133 research outputs found

    Examining the Effects of a Bike and E-Bike Lending Program on Commuting Behavior

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    In 2015, Google added a new transportation demand management (TDM) program to increase bike commuting to their two main campuses in Mountain View and Sunnyvale, California. An initial survey of employees indicated that bike ownership and worry about maintenance were primary bicycling barriers. With this information, Google began a program that loaned high-quality electric-assisted and conventional bicycles for a period of six months at no cost to interested employees. This research evaluates the effectiveness of the program at changing travel behavior to the corporate campuses by using self-reported and smartphone-integrated travel data. The lending program at Google represents one of, if not the largest, employer-sponsored bike and e-bike lending program in North America with over 1,000 bikes in its inventory. Thus, the evaluation of this program is a critical first step for understanding how bike lending can influence travel behavior in North American suburban contexts

    Phase II

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    UC-ITS-2020-05Dock-less, electric bike-share services offer cities a new transportation option with the potential to improve environmental, social, and health outcomes. But these benefits accrue only if bike-share use replaces car travel. The purpose of this study is to examine factors influencing whether bike-share substitutes for driving and the degree to which and under what circumstances bike-share use reduces car travel. Major findings in this report include (1) bike-share in the Sacramento region most commonly substitutes for car and walking trips, (2) each bike in the Sacramento bike-share fleet reduces users\u2019 VMT by an average of approximately 2.8 miles per day, (3) areas with a higher proportion of low-income households tend to use bike-share less, (4) bike-share availability appears to induce new trips to restaurants and shopping and for recreation, (5) bike-share trips from commercial and office areas were more likely to replace walking or transit trips, while bike-share trips from non-commercial areas (and trips to home or restaurants) were more likely to replace car trips, (6) expanding the bike-share service boundary at the same fleet density decreases system efficiency and VMT reductions per bike. Our result suggests the need for an efficient rebalancing strategy specific to areas by time of day to increase the service efficiency and its benefits. Further analysis of the data used in this study to examine questions such as how bike share can improve transit connections and factors inducing bike use at the individual level will contribute to the development of more robust models and provide additional insights for bike share operation strategies and policy implementation

    American Micromobility Panel: Part 1

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    Caltrans 65A0686 Task Order 024 USDOT Grant 69A3551747114This report presents preliminary findings from the American Micromobility Panel, the largest study of shared micromobility services in the United States incorporating riders from multiple major operators. Micromobility services (bike-share and scooter-share) have recently emerged in many U.S. cities. Given that the substitution of bicycling, scooting, and other small vehicle travel for car travel will help cities reach numerous planning goals (e.g., accessibility, emissions, climate, health, equity, etc.), there is a need for understanding the effects of these mobility services. The purpose of this study was to examine the impact of micromobility services on travel behavior and outcomes such as mode shift, car ownership, access, equity, safety, and physical activity

    Bicyclist Behavior in San Francisco: A Before-and-After Study of the Impact of Infrastructure Investments

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    65A0527 TO 012This study explores bicyclist behavior in San Francisco using data collected before and after major bike infrastructure investments. From early 2011 to December 2013, investments of $3.3 million correlated with a 14% increase in counts of bicyclists, part of a 96% increase in bicyclist counts from 2006 to 2013. To better understand the relationship between these investments and changes in bicycling behavior, the authors build on the successful GPS travel survey conducted in 2010 by the San Francisco County Transportation Authority (SFCTA). The authors used data from the smartphone based GPS data collection method (the CycleTracks application developed by SFCTA) which records bicyclists\u2019 routes. In addition, they administered a detailed web-based survey to CycleTracks users in order to better characterize the factors associated with their bicycling behavior. The authors examine the relationship between bicycle infrastructure and behavior of bicyclists so as to assess the effectiveness of existing investments, and to provide guidance on efforts that are effective at increasing bicycling

    Impacts of E-Bike Ownership on Travel Behavior: Evidence From Three Northern California Rebate Programs

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    Caltrans 65A0686 Task Order 060 USDOT Grant 69A3551747114E-bike incentive programs are being utilized across the United States to encourage the adoption of active transportation. This study assesses the impacts of three e-bike rebate programs in Northern California using survey results gathered by each agency. Three research questions are answered through this study: \u201cHow has e-bike ownership impacted the mode choices, trip purpose, and travel frequency of our sample?\u201d, \u201cHow much do e-bike rebate recipients reduce their mobile greenhouse gases (GHGs)?\u201d, and \u201cHow did the design of each program impact who was able to participate and the program outcomes?\u201d. To answer these, the research team merged and cleaned the survey data from the three programs, explored descriptive statistics, and undertook an estimation of GHG emissions reductions. This analysis highlighted changes in travel behavior, car travel replacement, the impact of program designs, and various equity impacts. E-bike recipients reported more regular bike use after getting their e-bike, although their frequency of bike travel began to decline in the long-term. Respondents also reported high rates of occasional car trip replacement (1-3 times per week and 1-3 times per month). The vast majority of e-bike use in the sample was for recreational travel. Although the GHG reductions analysis estimated a monthly diversion of 12-44 kilograms of CO2 per rebate participant. The authors conclude with an equity analysis that explores how program design influenced who participated in these rebate programs. This found that low-income requirements are successful at targeting those with the most need for financial assistance, though these requirements do not help meet other equity metrics by association
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