Modeling and Analysis of Excess Commuting with Trip Chains

TL;DR

This paper introduces a trip-chaining-based model to better understand excess commuting, revealing that traditional models underestimate actual and optimal commute times by accounting for multi-purpose stops.

Contribution

It develops a novel model incorporating trip-chaining behavior into excess commuting analysis, improving accuracy over traditional nonstop travel assumptions.

Findings

Traditional excess commuting underestimates actual commute times.

Trip-chaining increases mean minimum commute time by 70%.

Gaps vary across different trip-chaining types.

Abstract

Commuting, like other types of human travel, is complex in nature, such as trip-chaining behavior involving making stops of multiple purposes between two anchors. According to the 2001 National Household Travel Survey, about one half of weekday U.S. workers made a stop during their commute. In excess commuting studies that examine a region's overall commuting efficiency, commuting is, however, simplified as nonstop travel from homes to jobs. This research fills this gap by proposing a trip-chaining-based model to integrate trip-chaining behavior into excess commuting. Based on a case study of the Tampa Bay region of Florida, this research finds that traditional excess commuting studies underestimate both actual and optimal commute, while overestimate excess commuting. For chained commuting trips alone, for example, the mean minimum commute time is increased by 70 percent from 5.48 minutes to 9.32 minutes after trip-chaining is accounted for. The gaps are found to vary across trip-chaining types by a disaggregate analysis by types of chain activities. Hence, policymakers and planners are cautioned of omitting trip-chaining behavior in making urban transportation and land use policies. In addition, the proposed model can be adopted to study the efficiency of non-work travel.