Gender differences of young drivers on injury severity outcome of highway crashes

Problem: Gender differences of young drivers involved in crashes and the associated differences in risk factors have not been fully explored in the United States (U.S.). Accordingly, this study investigated the topic, where the Odds Ratios (OR) were used to identify differences in crash involvements between male and female young drivers. Method: Logistic regression models for injury severity of young male drivers and young female drivers were also developed. Different driver, environmental, vehicle, and road related factors that have affected young female drivers’ and young male drivers’ crash involvement were identified using the models. Results: Results indicated that some variables are significantly related to female drivers’ injury risk but not male drivers’ injury risk and vice versa.Variables such as driving with valid licenses, driving on weekends, avoidance or slow maneuvers at time of crash, non-collision and overturn crashes and collision with a pedestrian were significant variables in female driver injury severity model but not in young male driver severity model. Travel on unleveled roadways, travel on concrete surfaces, travel on wet road surfaces, collision with another vehicle, rear-end collisions were variables that were significant in male driver severity model but not in female driver severity model. Summary: Factors which increase young female drivers’ injury severity and young male drivers’ injury severity were identified. Some factors are significantly related to female drivers’ injury risk but not male drivers’ injury risk and vice versa. This study adds detailed information about gender differences and similarities in injury severity risk of young drivers

Modeling Injury Severity of Young Drivers Using Highway Crash Data from Kansas

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

Improving safety of teenage and young adult drivers in Kansas

Doctor of PhilosophyDepartment of Civil EngineeringSunanda DissanayakeYoung drivers have elevated motor vehicle crash rates compared to other drivers. This dissertation investigated characteristics, contributory causes, and factors which increase the injury severity of young driver crashes in Kansas by comparing them with more experienced drivers. Crash data were obtained from the Kansas Department of Transportation. Young drivers were divided into two groups: 15-19 years (teen) and 20-24 years (young adult) for a detailed investigation. Using data from 2006 to 2009, frequencies, percentages, and crash rates were calculated for each characteristic and contributory cause. Contingency table analysis and odds ratios (OR) analysis were carried out to identify overly represented factors of young-driver crashes compared to experienced drivers. Young drivers were more likely to be involved in crashes due to failure to yield-right-of way, disregarding traffic signs/signals, turning, or lane changing, compared to experienced drivers. Ordered logistic regression models were developed to identify severity affecting factors in young driver crashes. According to model results, factors that decreased injury severity of the driver were seat belt use, driving at low speeds, driving newer vehicles, and driving with an adult passenger. The models also showed that alcohol involvement, driving on high-posted-speed-limit roadways, ejection at the time of crash, and trapping at the time of crash can increase young drivers’ injury severity. Based on identified critical factors, countermeasure ideas were suggested to improve the safety of young drivers. It is important for teen drivers and parents/guardians to gain better understanding about these critical factors that are helpful in preventing crashes and minimizing driving risk. Parents/guardians can consider high-risk conditions such as driving during dark, during weekends, on rural roads, on wet road surfaces, and on roadways with high speed limits, for planning teen driving. Protective devices, crash-worthy cars, and safer road infrastructures, such as rumble strips, and forgiving roadsides, will particularly reduce young drivers’ risk. Predictable traffic situations and low complexity resulting from improved road infrastructure are beneficial for young drivers. The effectiveness of Kansas Graduated Driver Licensing (GDL) system needs to be investigated in the future

Modeling Frequency of Truck Crashes on Limited-Access Highways

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 model-fitting 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

Traffic Signal Controller Optimization Through VISSIM to Minimize Traffic Congestion, CO and NOx Emissions, and Fuel Consumption

