Commute Distance Research Articles

The determinants of commute mode usage frequency of post-secondary students in the Greater Toronto and Hamilton Area

An important aspect of post-secondary student travel behaviour is their commute mode usage frequencies. How frequently students use different transportation modes for their daily travel directly characterizes their habits, routines, and predispositions, which can ultimately affect long-term social welfare of the region, congestion of the transportation network, and fuel consumption. Thus, obtaining an in-depth and unbiased understanding of the various factors influencing this travel behavior is key to the sustainable transportation development of a region. Existing studies on this topic are not comprehensive enough in terms of the types of commute modes analyzed and most of them relied on relatively small samples for their investigation. This paper attempts to address the gap by modelling the influence of personal and household socio-demographic attributes, the built environment, commute characteristics, and personal attitudes of the students on the monthly usage frequency of five different types of commute modes (auto, transit, ride-hailing, bicycle, and walk). It uses data from a large-scale student travel survey conducted among 10 post-secondary institutions in the Greater Toronto and Hamilton Area. The study makes use of a sophisticated modelling approach, consisting of a multiple indicators multiple causes model and a zero-inflated ordered probit model to analyze the factors affecting the decision to use and the usage frequency of the exhaustive set of commute modes. The findings emphasize the importance of commute distance, available resources (in terms of mobility tools, living situation, household vehicles, and income), and built environment attributes in the usage frequency of different commute modes. Marginal effects are used to inform actionable policy recommendations for both the institutions and the regional municipalities. The recommendations include offering discounted and promotional transit passes to encourage students to use public transit frequently, increasing the capacity of student housing and enhancing the sidewalk and bicycle infrastructure within 3–5 km of the campus locations, and increasing the transit accessibility of the institutions by establishing subway stops in proximity to the campuses. These recommendations, when implemented, will help to adequately meet the travel needs of the students while also improving their overall campus life experience.

Age and domestic migration effects on workers' commuting distance.

The online version contains supplementary material available at 10.1007/s11116-022-10341-5.

The future of telecommuting post COVID-19 pandemic

COVID-19 caused unprecedented changes in the daily lives of many people worldwide, with many working from home for the first time. This shift in working arrangement has the potential to have a lasting impact in future. This paper investigates longer-term impacts of COVID-19 on work-arrangements, specifically, individuals’ preferences towards work-from-home post COVID-19. This study utilizes data from a stated preference component of a travel survey conducted in the Central Okanagan region of British Columbia. A random parameter ordered logit model is developed to accommodate the ordinal nature of the preference variable and capture unobserved heterogeneity. One of the key features of the study is to confirm the effects of residential choice in-terms of location characteristics and dwelling attributes on work-from-home preferences after the pandemic. For example, individuals’ dwelling attributes such as larger sized dwelling, larger sized apartments are likely to have positive effect on frequent work-from-home. The model confirms significant heterogeneity, in relation to location characteristics such as commute distance and distance to urban center. For instance, initially, females were less likely to work-from-home. However, they showed significant heterogeneity with large standard deviation, specifically their preference was found to vary by residential location. For instance, females residing farther from urban centers prefer a higher frequency of work-from-home. Elasticity analysis suggests that part-time female workers, mid-age individuals, full-time workers with children, and full-time workers with longer commutes have a significantly higher probability to work-from-home every day after the pandemic. The findings of the study provide important insights which will assist in developing effective work-from-home strategies post-the-pandemic.

Employment deconcentration and spatial dispersion in metropolitan areas: Consequences for commuting patterns

There is interest in understanding which characteristics of metropolitan areas impact the length of time or distance residents spend commuting. We utilize two measures recently introduced to the urban literature capturing distinct dimensions of employment decentralization –the level of employment deconcentration and employment spatial dispersion in metropolitan areas – to assess how they are related to commuting patterns across metropolitan areas. These two measures of urban/metropolitan spatial structure avoid challenges in identifying “job centers” and allow for a more systematic investigation of how employment decentralization affects commuting patterns. Furthermore, we detect key differences for the implications of these measures for commuting across 329 US metropolitan regions based on their population size. We find that greater employment deconcentration in very small MSAs is associated with longer commute times and distances, whereas greater employment deconcentration in large or very large MSAs is associated with shorter commutes. And whereas spatial dispersion is not related to commute times in very small MSAs, greater spatial dispersion is associated with longer commutes in very large MSAs. This study also shows that the spatial pattern of employment in regions, captured by these new measures, is associated with the proportion of very short and very long duration commutes.

