Commute Mode Research Articles

Evolution of Mode Use During the COVID-19 Pandemic in the United States: Implications for the Future of Transit

The COVID-19 pandemic has brought about transformative changes in human activity-travel patterns. These lifestyle changes were naturally accompanied by and associated with changes in transportation mode use and work modalities. In the United States, most transit agencies are still grappling with lower ridership levels, thus signifying the onset of a new normal for the future of transit. This paper addresses this challenge using a novel panel survey data set collected from a representative sample of individuals across the United States. The study involved the estimation of a panel multinomial probit model of mode choice to capture both socio-economic effects and period (pre-, during-, and post-COVID) effects that contribute to changes in mode choice. This paper provides rich insights into the evolution of commute mode use as a result of the pandemic, with a particular focus on public transit. Through a rigorous modeling approach, this paper provides a deep understanding of how transit use has evolved, how it is likely to evolve into the future, and the socio-economic and demographic characteristics that affect the evolution (and expected future use) of public transit. Results suggest that transit patronage is likely to remain depressed by about 30% for the foreseeable future, in the absence of substantial changes in service configurations. This study also shows that minority groups and those living in higher density regions are more likely to exhibit a return to transit use in the post-pandemic period.

The carbon footprint of health system employee commutes

Greenhouse gas emissions from employee commutes contribute to the carbon footprint of the healthcare sector. We sought to analyze the patterns of employee commutes and quantify the resulting greenhouse gas emissions for three large health systems in Cleveland, Ohio. An employee survey was obtained and reviewed for data regarding home location, mode of commute, distance traveled, and commute time. Emissions for each survey participant were estimated using the United States Environment Protection Agency Emission Factor Inventory. Emissions per survey participant were multiplied by the total number of employees to estimate commute-related emissions for each health system. A total of 3,192 employees responded to the survey, including 988 from Cleveland Clinic, 271 from VA Medical Center, and 1933 from MetroHealth System. The vast majority of survey participants drove by themselves to work in an automobile. Annual greenhouse gas emissions per participant (in kg CO2-eq) were 1750 (Cleveland Clinic), 3333 (VA Medical Center), and 2834 (MetroHealth System) with variations due to differences in commute distances. Total annual emissions per health system were estimated as: 36,006,250 (Cleveland Clinic), 11,998,800 (VA Medical Center), and 22,672,000 (MetroHealth System). Employee commutes are a potential area of intervention to decrease the carbon footprint of healthcare institutions. We recommend that health systems periodically survey employees to understand commute patterns and work with employees and policymakers to support and promote lower-emission transportation modes.

Effects of Individual and Built Environmental Features on Commuting Mode Shifts before and after the COVID-19 Outbreak in Guangzhou, China

The novel coronavirus disease (COVID-19) pandemic has had a significant impact on transportation. Understanding how the epidemic in China has affected people’s travel mode choices can help city managers analyze the travel mode of different populations in the postepidemic phase. Based on a travel behavior questionnaire conducted in Guangzhou, China, during the COVID-19 epidemic and points-of-interest data, this study explored the impact of the built environment, travel characteristics, and socioeconomic factors on changes in commuters’ travel modes during the postepidemic phase. We found that gender, age, occupation, the decline in the rate of travel frequency, and built environment characteristics significantly influenced the change in travel mode. When respondents had to give up public transport, those in different professions had different options for alternative means of transportation. The density of residential facilities, bus stations, and government institutions had a more significant impact on the change in the travel mode of commuters. The research results provide a theoretical basis for policy and practice. After the outbreak of a major public health event, urban transport managers and policymakers should consider individual heterogeneity and environmental factors when formulating strategies to address public travel in unconventional situations.

