Univ. Lyon, Univ. Gustave Eiffel, ENTPE, LICIT, Lyon, France.
PLoS One. 2020 Sep 17;15(9):e0238143. doi: 10.1371/journal.pone.0238143. eCollection 2020.
Even if shared mobility services are encouraged by transportation policies, they remain underused and inefficient transportation modes because they struggle to find their customer base. This paper aims to estimate the potential demand for such services by focusing on individual trips and determining the number of passengers who perform similar trips. Contrary to existing papers, this study focuses on the demand without assuming any specific shared mobility system. The experiment performed on data coming from New York City conducts to cluster more than 85% of the trips. Consequently, shared mobility services such as ride-sharing can find their customer base and, at a long time, to a significantly reduce the number of cars flowing in the city. After a detailed analysis, commonalities in the clusters are identified: regular patterns from one day to the next exist in shared mobility demand. This regularity makes it possible to anticipate the potential shared mobility demand to help transportation suppliers to optimize their operations.
即使交通政策鼓励共享出行服务，但由于难以找到客户群，它们仍然是未被充分利用且效率低下的交通方式。本文旨在通过关注个人出行并确定执行类似出行的乘客数量来估算此类服务的潜在需求。与现有文献不同，本研究侧重于需求，而不假设任何特定的共享出行系统。在来自纽约市的数据上进行的实验表明，可以对超过 85%的出行进行聚类。因此，像拼车这样的共享出行服务可以找到其客户群，并在长期内显著减少城市中流动的汽车数量。经过详细分析，确定了聚类中的共性：共享出行需求存在从一天到下一天的规律模式。这种规律性使得预测潜在的共享出行需求成为可能，从而帮助交通供应商优化其运营。