Housing affordability and commute distance

ABSTRACT The growing affordable housing crisis in high-cost metropolitan areas may force households to seek lower cost housing in the outer reaches of metropolitan areas contributing to the recent increase in commute distance. To explore this assertion, we test the relationship between the availability of affordable housing relative to jobs and commute distance in two diverse metropolitan statistical areas in Southern California: Los Angeles-Orange (higher cost, coastal, older, more urban) and Riverside-San Bernardino (lower cost, inland, newer, more suburban). A worse “fit” between the number of low-wage jobs and affordable housing rentals is associated with longer commute distances in LA-Orange, but is not statistically significant in Riverside-San Bernardino. This study’s findings highlight the differences in housing dynamics and commute distances between higher cost coastal regions and lower cost inland regions—and underscore the importance of protecting and expanding the supply of affordable housing in job-rich neighborhoods located in more expensive, coastal cities.

Impact analysis of residential relocation on ownership, usage, and carbon-dioxide emissions of private cars

Even though the effects of residential relocation on travel behaviours have been explored worldwide, few studies discuss the impact of relocation on carbon dioxide emissions from travel. This study utilises the Fifth Travel Survey of Beijing Inhabitants and land-use data to build a structural equation model with latent variables that explore the effects of relocating direction, changes in built environments, commute distance, and household socio-economic attributes on changes in household car carbon dioxide emissions before and after moving. It is concluded that the change in commuting distance, private vehicle purchases, family size, electric bicycles, and public transit smart cards have direct effects on the change in carbon emissions, while the relocating direction, built environment latent variables, home size, and the number of adolescents in the family are found to have indirect effects. In addition, recommendations for reducing carbon emissions from private car travel in response to urbanisation are proposed. This study can help urban planners and managers understand the impact of residential relocation on vehicle ownership, usage, and the corresponding carbon dioxide emissions, and can also provide a reference to formulate planning and management policies on sustainable city and green transportation.

Rational planning strategies of urban structure, metro, and car use for reducing transport carbon dioxide emissions in developing cities

Understanding transport carbon dioxide (CO2) emission impact factors’ effects is important for the rational planning strategy making in reducing the emissions. This study determines transport emission impact factors’ heterogeneous effects and proposes urban and transport planning strategies in typical developing cities. Quantile regression is applied to overcome the insufficiency of factors’ mean effects and to avoid the biased estimations when the outcome variable is non-normally distributed and heteroscedastic. It is found that, from the low emitters at the 10th quantile to the high emitters at the 90th quantile, transport emissions’ increasing rates are 8.8 times and 79.6 times that of car availability and home-to-center/subcenter distance (HCD/HSD), respectively. When commute distance reaches 5.8 km or farther, and car availability percentage is 41.2% or greater, the effects that metro services have on reducing emissions decrease by 37.8%. Polycentric and satellite city forms can greatly reduce emission increases, which are caused by HCD growth when HCD is more than 10–15 km. According to these findings, the following planning strategies are recommended, including limiting oil-fueled car use to about 40% among the urban residents, forming employment and life circles within a 5–6 km radius, allocating better public transit services around metro stations, providing high service levels of bicycle lanes, pedestrian streets, and greenways to attract more transfers to metros, controlling urban radius within 10–15 km under the monocentric pattern, and fostering polycentric structures and satellite cities when city continuously sprawls. This study can provide empirical evidence and reference value globally.

Impact of autonomous vehicles on household relocation: An agent-based simulation

The agent-based land use/transport model SILO/MITO/MATSim is adapted to simulate the impact of AVs on household relocation. The revised model accounts for the fact that households who own conventional cars are sensitive to parking availability at their dwelling. As AVs could park themselves anywhere, this sensitivity to parking is reduced for households who own AVs. Distance to work, which serves as a hard constraint for household relocation with conventional cars, becomes less critical for households who use an AV to commute as they may perform other activities while commuting. The induced demand of travel by AV is represented and leads to increased congestion.Several scenarios were designed to analyze the effects of reduced value of time for AV travel, parking restrictions and increase of congestion. The simulation shows that AVs will compete with transit and reduce transit ridership by three quarters. The average commute distance is expected to double, and the vehicle-kilometers traveled will increase by one third. The impact of AVs on the distribution of population, however, is marginal. The urban sprawl caused by less burdensome commuting is largely compensated by the increased attractiveness of core cities in the absence of parking issues for AVs.

