Better data on pedestrian volumes are needed to improve the safety, comfort, and convenience of pedestrian movement. This requires more carefully-developed methodologies for counting pedestrians as well as improved methods of modeling pedestrian volumes. This paper describes the methodology used to create a simple, pilot model of pedestrian intersection crossing volumes in Alameda County, CA. The model is based on weekly pedestrian volumes at a sample of 50 intersections with a wide variety of surrounding land uses, transportation system attributes, and neighborhood socioeconomic characteristics. Three alternative model structures were considered, and the final recommended model has a good overall fit (adjusted-R 2 =0.897). Statistically-significant factors in the model include the total population within a 0.5-mile radius, employment within a 0.25-mile radius, number of commercial retail properties within a 0.25-mile radius, and the presence of a regional transit station within a 0.1-mile radius of an intersection. The model has a simple structure, and it can be implemented by practitioners using geographic information systems and a basic spreadsheet program. Since the study is based on a relatively small number of intersections in one urban area, additional research is needed to refine the model and determine its applicability in other areas.

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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.

Resources for implementing countermeasures to reduce pedestrian collisions in urban centers are usually allocated on the basis of need, which is determined by risk studies. They commonly rely on pedestrian volumes at intersections. The methods used to estimate pedestrian volumes include direct counts and surveys, but few studies have addressed the accuracy of these methods. This paper investigates the accuracy of three common counting methods: manual counts using sheets, manual counts using clickers, and manual counts using video cameras. The counts took place in San Francisco. For the analysis, the video image counts, with recordings made at the same time as the clicker and sheet counts, were assumed to represent actual pedestrian volume. The results indicate that manual counts with either sheets or clickers systematically underestimated pedestrian volumes. The error rates range from 8-25%. Additionally, the error rate was greater at the beginning and end of the observation period, possibly resulting from the observer's lack of familiarity with the tasks or fatigue.

Washington State legalized recreational cannabis consumption in December 2012. We used data on all drivers involved in fatal crashes in Washington in years 2008–2019 (n=8,282) to estimate prevalence in fatal crashes of drivers with ∆9-tetrahydrocannabinol (THC, the main psychoactive compound in cannabis) in their blood before and after legalization. However, nearly half of the drivers were not tested for drugs; we used multiple imputation to estimate THC presence and concentration among them. We used logistic regression followed by marginal standardization to estimate the adjusted prevalence of THC-positive drivers after legalization relative to what would have been predicted without legalization. In the combined observed and imputed data, the proportion of drivers positive for THC was 9.3% before and 19.1% after legalization (adjusted Prevalence Ratio: 2.3, 95% Confidence Interval: 1.3, 4.1). The proportion of drivers with high THC concentrations increased substantially (adjusted Prevalence Ratio: 4.7, 95% Confidence Interval: 1.5, 15.1). Some of the increased prevalence of THC-positive drivers might have reflected cannabis use unassociated with driving; however, the increased prevalence of drivers with high THC concentrations suggests increased prevalence of driving shortly after using cannabis. Other jurisdictions should compile quantitative data on drug test results of drivers to enable surveillance and evaluation.