Commute Distance Research Articles

Sustainable Steps in the Snow: Exploring factors associated with active school commuting in Finland

Physical inactivity of children and youth is a major health problem. Active commuting could increase the amount of physical activity and be a more environmentally friendly traffic mode choice compared with motorized vehicles. Parents have an integral role in determining the options a child has for commuting. We explored parental barriers and motivators associated with the active commute of their children.This cross-sectional study used the CLIMATE NUDGE Survey data of parents with school-aged children. A set of 25 possible influencing factors included demographic factors, social aspects, and pro-environmental attitudes, and questions about the motivating role of journey characteristics and health effects. The data were analyzed with two multinomial logistic regression analyses (N = 320): first, comparing those who either never or sometimes commute actively to those who always do so, and second, those who always or sometimes commute actively to those who never do so.Results indicated the strongest determinants to differentiate those who always and those who never use active commute, were commute distance and parental beliefs about social commute norms. The longer the commute distance was, the more likely the child was to belong to the never active commuter group and less likely to belong to the always active commuter group. The more parents believed their child’s peers commuted actively, the more likely it was that their child always commuted actively and less likely they never did so.We found several factors associated with active commuting, yet somewhat surprisingly neither environmental attitudes nor climate change related reasons were significantly associated with active commuting. To conclude, instead of highlighting environmental aspects to promote active commute, we recommend highlighting social norms and perception of the commute distance, and addressing weather-related barriers and health related motivators.

Housing choice in an evolving remote work landscape

We estimate a joint model of housing choice along several dimensions to account for changing valuations of housing outcomes due to the COVID-19 pandemic. We consider housing outcomes including housing type, tenure type, the presence of a patio or yard, the number of bedrooms, neighborhood population density, median housing cost, accessibility of amenities, school quality, crime rate, and commute distance. Data used for this analysis were collected in October and November of 2021 from 24 metropolitan areas across the United States. A Generalized Heterogeneous Data Model (GHDM) is used to estimate these housing outcomes as a function of exogenous household sociodemographic characteristics and latent lifestyle propensities. The GHDM also captures jointness caused by unobserved factors, allowing for the estimation of accurate causal effects between outcomes. The results reveal that lifestyle preferences have significant impacts on housing outcomes. Specifically, individuals with a preference for teleworking are more likely to reside in single-family homes in highly populated areas, experience longer commute distances, and exhibit a higher sensitivity to the presence of amenities in their neighborhoods. Additionally, the analysis of tradeoffs between housing outcomes reveals the relative valuations of various housing outcomes. An increased commute distance is found to lead to an increase in single-family homes, reductions in density, and an increased crime rate. Choosing an apartment in a high-density neighborhood is found to lead to reductions in school quality and significant increases in crime rates. Implications of the results for land-use planning, travel demand analysis, and equity considerations are identified and discussed.

Limited Role of MSAFP Screening for Prenatal Omphalocele Detection.

Although serum screening for aneuploidies has become less prevalent, maternal-serum alpha-fetoprotein (MSAFP) screening for body-wall defects remains widespread. We explored whether MSAFP screening is associated with earlier omphalocele detection than ultrasound alone. This is a retrospective cohort study of prenatally detected omphalocele cases at our center from 2007 to 2023. We explored the association between MSAFP screening, gestational age at omphalocele detection, and clinical outcomes. Among 101 pregnancies with prenatally diagnosed omphalocele, 27 (26.7%) had MSAFP screening. The median gestational age at MSAFP screening was 17 weeks 4 days. Of those who received MSAFP screening, 11 (41%) had values ≥2.5 multiples of the median (MoM) and 16 (59%) were not elevated. MSAFP results did not correlate with omphalocele size and were not associated with prenatal or postnatal outcomes. MSAFP screening did not result in earlier suspicion for or confirmation of omphalocele (P = .97 and P = .87, respectively). In contrast, first-trimester ultrasound screening was associated with earlier suspicion for and confirmation of omphalocele (P < .01 and P = .01, respectively). There were no clinical or demographic differences between those who received MSAFP screening and those who did not (including body mass index or commute distance to an urban center). MSAFP screening is not associated with earlier omphalocele detection. Furthermore, in pregnancies with prenatally diagnosed omphalocele, the results of MSAFP screening are not predictive of clinical outcomes.