The Determinants of Walking Behavior before and during COVID-19 in Middle-East and North Africa: Evidence from Tabriz, Iran

To support the global strategy to raise public health through walking among adults, we added the evidence on predictors of walking behavior in the Middle East and North Africa (MENA) region by emphasizing the mediator—COVID-19. During the COVID-19 outbreak, public restrictions to encompass the spread of the disease have disrupted normal daily lifestyles, including physical activity and sedentary behavior. It was proposed that tremendous changes have occurred on predictors of physical activity in general and walking behavior in particular for three types of walking, including commute, non-commute, and social walking compared to pre-COVID-19 time. This study aimed to identify the determinants of the walking types mentioned above, including subjective and objective variables before COVID-19, and compare them during the COVID-19 period in a sample from Iran, which has not yet been addressed in previous research. Adults (N = 603) finalized an online survey between June 5 and July 15, 2021. This group reported their individual/socioeconomic locations (e.g., home/work) and perception features before and during COVID-19. The paper developed six Binary Logistic (BL) regression models, with two models for each walking type (commute, non-commute, and social walking). For commute trips before COVID-19, the findings showed that factors including BMI, residential duration, p. (perceived) neighborhood type, p. distance to public transport stations and job/university places, p. sidewalks quality, p. facilities attractiveness, p. existence of shortcut routes, commute distance, building density and distance to public transport were correlated with commute walking. At the same time, such associations were not observed for BMI, p. distance to public transport and job/university places, p. facilities attractiveness, building density, and distance to public transport during COVID-19. The variables include age, possession of a driving license, number of family members, p. neighborhood type, p. distance to grocery, restaurant, parking, and mall, p. existence of sidewalks, land-use mix, and distance to public transport indicated correlations with non-commute before COVID-19. However, p. distance to groceries and malls and the p. existence of sidewalks did not correlate with non-commute walking during COVID-19. Ultimately for social walking, age and income variables, and the considerable proportions of subjective variables (e.g., p. distance to services/land-uses, security, etc.), health status and building density were correlated with social walking before COVID-19. Nevertheless, most of the mentioned variables did not explicitly correlate with social walking during COVID-19. As for the implication of our study, apparently, special actions will be needed by urban authorities to encourage adults to enhance their walkability levels by fully considering both objective and subjective indicators and walking types, which will result in healthier lifestyles.

Telemedicine Hip and Knee Arthroplasty Experience During COVID-19

BackgroundAlthough telemedicine visits were essential and adopted by providers and patients alike, few studies have been conducted evaluating orthopedic patient perception of the care delivered during these visits. To our knowledge, no study has evaluated specific factors that affected patient satisfaction with telemedicine and the receptiveness to continue virtual visits post COVID-19 in total joint arthroplasty (TJA) patients. Thus, the purposes of our study are to determine the following: (1) patient satisfaction with using TJA telemedicine services, (2) whether patient characteristics might be associated with satisfaction, and (3) whether virtual clinic visits may be used post-COVID-19. MethodsA prospective, cross-sectional survey study was completed by 126 TJA patients who participated in telemedicine visits with TJA surgeons from May 1, 2020 to August 31, 2020. The survey consisted of questions regarding demographics, satisfaction, and telemedicine experiences. ResultsOne hundred one (80.2%) patients were satisfied with their telemedicine visit, with patients <80 years old (P = .008) and those with a longer commute time (P = .01) being more satisfied P = .01. There was a significant preference for in-person visits when meeting arthroplasty surgeons for the first time (P < .001), but patients were equally amenable to follow-up telemedicine visits once there was an established relationship with the surgeon. ConclusionYounger patients, patients with longer commute distances, and patients who had established relationships with their provider expressed higher satisfaction with telemedicine arthroplasty visits. Although >80% of patients were satisfied with their telemedicine visit, an established patient-provider relationship may be integral to the success of an arthroplasty telemedicine practice.

The association between commuting, mood and job performance: the structural equation modelling approach

Commuting demands a considerable amount of time from a worker’s daytime. Numerous studies have shown the relationship between commuting and mood, and even job performance. Contradictory findings are also present in the literature regarding the association between commuting, mood and job performance. Therefore, the aim of the present study is to examine the association between commuting, negative upon arrival at work and individual job performance, given the special focus on the mediating role of negative mood. Two hundred and twenty-six participants were recruited via snowball sampling. Participants were asked to answer questions about commute duration and distance, to rate their mood upon arrival at work and to fill in the individual work performance questionnaire. Results showed that commute duration and distance was associated with job performance through mood upon arrival at work. It is believed that this study provides valuable information regarding the mediating role of mood upon arrival at work.