Impacts of commute mode on body mass index: A longitudinal analysis before and during the COVID-19 pandemic

IntroductionCOVID-19 has impacted millions of commuters by decreasing their mobility and transport patterns. While these changes in travel have been studied, less is known about how commute changes may have impacted individuals’ body mass index (BMI). The present longitudinal study explores the relationship between commute mode and BMI of employed individuals in Montréal, Canada. MethodsThis study uses panel data drawn from two waves of the Montréal Mobility Survey (MMS) conducted before and during the COVID-19 pandemic (n = 458). BMI was modeled separately for women and men as a function of commuting mode, WalkScore©, sociodemographic, and behavioral covariates using a multilevel regression modeling approach. ResultsFor women, BMI significantly increased during the COVID-19 pandemic, but telecommuting frequency, and more specifically telecommuting as a replacement of driving, led to a statistically significant decrease in BMI. For men, higher levels of residential local accessibility decreased BMI, while telecommuting did not have a statistically significant effect on BMI. ConclusionsThis study's findings confirm previously observed gendered differences in the relations between the built environment, transport behaviors, and BMI, while offering new insights regarding the impacts of the changes in commute patterns linked to the COVID-19 pandemic. Since some of the COVID-19 impacts on commute are expected to be lasting, findings from this research can be of use by health and transport practitioners as they work towards generating policies that improve population health.

Meaningless Use: Assessing Compliance With a Clinically Meaningless Emergency Department Documentation Requirement.

Introduction A New York State initiative requests that Emergency Department (ED) providers document in the electronic health record (EHR) each admitted patient's employment status and, if applicable, their mode of commute. This initiative diverts them from their primary duties and increases the likelihood they will either disregard the request or input incorrect information to complete the data fields as fast as possible. This study intends to understand how well providers adhere to this regulation, which, while important for society as a whole, has little clinical relevance, especially in the ED, where the focus is to identify and treat emergent conditions. We hypothesized that clinician-collected employment data would contain many more "N/A" responses than registration-collected employment data (the "gold standard"). Methods We took a randomly selected convenience sample of 100 patients admitted from the EDand compared each patient's provider-entered response to the employment data field to the registration-recorded response. The EHR operates such that the "Employment" field must be completed in order to complete the admission electronically. Data fields collected were: last name, first name, date of birth, medical record number, date and time of arrival, date and time of admission, attending physician, resident physician (if there was one), mid-level provider (if there was one), provider-entered employment status, registration-entered employment status, admitting service (eg, Medicine, Surgery, OB/Gyn), and disposition level (eg, ICU). We assessed the percent of employment data that was concordant between the provider's entry and the registration clerk's entry. We also assessed for the potential confounding variable of how busy the ED was at time of admission,as providers may not take ask about employment or enter such data during particularly busy times. Finally, we interviewed providers to elicit reasons they did not enter accurate data. Statistical significance was set a priori at p <0.05. Results One hundred six patients were screened; six were excluded because one of the authors (MR) was their attending physician. For 92 of the remaining 100 patients, providers recorded employment as "N/A," and for eight patients they recorded "retired." For seven of these eight patients, provider entry matched registration entry (87.5% concordance). To adjust for whether how busy the ED was may have impacted the accuracy of data entry, admissions were categorized according to what time of day the patient was admitted. There was no statistically significant correlation between how busy the ED was andaccuracy of data entry.The majority of providers stated they responded "NA" because the employment information was unrelated to the ED visit. Conclusion In New York, for each patient admitted from the ED, the ED provider is requested to enter the patient's job information and, if they commute to work, the method they use. However, this takes providers' attention away from what they should be doing most: diagnosing and treating patients.This study highlights the unintended consequence of requesting data fields that are not clinically relevant and, from the patient and provider perspective, are not good investments of time and energy and distract from the clinical visit. Persons interpreting such clinically irrelevant data should do so with caution, as the results are unlikely to reflect the truth of what the questions intend to determine.

