxy for the susceptibility of a transit system to facilitating the transmission of disease among its riders. A case study of transit at Oregon State University is presented with multiple transit network solutions evaluated and the resulting encounter networks investigated. The improved transit network solution with the closest number of riders (1.2% more than baseline) provides a 10.7% reduction of encounter network edges.

The Optimal Route Selection Model of Fresh Agricultural Products Transportation Based on Bee Colony Algorithm

“…5) Judge whether the number of iterations reaches the maximum value [20][21]. If it is the maximum value, the optimal solution of the transportation path of fresh agricultural products will be output, that is, the distribution route of fresh agricultural products with the lowest distribution cost and the highest customer satisfaction will be obtained.…”