Car Ownership, Commute Distance, and Commute Mode Choice in the Dense Megacity of a Developing Country: The Direct and Indirect Role of the Built Environment

Despite much having been published about the effects of the built environment (BE) on urban travel in the developed world, few articles have so far been published based on studies using a megacity in a developing country. The paper addresses the existing gaps in research by conducting a study in Dhaka, one of the densest urban areas globally. An integrated framework based on the structural equation model and discrete choice model is used to examine how individual commute mode choice behavior is influenced by the BE, as mediated by car ownership and commute distance. Three BE features—population density, street connectivity, and job-to-household ratio—have a direct and total positive association with non-motorized transport use. Although being close to bus stops does not directly affect people’s choice to take non-motorized transport, it does promote non-motorized travel in an indirect way by decreasing car ownership and commute distance. Population density, job-to-household ratio, proximity to the nearest central business district, and bus stop proximity all have a positive direct and total impact on transit use, although larger employment densities directly support automobile use over transit. Understanding how the BE affects commute distance, car ownership, and mode choice is a useful reference for the development of practical measures to reduce demand for automobiles.

The interplay between teleworking choice and commute distance

In this study, we model the telework-commute distance decision process as a package choice, and use a latent segmentation approach to recognize that some individuals may choose their telework arrangement first and then choose their commute distance (their residential location), while others may adopt a reverse causal behavioral process. The latent segment modeling methodology combines a multiple discrete–continuous probit model for telework adoption/intensity as the proportion of monthly workdays worked from home, from the office, and from a variable third workplace, with a log-linear regression model for commute distance (the home-to-office distance). The data for our analysis is drawn from a 2022 survey of Texas residents. Results suggest that about 20% of the population are likely “movers”, whose telework choices led to a residential move that changed commute distance, while the other 80% are “stayers”, whose commute distance at their predetermined (to telework) residential location changed telework habits. We further quantify the impact of teleworking on commute distances by computing the expected commute distance for several telework distribution preferences. A shift from 100% in-person work to 100% home-based remote work increases average commute distance by 64.8%, while working from a third workplace for any portion of a month reduces commute lengths relative to only home-based telework. In terms of monthly commute vehicle miles traveled (VMT), working from home for less than 18.6% of the month (about once a week) per worker actually increases overall monthly commute VMT. Monthly commute VMT reductions become tangible only at about 30–40% telework for each worker (about two days of telework per week for full-time workers with 22 workdays per month). In general, between 0 and 40% home-based telework per worker (with the remaining percentage at the office), there is a 10% commute VMT reduction for each 10% increase in home-based telework. In the range of 40% to 80% home-based telework, there is about a 13% commute VMT reduction for every 10% increase in home-based telework. And in the range of 80%-100% home-based telework reduction, there is about a 15% commute VMT reduction for every 10% increase in home-based telework.

Retaining the transportation benefits of COVID-19 induced work from home: Understanding the role of worker productivity

The abrupt switch to work from home during the COVID-19 pandemic has not only altered people’s commutes but also changed their entire work-life balance. While some workers were quick to adapt and maintain or even increase productivity, others experienced a decrease in productivity. Self-assessed productivity changes after switching from traditional in-person work to work from home is studied using a survey of 3,780 workers (including full-time college students). A probabilistic statistical model is used to estimate the probabilities that workers’ self-reported productivity during the pandemic remained the same, decreased, in some ways increased and in other ways decreased, or increased. The model estimation results identify workers who were resilient and adaptable (having a higher probability of increasing their productivity) and those less adaptive workers, who were more likely to experience a decrease in productivity. It was found that race, ethnicity, household income, household size, education, gender, the presence of children in the household, level of life satisfaction, being a student, prior experience with online meetings, and commute distances all play a role in how the workers’ productivity changed. This study provides insights for the development of effective policies to improve equity (by targeting vulnerable populations) and sustainability (by retaining the transportation and environmental benefits of telework) in the post COVID-19 reality.

