What Counts as Commute Travel? Identification and Resolution of Key Issues around Measuring Complex Commutes in the National Household Travel Survey

We use travel diary data from the 2017 National Household Travel Survey (NHTS) Georgia subsample to address critical issues associated with analyzing complex work journeys. To define the work journey, we discuss the importance of defining commute anchors by both purpose and location. We then compare two alternate measures for determining what portion of each journey should be counted as commute distance: the last leg of the journey (the NHTS default), and a modeled counterfactual simple commute to estimate the distance that would have been traveled had no stops been made. The average complex commute distance obtained using the counterfactual method was 63% higher than the estimate based on using the last leg alone. Using the last-leg method may understate Georgia’s annual commute distance by 2.6 billion miles (10% of the total, including both simple and complex commutes). We argue that the last-leg method is not an accurate gauge of work travel, particularly among populations such as women, who are more likely to trip chain on their commutes.

Overlapping community detection in networks based on link partitioning and partitioning around medoids

In this paper, we present a new method for detecting overlapping communities in networks with a predefined number of clusters called LPAM (Link Partitioning Around Medoids). The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph employing link partitioning and partitioning around medoids which are done through the use of a distance function defined on the set of nodes. We consider both the commute distance and amplified commute distance as distance functions. The performance of the LPAM method is evaluated with computational experiments on real life instances, as well as synthetic network benchmarks. For small and medium-size networks, the exact solution was found, while for large networks we found solutions with a heuristic version of the LPAM method.

Urban Commute Travel Distances in Tehran, Istanbul, and Cairo: Weighted Least Square Models

A very large part of the literature on urban commute travels is related to high-income countries. The determinants of urban commute trip distances are not clear in the Middle East and North Africa (MENA); thus, this study attempts to shed light on this topic in relation to Tehran, Istanbul, and Cairo. The objective is to clarify which environmental and human factors are correlated with commuting distance in these cities. Using primary disaggregate data produced by surveys in the three cities (n = 8237) in 2017, weighted least square regressions showed that fifteen significant or highly significant variables, including individual and household characteristics, mobility decisions, residential location, and land use attributes, predict the lengths of urban commute trips in the MENA sample. Unlike western countries, age and gender are not significant predictors of commute distance in MENA large cities. The results of independent-sample Kruskal–Wallis test show that there is a significant difference between the mean one-way commute travel distances in the three cities (Tehran: 9096 m, Istanbul: 10839 m, and Cairo: 6670 m); however, there are some similarities in the determinants of commute distance in the three cities. The results can be adopted to reduce commute trip lengths by providing a more connected street network and accessible neighborhood-level facilities.

Low-Income Workers, Residential Location, and the Changing Commute in the United States

Numerous reports suggest that rising rents in some U.S. metropolitan areas are pushing workers to live further from their workplaces over time and contributing to lengthening commutes. Drawing on data from the last three vintages of the U.S. National Household Travel Survey (2001, 2009, and 2017), we test whether the relationship between residential location and commute distance varies significantly between low-income and higher-income workers and has changed over time. The data show that commute distances have increased for both low- and higher-income workers with much of the increase occurring in lower density areas. Statistical models show a strong positive relationship between living in a low-density neighbourhood and commute distance for all workers. This relationship appears stronger for low-income than higher-income workers but the strength of the relationship has not increased over time. The findings suggest that the growth in commute distance among low-income workers is largely due to a shift in their residential location towards low-density neighbourhoods.

Role of Spatial Configuration in Commuting Trade-Offs of Two-Worker Households: A Case Study in the Greater Toronto Area

This research investigates the commuting trade-offs between individuals in two-worker households with home and work locations in the Greater Toronto Area (GTA). A commuting trade-off occurs when a home relocation results in one worker incurring a higher commute distance for the other worker to work closer to the home location. This research uses stated preference data and multilevel modeling to demonstrate that two-worker households adjust their home–work spatial configuration which results in commuting trade-offs between individuals. This research uses the angle between the two workplaces, measured at the home location, as a variable in its empirical model. This variable is a descriptor of the home–work spatial configuration and a predictor of total household commute distance. The modeling results indicate an inverse relationship between total household commute distance and the difference between individual commutes. This suggests that individuals in two-worker households trade off their individual commute distances and, in that process, reduce total household commute distance. A key policy implication arising from this research relates to the jobs–housing balance within a catchment area. Two-worker households have been regarded as a hindrance to achieving jobs–housing balance as the two work locations present a constraint in relocating the home near both work locations. However, as this research shows, workers will trade off their individual commute distances such that a home relocation results in a shorter commute distance for one worker and longer commute distance for the other.

Integrating road carrying capacity and traffic congestion into the excess commuting framework: The case of Los Angeles

The excess commuting framework has advanced a series of metrics through which a city or a region’s jobs-housing balance and commuting efficiency can be measured. This study seeks to add to the conceptual development and extension of the excess commuting framework. Specifically, it considers the carrying capacity (of links) and related congestion issues in the excess commuting framework and demonstrates that overlooking these characteristics has important implications for excess commuting metrics. Drawing on an empirical case study, it shows that when carrying capacity and traffic congestion are accounted for, the observed commute is longer than otherwise. Excess commuting tends to be higher than its counterparts in previous excess commuting studies. The findings suggest that future excess commuting studies should take account of carrying capacity and congestion in determining excess commuting metrics. Moreover, high-quality connections (preferably via public transport) between jobs and housing allied with sufficient carrying capacity of popular links/routes for commuters are crucial preconditions for cities and regions to harvest the full benefits of jobs-housing balance policies targeted at the reduction of the average commute distance and vehicle miles travelled.

Factors affecting smart working: evidence from Australia

Purpose The purpose of this paper is to analyse data from the New South Wales Government’s Pilot Programme of establishing Smart Work Hubs (SWHs) for enabling teleworking in two busy commuter corridors. The paper analyses the relationships between various firm, job and personal factors and the perceived value, attitudes and expected usage by users of the SWHs. Design/methodology/approach Employing a cross-sectional survey design, the characteristics, values and attitudes of 117 SWH users were analysed using partial least squares (PLS) method of structural equation modelling (SEM). SEM-PLS approach is considered appropriate especially in prediction-based studies and to estimate an endogenous target construct. Findings Results revealed that perceived SWH value significantly influenced attitude towards the SWH, which then had a significant influence on SWH usage intentions, with personal, job and firm factors also playing a role. Further analysis revealed four variables that significantly influenced the perception of family-value benefits (age, income, hub commute distance, work commute distance), however, there were none that significantly influenced the perception of work benefits. Research limitations/implications The small sample size limits statistical inferences and generalisations to be drawn. Further, this paper also discusses how the low and uneven uptake of teleworking at a SWH raises several managerial and policy implications needing attention. Originality/value To the best of the authors’ knowledge, this is the first empirical study analysing the expected values, attitudes and usage intentions of teleworkers in a SWH context. This study adds to the emerging body of human resource management studies on an outward-looking approach. The novel context will provide a useful base for subsequent studies.