Crash Frequency Analysis

Modeling highway traffic crash frequency is an important approach for identifying high crash risk areas that can help transportation agencies allocate limited resources more efficiently, and find preventive measures. This paper applies a Poisson regression model, Negative Binomial regression model and then proposes an Artificial Neural Network model to analyze the 2008-2012 crash data for the Interstate I-90 in the State of Minnesota in the US. By comparing the prediction performance between these three models, this study demonstrates that the Neural Network is an effective alternative method for predicting highway crash frequency.

Predicting injury severity and crash frequency:Insights into the impacts of geometric variables on downgrade crashes in Wyoming

Road deaths,injuries and property damage places a huge burden on the economy of most nations.Wyoming has one of the highest truck-related fatality rates among the states in the US.The high crash rates observed in the state is as a result of many factors mainly related to the challenging mountainous terrain in the state,which places extra burden on truck drivers in terms of requiring higher levels of alertness and driving skills.The difficult geometry of roads characteristic of mountainous terrain in terms of steep grade lengths adds extra risks of fatalities or injuries occurring as a result of a crash.These risks are more pronounced for truck-related crashes due to their weight and sizes.As part of the measures to reduce the incidence of truck-related crashes on mountainous areas,the Wyoming Department of Transportation(WYDOT)initiated a study to investigate causes of truck crashes on downgrade areas of Wyoming.Several studies have investigated the contributory factors to severe injury crashes but the focus has mostly been on level sections.This study analyzed the contributory geometric factors of truck crashes on downgrades by estimating three crash prediction negative binomial models.These models took into account the injury severity of the crashes.The results indicate that downgrade length,shoulder width,horizontal curve length,number of lanes,number of access points and truck traffic on the highway all impact truck-related crashes and injury frequencies ondowngrades in Wyoming.The results of this study will be helpful to future downgrade road design policy aimed at reducing downgrade truck related crashes.

Statistical modeling of total crash frequency at highway intersections

Intersection-related crashes are associated with high proportion of accidents involving drivers, occupants, pedestrians, and cyclists. In general, the purpose of intersection safety analysis is to determine the impact of safety-related variables on pedestrians, cyclists and vehicles, so as to facilitate the design of effective and efficient countermeasure strategies to improve safety at intersections. This study investigates the effects of traffic, environ- mental, intersection geometric and pavement-related characteristics on total crash fre- quencies at intersections. A random-parameter Poisson model was used with crash data from 357 signalized intersections in Chicago from 2004 to 2010. The results indicate that out of the identified factors, evening peak period traffic volume, pavement condition, and unlighted intersections have the greatest effects on crash frequencies. Overall, the results seek to suggest that, in order to improve effective highway-related safety countermeasures at intersections, significant attention must be focused on ensuring that pavements are adequately maintained and intersections should be well lighted. It needs to be mentioned that, projects could be implemented at and around the study intersections during the study period （7 years）, which could affect the crash frequency over the time. This is an important variable which could be a part of the future studies to investigate the impacts of safety related works at intersections and their marginal effects on crash frequency at signalized intersections.

Role of street patterns in zone-based traffic safety analysis

Although extensive analyses of road segments and intersections located in urban road networks have examined the role of many factors that contribute to the frequency and severity of crashes, the explicit relationship between street pattern characteristics and traffic safety remains underexplored. Based on a zone-based Hong Kong database, the Space Syntax was used to quantify the topological characteristics of street patterns and investigate the role of street patterns and zone-related factors in zone-based traffic safety analysis. A joint probability model was adopted to analyze crash frequency and severity in an integrated modeling framework and the maximum likelihood estimation method was used to estimate the parameters. In addition to the characteristics of street patterns, speed, road geometry, land-use patterns, and temporal factors were considered. The vehicle hours was also included as an exposure proxy in the model to make crash frequency predictions. The results indicate that the joint probability model can reveal the relationship between zone-based traffic safety and various other factors, and that street pattern characteristics play an important role in crash frequency prediction.