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Bicycle volume data are useful for practitioners and researchers to understand safety, travel behavior, and development impacts. Several simple models of bicycle intersection volumes were developed for Alameda County, California. The models were based on 2-h bicycle counts performed at a sample of 81 intersections in the spring of 2008 and 2009. Study sites represented areas with a wide range of population density, employment density, proximity to commercial property, neighborhood income, and street network characteristics. The explanatory variables considered for the models included intersection site, land use, transportation system, and socioeconomic characteristics of the areas surrounding each intersection. Four alternative models were developed with adjusted R2 values ranging from .39 to .60. The models showed that bicycle volumes tended to be higher at intersections surrounded by more commercial retail properties within1.10mi, closer to a major university, with a marked bicycle facility on at least one leg of the intersection, surrounded by less hilly terrain within1.2mi, or surrounded by a more connected roadway network. The models also showed several important differences between weekday and weekend intersection volumes. The positive association between bicycle volume and proximity to retail properties or a large university was greater on weekdays than on weekends, whereas bicycle facilities had a stronger positive association and hilly terrain had a weaker negative association with bicycle volume on weekends than on weekdays. The study found that further testing and refinement was necessary before accurate count predictions could be made in Alameda County or other communities.

The maximal rates that buses can discharge from bus stops are examined. Models were developed to estimate these capacities for curbside stops that are isolated from the effects of traffic signals. The estimates account for key features of the stops, including their target service levels assigned to them by a transit agency. Among other things, the models predict that adding bus berths to a stop can sometimes return disproportionally high gains in capacity. This and other of our findings are at odds with information furnished in professional handbooks.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Because hit-and-run crashes account for a significant share of pedestrian fatalities, a better understanding of these crashes will assist efforts to reduce pedestrian fatalities. Of the more than 48,000 pedestrian deaths that were recorded in the United States between 1998 and 2007 (Fatality Accident Reporting System [FARS]), 18.1% of them were the victims of hit-and-run crashes, and the percentage of fatal pedestrian hit-and-runs has been rising as the number of all pedestrian fatalities has decreased. Using FARS data on single pedestrian fatal victim crashes between 1998-2007, logistic regression analyses were conducted to identify factors related to hitand-run and to identify factors related to the identification of the hit-and-run driver. Results indicate an increased risk of hit-and-run in the early morning, during non-daylight, and on the weekend. Results also indicate that certain driver demographic characteristics (young, male), behavior (notably alcohol use), and history (e.g., suspended license or history of DWI/DUI convictions) are associated with hit-and-run. There also appears to be an association between the type of victim and the likelihood of the driver being identified. Alcohol use and early morning, the time frame when persons may be leaving bars, were among the leading factors that increased the risk of hit-and-run. Reducing alcohol-related crashes could substantially reduce pedestrian fatalities as a result of hit-and-run. Driver characteristics will assist in the development of countermeasures, however, more information about this population may be necessary.TRB 2010 Annual Meeting CD-ROM Paper revised from original submittal.

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