Contextual Bayesian optimization of congestion pricing with day-to-day dynamics

Congestion pricing is a common approach to alleviate urban traffic congestion. The design of second-best congestion pricing schemes is typically formulated as non-linear programming and bi-level optimization problems, where the lower-level problem involves either a static or dynamic network equilibrium model. The complexity of these bi-level toll optimization problems increases considerably when incorporating day-to-day dynamic models of travel behavior and dynamic models of network congestion. These models are often operationalized using simulation, and consequently, the toll design problem is a computationally challenging simulation-based optimization problem where the evaluation of a single candidate pricing scheme involves simulating the day-to-day model until convergence. In order to circumvent this issue, we propose a contextual Bayesian optimization (BO) framework, where the BO scheme is embedded within the day-to-day dynamic model by using temporal contextual information. The framework implicitly incorporates the relationship between the objective function across days and uses past days’ observations (function evaluations) as weak priors when constructing the Gaussian process underlying the BO algorithm for the current day’s toll optimization problem, resulting in gains in computational efficiency.The contextual BO approach is applied to the design of distance-based pricing schemes for the morning commute problem. We demonstrate numerically that the scheme converges to the system optimum, and moreover, utilizes a significantly smaller number of simulation evaluations (ten-fold reduction) than the standard approach wherein each function evaluation involves simulating the day-to-day model until convergence. From a policy perspective, we find that the distance-based schemes yield significant welfare gains relative to area-based schemes and show that the design of the distance-based tariff scheme can significantly affect distributional impacts. A suitably designed two-part tariff structure can partially offset the relatively large welfare losses of travelers with longer commute distances while maintaining overall welfare. The proposed contextual BO scheme is also extended to incorporate context specific demand and supply information, which can be of value to policy-makers when evaluating optimal toll design schemes under a wide range of scenarios in a computational tractable manner.

The socialist metropolis in flux: Urban structure and commuting patterns in Poland, 1973–1983

There is a significant dearth of knowledge regarding changes in intra-metropolitan local labor markets in former socialist countries, which encompass extensive parts of Eurasia and where industrialization and urbanization were centrally controlled by the state. This paper aims to contribute to the existing body of research by (a) illuminating and explaining changes in the spatial structure of urban regions in Poland during the transition from 'extensive' to 'intensive' socialist urban development (1973-1983), and (b) investigating changes in commuting patterns and their socio-demographic composition during the transition to 'mature' socialism. We selected four major city-regions for analysis: gdański, katowicki, łódzki, and warszawski. These regions were carefully chosen to represent different types of polycentric development and diverse economic profiles. The main findings of our paper are as follows: (a) despite the substantial population growth and the unprecedented rate of new housing development in the four city-regions, the overall impact of the socialist transition to intensive urbanization on their spatial structure was relatively minor, (b) the intensification of urbanization process in Poland contributed to the reduction in commute distance for all socioeconomic and demographic groups of workers.

Comparing Commute Distances Between Hybrid and Conventional Schools

ABSTRACT One schooling sector that had been growing before the pandemic, hybrid schools, in which students attend physical classes fewer than five days per week and are homeschooled on the other days, has seen recent continued growth. One factor that has not been explored is the commuting patterns families follow to use these schools. Whether families drive more miles to attend these part-time schools, or fewer, or neither, is the question this study seeks to address. In this manuscript, using individual student address data, we examine the commute distances made by students at four schools: two conventional, 5-day private schools, and two hybrid schools. We find that suburban hybrid school students do commute longer distances, but fewer total miles per week.