Do long school commutes undermine teenagers’ wellbeing? Evidence from a nation-wide survey in China

IntroductionThere is reason to believe that a long school commute negatively affects the wellbeing of teenagers, but the empirical evidence is limited. The focus of past research has usually been on the commute mode or type rather than the commute length. Virtually all existing studies are based on surveys of a single domain of wellbeing; most have only established associations rather than causation and are set in highly developed countries in North America and Europe. Very few studies on this topic have been conducted in China. MethodsThis study employs a sizable nation-wide survey called China Education Panel Survey (CEPS). The sample includes nearly 7500 teenagers, ranging in age from 12 to 19, nearly all of whom attend public schools. Their wellbeing in relation to the length of the school commute is explored across three domains (health, activities, and cognition). An econometric technique called Propensity Score Matching (PSM) is applied to estimate causal effects; we control for 13 covariates. A long commute is defined as lasting at least 20 min one-way. ResultsLong commutes lead to: lower self-reported health and mental health scores, lower grades (in English, Chinese, mathematics, and cognitive tests), less time spent surfing the Internet, playing games, and sleeping, higher BMI and absenteeism, and more time completing household chores. When we control for commute mode, we find that even active long commutes are associated with lower physical and mental health status and reduced cognitive abilities and academic performance. ConclusionLong commutes negatively affect teenagers’ wellbeing regardless of transport mode (active or passive). To moderate this effect, the Chinese education and transport sectors should come together to (a) create and maintain a dense network of good schools; (b) improve transport services around schools; (c) provide publicly funded school-bus services; (d) build dormitories in very large school districts.

Detection of anomalies in cycling behavior with convolutional neural network and deep learning

BackgroundCycling has always been considered a sustainable and healthy mode of transport. With the increasing concerns of greenhouse gases and pollution, policy makers are intended to support cycling as commuter mode of transport. Moreover, during Covid-19 period, cycling was further appreciated by citizens as an individual opportunity of mobility. Unfortunately, bicyclist safety has become a challenge with growing number of bicyclists in the 21st century. When compared to the traditional road safety network screening, availability of suitable data for bicycle based crashes is more difficult. In such framework, new technologies based smart cities may require new opportunities of data collection and analysis.MethodsThis research presents bicycle data requirements and treatment to get suitable information by using GPS device. Mainly, this paper proposed a deep learning-based approach “BeST-DAD” to detect anomalies and spot dangerous points on map for bicyclist to avoid a critical safety event (CSE). BeST-DAD follows Convolutional Neural Network and Autoencoder (AE) for anomaly detection. Proposed model optimization is carried out by testing different data features and BeST-DAD parameter settings, while another comparison performance is carried out between BeST-DAD and Principal Component Analysis (PCA).ResultBeST-DAD over perform than traditional PCA statistical approaches for anomaly detection by achieving 77% of the F-score. When the trained model is tested with data from different users, 100% recall is recorded for individual user’s trained models.ConclusionThe research results support the notion that proper GPS trajectory data and deep learning classification can be applied to identify anomalies in cycling behavior.

Associations between commute mode use and self-rated health and work ability among Finnish public sector employees.

To determine the extent to which level of active commute mode use is associated with self-rated health and work ability. The data were sourced from the Finnish Public Sector Study survey in 2020 (n = 38,223). The associations between active commuting - assessed with the frequency of using active commute modes - and self-rated health and work ability were examined with negative binomial regression analyses. Passive commuting and low-to-moderate levels of active commuting were compared with active commuting, and the models were adjusted for sociodemographic factors, working time mode, and lifestyle risk factors. We also assessed separate associations between walking and cycling as a mode of commuting by additionally considering the commuting distance and the outcomes. After adjustment, when using active commuters as a reference, passive commuters had a 1.23-fold (95% confidence intervals (CI) 1.19 to 1.29) risk of suboptimal self-rated health and a 1.18-fold (95% CI 1.13 to 1.22) risk of suboptimal work ability. More frequent and/or longer distance by foot and especially by bicycle, was positively associated with health and work ability. Never commuting by bicycle was associated with a 1.65-fold (95% CI 1.55 to 1.74) risk of suboptimal health and a 1.27-fold (95% CI 1.21 to 1.34) risk of suboptimal work ability when using high-dose bicycle commuting as a reference. Passive commuting was associated with suboptimal self-rated health and suboptimal work ability. Our results suggest that using active commute modes, particularly cycling, may be beneficial for employee health and work ability.

