基于旅行矩阵分解的空间长途旅行结构研究

Hiromichi Yamaguchi, Mashu Shibata, Shoichiro Nakayama
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引用次数: 0

摘要

手机定位数据使我们能够获得准确的、时间上详细的长途旅行分布。然而,传统的长途旅行分布模型无法正常处理这些详细的时间信息。本研究提出了一种处理长途旅行分布时间细节信息的方法。考虑到这种方法,起点-目的地矩阵分解为两个变量(指标):目的地舒适度和旅行成本。它们可以被解释为在旅游目的地选择多项逻辑模型中处理的几个变量的复合指标。因为它们只从原点和目的地计算,所以我们可以详细讨论它们的时间变化。在本研究中,计算了目的地便利设施的时间变化和日本解说旅行的旅行成本,以确认该方法的价值。这些指标成功地描述了日本国内长途旅行的模式。这些量化指标有助于了解国家土地结构。它们可以作为决策的结果衡量标准。此外,这些指标解释了目的地选择模型的时间适用性。具体来说,目的地便利设施的结果有很大的季节变化。这说明目的地舒适度模型的参数(即目的地变量的系数)不具有季节稳定性。因此,在选择长途旅行的目的地时,必须考虑到这一点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Travel Matrix Decomposition for Understanding Spatial Long-Distance Travel Structure
Mobile phone location data enable us to obtain accurate and temporally detailed long-distance travel distribution. However, the traditional long-distance travel distribution model cannot normally handle this detailed temporal information. This study proposes an approach for handling temporally detailed information of long-distance travel distribution. Considering this approach, the origin-destination matrix decomposes into two variables (indicators): destination amenity and travel cost. They can be interpreted as composite indicators of several variables that are treated in the travel-destination choice multinomial logit model. Because they are calculated only from the origin destination, we can discuss their detailed temporal variations. In this study, time changes in destination amenities and travel costs of interprefectural travel in Japan are calculated to confirm the value of this approach. These indicators have succeeded in describing the pattern of domestic long-distance travel in Japan. These quantified indicators have facilitated the understanding of the national land structure. They are useful as outcome measures for policy-making. Moreover, these indicators explain the temporal applicability of the destination choice model. Specifically, the results of destination amenities have a large seasonal variation. This indicates that the parameters of the destination amenity model (i.e., the coefficients of the destination variables) are not seasonally stable. Therefore, this must be considered when dealing with destination choice for long-distance travel.
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CiteScore
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