Do employment centers matter? Consequences for commuting distance in the Los Angeles region, 2002–2019

The presence of employment centers provides the potential for reducing commuting distance. However, employment centers have distinct attributes, which may lead to varied impacts on commuting outcomes. We examine how proximity to employment centers can influence commuting distance with consideration of the heterogeneity of employment centers and workers. Specifically, we consider various attributes of employment centers related to location, persistency, job density, industry diversity, and size and analyze their impacts on the commuting patterns of low- and high-income workers using panel (2002-2019) data. Our analysis of the Los Angeles region shows that increasing proximity to the nearest employment center decreases commuting distance even after controlling for the job attributes located in the neighborhood of workers. The results further suggest that employment centers are not equal in terms of their impact on commute distance and that their impact is different for commuters from different income groups. Our findings contribute to the literature by deciphering the location and attributes of employment centers that may exert a greater impact on commuting patterns.

Impact of Commute Distance on Volunteering Rates in the Greater Sydney Region, Australia

Impact of Commute Distance on Volunteering Rates in the Greater Sydney Region, Australia

What drives the joint demand for ride-hailing and carsharing services? Understanding consumers’ behaviors, attitudes, & concerns

Driven by the emerging collaborative consumption trends, new shared ownership-based business models provide more flexible and accessible on-demand mobility options. This study simultaneously analyzes factors correlated with the consumers’ use of two interrelated disruptive on-demand mobility services, including ride-hailing (RH) and carsharing (CS). A comprehensive behavioral framework is presented to explicitly address important methodological concerns regarding the complex stochastic dependence between the use of CS and RH programs and the underlying behavioral heterogeneity in latent factors influencing the use of the two shared mobility options. Using unique data from the California Vehicle Survey, a rigorous elliptical and Archimedean copula-based finite mixture bivariate ordered probit (BOP) modeling methodology is used to understand behavioral, attitudes, and concern-related correlates of households’ participation levels in CS and RH programs. Characterized by a clayton-copula based finite mixture BOP model, participation levels in CS and RH programs exhibited complex (both synergistic and competing) non-linear stochastic dependence patterns. With a stronger left tail dependence, majority of the households having lower levels of participation in RH also had lower participation levels in CS programs. Contrarily, of the households with higher levels of participation in RH programs, the majority had lower levels of participation in CS programs (revealing weaker right tail dependence). Taste heterogeneity was observed in the unobserved determinants of CS use. Results show that current users of CS and/or RH programs tend to be those with greater awareness, owners of plug-in electric vehicles, those making greater transit trips, workers with greater commute distances, and those lacking free parking at their residences. Compared to ride-hailing, the negative effect of more frequent drivers in a household on CS use was less pronounced. Analysis of marginal and joint marginal effects provided deeper insights into the (interactive) effects of other behavioral and socio-demographic correlates. Treatment effects were simulated and discussed to further demonstrate the policy implications of our results. The new empirical insights provide a more granular understanding of the use patterns of on-demand shared mobility services and can inform more context-sensitive travel forecasts for planning and programming purposes.

Changes in active commuting and changes in health

Abstract Background Previous studies have shown that regular physical activity through active commuting, such as walking or cycling, is positively associated with physical and mental health. However, because longitudinal within-individual studies on the association between active commuting and health are scarce, the results may have been biased due to unmeasured confounding. Additionally, information about commute length has rarely been included in studies on within-individual change in active commuting. A better understanding of the association between active commuting and health could inform public health policies and interventions to promote active transportation. Methods Using two waves (2020, T1 and 2022, T2) of self-report data from of the Finnish Public Sector study (N = 16881), we examined the association between within-individual changes in active commuting and health. Active commuting was measured by kilometers/week (i.e., by multiplying the number of walking or cycling days/week with the daily commute distance). The primary outcome, self-rated health, was measured with a single item (scale from 1=poor to 5=good) at T1 and T2. The secondary outcomes, psychological distress and sleep problems were measured only at T2 and were therefore analyzed with a between-individual model. Results After adjustment for potential time-varying confounders such as socioeconomic factors, BMI, and health behaviors, an increase of 10 additional active commuting kilometers per week was associated with a slight improvement in self-rated health (unstandardized B = 0.01, p = 0.001). No associations were observed between changes in active commuting and psychological distress or sleep problems. Conclusions This study provides evidence supporting a modest causal association between active commuting and improved health. Promoting walking or cycling to work may have potential benefits for public health, but further intervention research is needed to better understand the extent of these benefits. Key messages • The study provides evidence supporting a modest causal association between active commuting and improved self-rated health. • Promoting walking or cycling to work may have potential benefits for public health.

