Vehicle Miles Traveled Reduction Research Articles

An Analysis of the Health Effects of Physical Activity due to Active Travel Policies in Rennes, France

Background Rennes, a midsize city in France, features many opportunities for active travel. City officials seek to increase walking and cycling by 2030 to improve public health. Physical inactivity, a leading risk factor for premature mortality around the globe, has been shown to be associated with many chronic diseases including heart disease, type 2 diabetes, and cancer. Methods Using the 2018 household travel survey of Rennes residents, we apply the Health-Oriented Transportation statistical model to assess health impacts associated with population-level rates of walking and cycling. We consider two proposed mobility and climate objectives which outline sustainable transportation goals by 2030. These include a shift in transportation mode share to increase walking and cycling trips, as well as a broad reduction in vehicle miles traveled (VMT) across the metropolitan area. Results Our regression analysis demonstrated that factors of household car access and inner-city residency were predictors of prevalence (observed one-day proportion engaging in walking or cycling), participation (weekly proportion), and intensity (mean individual physical activity achieved through walking/cycling) of active travel. Age and education were additionally associated with prevalence. The 2030 mobility objective (mode share: 9% cycle, 35% walk) was associated with a reduction of 1,051 DALYs (disability-adjusted life-years), translating to $73 million USD ($23-$177) in averted costs. The climate objective (10% reduction in VMT) was associated with a reduction of 369 DALYs when replaced entirely by walking and 714 DALYs with cycling, translating to $26 million ($8-$62) and $50 million ($15-$121) saved, respectively. Conclusions Rennes residents experience high participation in active travel, particularly those in the inner city. If residents achieve the city’s active travel goals for 2030, there is potential for a large reduction in health burden and subsequent costs. Reaching these goals may require significant investment in transportation programming and infrastructure to improve active travel opportunities.

Whose Miles Are These Anyway?

Problem, research strategy, and findings State, local, and private-sector climate policies increasingly require estimates of site-generated vehicle miles traveled (VMT) for individual properties. No standard, agreed-upon method exists for this purpose. If alternative methods produce reasonably consistent results, then an analyst may select a method based on convenience. The purpose of this study was to determine whether this is the case. We reviewed existing applications and methods for estimating site-generated VMT, which can be conceptualized as the product of site-level trip generation and average length of a site-generated trip. We identified four plausible methods for estimating site-level trip generation and three for estimating average lengths of site-generated trips. Combining these yielded 12 methods for estimating site-generated VMT. We applied these methods to estimate trip generation, average trip length, and total VMT generated by eight existing office sites in the San Francisco Bay Area (CA) and the Wasatch Front Region of Utah. We found that trip generation and trip length estimates varied widely, with greater variation in trip generation estimates than in trip length estimates. Variation was magnified when the estimates were multiplied to produce site-generated VMT estimates, with the minimum estimate at a single site ranging from 84% to 96% lower than the corresponding maximum. Takeaway for practice In the short term, planning policies requiring site developers or occupants to produce site-level estimates of VMT should specify consistent methods to allow for comparison across sites and over time as needed. In the long term, if VMT is to be used as a performance metric, industry-wide standards for estimating it for both existing and prospective projects are needed. Alternatively, policymakers should consider whether the goals of VMT reduction might be better met with a different performance metric.

Can Electric Vehicle Carsharing Bridge the Green Divide? A Study of BlueLA’s Environmental Impacts among Underserved Communities and the Broader Population

This study aims to evaluate the potential of electric vehicle (EV) carsharing services to address social and environmental disparities in urban transportation through an evaluation of BlueLA, a one-way station-based carsharing service in Los Angeles, California. BlueLA provides a clean and affordable mobility option in underserved communities that face significant air quality burdens and have historically been excluded from environmental benefits. By incorporating BlueLA trip activity data from January 2021 to December 2022 (n = 59,112 trips) and an online user survey implemented in early December 2022 (n = 215 responses), we estimate the impacts of BlueLA on personal vehicle ownership patterns, vehicle miles traveled (VMT), and associated greenhouse gas (GHG) emissions. The results show an overall net reduction in VMT and GHG emissions of 463,845 miles and 656 metric tons, respectively, among the BlueLA user population (3074 registered users). When disaggregating impacts by BlueLA member type, our findings show a net reduction of 234 and 371 metric tons in GHG emissions for Standard (general population) and Community (low-income qualified) members, respectively. Additionally, our socio-demographic analysis highlights clear disparities between these two member groups, with Community members typically having lower incomes (i.e., 74% earning below USD 50,000 annually); lower educational attainment (i.e., 46% with at most an associate’s degree); and larger households (i.e., 23% living in households of four or more) compared to Standard members (i.e., 19% earning below USD 50,000, 24% with at most an associate’s degree, and 9% in households of four or more). Moreover, when comparing the VMT and associated GHG emissions due to BlueLA, we find that the presence of BlueLA reduces VMT and GHG emissions by 34% and 48% respectively, and each BlueLA vehicle replaces 16 personally owned vehicles (shed and postponed purchases). Last, when comparing the emissions produced by the electric fleet of BlueLA to those of a comparable fleet of internal combustion engine vehicles, we find that the use of an EV fleet reduces GHG emissions by 43% in comparison. The BlueLA carsharing service has led to notable net reductions in VMT and thus GHG emissions, with a major share of these reductions observed among Community members.