Using conjoint analysis to incorporate heterogeneous preferences into multimodal transit trip simulations

AbstractUrban transportation systems involve thousands of individuals making choices between routes with multiple modes and transfers. For transportation system simulations to produce realistic results, modelers need to incorporate these users and their choices. Choice‐based conjoint surveys provide an attractive solution for obtaining flexible utility models that can be used to predict choices for a wide variety of trips. In this study, we demonstrate an example using conjoint survey data of commuter mode choice in the Washington, D.C. metro area (N = 1651). We sample commuters who primarily drive and those that take transit. We examine preferences for different types of multimodal trips, including those with intramodal and intermodel transfers. We find that trips involving a bus transfer are the least preferred while both drivers and transit users both value metro similarly to driving. We also find that walking during transit trips is an important barrier, with the travel time penalty for walking being 60% higher than that of time in a vehicle. Our findings highlight the significance of accounting for differences in modal transfer types in transportation system simulations. Reducing arrival time uncertainty was not a significant factor in commuter mode choice, and commuters' value of time was similar across all vehicle types, suggesting that increasing the relative speed of transit modes may only have a marginal effect on commuter substitution away from personal vehicles.

Exploring the Nonlinearity of Commute-Time Utility Considering Individual Ideal Preferences and Tolerance ThresholdsC: ase Study in Kunming

Exploring the nonlinearity of commute-time utility is useful in predicting urban travel demand. However, existing studies assume that commute utility decreases linearly with commute time. This ignores the influence of commuters’ ideal preferences and tolerance thresholds on commute utility. To reveal the nonlinear variation of commute utility with commute time and its influence on the choice of commute mode, the ideal commute time (ICT) and the tolerance threshold for commute time (TTCT) were introduced. Three-piecewise linear utility models (Models 2, 3, 4) were constructed and compared with the linear utility multinomial logit model (Model 1). The results of empirical study showed that: (a) the goodness of fit of these three modified models is higher than that of Model 1, indicating that the fitting effect of the commute-time utility model can be improved with either or both the ICT and TTCT; (b) there is a nonlinear relationship between the commute utility and the commute time. The commute-time utility decreases slowly before ICT, declines steadily between the ICT and the TTCT, and falls significantly after TTCT; and (c) when the commute time exceeds the TTCT, the perceived utility of commuters traveling by walking or cycling decreases significantly, and there are few changes in the perceived utility of commuters by car, which increases the probability that commuters who use active modes will transfer to commuting by car. The research results have implications for improving the prediction capacity of commute mode choice model and could guide commuters to switch to more sustainable commute modes.

Comparing the Effect of Lifestyle, Travel and Residential Attitudes on Male and Female Commute Mode Choice

Comparing the Effect of Lifestyle, Travel and Residential Attitudes on Male and Female Commute Mode Choice

Risk Factors and Inequities in Transportation Injury and Mortality in the Canadian Census Health and Environment Cohorts (CanCHECs).

