Reaching Cruising Altitude: Optimizing Airport Arrivals for the Last Mile

Abstract

In the United States, the Average Vehicle Occupancy (AVO) of a car is 1.55 people. This number is likely even lower when considering that many of these trips are rides in which the driver is merely the chauffeur and gains no utility from traveling to a destination, other than to drop off their passengers. Consequently, the stark realization is that average vehicle occupancy lies somewhere less than 1. Being both an incredible waste of energy and carbon footprint, a change must occur to remedy this poor performance. Autonomous vehicles offer an avenue to raise this low average, which in doing so, would declutter roadways, make commutes easier, and produce a net benefit to both the environment and public health. Through autonomous taxi (aTaxi) networks, companies can pool riders together that share similar destinations, and offer competitive prices to draw users and break the stigma of ridesharing. Thus, this thesis will attempt to model trips that an aTaxi network would service while maximizing the number of passengers per vehicle. Furthermore, the model will examine trips that originate at airports, and change their route such that passengers utilize ridesharing aTaxis to complete their trip rather than individual vehicles. Such a modification should provide an additional reduction of vehicles on the road, and presents an optimal case to study ridesharing, as clusters of people will be heading to and exiting from the airport at the same chronological period.