Examining Impacts of COVID-19-Related Stay-At-Home Orders through a Two-Way Random Effects Model.

The COVID-19 pandemic has had far-reaching impacts on public health and safety, economics, and the transportation system. To reduce the spread of this disease, federal and local governments around the world have introduced stay-at-home orders and other restrictions on travel to "non-essential" businesses to implement social distancing. Preliminary evidence suggests substantial variability in the impacts of these orders in the United States, both across states and over time. This study examines this issue using daily county-level vehicle miles traveled (VMT) data for the 48 continental U.S. states and the District of Columbia. A two-way random effects model is estimated to assess changes in VMT from March 1 to June 30, 2020 as compared with baseline January travel levels. The implementation of stay-at-home orders was associated with a 56.4 percent reduction in VMT on average. However, this effect was shown to dissipate over time, which may be attributable to "quarantine fatigue." In the absence of full shelter-in-place orders, travel was also reduced where restrictions on select businesses were introduced. For example, restrictions on entertainment, indoor dining, and indoor recreational activities corresponded to reductions in VMT of 3 to 4 percent while restrictions on retail and personal care facilities showed 13 percent lower traffic levels. VMT was also shown to vary based on the number of COVID case reports, as well as with respect to other characteristics, including median household income, political leanings, and how rural the county was in nature.

Understanding the Temporal, Regional, Demographic, and Policy Factors Influencing Counties' Daily Traffic Volume Reductions in Response to COVID-19.

This research aims to understand temporal, regional, demographic, and policy factors that influenced travel reduction within the contiguous United States during the early period of the COVID-19 pandemic. Particularly, this research combines U.S. Census data, infection rates, and state-level mandates to determine their effects on daily, county-level vehicle miles traveled (VMT) estimations from March 1, 2020 to April 21, 2020. Specifically, this work generates metrics of VMT per capita, daily change in VMT, and VMT immediate reaction rates for every county in the U.S.A. and develops regression models to determine how these factors influence VMT rates over time. Results show that state-mandated orders were deployed in a pattern relative to their expected economic impact. Model results showed infection rates may have had a greater influence on forcing state policy adoption, ensuring reduced VMT, rather than the number of cases directly influencing individual travel to a significant degree. Additionally, counties with higher populations or labeled as urban counties saw a greater reduction in VMT across all three models compared with lower population and rural counties. Planners and policy makers in the future can utilize the results of this research to make better informed responses as well as to know the expected results of their actions.

Do high income households reduce driving more when living near rail transit?

Transportation planning today requires an understanding of how income and near-rail residence jointly influence household travel behavior. This article fills a gap in the literature by showing how vehicle miles traveled (VMT) and transit trips taken (TT) vary with income and rail transit access by neighborhood type. Results indicate that, when comparing households with similar incomes and examining how the “near-rail” versus “far from rail” VMT and TT gap varies by income, the cross-sectional reduction in nominal VMT and the increase in TT on a percentage basis is generally larger for higher-income households (>$50,000), and particularly so in neighborhoods dense with both jobs and population. These findings offer support for the notion that near-transit housing targeting higher-income households can have both sustainability and transit use benefits. We note, though, that equity considerations are a strong reason to include low-income housing near rail transit, and argue that policies focusing overly singly on either low-income or high-income housing near rail transit will not be as impactful as a robust focus on mixed-income housing developments in rail transit-oriented developments (TODs).

