Commuting, which bridges residences and workplaces, is a fundamental trip that supports daily life and economic activities, while maintaining close ties to labor market accessibility, productivity, and social quality of life. The government has made continuous efforts to reduce commuting distances and times by alleviating the spatial imbalance between residential and employment facilities and expanding the housing supply for diverse social classes near job-dense areas.

The purpose of this study is to empirically analyze the impact of urban spatial development and transport infrastructure supply policies on commuting efficiency. To this end, we established commuting efficiency indicators―including average and external commute times, external commute ratios, and car usage rates for external commutes―at the level of cities (si), counties (gun), and districts (gu) (hereafter si-gun-gu). By constructing a staggered Difference-in-Differences (DiD) model that accounts for the varying implementation timelines of urban development and rail projects across different regions, this research analyzes the heterogeneity of commuting efficiency improvements according to the timing of development and supply.

Based on the staggered DiD model, which considers the interaction effects between rail and housing land supply, the results reveal that despite the steady expansion of physical infrastructure―such as urban development, transport expansion, and public transit improvements―over the past 20 years, the actual effects on improving commuting efficiency have been limited. The average commute time across nationwide si-gun-gu slightly increased from 23.02 minutes in 2000 to 24.18 minutes in 2020. Furthermore, the car usage rate for external commutes across these si-gun-gu regions rose significantly from 54.61% to 69.69%, and the external commuting ratio also saw a slight increase from 23.18% to 24.75% during the same period.

The empirical findings suggest that to enhance commuting efficiency in the future, a policy shift is required: moving away from the traditional focus on expanding transport and land supply toward Transportation Demand Management (TDM) and the refinement of urban spatial structures. Specifically, car demand management policies―such as congestion charging, adjusting parking fees to realistic levels, and expanding bus-only lanes―should be strengthened. Concurrently, public transit service quality, including fare systems, transfer convenience, accessibility, and reliability, must be improved to actively encourage transit use. Additionally, strategies to distribute transport demand through mixed-use urban developments that promote jobs-housing proximity are essential.

Furthermore, to improve commuting efficiency, policies should focus on creating polycentric urban structures to alleviate commute concentration in specific areas, establishing walkable and bikeable communities through mixed-use development, and strengthening jobs-housing proximity via Transit-Oriented Development (TOD). Moreover, "soft" policies such as telecommuting, flexible work hours, decentralized office spaces, and staggered commuting should be implemented in parallel to reduce the necessity of travel or mitigate peak-hour congestion.

Commuting efficiency cannot be improved solely by the supply of land and transport facilities. This is because commute times are influenced by a complex interplay of factors, including induced demand, jobs-housing imbalances, and individual mobility behaviors. Therefore, the policy focus must shift fundamentally toward improving spatial accessibility between housing and employment opportunities, prioritizing demand management and structural urban improvements over simple infrastructure expansion.

Abstract (ENG)
Preface
Summary (KOR)

Chapter 1 Introduction
　Section 1 Research Background and Purpose
　Section 2 Research Scope and Data

Chapter 2 Changes in Commuting Status
　Section 1 Average Commute Time
　Section 2 Car Usage Rate
　Section 3 Commuting Ratio

Chapter 3 Building a Sequential Difference-in-Differences Model
　Section 1 Outcome (Dependent) Variables
　Section 2 Control Variables
　Section 3 Building a Sequential Difference-in-Differences Model

Chapter 4 Model Estimation and Analysis Results
　Section 1 Commute Time
　Section 2 Automobile Modal Share for External Commutes
　Section 3 External Commuting Ratio

Chapter 5 Synthesis and Policy Implications

References
Appendix