Carbon Footprint of Photon Therapy

Rising carbon dioxide levels have hazardous impact on human health and climate change. This study estimates the energy utilization of radiation therapy and estimates corresponding carbon footprint. Patients treated between 07/2020 and 06/2021 using a Varian LINAC system were evaluated. Power draw was directly measured in 1 second increments, from the LINAC machine and the radiation department facility. Patients treated were reviewed for number of fractions and beam duration during each fraction. kWh power consumption was calculated per fraction and per treatment course. Patient commute distance was evaluated using Google Maps. EPA calculator was used to calculate CO2 equivalent (CO2e) footprint. There were 176 patients treated for 191 treatment courses. Total of 4,517 fractions were delivered (avg 23.6, range 1 to 48). Average BeamOn time was 141 seconds per fraction (22 to 310 sec); electron plans (n = 8) 29 sec, 2D/3D (n = 63) 77 sec, and IMRT (n = 120) 182 sec. BeamOn power draw was ∼36.3 kW. Power consumption per fraction was 1.4 kWh (0.2 to 3.0 kWh), and per course 37.5 kWh (0.9 to 109.1 kWh). LINAC was otherwise in standby mode 71% of time (∼7.3 kW) and ON/ready mode 25% (∼12.5 kW). Patient BeamOn time accounted for 2% of total its time and 8% of its power use during the year. Incremental standby/ready energy contribution per treatment course was 410 kWh (78,332/191). The building power draw was 12.5 kW, for incremental contribution of 576 kWh per course. Average patient travel distance was 9.6 miles (0.7 miles to 73.5 miles), resulting in 711-mile round trip per course (6 miles to 5620 miles). Corresponding carbon footprint for BeamOn time was 0.6 kg CO2e per fraction (electrons 0.1 kg, 2D/3D 0.3 kg, and IMRT 1.1 kg per fx). The treatment course footprint was 16.2 kg CO2e (0.4 kg to 47.2 kg). The attributed standby/ready LINAC footprint was 177.8 kg CO2e per course. The attributed facility footprint was further 250.0 kg CO2e per course. The attributed commute footprint was further 286.3 kg CO23 per course (2.4 kg to 2264.0 kg). The entire treatment course was 730.4 kg CO2e. As such, single fraction was 0.1% of the total CO2e course (0.01% to 0.27%), BeamOn was 2.3% (0.1% to 7.1%), LINAC standby/ready was 28.0% (6.5% to 41.3%), building was 39.4% (9.2% to 58%), and commute was 30.3% (0.6% to 82.9%). The entire program generated 139.5 metric tons of CO2e during the year. Linear accelerator BeamOn carbon footprint varied as a function of technique but was only 2% of the overall patient treatment footprint. The LINAC standby electricity contributed 28%, building electricity 39%, and commute 30%, though this varied significantly per patient, as a function of number of fractions and treatment distance. Fixed CO2e contribution from the standby LINAC and the building draw accounted for ∼67% of the footprint. However, radiation oncologists can potentially impact the overall carbon footprint by prescribing fewer fractions to lower total commute, as clinically indicated.

