Optimal Excess Commuting Evaluation Based on Local Minimal Costs

Excess commuting refers to the value of unnecessary commuting or distance costs. Traditional commuting distance models adapt the most efficient scenario with people working in the nearest workplace geographically. Even though there have been some attempts to include constraints with commuter attributes and neighborhood features, problems arise with traditional geographical space and the subjectivity of these predefined characteristics. In this paper, we propose a method to calculate theoretical local minimal costs, which considers preferences that are inherently behavioral based on current work–home trips in the process of reassigning the work–home configuration. Our method is based on a feature space with a higher dimension and with the enlargement of attributes and relations of and between commuters and neighborhoods. Additionally, our solution is arrived at innovatively by improved Fuzzy C-Means clustering and linear programming. Unlike traditional clustering algorithms, our improved method adapts entropy information and selects the initial parameters based on the actual data rather than on prior knowledge. Using the real origin–destination matrix, theoretical minimal costs are calculated within each cluster, referred to as local minimal costs, and the average sum of local minimal costs is our theoretical minimal cost. The difference between the expected minimal cost and the actual cost is the excess commuting. Using our method, experimental results show that only 13% of the daily commuting distance in Wuhan could be avoided, and the theoretical distance is approximately 1.06 km shorter than the actual commuting distance.

Understanding the Spatial Effects of Unaffordable Housing Using the Commuting Patterns of Workers in the New Zealand Integrated Data Infrastructure

Commuting behaviour has been intensively examined by geographers, urban planners, and transportation researchers, but little is known about how commuting behaviour is spatially linked with the job and housing markets in urban cities. New Zealand has been recognised as one of the countries having the most unaffordable housing over the past decade. A group of middle-class professionals called ‘key workers’, also known during the pandemic as ‘essential workers’, provide essential services for the community, but cannot afford to live near their workplaces due to a lack of affordable housing. As a result, these key workers incur significant sub-optimal commuting. Such job-housing imbalance has contributed to a so-called spatial mismatch problem. This study aims to visualise the excess commuting patterns of individual workers using the Integrated Data Infrastructure (IDI) from Statistics New Zealand. The visualisation suggests that over the last demi-decade, housing unaffordability has partially distorted the commuting patterns of key workers in Auckland. More of the working population, in particular those key workers, are displaced to the outer rings of the city. While there is an overall reduction in excess commuting across three groups of workers, key workers remain the working population with a disproportionate long excess commute.

Categorizing Urban Form for the Largest Metro Regions in the U.S. Using the Excessive Commuting Framework

The degree to which U.S. cities, metro regions, and general urbanized areas have distinct centres of economic activity has been a matter of debate for many decades. In the jobs–housing literature, there is related debate about whether having many distinct mixed-use centres in cities leads to longer or shorter commutes. The excess commuting framework has been increasingly refined and applied to assess urban areas' jobs–housing balance. The framework has expanded over the years but an issue in the present research is whether its various theoretical measurements and efficiency calculations might be used to assess the degree of poly- or mono-centricity of a region, thereby contributing to debates about what kind of urban form facilitates shorter commutes. In this paper, a suite of excess commuting (EC) measures are calculated for fifty-three of the largest Metropolitan Statistical Areas (MSAs) in the U.S. From there a hierarchical clustering approach is developed and applied to demonstrate which of these metrics are most useful in describing urban form. We examine how these metrics perform for particular built environments, which gives further insights into commuting and land use trends. Results of the research show how various urban forms have specific commuting outcomes: specifically, that polycentric urban forms have shorter average commute distances than sprawling ones. This should inform policy questions about the most effective land-use planning strategies to pursue in efforts to manage travel demand via built environment interventions.