Evaluating ridesharing algorithms using the jargo real-time stochastic simulator

Abstract

Ridesharing algorithms operate in environments that are dynamic and uncertain due to traffic effects. Evaluating an algorithm by deploying it in a real environment is costly and often inaccessible, yet the traditional approach of using static inputs and applying an objective function on the outputs may give unrealistic results. Jargo is a novel real-time simulator that provides more realistic evaluation. It lets users implement their own algorithms, speed field functions, and evaluators, and then it reports on multiple quality metrics that are useful to service providers. To support any new and existing algorithm, simulate traffic, and compute the metrics, it is supported by a new relational model of ridesharing. Relations naturally express empirical concepts such as customer pick-up time, and their flexibility can allow any feasible routing strategy. Relational algebra is also convenient for defining operations on the system as well as formalizing service-related metrics. We will show how a service provider considering whether or not to deploy the well-known greedy insertion algorithm could use Jargo to uncover its limits and guide the development of new techniques.