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Construction of bypass lanes at rural intersections has typically been considered a low-cost highway safety improvement by the transportation community. However, this needs to be quantitatively evaluated so that the decisions could be made on whether to continue with adding bypass lanes. Highway safety analyses utilize two common approaches to evaluate the effectiveness of a geometric treatment: before-and-after study and cross-sectional study. This paper explains the results using a cross-sectional study approach, where intersections with bypass lanes were compared to intersections with no bypass lanes for which crash data were obtained for more than 1,100 intersections in Kansas. Both 3-legged and 4-legged intersections were taken into consideration separately by looking at intersection-related crashes and crashes within an intersection box. According to the results, the number of crashes and crash severities were lower at 3-legged intersections with bypass lanes compared with 3-legged intersections without bypass lanes, even though these reductions were not statistically significant at 95% level. When considering a 300-ft. intersection box, statistically significant crash reductions were observed at 4-legged intersections, for all considered crash and crash rate categories. When considering 90% level, crash reduction at 3-legged intersections was also statistically significant when considering a 300-ft. intersection box. Crash modification factors (CMFs) calculated to evaluate safety effectiveness of bypass lanes at unsignalized rural intersections in Kansas showed values less than 1.0 for almost all cases, indicating safety benefits of bypass lanes. Accordingly, it is beneficial to continue with the practice of adding shoulder bypass lanes at rural unsignalized intersections on two-lane roads where the traffic volumes are relatively low.

Transportation Statistics and Microsimulation (Clifford H. Spiegelman, Eun Sug Park, Laurence R. Rilett)

Freight can be efficiently transported between most locations in the U.S. using large trucks. Involvement of large trucks in crashes can cause much damage and serious injuries, due to their large sizes and heavy weights. The purpose of this study was to identify the relationships between large truck crashes and traffic and geometric characteristics on limited access highways. Crash and traffic and geometric-related data for Kansas were utilized to develop a Poisson regression model and a negative binomial regression model for understanding the relationships. Based on modelfitting statistics, the negative binomial model was found to be the better model, which was used to identify the important characteristics. By addressing identified factors, safety could be promoted through introduction of appropriate engineering improvements.

Young drivers have higher motor vehicle crash rates compared to other drivers, and understanding the reasons for this would help to improve safety. This study, therefore, investigated characteristics and contributory causes of young-driver crashes and developed multinomial logit models to identify severity affecting factors. It was found that teen drivers were more likely to be involved in crashes due to failure to give time and attention and falling asleep. Among other factors, alcohol involvement, not wearing a seat belt, driving without a valid license, having restrictions on driver’s license, and involvement in off-roadway crashes were factors that increased young-driver injury severity. Based on identified factors, countermeasure ideas for improving safety have also been suggested.

Young drivers have elevated motor vehicle crash rates compared to other drivers. The study investigates characteristics of young driver crashes that took place in Kansas from year 2006 to 2008 by comparing them with more experienced drivers. In order to calculate the population-based rates, number of licensed drivers in each group was taken into account, where the data were taken from the United States Department of Transportation (USDOT). Annual vehicle miles traveled were obtained from National Household Travel Survey data to calculate the exposure-based crash rates. Young drivers were divided into two groups: aged 15-19 (teen) and aged 20-24 (young-adult) for detailed investigation. Multinomial logit models were developed, and likelihood ratio (LR) estimates and odds ratios (ORs) were used to identify overly represented characteristics and contributory causes of young driver crashes. Teen drivers were more likely to involve in crashes due to failure to give time and attention, falling asleep, failure to yield right of way, and distractions. Alcohol involvement, driving without a valid license, having restrictions on driver’s license, and involvement on off-roadway crashes were significant factors which increase the young driver injury severity. Driving with a valid license and wearing seat belt decrease the young driver injury severity. Based on the identified factors, crash mitigation strategies are presented.

This study examines the trends, characteristics, as well as contributory causes associated with run-off-road (ROR) and non-run-off-road (NROR) crashes. Likelihood ratios of these causes for ROR crashes with respect to NROR crashes are assessed using the Bayesian Statistical Approach. Nighttime, weekends, adverse weather, rural area, gravel and curved roads, higher speed limits, wet and icy road surface, and utility vehicles are found to be the common characteristics of ROR crashes. Fell asleep, ill or medical condition, driving under the influence, too fast for conditions, tires and wheels, strong winds, freezing rain, shoulders, ruts, holes, and bumps are found to have the greatest likelihood ratios and as such have a greater role in contributing to ROR crashes than NROR crashes.

Older drivers tend to be involved in more severe crashes compared to middle-aged drivers, and U.S. Census Population statistics indicate that the older-driver population is rapidly increasing. Therefore, an improvement in older-driver safety is both important and necessary. In this analysis, a statistical modeling technique was used to identify factors contributing to older-driver injury severity. Two separate models were developed for rural and urban locations, which incorporated several potential explanatory variables. Speed, gender, presence of passengers, road type and street-lighting conditions were found to be important factors affecting injury severity of older drivers on both rural and urban roads.

This study uses the multiple logistic regression method to estimate the effectiveness of seat belts in reducing fatal and nonfatal injuries to occupants involved in motor vehicle crashes. Two vehicle groups - passenger cars, and other passenger vehicles that included vans and pickup trucks - were considered in estimating seat belt effectiveness values using highway crash data in Kansas. According to the results, the estimated effectiveness of seat belts in reducing fatal injuries is 56% for passenger car occupants and 61% for others. In passenger cars, seat belts are 53% and 55% effective in reducing incapacitating and non-incapacitating injuries, respectively. In the case of other passenger vehicles, corresponding values are found to be 52% and 51%.

This study made an effort to identify critical factors contributing to increased crash severities on rural highways. Crash data from the Kansas Accident Reporting System (KARS) database was analyzed and crash severity was modeled using ordered choice models. Many driver-related factors, such as alcohol involvement, lack of seat belt usage, excessive speed, and driver ejections because of the crash contribute to the increased severity of crashes in rural areas. Also, severities of singlevehicle crashes are higher than two-vehicle and animal-vehicle crashes. Factors related to roadway geometry such as sharp curves and steep grades are also found to contribute to the increased crash severity in rural areas.