Travel Matrix Decomposition for Understanding Spatial Long-Distance Travel Structure

Hiromichi Yamaguchi, Mashu Shibata, Shoichiro Nakayama and Hiroki Sayama

Complexity, 2023, vol. 2023, 1-15

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:1090277

DOI: 10.1155/2023/1090277

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