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Crashes that occur on freeway ramps and in the vicinity of ramps were analyzed using a variety of statistical methods. The data used in this study are the 6 year records of crashes that occurred on 98 ramps of a 25 mile section of Interstate-4 freeway in the Orlando, Florida, metro area. Log linear models were used to identify the relationship among the frequency of ramp crashes, ramp geometry, and ramp traffic volume. Binary logit models and bivariate probit models were developed to observe the propensity of specific type of ramp crashes in terms of curvature of ramps, number of vehicles involved in crashes, and collision type. The binary logit model was also used to identify the factors correlated with injury severity of drivers involved in ramp crashes. The results of model estimates showed that crash characteristics differ by the location of crashes with respect to ramps: ramp related and ramp intersection related crashes. It was found that the presence of advisory speed signs potentially reduces the likelihood of the occurrence of crashes on off ramps and rear-end crashes on ramps. Finally, it was found that female drivers, single vehicle crashes, and shorter ramp length are correlated with higher probability of fatal and severe crashes on ramps. The findings suggest some important factors closely related to ramp crash risk and hidden relationships that have not been identified in the past. [ABSTRACT FROM AUTHOR]

Researchers have put great efforts in quantifying Crash Modification Factors (CMFs) for diversified treatment types. In the Highway Safety Manual (HSM), CMFs have been identified to predict safety effectiveness of converting a stop-controlled to a signal-controlled intersection (signalization) and installing Red Light Running Cameras (RLCs). Previous studies showed that both signalization and adding RLCs reduced angle crashes but increased rear-end crashes. However, some studies showed that CMFs varied over time after the treatment was implemented. Thus, the objective of this study is to investigate trends of CMFs for the signalization and adding RLCs over time. CMFs for the two treatments were measured in each month and 90-day moving windows respectively. The ARMA time series model was applied to predict trends of CMFs over time based on monthly variations in CMFs. The results of the signalization show that the CMFs for rear-end crashes were lower at the early phase after the signalization but gradually increased from the 9th month. On the other hand, the CMFs for angle crashes were higher at the early phase after adding RLCs but decreased after the 9th month and then became stable. It was also found that the CMFs for total and fatal/injury crashes after adding RLCs in the first 18 months were significantly greater than the CMFs in the following 18 months. This indicates that there was a lag effect of the treatments on safety performance. The results of the ARMA model show that the model can better predict trends of the CMFs for the signalization and adding RLCs when the CMFs are calculated in 90-day moving windows compared to the CMFs calculated in each month. In particular, the ARMA model predicted a significant safety effect of the signalization on reducing angle and left-turn crashes in the long term. Thus, it is recommended that the safety effects of the treatment be assessed using the ARMA model based on trends of CMFs in the long term after the implementation of the treatment. [ABSTRACT FROM AUTHOR]

This study examines the impact of passengers on the driver's crash potential on freeways. To estimate the impact, a set of bivariate probit models were developed using the 5-year (1999-2003) crash records on a 36.3-mile stretch of Interstate-4 freeway (I-4) in Orlando, Florida. Bivariate probit models identify the correlation between potentially inter-related choices of three passenger characteristics and three crash characteristics. The analysis using bivariate probit models showed that there exist strong correlations between passenger and crash characteristics. It was found that drivers generally display safer driving behavior when they are accompanied by passengers, and more passengers reduce driver's crash potential. It was also found that younger driver's crash potential increases with the presence of a younger passenger only. In addition, the analysis of crash type using traffic flow parameters at the time of crashes showed that young drivers with only younger passengers are more likely to be involved in single-vehicle crashes in high-speed and low-volume conditions. The findings in this study provide insight into how the presence of passengers has an impact on driver behavior and traffic safety in various conditions.

Problem: There is a growing concern with the safety of school-aged children. This study identifies the locations of pedestrian/bicyclist crashes involving school-aged children and examines the conditions when these crashes are more likely to occur., Method: The 5-year records of crashes in Orange County, Florida where school-aged children were involved were used. The spatial distribution of these crashes was investigated using the Geographic Information Systems (GIS) and the likelihoods of crash occurrence under different conditions were estimated using log-linear models., Results: A majority of school-aged children crashes occurred in the areas near schools. Although elementary school children were generally very involved, middle and high school children were more involved in crashes, particularly on high-speed multi-lane roadways. Driver's age, gender, and alcohol use, pedestrian's/bicyclist's age, number of lanes, median type, speed limits, and speed ratio were also found to be correlated with the frequency of crashes., Discussion: The result confirms that school-aged children are exposed to high crash risk near schools. High crash involvement of middle and high school children reflects that middle and high schools tend to be located near multi-lane high-speed roads., Impact on Industry: The pedestrian's/bicyclist's demographic factors and geometric characteristics of the roads adjacent to schools associated with school children's crash involvement are of interest to school districts.

This study analyzes vehicle-pedestrian crashes at intersections in Florida over 4 years, 1999-2002. The study identifies the group of drivers and pedestrians, and traffic and environmental characteristics that are correlated with high pedestrian crashes using log-linear models. The study also estimates the likelihood of pedestrian injury severity when pedestrians are involved in crashes using an ordered probit model. To better reflect pedestrian crash risk, a logical measure of exposure is developed using the information on individual walking trips in the household travel survey. Lastly, the impact of average traffic volume on pedestrian crashes is examined. As a result of the analysis, it was found that pedestrian and driver demographic factors, and road geometric, traffic and environment conditions are closely related to the frequency and injury severity of pedestrian crashes. Higher average traffic volume at intersections increases the number of pedestrian crashes; however, the rate of increase is steeper at lower values of average traffic volume. Based on the findings in the analysis, some countermeasures are recommended to improve pedestrian safety.