Reflecting economic activity in traffic volume estimation: An analysis using the gravity model

19 Pages Posted: 23 Apr 2020 Last revised: 31 Aug 2020

See all articles by Changgi Lee

Changgi Lee

Korea Advanced Institute of Science and Technology (KAIST)

Hyungsoo Woo

Korea Advanced Institute of Science and Technology (KAIST)

Jae-Suk Yang

Korea Advanced Institute of Science and Technology (KAIST)

Date Written: August 31, 2020

Abstract

The gravity model is widely used to estimate the movement of people and goods. We use the gravity model to explain the characteristics of the movement of Korean people between and within cities via various modes of transportation using data from 246 county/district-level municipalities. We use a PPML gravity model to run regression analyses of traffic at three spatial levels: the national level (all of Korea), metropolitan level (in the Seoul and Busan Metropolitan Transportation Areas (MTAs)), and city-level (in Seoul and Busan). For these geographical areas, we use two variables to represent gravitational mass in the model – regional population and regional GDP per capita (RGDPPC). Introducing regional GDP per capita improves the model fit over all the spatial ranges, but the greatest improvement is at the city level. While the exponent parameter for distance diminishes as spatial ranges decrease, the parameters for population and RGDPPC and their relative ratio remain fairly constant. More traffic is associated with both higher population and higher levels of economic activity, represented by RGDPPC. These parameters can be understood in the model as corresponding to the number of particles and specific density of masses in Newton’s gravity law. Based on this result, we suggest the concept of economic activity-adjusted population.

Keywords: Gravity model,Traffic volume,Transportation, mobility, PPML, GDP, RGDPPC

Suggested Citation

Lee, Changgi and Woo, Hyungsoo and Yang, Jae-Suk, Reflecting economic activity in traffic volume estimation: An analysis using the gravity model (August 31, 2020). Available at SSRN: https://ssrn.com/abstract=3563913 or http://dx.doi.org/10.2139/ssrn.3563913

Changgi Lee

Korea Advanced Institute of Science and Technology (KAIST) ( email )

373-1 Kusong-dong
Yuson-gu
Taejon 305-701, 130-722
Korea, Republic of (South Korea)

Hyungsoo Woo

Korea Advanced Institute of Science and Technology (KAIST) ( email )

373-1 Kusong-dong
Yuson-gu
Taejon 305-701, 130-722
Korea, Republic of (South Korea)

Jae-Suk Yang (Contact Author)

Korea Advanced Institute of Science and Technology (KAIST) ( email )

291 Daehak-ro
Yuseong-gu
Daejeon, 34141
Korea, Republic of (South Korea)

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