Exploring patterns of demand in bike sharing systems via replicated point process models

TL;DR

This paper models bike sharing demand as a multivariate temporal point process, enabling nonparametric intensity estimation and revealing usage patterns through station correlation analysis.

Contribution

It introduces a replicated point process framework for analyzing bike demand, allowing for nonparametric estimation and clustering of stations based on usage patterns.

Findings

Demand can be modeled as a multivariate temporal point process.

Nonparametric estimation of intensity functions is feasible even for low-count stations.

Station correlations reveal distinct bike usage patterns.

Abstract

Understanding patterns of demand is fundamental for fleet management of bike sharing systems. In this paper we analyze data from the Divvy system of the city of Chicago. We show that the demand of bicycles can be modeled as a multivariate temporal point process, with each dimension corresponding to a bike station in the network. The availability of daily replications of the process allows nonparametric estimation of the intensity functions, even for stations with low daily counts, and straightforward estimation of pairwise correlations between stations. These correlations are then used for clustering, revealing different patterns of bike usage.