Commute distance and jobs-housing fit

Anecdotal evidence suggests that the affordable housing crisis is forcing households to seek lower cost housing in the outer reaches of major metropolitan areas, helping to explain recent increases in commute distance. To test this relationship, we use spatial regression to examine the relationship between the availability of affordable housing in close proximity to jobs (jobs-housing fit) and commute distance in the Los Angeles metropolitan area. The analysis draws on 2015 Longitudinal Employer-Household Dynamics (LEHD) Origin–Destination Employment Statistics (LODES) by workplace supplemented with data from the 2013–2017 5-Year American Community Survey on affordable housing units. We find substantial variation in jobs-housing fit across Los Angeles neighborhoods. The imbalance is greatest in higher-income neighborhoods located along the coast and in Orange County, south of Los Angeles. Controlling for other determinants of commute distance, a higher ratio of jobs to affordable housing is associated with longer distance commutes. To address growing commute distances, policymakers must greatly expand and protect the supply of long-term rental housing particularly in job-rich neighborhoods.

Determinants of Commute Time in an Indian City

Given the conspicuous lack of adequate research on commute time in the Indian intra-urban context, the research questions we answer in this article are: what is the average commute time to work for residents? What does this imply for the city’s effective labour market? What are the determinants of commute time to work? This article studies the questions for a large Indian city, known for its traffic gridlocks. Given that published data on journey-to-work is not readily available for Indian cities, we report findings from primary surveys of commuters travelling to work in Bengaluru. The actual time taken by commuters to reach their workplace is 42.45 minutes one-way, implying an insignificant reduction in the city’s effective labour market since 2001. We use geographic information systems to map the sectoral flow of workers and their origins and destinations within the city. Estimating regressions of the determinants of commute time, we find that workers in government jobs travel longer to get to work, elevation has a negative effect on commute time, and commute distance positively impacts travel time. The contribution of the article is the report of an Indian city’s commute time and its determinants, which can be used to influence policy. JEL Classification: R12, R41, R42

Border enforcement and the sorting and commuting patterns of Hispanics

AbstractI analyze the effects of immigration enforcement by the US Border Patrol on the sorting and commuting patterns of Hispanics. Using a regression discontinuity design based on a 100‐Mile Border Zone, which permits Border Patrol agents to conduct warrantless searches within 100 air miles of the US border, I find that the share of Hispanics in southwestern states increases outside the Border Zone. This sorting effect disappears, however, when focusing on within‐county differences in shares of Hispanics. I also find no significant commuting effect on Hispanics at the 100‐mile cutoff. On the contrary, I show that Hispanics near Border Patrol checkpoints exhibit significantly different commuting patterns, where a 1 percentage point rise in Hispanic share is associated with a 0.08 percentage point decrease in the probability of commuting toward checkpoints and a 0.2 mile decrease in the average commute distance.

The use of E-moped increases commute satisfaction and subjective well-being: Evidence from Shanghai, China

Increasing residents' commute satisfaction and subjective well-being contribute to a healthy, sustainable, and successful society. In this respect, transport mode choice is commonly recognized as one of the key factors associated with commute satisfaction and commuters' subjective well-being. However, few studies have investigated how e-moped, one of the most popular commute transport modes in China, shapes residents' commute satisfaction and subjective well-being, although e-moped ownership in China is extremely great. Thus, we conducted an empirical study on the impact of commuting by e-mopeds, using the survey data from eight residential communities in Shanghai. A path analysis model was established to investigate the relationships between commute characteristics (including commute distance, commute time, commute costs, transport mode choice, transport environment), socio-demographics, commute satisfaction, and subjective well-being. The model results showed that commute satisfaction positively correlated with commuters’ subjective well-being. However, the relationship between transport mode choice and commute satisfaction was demonstrated not straightforward but fully mediated by commute time and commute costs which both showed a negative impact on commute satisfaction. In this case, using active modes was revealed to lead to a happier work commute than using private cars, whereas bus riders were at the bottom of the list as expected. Surprisingly, the use of e-mopeds resulted in the second highest total effects on the increase of commute satisfaction and subjective well-being, due to less commute time and lower commute costs. Moreover, the substitution effects of e-mopeds were examined. The results indicated that bus commuters held the most possibility to shift to e-mopeds; while, it was found hard to let e-moped users alter to the other transport modes of commuters. In this context, it was reasonable to further promote the development of e-mopeds in Shanghai, and targeted practical implications were given in this study.

Using Community Health Workers to Address Barriers to Participation and Retention in Diabetes Prevention Program: A Concept Paper.

