This paper examines three types of pedestrian volume models in light of their usefulness for estimating pedestrian exposure for pedestrian safety research. The need for pedestrian flow data as part of pedestrian exposure and safety analysis is outlined, and the background of each type of model is discussed. It then selects the space syntax network analysis model to estimate pedestrian volumes for the city of Boston, Massachusetts. It was found that the model was able to accurately predict pedestrian flows (r-squared 0.81, p-value < 0.0001) after incorporating distance to transit stops and major tourist attractions. These findings suggest that in addition to estimating pedestrian volumes in geographic locations where data is not available, pedestrian volume modeling can also be useful for estimating pedestrian volumes in future conditions. Planning and policy implications are discussed, as are directions for future research.

This paper describes an innovative pedestrian modeling technique known as Space Syntax, which was used to create estimates of pedestrian volumes for the city of Oakland, California. These estimates were used to calculate pedestrian exposure rates and to create a Relative Risk Index for the city’s first pedestrian master plan. A major challenge facing planners, transportation engineers, and pedestrian-safety advocates is the lack of detailed and high quality pedestrian-exposure data. Exposure is defined as the rate of contact with a potentially harmful agent or event. Pedestrian exposure is therefore defined as the rate of pedestrian contact with potentially harmfully situations involving moving vehicles (e.g., crossing an intersection). Pedestrian risk is defined as the probability that a pedestrian-vehicle collision will occur, based on the rate of exposure. To estimate exposure, pedestrian volume measurements must be made, but such measurements not easily available. In the absence of accurate exposure data, pedestrian-safety decisions are often made by estimation, rules of thumb, or political influence, resulting in mixed and potentially less effective outcomes. This paper also explores the value of the Space Syntax volume-modeling approach for generating estimates of pedestrian exposure, using the City of Oakland as a case study. It discusses the method’s theoretical background, data requirements, implementation, and results. The author suggests that the output of the model - city-wide pedestrian volume estimates - is useful to pedestrians, planners and transportation engineers, and he discusses the value of the pedestrian-exposure concept for the planning professional.

This paper examines three types of pedestrian volume models in light of their usefulness for estimating pedestrian exposure for pedestrian safety research. The need for pedestrian flow data as part of pedestrian exposure and safety analysis is outlined, and the background of each type of model is discussed. It then selects the space syntax network analysis model to estimate pedestrian volumes for the city of Boston, Massachusetts. It was found that the model was able to accurately predict pedestrian flows (r-squared 0.81, p-value < 0.0001) after incorporating distance to transit stops and major tourist attractions. These findings suggest that in addition to estimating pedestrian volumes in geographic locations where data is not available, pedestrian volume modeling can also be useful for estimating pedestrian volumes in future conditions. Planninimplications are discussed, as are directions for future research.

This paper presents a new method for forecasting cyclist volume and route choice based on space syntax techniques for urban analysis. Space syntax has been shown to correlate strongly with pedestrian and vehicular trips in a number of international studies, but little research to date has focused on the role of urban form and street network design in cyclist route choice. This paper addresses this gap by analyzing the distribution of cycling trips in the central London area, focusing on a sample of work-based commuting trips. A sample of 423 cyclists from 50 organizations was combined with cordon volume counts at several Central London locations. It was found that individual cycle trips were subject to a wide range of variables that made individual traces difficult to predict, but that total cyclist volumes corresponded strongly with the most accessible, direct streets in the urban network. This research suggests that angular minimization is an important factor in cyclist route choice and that measurement of least angle routes in urban environments can be a useful way of predicting cyclist volumes and route choice. Such techniques have the potential to save planners and policymakers the expense of performing origin destination studies and may offer a useful tool for cyclist volume prediction.

The primary objective of this paper is to review the appropriate use of ratio variables in the study of pedestrian injury exposure. We provide a discussion that rejects the assumption that the relationship between a random variable (e.g., a population X) and a ratio (e.g., injury or disease per population Y/X) is necessarily negative. In the study of pedestrian risk, the null hypothesis is that pedestrian injury risk is constant with respect to pedestrian volume. This study employs a unique data set containing the number of pedestrian collisions, average annual pedestrian volume, average annual vehicle volume, and physical intersection characteristics for 247 intersections in Oakland, California. We use a GLM to estimate the expected injury risk given average annual pedestrian volume and other explanatory variables. Consistent with studies by Leden, Ekman and Jacobsen, the null hypothesis is rejected. Indeed, the risk of collision for pedestrians decreases with increasing pedestrian flows, and it increases with increasing vehicle flows. We also find that pedestrians are more likely to be struck by motorists in commercial and mixed areas than in residential areas.

The primary objective of this paper is to review the appropriate use of ratio variables in the study of pedestrian injury exposure. We provide a discussion that rejects the assumption that the relationship between a random variable (e.g., a population X) and a ratio (e.g., injury or disease per population Y/X) is necessarily negative. In the study of pedestrian risk, the null hypothesis is that pedestrian injury risk is constant with respect to pedestrian volume. This study employs a unique data set containing the number of pedestrian collisions, average annual pedestrian volume, average annual vehicle volume, and physical intersection characteristics for 247 intersections in Oakland, California. We use a GLM to estimate the expected injury risk given average annual pedestrian volume and other explanatory variables. Consistent with studies by Leden, Ekman and Jacobsen, the null hypothesis is rejected. Indeed, the risk of collision for pedestrians decreases with increasing pedestrian flows, and it increases with increasing vehicle flows. We also find that pedestrians are more likely to be struck by motorists in commercial and mixed areas than in residential areas.