Aim and Introduction 
Commuting is a socio-economic phenomenon that arises from spatial imbalances between labor supply and demand across different locations. While some trips are recreational or incidental, a significant proportion occurs due to the inability of individuals to meet essential needs—such as employment—at their place of residence. In this context, commuting serves as a practical response to spatial mismatches. However, constraints in transportation infrastructure and increased demand for urban travel have made trip reduction an effective strategy for improving the performance of urban transportation systems. 
Since a considerable share of daily trips is generated by land patterns—particularly workplace locations—modifying commuting patterns by relocating workers closer to their places of employment can significantly reduce trip generation. This study assumes that all workers currently living in Isfahan but employed elsewhere relocate to reside in their respective places of work. As a result, transportation costs associated with commuting to and from Isfahan would be eliminated, thereby creating a negative shock to the city’s final demand. 
Conversely, the inflow and outflow of workers and their families would induce changes in local economic dynamics. Specifically, increased demand for housing would raise real estate rental prices, generating a positive shock in final demand. This research explores the economic consequences of such shifts through a regional input-output framework.
Methodology
To estimate interregional economic changes, this study employs a multi-regional input–output (MRIO) model. Given the availability of regional account data in Iran, regional tables were constructed using the Location Quotient (LQ) method. To address the common shortcomings of traditional LQ techniques—namely, the overestimation of regional coefficients and underestimation of imports—the Flag method was adopted. This approach incorporates three economic dimensions and addresses spatial factors, improving the accuracy of regional estimates.
A key challenge in compiling MRIO tables is obtaining reliable interregional trade data to calculate import and export coefficients. To this end, the gravity model—based on Newton’s law of gravitation—was utilized to estimate economic flows. The model correlates the volume of interregional trade with the economic size of the origin and destination and inversely with the distance between them. Thus, this study combines the LQ and gravity methods to model economic interactions among three regions in Iran: (1) Isfahan city, (2) other cities within Isfahan province, and (3) other provinces nationwide. Data sources include the national input-output table (1395) from the Central Bank of Iran and regional accounts provided by the Statistical Center of Iran.
Results and Discussion 
Findings indicate that the reduction in transportation costs within Isfahan city leads to a decline in production across all three regions, with the most pronounced effects observed in the industrial production and wholesale/retail sectors. Conversely, rising real estate rental costs initially stimulate employment growth in the construction, financial, insurance, industrial, and transportation sectors.
The simultaneous impact of reduced commuting costs and increased housing expenses results in a net rise in employment in Isfahan’s construction and real estate sectors. Similar employment gains are observed in the real estate, construction, and financial sectors in other cities within Isfahan province. In other provinces, the positive effects extend to the real estate, construction, financial, insurance, and water and sewage sectors. However, most other economic sectors across all regions experience a decline in employment. 
Conclusion 
This study underscores the complex economic implications of altering commuting patterns. Future research should explore the broader effects of these shocks on variables such as energy savings, reduced fossil fuel consumption, decreased air pollution and greenhouse gas emissions, fewer traffic accidents, lower healthcare costs, and less congestion—especially during peak commuting hours. Additionally, reduced commuting times can increase employees’ available time, some of which could be allocated to productive activities, warranting supply-side investigations. Furthermore, lower transportation costs may function as increased household income, potentially influencing household consumption patterns—an area that merits further exploration in subsequent studies.