In developing countries with heterogeneous traffic, such as Sri Lanka, it is possible to observe severe traffic congestion at intersections and traffic corridors. The main objective of this study was to demonstrate the optimization of traffic signal controllers using VISSIM microsimulation software. It aimed to minimize traffic congestion, emissions, and fuel consumption. This study focused on developing a traffic signal controller optimization program for a congested traffic corridor which consisted of a three-legged signalized intersection, a four-legged unsignalized intersection, and a three-legged unsignalized intersection. The entire corridor was modeled here, and the already signalized three-legged intersection was optimized. Traffic signal controller optimization was done separately through the built-in optimization features in VISSIM and Webster’s Method. The results showed that emissions and fuel consumption were reduced by 14.89 % in VISSIM optimization and 14.11% in optimization using Webster’s Method. Through the comparison between the VISSIM optimized signal timing and manually calculated signal timing, it was found that the signal timing optimization provides much more improved results than the manual signal timing calculations. Using the proposed methodology, the traffic signal controllers can be optimized within a short duration in very few steps without any iterations compared to the existing traffic signal controller optimization techniques. Therefore, the proposed methodology is a good alternative method to optimize the traffic signal controllers

Optimization of VISSIM Driver Behavior Parameter Values Using Genetic Algorithm

Modeling effective vehicular traffic is a highly contested topic, especially in developing countries like Sri Lanka, which has a wide range of driving conditions. VISSIM microsimulation software is currently used by Road Development Authority (RDA) and relevant authorities to perform traffic management solutions in Sri Lanka. However, it is required to do modifications to the existing driver behavior parameter values to effectively reflect the realistic traffic conditions observed in the real-world in the simulated model. The main purpose of this study is to calibrate the VISSIM driver behavior parameter values using a genetic algorithm (GA). The methodology and results of the VISSIM model’s sensitivity analysis and calibration, which was developed for the Malabe three-legged signalized intersection, are presented in this study. A sensitivity analysis was used to find the most sensitive driver behavior parameters. Using the multi-objective GA optimization tool in the MATLAB software's optimization toolbox, the optimum driver behavior parameter values for these identified most sensitive driver behavior parameters were determined. The findings revealed that GA optimization is effective in reducing the difference between observed and simulated results

Effects of Geometric Design Features on Truck Crashes on Limited-Access Highways

Freight can be transported between most points in the country quite efficiently using trucks. However, involvement of large trucks in crashes can cause much damage and serious injuries, due to their large sizes and heavy weights. Large truck crashes occurring on limited-access highways may be more severe than crashes occurring on other roadways due to high speed limits, and traffic- and geometric-related characteristics. The purpose of this study is to describe the relationships between large truck crash probability, and traffic and geometric characteristics. Crash data from 2005 to 2010 were obtained from the Kansas Department of Transportation (KDOT), which included 5,378 large track crashes that occurred on Kansas limited-access highway sections. The traffic- and geometric-related details of highways were obtained from the Control Section Analysis System (CANSYS) database, which is maintained by KDOT as a highway inventory system. Homogeneous road sections in terms of speed limit, AADT, percent of trucks, horizontal curvature, horizontal grade, lane width, shoulder width, median width, and existence of rumble strips were identified. The total number of crashes occurring within each segment from 2005 to 2010 was determined, resulting in 7,273 analysis segments used in the modeling. A Poisson regression model and a negative binomial regression model were developed for identifying the relationships between the occurrence of truck crashes, and traffic and geometric characteristics. According to the models, highway design features such as horizontal curvature, vertical grade, lane width, and shoulder width are factors which can be used to change the occurrence of large truck crashes. Identifying the effect of traffic and geometric characteristics is important to promote safety treatments through engineering improvements

Factors associated with rural run-off-road and urban run-off road crashes: a study in the United States

Run-Off-Road (ROR) crash occurs when a vehicle leave the lane resulting in a collision. ROR crashes in recent years have become a major cause of serious injuries and fatalities. Data from Kansas Crash and Analysis Reporting System (KCARS) database during the period 2007 to 2011 were used in this study to examine the ROR crashes. Identification of various characteristics such as environment, roadway, driver, and vehicle as well as factors contributing to rural ROR and urban ROR crashes is important because potential countermeasures can be developed to improve roadside safety. It was found that avoidance/evasive or slow; ill, falling asleep or fatigued; or animal at the road is more common on rural roadways than urban roadways

Modeling Frequency of Truck Crashes on Limited-Access Highways

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