Graph structure reforming framework enhanced by commute time distance for graph classification

As a graph data mining task, graph classification has high academic value and wide practical application. Among them, the graph neural network-based method is one of the mainstream methods. Most graph neural networks (GNNs) follow the message passing paradigm and can be called Message Passing Neural Networks (MPNNs), achieving good results in structural data-related tasks. However, it has also been reported that these methods suffer from over-squashing and limited expressive power. In recent years, many works have proposed different solutions to these problems separately, but none has yet considered these shortcomings in a comprehensive way. After considering these several aspects comprehensively, we identify two specific defects: information loss caused by local information aggregation, and an inability to capture higher-order structures. To solve these issues, we propose a plug-and-play framework based on Commute Time Distance (CTD), in which information is propagated in commute time distance neighborhoods. By considering both local and global graph connections, the commute time distance between two nodes is evaluated with reference to the path length and the number of paths in the whole graph. Moreover, the proposed framework CTD-MPNNs (Commute Time Distance-based Message Passing Neural Networks) can capture higher-order structural information by utilizing commute paths to enhance the expressive power of GNNs. Thus, our proposed framework can propagate and aggregate messages from defined important neighbors and model more powerful GNNs. We conduct extensive experiments using various real-world graph classification benchmarks. The experimental performance demonstrates the effectiveness of our framework. Codes are released on https://github.com/Haldate-Yu/CTD-MPNNs.

O.5.1-10 Promoting mobility among wellbeing employees in Lahti, Finland

Abstract Versatile everyday physical activity outdoors increases the wellbeing of people of all ages and prevents illnesses. The amount of physical activity among working-age people affects coping and recovery at work and wellbeing after work. A healthy, safe, and sustainable living environment supports everyday physical activity and mobility outdoors. Within the NatureStep to Health programme 2022-2032 the Wellbeing services county of Päijät-Häme, Lahti, Finland promotes mobility among employees to in-crease nature contacts and to improve health. The Wellbeing services county of Päijät-Häme piloted sustainable mobility among employees. The county employs approximately 7600 persons, of which 89% are women, for health, social and rescue services. The Wellbeing services county of Päijät-Häme aimed to build recommendations to provide occupational benefit packages for employees. The pilots were expected to provide information on short-term, intermediate, and long-term outcomes for planning, developing and selecting further actions on sustainable mobility. The pilots focused on how employer provided sustainable mobility initiatives were adopted by employ-ees. The pilots included employer-subsidized commuter tickets for local transport, employer-provided bicycle benefits and shared city e-bikes and e-scooters during the working day covered by employer. The employer-subsidized commuter tickets are personal tickets meant for commuting free of taxes up to 3 400 €/year. An employer-provided bicycle benefit is a bicycle intended for the employeés personal use for commuting by tax-deductible income up to 1 200€/year. Despite the short pilot approach to increase mobility and the limited number of participants, they were loyal and eager to continue the provided mobility modes after the pilot period. The participants' com-muting distances were short to switch to alternative modes of transport in a strongly female-dominated workplace. The pilot confirms that sustainable mobility supports employees’ wellbeing, increase physical activity, and decrease the costs of commuting spilling over to increase free time mobility as well. Employers have an active role in both implementing sustainable mobility incentives and in supporting the continua-tion by human resource policies. The maintenance of mobility requires appropriate facilities, such as safe and covered well-located bicycle parking, dressing rooms, lockers, and showers. The initiative shows employer's commitment to the mobility shift and corporate responsibility.

Analysis and optimization of equitable US cancer clinical trial center access by travel time.

