Disaggregate Journey-to-Work Data: Implications for Excess Commuting and Jobs–Housing Balance

Abstract

One suggested goal has been the reduction of the spatial separation between workplace and residence through patterns of concentrated multiuse developments. This strategy tends to promote jobs^housing balance, which consequently decreases excess commuting (Levinson, 1998;Scott et al, 1997). The degree of jobs^housing balance (or imbalance) is often measured simplistically as a ratio of jobs to workers (Bookout, 1990;Levine, 1998) and excess commuting is expressed as the difference between the observed average trip length and the theoretical minimum average length (Small and Song, 1992;White, 1988). Commuting is bound tightly with urban structure in the sense that excess commuting in the aggregate is caused by multiple factors, including a locational imbalance of jobs and housing. Household activity schedules and time budgets are critically important too, though such constraints are best tackled using microsimulation rather than the tools in this present paper. We are primarily concerned with the broad-scale analysis of the intrinsic amounts of interaction needed to match workers to places of employment, and we choose to disaggregate zonal data by worker type rather than according to the individual

Calculating minimum energy urban layouts with mathematical programming and Monte Carlo analysis techniques

“…This can be implemented via a heuristic approach for planning new developments (Scott and Getis, 1998) or, more recently, with optimization formulations that address how excess commuting might be reduced either by relocating jobs and workers (Horner and Murray, 2003) or by targetted "in-filling" with new development (Horner, 2008). Although much of this work is based on an aggregate approach, several studies have looked to disaggregate individuals and households within these calculations, for example to account for households with two working persons who need to live together but work in separate locations (Buliung and Kanaroglou, 2002;O'Kelly and Lee, 2005).…”

Is non-work travel excessive?

“…Many versions of the transportation problem that have appeared in the excess commuting literature are applied to very aggregate, zonal-level data, and the solutions to said models will fluctuate with the scale of input data (Horner and Murray, 2002;O'Kelly and Lee, 2005). While this level of analysis may be justifiable in some cases, it may obscure too much of the local detail in activity locations to be a viable modeling strategy for non-work travel.…”

“…Under the rubric of 'excess commuting', this topic has captured the attention of transport geographers, economists, regional scientists, urban planners, and other spatial scientists (e.g. Hamilton, 1982;White, 1988;Giuliano and Small, 1993;Merriman et al, 1995;Scott et al, 1997;Frost et al, 1998;Buliung and Kanaroglou, 2002;Horner, 2002;Rodriguez, 2004;O'Kelly and Lee, 2005;Horner, 2007). The excess commuting approach is characterized by comparison of an estimated theoretical minimum commute, or required amount of commuting for a given city, to that same city's observed level of commuting.…”

“…Most methodological and substantive developments in this area have been aimed principally at refining and enhancing the measurement of excess seven commuting (see Rodriguez, 2004 andLee, 2005 for recent reviews). However, one relatively unexplored idea in the literature thus far has been whether or not 'excess-commuting' and its underpinning notion of excess travel can be used to analyze trips made for purposes other than a journey-to-work.…”

Empirical Analysis of Spatial Mismatch of Living-Working: Based on a Field Survey in Downtown Beijing

“…A still-growing number of studies and publications concerning this subject has emerged since they published their works. Most of them regards spatial distribution of journey sources and destinations (Cropper & Gordon 1991;Giuliano & Small 1993;Kim 1995;Manning 2003;O'Kelly & Lee 2005;Niedzielski 2006;Charron 2007;Yang 2008;O'Kelly & Niedzielski 2009;Murphy 2009;Loo & Chow 2011;Boussauw et al 2011;Chowdhury et al 2013).…”