A business class for autonomous mobility-on-demand: Optimization- and learning-based strategies to model service quality contracts in ridesharing systems
With the popularization of transportation network companies (TNCs) (e.g., Uber, Lyft) and the rise of autonomous vehicles (AVs), even major car manufacturers are increasingly considering themselves as autonomous mobility-on-demand (AMoD) providers rather than individual vehicle sellers. However, matching the convenience of owning a vehicle requires providing consistent service quality, taking into account individual expectations. Typically, different classes of users have different service quality expectations, especially in terms of reliability and responsiveness. Nonetheless, planning models presented in the AMoD literature do not enable active control of service quality, sometimes allowing extensive delays and user rejections. In this study, we propose both learning- and optimization-based strategies to actively control service quality in AMoD systems, increasing and decreasing the number of used vehicles in the short term to meet diversified user expectations. We have used these expectations to establish service quality contracts, allowing heterogeneous users to choose ride experiences that best match their preferences. Based on an experimental study using New York City taxi data, we show how providers can adequately cater to each segment of the customer base without necessarily owning large fleets through a service-quality-oriented on-demand hiring approach.
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