Affiliation:
1. LX Spatial Information Research Institute, Korea Land and Geospatial Informatix Corporation, Jeonju 55365, Republic of Korea
2. Department of Convergence System Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
Abstract
This study explored how transportation accessibility and traffic volumes for automobiles, buses, and trucks are related. This study employed machine learning techniques, specifically the extreme gradient boosting decision tree model (XGB) and Shapley Values (SHAP), with national data sources in South Korea collected from the Korea Transport Institute, Statistics Korea, and National Spatial Data Infrastructure Portal. Several key findings of feature importance and plots in non-linear relationships are as follows: First, accessibility indicators exhibited around 5 to 10% of feature importance except for Mart (around 50%). Second, better accessibility to public transportation infrastructures, such as bus stops and transit stations, was associated with higher annual average daily traffic (AADT), particularly in metropolitan areas including Seoul and Busan. Third, access to large-scale markets may have unintended effects on traffic volumes for both vehicles and automobiles. Fourth, it was shown that lower rates of AADT were associated with higher accessibility to elementary schools for all three modes of transportation. This study contributes to (1) understanding complex relationships between the variables, (2) emphasizing the role of transportation accessibility in transportation plans and policies, and (3) offering relevant policy implications.
Funder
Ministry of Trade, Industry, and Energy
Subject
Pollution,Urban Studies,Waste Management and Disposal,Environmental Science (miscellaneous),Geography, Planning and Development