Factors associated with round-trip carsharing frequency and driving-mileage impacts in London

This study draws on respondents from Greater London within the 2016/17 wave (n = 2640) of Britain’s Annual Survey of carsharing users, which we enrich with external data from the 2011 England and Wales Census and small-area income estimates. Focusing on round-trip carsharing users, we present multivariate analyses of frequency-of-usage of carsharing vehicles and impacts on annual vehicle miles traveled (VMT). Published attribute effects from other geographic and contextual circumstances are compiled and compared (where direct comparison is possible) with the specific attribute effects that we report in this paper. We demonstrate a statistically significant link between customer satisfaction with proximity of carsharing vehicles and VMT impacts. Car ownership (both current, and changes upon joining a carsharing service) is shown to have intuitive structural impacts. We find that frequent usage is associated ceteris paribus with increasing VMT after joining a carsharing service, and that subscribing to multiple types of carsharing is associated with frequent carsharing usage and a reduction in VMT. Interestingly, we did not find any significant effect of household income on either frequency-of-usage or VMT impacts.

Assessment of effects of street connectivity on traffic performance and sustainability within communities and neighborhoods through traffic simulation

Street connectivity measures the density of networks and directness of paths. Increasing street connectivity is one of the ways to increase network capacities, achieve a better distribution of traffic flows, improve accessibility and encourage the use of non-motorized traffic modes. This paper analyzes the effects of enhanced street connectivity on traffic performance and sustainability through transportation modeling of three community-scale (mesoscopic) and three neighborhood-scale (microscopic) networks in Utah. It discusses traffic performance and sustainability effects that increased street connectivity has on different types and sizes of networks simultaneously. The analysis was performed as a part of the Utah Street Connectivity Study. On the community-scale level, the results showed a significant reduction in network travel times and delays after the implementation of increased street connectivity alternatives. Increased street connectivity on the community-scale level was compared to street widening, and outperformed it in most cases. The distribution of traffic flows was more balanced in the networks with more connectivity, with a reduction in Vehicle Miles Traveled (VMT). This study relates street connectivity performance measures on different levels to the sustainability of transportation networks. It shows that increased connectivity, especially in neighborhoods, leads to more sustainable environments.

Effects of Traffic Congestion on Vehicle Miles Traveled

The effects of traffic congestion on travel behavior are complex and multidimensional because they are related to various factors such as density, land use patterns, network connectivity, and individual preferences. Traffic congestion is a phenomenon that not only affects transportation systems but also influences commuters’ quality of life and population mobility. The present research aims to analyze the effects of traffic congestion on individuals’ travel behaviors, addressing both direct and indirect effects of congestion on vehicle miles traveled (VMT) per driver by implementing structural equation modeling (SEM) techniques. In addition to the causal analysis between traffic congestion and VMT, this study examined the complex relationship between an individual’s socioeconomic characteristics, the built environment, congestion, and VMT. Measuring local congestion at a national level is also a key contribution of this research. This study used the same methodology as the Texas A&M Transportation Institute to compute a road congestion index and quantify local congestion for 93,769 drivers within 337 metropolitan areas. Our findings suggest that congestion is the main driver of VMT reduction. The findings also confirm that residents in compact development regions have lower daily VMTs because of the proximity of origins and destinations in denser areas with higher job–population balances. Therefore, rather than expanding highway networks, public transit investment might address traffic congestion more efficiently—not only by providing residents with more equitable and sustainable means of transportation, but also by encouraging people to reside in more compact and location-efficient areas.

Leaving level-of-service behind: The implications of a shift to VMT impact metrics

Concern about climate change has led to policies in California that aim to decrease greenhouse gas (GHG) emissions from transportation. Although these policies mostly promote technological innovations, some policies aim to reduce GHG emissions by reducing the amount of driving, measured in vehicle miles traveled (VMT), through land use and transportation planning. The focus on VMT reduction represents a dramatic shift for the land use and transportation planning fields, which have traditionally prioritized auto mobility by reducing vehicle delay, measured as level of service (LOS). California has taken the bold step to replace LOS with VMT as the metric of transportation impact in the environmental review process for land use and transportation plans and projects under the California Environmental Quality Act (CEQA). This study compares these two metrics – VMT and LOS – and their implications for a sample of land use projects located in Davis, California. We compare the LOS impacts analyzed in the environmental impact reports for the projects to forecasted VMT impacts that we quantify using several available VMT estimation models. Our analysis of LOS mitigation shows how the CEQA process per se impacts the built environment, often in ways that increase vehicle capacity and thus VMT. We find that a switch to VMT metrics may lead to streamlining for projects that reduce travel demand because of their location or design, whereas LOS metrics have led communities to build expensive, capacity-increasing mitigation measures to ease vehicle delay. Finally, we show that the vehicle capacity constructed to mitigate LOS may contravene the goals and aspirations of many communities in California, as well as the state's goals for GHG reductions, and is unlikely to solve the congestion problem caused by misplaced land use development.