6524 Background: Racially minoritized and socioeconomically disadvantaged populations are underrepresented in cancer clinical trials. Data-driven, quantitative approaches are required to address this inequity. Here, we systematically analyze the geographical distribution of self-identified racial and socioeconomic demographics within commuting distance to cancer clinical trial centers and all hospitals U.S.-wide. Methods: We calculate self-identified racial and socioeconomic demographics of populations living within 30-, 60-, and 120-minute one-way driving commute times from U.S. cancer clinical trial centers and all U.S. hospitals. Data sources include the U.S. 2020 census (for race and socioeconomic data), OpenStreetMaps (for calculation of commuting times), NCT trial registry (for cancer clinical trial quantification), NCI Designated Cancer Centers List, Nature Index of Cancer Research Health Institutions and National Homeland Infrastructure Foundation-Level Data (for U.S.-wide hospital and cancer clinical trial center identification). Data analysis was conducted in R, and Ggplot2 used for all data visualizations. Results: Analysis of clinical trial numbers between 2012–2022 identified 78 high-volume U.S. cancer trial centers. Compared to U.S. national averages, these centers are in areas with socioeconomically more affluent populations (+27.6K USD (95% CI +23.1 to 32.8K) median income unpaired mean difference; -0.073 (95% CI -0.063 to -0.084) deprivation index unpaired mean difference), with higher proportions of affluent self-identified White individuals (+10.1% unpaired mean difference (95% CI +6.8 to +13.7%)). The top 10th percentile of all U.S. hospitals (N=7,623) show a range of absolute sum difference from 2.4% to 35% from equal racial representation of White, Black/African American and Asian/Mixed/Other groups. For each U.S. city above 500,000 inhabitants, we compile map overlays that display all prospective hospitals within commutable distance to racially diverse and socioeconomically disadvantaged populations. These maps may inform the design of future clinical trials or investigations in enrollment and retention strategies for clinical trials. Conclusions: Our analysis identifies biases in the sociodemographics of populations living within commuting distance to U.S.-based cancer trial sites and enables the determination of more equitably commutable prospective satellite hospital sites that could be mobilized for enhanced racial and socioeconomic representation in clinical trials. Other recruitment barriers still need to be tackled to ensure racial and socioeconomic demographics within the geographical vicinity of a clinical site can translate to equitable trial participant representation.

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.

Modelling transit and automobile trip-generation propensities of post-secondary students in the Greater Toronto and Hamilton area: a cross-sectional study

The post-secondary students in the Greater Toronto and Hamilton Area (GTHA) maintain a constant source of demand and ridership for the region’s transit infrastructure. With the province investing billions of dollars to meet the transit needs of the residents of the GTHA, a comprehensive analysis establishing the correlation between transit and automobile trips and the factors that influence the trip generation for these modes and this subpopulation is warranted. Using data from 2015 and 2019, a cross-sectional study to gain behavioural insights into travel by post-secondary students is performed. Using a bivariate ordered probit approach, the effect of land-use attributes and socio-demographics on the propensities of making transit and automobile trips is determined, followed by a marginal effects analysis. The results indicate that the propensity of making transit and automobile trips decreases if the commute distance to campus is below 5 km, and improvement in areas with low transit accessibility can considerably increase the transit trip-making propensity.

The perfect match? Assessment of excess commute and transport externalities using an agent-based transport model

Excess commute (EC) refers to the sub-optimal allocation of workers to jobs in an urban region in terms of minimal commute distances. This article investigated EC for the Munich metropolitan area using a microscopic agent-based modeling suite. We first considered the optimization strategy, comparing the traditional zone-based transportation problem to a microscopic routine. Zone-based optimization is computationally efficient, but it underestimates EC due to spatial biases, even when the zone system is highly disaggregate. We then applied the optimized job-worker assignment to a microscopic travel demand model, assigning workers to jobs of the same job sector at a shorter distance from their home, accounting for multi-worker households. With shorter commutes, there was a substantial shift toward active modes. However, we only observed moderate impacts on total travel demand, with the overall effect dampened due to compensatory behavior between mandatory and discretionary travel. Finally, the outputs from the demand model were used by a microscopic assignment model to calculate externalities attributable to EC. Because of the complexity of travel demand, we conclude that the EC indicator alone would overestimate the benefits of policies targeting jobs-housing balance.