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.

本论文研究基于 Web 的数字记录，分析了 10 个欧洲城市中一项主要积极的汽车共享运营商的车辆可用性，并确定了与车辆共享需求变化相关的社会人口和城市活动指标。同时分析了如何预测取车和还车事件，以及如何利用关于车辆可用性的时空信息推断城市的不同区域如何被顾客使用。最后给出了一个直接应用分析数据集的实例，旨在确定车辆共享运营区内维护设施的位置。

移动领域中的弱信号：十个欧洲城市的汽车共享

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

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.

本文研究了欧洲 10 个城市中一家主要租车公司的车辆可用性相关的网络数字记录，以分析及推测车共用旅行和不同区域的客户用途以确定维修设施的位置。