Road traffic injury contributes substantially to morbidity and mortality. Canada stands out among developed countries in not conducting a national household travel survey, leading to a dearth of national transportation mode data and risk calculations that have appropriate denominators. Since traffic injuries are specific to the mode of travel used, these risk calculations should consider travel mode. Census data on mode of commute is one of the few sources of these data for persons aged 15 and over. This study leveraged a national data linkage cohort, the Canadian Census Health and Environment Cohorts, that connects census sociodemographic and commute mode data with records of deaths and hospitalizations, enabling assessment of road traffic injury associations by indicators of mode of travel (commuter mode). We examined longitudinal (1996-2019) bicyclist, pedestrian, and motor vehicle occupant injury and fatality risk in the Canadian Census Health and Environment Cohorts by commuter mode and sociodemographic characteristics using Cox proportional hazards models within the working adult population. We estimated positive associations between commute mode and same mode injury and fatality, particularly for bicycle commuters (hazard ratios for bicycling injury was 9.1 and for bicycling fatality was 11). Low-income populations and Indigenous people had increased injury risk across all modes. This study shows inequities in transportation injury risk in Canada and underscores the importance of adjusting for mode of travel when examining differences between population groups.

How is commute mode choice related to built environment in a high-density urban context?

Mainstream studies on the relationship between built environment and travel behavior have mostly concerned about relatively low-density urban contexts in North American and European countries. Using Hong Kong as such a case, this study addresses the unique challenges and problems associated with this topic in extremely dense urban settings. In addition to investigating how built environment features are related to the choices between public transport and cars, we also divide public transportation into three sub-modes to study the impacts of built environment. Furthermore, we test how these relationships differ between millennials and older generations. Results indicate that built environment characteristics are more influential in people's choices among different public transport sub-modes than in their choice between public transport and cars. Compared to older commuters, millennials' choices of rail-based and mixed-mode public transport are more susceptible to built environment attributes whereas their effects on road-based transit usage are stronger for older commuters. These investigations provide important insights into individuals' commute mode choices in highly dense, transit dominated urban contexts, and hence provide more reliable grounds for policymaking in these cities to encourage the usage of specific public transit sub-modes, as well as to meet the needs of different age groups.

Estimating road traffic impacts of commute mode shifts.

This work considers the sensitivity of commute travel times in US metro areas due to potential changes in commute patterns, for example caused by events such as pandemics. Permanent shifts away from transit and carpooling can add vehicles to congested road networks, increasing travel times. Growth in the number of workers who avoid commuting and work from home instead can offset travel time increases. To estimate these potential impacts, 6-9 years of American Community Survey commute data for 118 metropolitan statistical areas are investigated. For 74 of the metro areas, the average commute travel time is shown to be explainable using only the number of passenger vehicles used for commuting. A universal Bureau of Public Roads model characterizes the sensitivity of each metro area with respect to additional vehicles. The resulting models are then used to determine the change in average travel time for each metro area in scenarios when 25% or 50% of transit and carpool users switch to single occupancy vehicles. Under a 25% mode shift, areas such as San Francisco and New York that are already congested and have high transit ridership may experience round trip travel time increases of 12 minutes (New York) to 20 minutes (San Francisco), costing individual commuters $1065 and $1601 annually in lost time. The travel time increases and corresponding costs can be avoided with an increase in working from home. The main contribution of this work is to provide a model to quantify the potential increase in commute travel times under various behavior changes, that can aid policy making for more efficient commuting.

Factors Influencing Electric Motorcycle Adoption: A Logit Model Analysis

This research investigates the factors influencing the adoption of electric motorcycles in Indonesia. The background of the study lies in the growing interest in sustainable transportation and the potential of electric motorcycles as an eco-friendly alternative. The objective is to understand the impact of travel behavior and knowledge levels on the choice of motorcycles. The research utilizes a logit model to analyze the relationship between independent variables (knowledge levels and travel behavior) and the dependent variable (choice of motorcycles). The findings reveal that travel behavior, such as the usual commute mode and the number of weekend trips for hybrid electric motorcycles, significantly influence adoption. Additionally, knowledge factors like maintenance and charging costs play an essential role. These findings contribute to a better understanding of the decision-making process behind adopting electric motorcycles and provide insights for policymakers and manufacturers to promote the adoption of electric motorcycles in Indonesia.

