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-R2=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.

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 hit-and-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.

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.

Accurate estimates of pedestrian volume are important for analyzing pedestrian movement and safety; methods to estimate these volumes are continuously evolving and being improved. However, relatively little is known about the impact of weather conditions on pedestrian activity. This paper evaluates the effect of weather by including temperature, cloud cover, wind, and precipitation variables in a linear regression model of pedestrian volumes. Pedestrian volumes were collected over approximately one year using automated counters at 13 different locations in Alameda County, California. These volumes were compared with weather data available from nearby weather stations. Results show that several weather variables had a significant influence on pedestrian volumes during certain time periods. Rain had the largest effect on pedestrian volumes at a given location, though clouds, wind, and both hot and cold temperatures were also shown to decrease volumes. This study demonstrates the importance of accounting for weather when analyzing pedestrian volumes. Future research should attempt to understand how the effect of weather conditions on pedestrian volumes varies by geographic region, time period, and local land use and site characteristics.

The purpose of this guide is to present the major risk factors associated with teen driving in California and to highlight policy and program strategies that may be influential in reducing risk.

Each year from 1998 to 2007, an average of approximately 4,800 pedestrians were killed and 71,000 pedestrians were injured in United States traffic crashes. Because many pedestrian crashes occur at roadway intersections, it is important to understand the intersection characteristics that are associated with pedestrian crash risk. This study uses detailed pedestrian crash data and pedestrian volume estimates to analyze pedestrian crash risk at 81 intersections along arterial and collector roadways in Alameda County, California. The analysis compares pedestrian crash rates (crashes per 10,000,000 pedestrian crossings) with intersection characteristics. In addition, more than 30 variables were considered for developing a statistical model of the number of pedestrian crashes reported at each study intersection from 1998 to 2007. After accounting for pedestrian and motor vehicle volume at each intersection, negative binomial regression shows that there were significantly more pedestrian crashes at intersections with more right-turn-only lanes, more non-residential driveways within 50 feet (15 m), more commercial properties within 0.1 miles (161 m), and a greater percentage of residents within 0.25 miles (402 m) who are younger than age 18. Raised medians on both intersecting streets were associated with lower numbers of pedestrian crashes. These results, viewed in combination with other research findings, can be used by practitioners to design safer intersections for pedestrians. This exploratory study also provides a methodological framework for future pedestrian safety studies.