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Autonomous Mobility On Demand (MOD) systems can utilize fleet managementstrategies in order to provide a high customer quality of service (QoS).Previous works on autonomous MOD systems have developed methods for rebalancingsingle capacity vehicles, where QoS is maintained through large fleet sizing.This work focuses on MOD systems utilizing a small number of vehicles, such asthose found on a campus, where additional vehicles cannot be introduced asdemand for rides increases. A predictive positioning method is presented forimproving customer QoS by identifying key locations to position the fleet inorder to minimize expected customer wait time. Ridesharing is introduced as ameans for improving customer QoS as arrival rates increase. However, withridesharing perceived QoS is dependent on an often unknown customer preference.To address this challenge, a customer ratings model, which learns customerpreference from a 5-star rating, is developed and incorporated directly into aridesharing algorithm. The predictive positioning and ridesharing methods areapplied to simulation of a real-world campus MOD system.A combined predictivepositioning and ridesharing approach is shown to reduce customer service timesby up to 29% and the customer ratings model is shown to provide the bestoverall MOD fleet management performance over a range of customer preferences.

Predictive Positioning and Quality Of Service Ridesharing for Campus Mobility On Demand Systems