Dynamic Ridesharing: Exploration of Potential for Reduction in Vehicle Miles Traveled

It is widely recognized that new vehicle and fuel technologies are necessary but not sufficient to meet deep greenhouse gas reduction goals in the United States. Demand management strategies, such as land use, transit, and auto pricing policies, are also needed. These measures, however, have historically faced political challenges and have been difficult to implement. Emerging ridesharing systems now suggest the possibility of a new demand management strategy that may be more politically palatable and reduce the number of vehicle miles traveled (VMT). To date, however, little research has evaluated their potential travel effects, especially on a regional scale. This study used the San Francisco, California, Bay Area activity-based travel demand model to simulate business-as-usual, transit-oriented development, and auto pricing scenarios with and without high, medium, and low ridesharing participation levels. The analysis suggests that relatively large VMT reductions are possible from moderate and high participation levels, but at low participation levels, VMT reductions are negligible. Moderate dynamic ridesharing alone compares favorably, with a 9% reduction in VMT, to transit-oriented development and auto pricing scenarios. The analysis also suggests a potentially promising policy combination: a moderately used regional dynamic ridesharing system with a 10- to 30-cent increase in the per mile cost of auto travel, which together may reduce VMT on the order of 11% to 19%.

How much can trip chaining reduce VMT? A simplified method

Trip chaining represents a way to reduce vehicle miles traveled (VMT) that does not require people to shift away from driving private automobiles. While the existing literature on trip chaining acknowledges this potential, little has been done by way of quantifying this. This research seeks to fill this gap by using a large travel survey from the San Francisco Bay area to model the VMT generated by automobile tours as a function of tour composition (i.e., the number and type of destinations on that tour). The model results indicate that many tours involving trips chains (i.e., those tours with more than one destination) generate significantly less VMT than would occur if the destinations in these tours were split into multiple tours with single destinations. Tours that combine a work and non-work destination (which are the most common types of trip chains) particularly demonstrate potential for VMT reduction. Adding a non-work destination to a work tour is usually (depending on the specific type of destination) predicted to result in a reduction of 6–11 VMT, or about 20–30 %. Adding two non-work destinations to a work tour is usually predicted to result in a reduction of 10–22 VMT, or about 25–50 %.

Are we successful in reducing vehicle miles traveled in air quality nonattainment areas?

An important planning and policy question in the transportation, energy, and environment areas is whether or not air quality control and the associated funding preference and mitigation efforts to attain air quality conformity have indeed led to traveler behavior changes such as reduction in vehicle miles traveled (VMT) or VMT growth rates. In this research, we develop statistical models to analyze the relationship between air quality nonattainment designation and VMT between 1966 and 2004 based on observed data. These models employ different statistical methods, including hypothesis testing and simultaneous equations. Findings from these statistical models and datasets are consistent, and suggest there is a statistically significant negative correlation between nonattainment designation and VMT/VMT growth. For instance, the simultaneous equation model in this research, suggests that if a nonattainment area and an attainment area that are similar in all other aspects (population composition, socio-economics, urbanization, fuel price, vehicle stock, etc.) are compared, the VMT in the nonattainment area will be 1.80% less than that in the attainment area in the short run, and 7.61% less in the long run. While these results show strong statistical evidence that efforts in reducing VMT in nonattainment areas have been successful, future research should be conducted to attribute the VMT reduction effects to specific policy instruments for decision-making (e.g. the Congestion Management and Air Quality Improvement program, the conformity regulation in the transportation planning process, etc.).

Sustainability Tools in Action

This paper discusses how a regional greenhouse gas inventory is an important sustainability tool for coordinating land use and transportation planning across levels of government to reduce vehicle miles traveled (VMT). Many metropolitan planning organizations have taken regional leadership to reduce greenhouse gas emissions associated with the transportation sector. However, the direct connection between transportation and land use patterns requires the integration of greenhouse gas mitigation strategies into local land use planning for successful VMT reduction. This paper reviews tools developed by two metropolitan planning organizations, the North Jersey Transportation Planning Authority (NJTPA) and the Delaware Valley Regional Planning Commission, and discusses the practical application of these tools to land use planning at the county and municipal levels. The master plan for Passaic County, New Jersey, provides a case study that demonstrates successful use of the NJTPA greenhouse gas management tool for integration of greenhouse gas mitigation strategies into local planning. The paper concludes with a discussion about potential applications of greenhouse gas inventory tools and valuable lessons learned, including limitations of the tools.

