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San Francisco, CA, United States

Schneider R.,University of California at Berkeley | Henry T.,Fehr and Peers Transportation Consultants | Mitman M.,Fehr and Peers Transportation Consultants | Stonehill L.,San Francisco Municipal Transportation Agency | Koehler J.,San Francisco County Transportation Authority
Transportation Research Record | Year: 2012

The process of modeling pedestrian volume in San Francisco, California, refined the methodology used to develop previous intersection-based models and incorporated variables that were tailored to estimate walking activity in the local urban context. The methodology included two main steps. First, manual and automated pedestrian counts were taken at a sample of 50 study intersections with a variety of characteristics. A series of factor adjustments was applied to produce an estimate of annual pedestrian crossings at each intersection. Second, log-linear regression modeling was used to identify statistically significant relationships between the estimate of annual pedestrian volume and land use, transportation system, local environment, and socioeconomic characteristics near each intersection. Twelve alternative models were considered, and the preferred model had a good overall fit (adjusted R 2=.804). As identified in other communities, pedestrian volumes were positively associated with the number of households and the number of jobs near each intersection. This San Francisco model also found significantly higher pedestrian volumes at intersections (a) in high-activity zones with metered on-street parking, (b) in areas with fewer hills, (c) near university campuses, and (d) under the control of traffic signals. Because the model was based on a relatively small sample of intersections, the number of significant factors was limited to six. Results are being used by public agencies in San Francisco to understand the risks of pedestrian crossings better and to inform citywide pedestrian safety policy and investment. Source


Feldman M.,Fehr and Peers Transportation Consultants | Manzi J.G.,San Francisco Municipal Transportation Agency | Mitman M.F.,Fehr and Peers Transportation Consultants
Transportation Research Record | Year: 2010

The empirical Bayesian method, currently the industry standard for before-and-after collision analysis, was used to perform post hoc tests on the efficacy of high-visibility school (yellow, continental-style) crosswalks in the city of San Francisco, California. Statistical analysis compared the number of collisions predicted for the after period had the enhanced crosswalks not been installed with the number of collisions observed. The analysis used data for 54 treated intersections with high-visibility crosswalks and 54 control intersections, each chosen for its geographical proximity to a treated intersection. The results from this analysis suggest a statistically significant reduction in the numbers of collisions at the intersections with high-visibility crosswalks. The estimated reduction is 37%, with the 95% confidence interval ranging from 13% to 60%. Potential limitations of this analysis, including a constant traffic volume input over time and a background reduction in collisions citywide, are discussed. In addition to the safety benefit attributable to high-visibility crosswalk markings, high-visibility crosswalks likely contribute to a sense of pedestrian comfort and overall design amenity. Future studies would enhance these results by evaluating other factors that may affect pedestrian safety at school crosswalks, such as changes in driver or pedestrian behavior and increased awareness of crosswalks and pedestrian activity. Source


Lee J.,San Francisco Municipal Transportation Agency
Transportation Research Record | Year: 2011

When transit customers pay fares, they contribute their fair share to help fund service. In San Francisco, California, anecdotal observations had reinforced perceptions that a high percentage of Muni (the San Francisco Railway transit system) riders were not paying, possibly costing the San Francisco Municipal Transportation Agency (SFMTA) tens of millions of dollars annually in lost revenue. In 2009, SFMTA, which operates Muni, conducted a proof-of-payment study to answer long-standing questions about fare payment patterns and identify strategies to improve fare enforcement. The resulting survey of 41,239 customers on 1,141 vehicle runs provided enough samples by time period, route, and vehicle occupancy to identify fare payment patterns at a disaggregated level. The study found a 9.5% minimum systemwide fare evasion rate that varied by route, location, time period, level of enforcement, and door of entry and amounted to an estimated $19 million annually in uncaptured revenue on the basis of 2009 fares. Although surveyors observed that there was no typical violator, the data showed that fare evasion was more prevalent on certain routes and during the afternoon and evening hours. Besides providing base data to measure future progress, the study enabled SFMTA to educate its customers about proof-of-payment requirements and deploy its fare enforcement personnel more efficiently and cost-effectively in an effort to improve fare compliance. Source


Wang X.,University of Southern California | Lindsey G.,University of Minnesota | Schoner J.E.,University of Minnesota | Harrison A.,San Francisco Municipal Transportation Agency
Journal of Urban Planning and Development | Year: 2016

The purpose of this research is to identify correlates of bike station activity for Nice Ride Minnesota, a bike share system in the Minneapolis-St. Paul Metropolitan Area in Minnesota. The number of trips to and from each of the 116 bike share stations operating in 2011 was obtained from Nice Ride Minnesota. Data for independent variables included in the proposed models come from a variety of sources, including the 2010 U.S. Census; the Metropolitan Council, a regional planning agency; and the Cities of Minneapolis and St. Paul. Log-linear and negative binomial regression models are used to evaluate the marginal effects of these factors on average daily station trips. The models have high goodness of fit, and each of 13 independent variables is significant at the 10% level or higher. The number of trips at Nice Ride stations is associated with neighborhood sociodemographics (i.e., age and race), proximity to the central business district, proximity to water, accessibility to trails, distance to other bike share stations, and measures of economic activity. Analysts can use these results to optimize bike share operations, locate new stations, and evaluate the potential of new bike share programs. © 2015 American Society of Civil Engineers. Source


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San Francisco Municipal Transportation Agency | Date: 2015-09-21

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