The Prevalence of Active Commuting to School and the Factors Influencing Mode Choice: A Study of University Students in a Secondary City of Bangladesh

Physical activity among people of all ages has been decreasing at an alarming rate in recent years. Active commuting is recognized as a public health strategy to increase physical activity. The objectives of the study were to determine the habit of active commuting and the associated factors of commute mode choice among university students in the secondary city of Sylhet, Bangladesh. The study was cross-sectional in nature, and information from three hundred and forty-eight students was collected through an online survey using Google Forms. In addition to basic statistical tools, a multiple logistic regression model was applied to identify the factors that were associated with the commuting mode choice of the students. The results have shown that the prevalence of using an active commuting mode is not at a satisfactory level (43%). No remarkable difference in commuting behavior was found between males and females. The “distance between campus and students’ residences” and the “mode of commuting preferred by roommate or classmate” are the most significant factors influencing students’ commuting choices and related actions. In addition, respondents with lower socio-economic conditions have more of a tendency to use active modes of commuting. Weather, time constraints, road safety, and family residence in rural or urban areas all have an impact on choosing an active mode of transportation for attending classes, but none is overly significant. It is recommended to promote the health and financial benefits of active commuting. Necessary facilities should be constructed to increase the level of active commuting, such as additional residential halls near campus and developing a built environment on the campus for walking and cycling for both male and female students.

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.

Commute based learning: Integrating literature across transportation, education, and i-o psychology

Though commuting is often seen as a source of stress, commuters may take advantage of travel time to pursue learning and developmental goals—a concept we refer to as Commute-Based Learning (CBL). We draw on self-regulation and learning and development theories to define CBL in terms of its context, content, and process and present the findings of a systematic review of multitasking activities in the transportation literature. This review demonstrates that (a) travel-based activities related to learning are increasingly prevalent, (b) people enjoy being productive during their commutes, and (c) commute mode and environmental characteristics impact multitasking and evaluations of the commute itself. We then integrate these review findings with psychological theories to propose a framework specifying the predictors of CBL, its benefits, and drawbacks, and the commute mode's moderating influence. These efforts yield several practical implications and future research directions to increase CBL's potential benefits while reducing potential harm.

How School Travel Affects Children's Psychological Well-Being and Academic Achievement in China.

Previous research on the role of school travel in children's well-being (WB) has paid little attention to developing countries. Using national survey data across China, this study examines how children's psychological well-being (PWB) and academic performance differ across commute duration and mode among urban, rural, and urban fringe areas. Our findings show that commute times are significantly negatively associated with children's PWB and academic achievements, and this correlation varies across areas. Children living in the urban fringe have the longest average one-way commuting time (18.6 min), but they have a better acceptance of longer commuting duration, whereas commuting time is more influential in the city center and rural areas. Regarding travel mode, walking to school is positively associated with PWB in the center area, while bicycles and public transport positively affect the rural student scores. Results from quantile regression show that students on the lower quantiles of the conditional distribution of PWB tend to suffer more than the others when commuting time increases; students with middle scores respond similarly to marginal changes in commuting time. Recommendations for urban planners and policymakers to enhance child WB include fostering school-home balance, improving public transit services, and investing in pedestrian and bicycle infrastructure for those vulnerable groups.

Campus commute mode choice in a college town: An application of the integrated choice and latent variable (ICLV) model

This study examined commute mode choice in a small college town by employing an integrated choice and latent variable (ICLV) model to adequately address the role of attitudinal variables, such as pro-car, pro-bus, and pro-environmental attitudes. The campus commute data (n = 554) came from an individual travel survey of the faculty and staff at the University of Illinois at Urbana-Champaign. The ICLV model provided a richer and more nuanced understanding of the commute mode choice of campus members than a traditional multinomial logit (MNL) model by illustrating how various sociodemographic characteristics influence mode decisions through mode preference and environmental attitudes. Results of a scenario analysis show that policy efforts that aim to shift commuter attitudes toward sustainable modes can be as effective as measures that improve actual transportation systems.