Decomposing Excess Commuting: A Monte Carlo Simulation Approach

TL;DR

This paper introduces a Monte Carlo simulation method to more accurately measure excess commuting by accounting for reporting errors and zonal data aggregation biases, improving upon traditional survey-based approaches.

Contribution

It develops a novel Monte Carlo approach to estimate optimal and actual commutes at the individual level, reducing biases from survey and aggregate data limitations.

Findings

Reporting errors significantly affect excess commuting estimates.

Zonal aggregation introduces bias in optimal commute calculations.

Monte Carlo simulation improves accuracy of excess commuting measurement.

Abstract

Excess or wasteful commuting is measured as the proportion of actual commute that is over minimum (optimal) commute when assuming that people could freely swap their homes and jobs in a city. Studies usually rely on survey data to define actual commute, and measure the optimal commute at an aggregate zonal level by linear programming (LP). Travel time from a survey could include reporting errors and respondents might not be representative of the areas they reside; and the derived optimal commute at an aggregate areal level is also subject to the zonal effect. Both may bias the estimate of excess commuting. Based on the 2006-2010 Census for Transportation Planning Package (CTPP) data in Baton Rouge, Louisiana, this research uses a Monte Carlo approach to simulate individual resident workers and individual jobs within census tracts, estimate commute distance and time from journey-to-work trips, and define the optimal commute based on simulated individual locations. Findings indicate that both reporting errors and the use of aggregate zonal data contribute to miscalculation of excess commuting.