Car sharing through the data analysis lens

TL;DR

This paper analyzes digital vehicle availability data from European car sharing services to understand usage patterns, predict events, and optimize maintenance facility locations, offering insights beyond traditional demand modeling methods.

Contribution

It introduces a novel data-driven approach using web-based records to study car sharing demand and spatial usage, overcoming limitations of traditional travel diaries.

Findings

Vehicle usage patterns vary across zones and times.

Event predictability depends on spatial-temporal factors.

Optimal maintenance locations can be identified from usage data.

Abstract

Car sharing is one the pillars of a smart transportation infrastructure, as it is expected to reduce traffic congestion, parking demands and pollution in our cities. From the point of view of demand modelling, car sharing is a weak signal in the city landscape: only a small percentage of the population uses it, and thus it is difficult to study reliably with traditional techniques such as households travel diaries. In this work, we depart from these traditional approaches and we rely on web-based, digital records about vehicle availability in 10 European cities for one of the major active car sharing operators. We discuss how vehicles are used, what are the main characteristics of car sharing trips, whether events happening in certain areas are predictable or not, and how the spatio-temporal information about vehicle availability can be used to infer how different zones in a city are used by customers. We conclude the paper by presenting a direct application of the analysis of the dataset, aimed at identifying where to locate maintenance facilities within the car sharing operational area.