Quantifying the Self-Selection Effects in Residential Location Choice with a Structural Equation Model

Land use policy is viewed as a way to mitigate congestion and alleviate greenhouse gas emissions. Many studies have confirmed the reduction effect for vehicle miles traveled (VMT) of compact and mixed land use development. However, debates on self-selection effects have arisen in recent years. Researchers argue that the correlation between land use pattern and VMT may be caused by self-selection of residential location based on a person's attitude toward traveling. This paper develops an urban form indicator for describing local land use patterns and then establishes a structural equation model (SEM) with VMT, vehicle ownership, and the urban form of residential location estimated simultaneously. Residential self-selection was controlled in two ways in the model: implicitly through the correlated error terms in multiple equations and explicitly through the incorporation of expected VMT in the equation of residential location choice. The model was estimated with household travel survey data collected in 2007 in the Washington, D.C., area. The results showed that land use itself could influence travel behavior after self-selection effects were removed. A comparison of the results of the full SEM results with that of a reference SEM confirmed the existence of self-selection effects. The comparison verified that the VMT reduction effect of land use would be exaggerated without consideration of self-selection, and self-selection effect accounted for a larger part of the total effect in more compact and better mixed development areas. However, the self-selection effect was small compared with the effect of land use itself in the analyzed case.

Separating Contextual from Endogenous Effects in Automobile Ownership Models

Using the 1997/98 New York Metropolitan Transportation Council household survey and United States Census, we estimate an instrumental variable probit model to test the impact of contextual and endogenous social interaction effects on auto ownership and determine that the probability of car ownership is affected by both types of social interaction effects. Previous research focused only either on contextual effects, or, increasingly, on endogenous effects using contextual effects variables as instruments. Therefore we were unable to find studies looking at both social interaction effects simultaneously. Consistent with earlier results, we find that households have a higher probability of possessing a vehicle if they are surrounded by other automobile-owning households (endogenous effect). However, we find that contextual effects are correctly measured only when the endogenous effect is included. In our case, everything else being equal, households in poorer neighborhoods are more likely to own vehicles, and households in neighborhoods with higher proportions of people with graduate degrees are less likely to own vehicles. This suggests that car ownership in New York City is a status symbol for poorer households and that non-car-ownership is a status symbol for people with post baccalaureate education. The results are important in two policy contexts: as auto ownership is a precursor to trip generation and mode choice, auto ownership estimation is important to effective travel forecasting; as vehicle miles traveled (VMT) is tied to auto ownership, VMT reduction strategies, as a way to improve air quality, reduce congestion, and reduce greenhouse gas emissions, may depend on strategies to reduce auto ownership. In either case, correct modeling of auto ownership will lead to more effective policy outcomes.

Are Land Use Planning and Congestion Pricing Mutually Supportive?

Problem: Congestion pricing and land use planning have been proposed as two promising strategies to reduce the externalities associated with driving, including traffic congestion, air pollution, and greenhouse gas emissions. However, they are often viewed by their proponents as substitutive instead of complementary to each other. Purpose: Using data from a pilot mileage fee program run in Portland, OR, we explored whether congestion pricing and land use planning were mutually supportive in terms of vehicle miles traveled (VMT) reduction. We examined whether effective land use planning could reinforce the benefit of congestion pricing, and whether congestion pricing could strengthen the role of land use planning in encouraging travelers to reduce driving. Methods: VMT data were collected over 10 months from 130 households, which were divided into two groups: those who paid a mileage charge with rates that varied by congestion level (i.e., congestion pricing) and those who paid a mileage charge with a flat structure. Using regression models to compare the two groups, we tested the effect of congestion pricing on VMT reduction across different land use patterns, and the effect of land use on VMT reduction with and without congestion pricing. Results and conclusions: With congestion pricing, the VMT reduction is greater in traditional (dense and mixed-use) neighborhoods than in suburban (single-use, low-density) neighborhoods, probably because of the availability of travel alternatives in the former. Under the same land use pattern, land use attributes explain more variance of household VMT when congestion pricing is implemented, suggesting that this form of mileage fee could make land use planning a more effective mechanism to reduce VMT. In summary, land use planning and congestion pricing appear to be mutually supportive. Takeaway for practice: For policymakers considering mileage pricing, land use planning affects not only the economic viability but also the political feasibility of a pricing scheme. For urban planners, congestion pricing provides both opportunities and challenges to crafting land use policies that will reduce VMT. For example, a pricing zone that overlaps with dense, mixed-use and transit-accessible development, can reinforce the benefits of these development patterns and encourage greater behavioral changes. Research support: This project was supported by the Mineta Transportation Institute, where the authors are research associates.

