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.
翻译:汽车共享是智能交通基础设施的支柱之一,因为它有望减少交通堵塞、停车需求和城市污染。从需求建模的角度来看,汽车共享是城市地貌的一个薄弱信号:只有一小部分人口使用汽车,因此难以用传统技术,如家庭旅行日记等可靠地研究汽车共享。在这项工作中,我们背离了这些传统方法,我们利用10个欧洲城市中10个主要活跃汽车共享运营商的车辆可用率的网络数字记录。我们讨论了哪些社会人口和城市活动指标与汽车共享需求的变化相关联,这种预测方法(在相关文献中最为流行的)更适合预测接车和落车事件,以及如何利用关于车辆可用性的空闲信息来推断城市用户使用不同区域的情况。我们通过直接应用数据集分析来完成文件,目的是确定汽车共享运营区内维护设施的位置。