Weak Signals in the Mobility Landscape: Car Sharing in Ten European Cities

TL;DR

This paper analyzes weak signals of car sharing demand in ten European cities using digital vehicle availability data, revealing sociodemographic and urban activity factors, forecasting methods, and spatial usage patterns to optimize operational decisions.

Contribution

It introduces a novel approach leveraging digital records to study car sharing demand and spatial usage, surpassing traditional survey-based methods.

Findings

Certain sociodemographic factors correlate with demand variations

Forecasting models differ in accuracy for pickup/drop-off prediction

Spatial analysis informs optimal maintenance facility locations

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 leverage web-based, digital records about vehicle availability in 10 European cities for one of the major active car sharing operators. We discuss which sociodemographic and urban activity indicators are associated with variations in car sharing demand, which forecasting approach (among the most popular in the related literature) is better suited to predict pickup and drop-off events, 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 operation area.