The role of VMT reduction in meeting climate change policy goals

This article evaluates the case for vehicle miles traveled (VMT) reduction as a core policy goal for reducing greenhouse gases (GHGs), concluding the economic impacts and social consequences would be too severe given the modest potential environmental benefits. Attempts to reduce VMT typically rely on very blunt policy instruments, such as increasing urban densities, and run the risk of reducing mobility, reducing access to jobs, and narrowing the range of housing choice. VMT reduction, in fact, is an inherently blunt policy instrument because it relies almost exclusively on changing human behavior and settlement patterns to increase transit use and reduce automobile travel rather than directly target GHGs. It also uses long-term strategies with highly uncertain effects on GHGs based on current research. Not surprisingly, VMT reduction strategies often rank among the most costly and least efficient options. In contrast, less intrusive policy approaches such as improved fuel efficiency and traffic signal optimization are more likely to directly reduce GHGs than behavioral approaches such as increasing urban densities to promote higher public transit usage. As a general principle, policymakers should begin addressing policy concerns using the least intrusive and costly approaches first. Climate change policy should focus on directly targeting greenhouse gas emissions (e.g., through a carbon tax) rather than using the blunt instrument of VMT reduction to preserve the economic and social benefits of mobility in modern, service-based economies. Targeted responses are also more cost effective, implying that the social welfare costs of climate change policy will be smaller than using broad-brushed approaches that directly attempt to influence living patterns and travel behavior.

Does telecommuting reduce vehicle-miles traveled? An aggregate time series analysis for the U.S.

. This study examines the impact of telecommuting on passenger vehicle-miles traveled (VMT) through a multivariate time series analysis of aggregate nationwide data spanning 1966–1999 for all variables except telecommuting, and 1988–1998 for telecommuting. The analysis was conducted in two stages. In the first stage, VMT (1966–1999) was modeled as a function of conventional variables representing economic activity, transportation price, transportation supply and socio-demographics. In the second stage, the residuals of the first stage (1988–1998) were modeled as a function of the number of telecommuters. We also assessed the change in annual VMT per telecommuter as well as VMT per telecommuting occasion, for 1998. The models suggest that telecommuting reduces VMT, with 94% confidence. Together with independent external evidence, the results suggest a reduction in annual VMT on the order of 0.8% or less. Even with impacts that small, when informally compared to similar reductions in VMT due to public transit ridership, telecommuting appears to be far more cost-effective in terms of public sector expenditures.

Impacts of center-based telecommuting on travel and emissions: Analysis of the Puget Sound Demonstration Project

Center-based telecommuting has many hypothesized benefits. To determine its value as a transportation demand management strategy, however, its travel-related benefits must be established quantitatively. This research provides the first analysis of the impacts of center-based telecommuting on individual travel behavior and emissions, using travel diary data from the Puget Sound Telecommuting Demonstration Project. An analysis of personal vehicle usage for this small sample of workers showed that the number of vehicle-miles traveled (VMT) was reduced significantly as a result of center-based telecommuting (from 63.25 miles per person-day on non-telecommuting days to 29.31 miles on telecommuting days). The reductions in weekday VMT comprise significant reductions in commute-related VMT with insignificant changes in non-commute-related VMT. The number of personal vehicle trips did not change significantly. In essence, center-based telecommuters behave as conventional commuters in terms of their number of trips, but are more similar to home-based telecommuters in terms of VMT reductions. The significant reduction in VMT translates into a 49% decrease in oxides of nitrogen (NO x ) emissions and a 53% decrease in particulate matter (PM) emissions comparing telecommuting days to non-telecommuting days for the small sample. Because the number of daily trips was not impacted by telecommuting, the levels of emissions associated with the cold start process, total organic gases (TOG) and carbon monoxide (CO), were essentially unaffected. Of course, region-wide impacts will be much smaller when the proportion of telecommuters in the workforce and the frequency of telecommuting is considered.