Bayesian hierarchical models for the prediction of the driver flow and passenger waiting times in a stochastic carpooling service

Abstract

Carpooling is an integral component in smart carbon-neutral cities, in particular to facilitate home-work commuting. We study an innovative carpooling service which offers stochastic passenger-driver matching. Stochastic matching is when a passenger makes a carpooling request, and then waits for the first driver from a population of drivers who are already en route. Crucially a designated driver is not assigned as in a traditional carpooling service. For this new form of stochastic carpooling, we propose a two-stage Bayesian hierarchical model to predict the driver flow and the passenger waiting times. The first stage focuses on prediction of the aggregated daily driver flows, and the second stage processes these daily driver flow into hourly predictions of the passenger waiting times. We demonstrate, for an operational carpooling service, that the predictions from our Bayesian hierarchical model outperform the predictions from a frequentist model and a Bayesian non-hierarchical model. The inferences from our proposed model provide insights for the service operator in their evidence-based decision making.

Keywords:

• Hierarchical modelling
• Gamma regression
• GPS traces
• MCMC
• multi-level moving average

Acknowledgements

The authors thank Ecov for providing the data sets of the driver GPS traces and the passenger waiting times. The authors also thank Safa Fennia, Madeleine Zuber, Flavien Sindou and Constant Bridon from Ecov, and Gérard Biau from Sorbonne University for their feedback.

Disclosure statement

No potential conflict of interest was reported by the author(s).