Abstract

Ridesharing has been attracting attention in Western countries in accordance with the rise of social and economic systems in which goods and services are shared between individuals. In accordance with the spread of ridesharing services, users’ data, which concern how, when, where, and why they use these services, are being gradually collected around the world. However, there are only a few studies that deal with the empirical situation in Japan. In addition, ridesharing services in the country are not yet as popular as those in Western countries. Therefore, in this study, we aim to show the present situation of the ridesharing behavior in Japan. We conduct an empirical analysis by using actual long-distance peer-to-peer ridesharing data in the country. Firstly, we classify ridesharing drives into three classes by OD pairs: Inter-metropolitan, Low-density area, and Others. Next, we formulate a binomial probit model that explains the matching success for each drive class. The estimated model shows that the departure time and day, days to departure from registration date, page views, and driver’s past experiences are important factors for successful matching. Moreover, the similarities and differences across the drive classes are discussed through the estimated model. The sensitivity of each drive attribute is explained, and ways of promoting this ridesharing service are suggested by using the estimated parameters.

摘要

随着商品和服务在个人之间共享的社会和经济体系的兴起，拼车在西方国家引起了人们的注意。随着拼车服务的普及，有关用户如何、何时、何地以及为何使用这些服务的数据正在全球范围内逐步收集。然而，只有少数研究涉及日本的经验情况。此外，该国的拼车服务尚未像西方国家那样受欢迎。因此，在本研究中，我们旨在展示日本拼车行为的现状。我们使用该国实际的长途点对点拼车数据进行实证分析。首先，我们通过 OD 对将拼车驱动分为三类：Inter-metropolitan，低密度区等。接下来，我们制定了一个二项式概率模型来解释每个驱动器类别的匹配成功。估计模型显示出发时间和日期、从注册日期到出发的天数、页面浏览量和驾驶员过去的经验是成功匹配的重要因素。此外，通过估计模型讨论了驱动器类别之间的异同。解释了每个驱动器属性的敏感性，并通过使用估计的参数建议了推广这种拼车服务的方法。和司机过去的经验是成功匹配的重要因素。此外，通过估计模型讨论了驱动器类别之间的异同。解释了每个驱动器属性的敏感性，并通过使用估计的参数建议了推广这种拼车服务的方法。和司机过去的经验是成功匹配的重要因素。此外，通过估计模型讨论了驱动器类别之间的异同。解释了每个驱动器属性的敏感性，并通过使用估计的参数建议了推广这种拼车服务的方法。