Empirical analysis on long-distance peer-to-peer ridesharing service in Japan
Empirical analysis on long-distance peer-to-peer ridesharing service in Japan
复制标题
日本长途P2P拼车服务实证分析
DOI：
10.1080/15568318.2020.1785595
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发表时间：
2020
影响因子：
3.9
通讯作者：
Asakura Yasuo
中科院分区：
文献类型：
--
作者：
Nakanishi Wataru;Yamashita Yuki;Asakura Yasuo
随着个人之间共享商品和服务的社会和经济制度的兴起，拼车在西方国家引起了人们的关注。随着拼车服务的普及，在全球范围内逐渐收集到关于用户如何、何时、何地、为何使用这些服务的数据。但是，关于日本的实证研究却很少。此外，该国的拼车服务还不像西方国家那样受欢迎。因此，在本研究中，我们的目的是显示日本的拼车行为的现状。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，我们将共乘驱动器按OD对分为三类：城际，低密度区域和其他。接下来，我们制定了一个二项概率单位模型来解释每个驱动器类别的匹配成功。估计模型表明，出发时间和日期，从注册日期出发的天数，页面浏览量和司机的过去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类之间的相似性和差异。解释了每个驱动属性的敏感性，并通过使用估计的参数提出了促进这种拼车服务的方法。
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.
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