The PreventionLink of Southern Maryland is a 5-year project to eliminate barriers to participation and retention in the National Diabetes Prevention Program (DPP) lifestyle change program to prevent or delay the onset of type 2 diabetes in adults with prediabetes. This is the study to identify the obstacles to participation and retention in the DPP lifestyle change program among high burden populations and learn how CHWs have reduced the identified barriers to participation and retention for high burden populations. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to conduct this literature review. We have used the Scopus and PubMed, including all types of studies and peer-reviewed documents published in English between 2010 and 2020. From 131 identified articles, 18 articles were selected for qualitative synthesis. The reviewed literature documented following as main barriers to participate in a DPP lifestyle change program: time, cost, lack of transportation, cost of transportation, commute distance, technology access, access to facilities and community programs, caregiver responsibilities, lack of health literacy and awareness, and language. CHWs can address these barriers to participation and retention, they were involved in educating and supporting roles; they worked as bridges between healthcare providers and participants and as intervention team members. Diabetes prevention program participants with social determinant risk factors who most need CHW services are unlikely to have financial resources to pay for CHW services out-of-pocket. Hence, the public and private health plans that pay for their prediabetes care should consider paying for these CHW services and there is a need to trust more to CHW and have them as a "community health teams" member.

Free Energy Node Embedding via Generalized Skip-gram with Negative Sampling

A widely established set of unsupervised node embedding methods can be interpreted as consisting of two distinctive steps: i) the definition of a similarity matrix based on the graph of interest followed by ii) an explicit or implicit factorization of such matrix. Inspired by this viewpoint, we propose improvements in both steps of the framework. On the one hand, we propose to encode node similarities based on the free energy distance, which interpolates between the shortest path and the commute time distances, thus, providing an additional degree of flexibility. On the other hand, we propose a matrix factorization method based on a loss function that generalizes that of the skip-gram model with negative sampling to arbitrary similarity matrices. Compared with factorizations based on the widely used <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula> loss, the proposed method can better preserve node pairs associated with higher similarity scores. Moreover, it can be easily implemented using advanced automatic differentiation toolkits and computed efficiently by leveraging GPU resources. Node clustering, node classification, and link prediction experiments on real-world datasets demonstrate the effectiveness of incorporating free-energy-based similarities as well as the proposed matrix factorization compared with state-of-the-art alternatives.

An L.A. story: The impact of housing costs on commuting

The empirical impact of housing costs on commuting is still relatively poorly understood. This impact is especially salient in California given the state's notoriously high housing costs, which have forced many lower- and middle-class households to move inland in search of affordable housing at the cost of longer commutes. To investigate this linkage, we relied on Generalized Structural Equation Modeling and analyzed 2012 CHTS data for Los Angeles County – the most populous county in the U.S. Our model, which jointly explains commuting distance and time, accounts for residential self-selection and car use endogeneity, while controlling for household characteristics and land use around residences and workplaces. We find that households who can afford more expensive neighborhoods have shorter commute distances (−2.3% and − 3.1% per additional $100 k to median home values around workplaces and residences respectively). Job density, distance to the CBD, and land-use diversity around workplaces have a relatively greater impact on commuting than the corresponding variables around commuters' residences. Compared to non-Hispanics, Hispanic workers commute longer distances (+3.5%), and so do African American (+5.1%) and Asian (+2.0%) workers compared to Caucasians, while college educated workers have shorter (−2.6% to −3.6%) commutes. Furthermore, commuters in the top income brackets tend to have faster commutes than lower income workers. Finally, women's commutes are ~41% shorter than men's, possibly because they are balancing work with domestic responsibilities. Better understanding the determinants of commuting is critical to inform housing and transportation policy, improve the health of commuters, reduce air pollution, and achieve climate goals.

An empirical comparison of connectivity-based distances on a graph and their computational scalability

Abstract In this study, we compare distance measures with respect to their ability to capture vertex community structure and the scalability of their computation. Our goal is to find a distance measure which can be used in an aggregate pairwise minimization clustering scheme. The minimization should lead to subsets of vertices with high induced subgraph density. Our definition of distance is rooted in the notion that vertices sharing more connections are closer to each other than vertices which share fewer connections. This definition differs from that of the geodesic distance typically used in graphs. It is based on neighbourhood overlap, not shortest path. We compare four distance measures from the literature and evaluate their accuracy in reflecting intra-cluster density, when aggregated (averaged) at the cluster level. Our tests are conducted on synthetic graphs, where clusters and intra-cluster densities are known in advance. We find that amplified commute, Otsuka–Ochiai and Jaccard distances display a consistent inverse relation to intra-cluster density. We also conclude that the computation of amplified commute distance does not scale as well to large graphs as that of the other two distances.

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.