Tailgating (maintaining an inadequate headway) is a major road safety concern that emerges because drivers tend to misperceive their following distance. Drivers seldom receive enough feedback to correct this error, leaving them vulnerable to rear-end collisions. While there have been attempts to address this issue through vehicle automation and warning systems, these approaches have not been sensitive to the needs of young drivers. The present investigation sought to implement a headway evaluation system: an in-vehicle display designed to provide motorists with real-time as well as aggregate headway feedback. This system was designed to teach drivers to recognize safe headways and to motivate adherence. Compared to drivers selecting their own headway or those attempting to count a two second headway, drivers using the headway evaluation system maintained longer headways that would be conducive to collision avoidance. This system may be beneficial as a training device for new drivers. / AUTO21, the Ontario Innovation Trust, the Canada Foundation for Innovation, and the Natural Sciences and Engineering Research Council of Canada

Background: The latest generation of rear-end whiplash protection systems, as found in the WHIPS Volvo and SAHR Saab, have reduced injury rates by almost 50% in comparison with the previous generation of seat/head restraint systems. Occupant behaviour, such as seated posture and seat adjustment settings, may affect the injury risk. Method: Five studies were conducted. Studie I was an injury outcome study based on insurance data. Studies II-IV investigated seat adjustment, occupant backset, and cervical retraction for drivers and occupants in different postures and positions in the car, during stationary and driving conditions. Study V compared the occupant data from studies II and III with a vehicle testing tool, the BioRID dummy, using the protocols of the ISO, RCAR, and the RCAR-IIWPG. Results: Female drivers and passengers had a threefold increased risk for medically-impairing neck injury in rear-end impacts, compared to males. Driver position had a double risk compared with front passenger seat position. Female drivers adjusted the driver seat differently to male drivers; they sat higher and closer to the steering wheel and with more upright back support. The volunteers also adjusted their seat differently to the ISO, RCAR, and RCAR-IIWPG protocol settings; both sexes sat further away from the steering wheel, and seat back angle was more upright then in the protocols. In stationary cars, backset was highest in the rear seat position and lowest in the front passenger seat position. Males had a larger backset than females. Cervical retraction decreased and backset increased for both sexes when posture changed from self-selected posture to a slouched posture. The BioRID II dummy was found to represent 96th percentile female in stature, and a 69th percentile female in weight in the volunteer group. Conclusions: Risks in car rear-end impacts differ by sex and seated position. This thesis indicates the need for a 50th percentile female BioRID dummy and re-evaluation of the ISO, RCAR, and RCAR-IIWPG protocols, and further development of new safety systems to protect occupants in rear-end impacts.

Intersection related crashes form a significant proportion of the crashes occurring on roadways. Many organizations such as the Federal Highway Administration (FHWA) and American Association of State Highway and Transportation Officials (AASHTO) are considering intersection safety improvement as one of their top priority areas. This study contributes to the area of safety of signalized intersections by identifying the traffic and geometric characteristics that affect the different types of crashes. The first phase of this thesis was to classify the crashes occurring at signalized intersections into rear-end, angle, turn and sideswipe crash types based on the traffic and geometric properties of the intersections and the conditions at the time of the crashes. This was achieved by using an innovative approach developed in this thesis "Neural Network Trees". The first neural network model built in the Neural Network tree classified the crashes either into rear end and sideswipe or into angle and turn crashes. The next models further classified the crashes into their individual types. Two different neural network methods (MLP and PNN) were used in classification, and the neural network with a better performance was selected for each model. For these models, the significant variables were identified using the forward sequential selection method. Then a large simulation database was built that contained all possible combinations of intersections subjected to various crash conditions. The collision type of crashes was predicted for this simulation database and the output obtained was plotted along with the input variables to obtain a relationship between the input and output variables. For example, the analysis showed that the number of rear end and sideswipe crashes increase relative to the angle and turn crashes when there is an increase in the major and minor roadways' AADT and speed limits, surface conditions, total left turning lanes, channelized right turning lanes for the major roadway and the protected left turning lanes for the minor roadway, but decrease when the light conditions are dark. The next phase in this study was to predict the frequency of different types of crashes at signalized intersections by using the geometric and traffic characteristics of the intersections. A high accuracy in predicting the crash frequencies was obtained by using another innovative method where the intersections were first classified into two different types named the "safe" and "unsafe" intersections based on the total number of lanes at the intersections and then the frequency of crashes was predicted for each type of intersections separately. This method consisted of identifying the best neural network for each step of the analysis, selecting significant variables, using a different simulation database that contained all possible combinations of intersections and then plotting each input variable with the average output to obtain the pattern in which the frequency of crashes will vary based on the changes in the geometric and traffic characteristics of the intersections. The patterns indicated that an increase in the number of lanes of the major roadway, lanes of the minor roadway and the AADT on the major roadway leads to an increased crashes of all types, whereas an increase in protected left turning lanes on the major road increases the rear end and sideswipe crashes but decreases the angle, turning and overall crash frequencies. The analyses performed in this thesis were possible due to a diligent data collection effort. Traffic and geometric characteristics were obtained from multiple sources for 1562 signalized intersections in Brevard, Hillsborough, Miami-Dade, Seminole and Orange counties and the city of Orlando in Florida. The crash database for these intersections contained 27,044 crashes. This research sheds a light on the characteristics of different types of crashes. The method used in classifying crashes into their respective collision types provides a deeper insight on the characteristics of each type of crash and can be helpful in mitigating a particular type of crash at an intersection. The second analysis carried out has a three fold advantage. First, it identifies if an intersection can be considered safe for different crash types. Second, it accurately predicts the frequencies of total, rear end, angle, sideswipe and turn crashes. Lastly, it identifies the traffic and geometric characteristics of signalized intersections that affect each of these crash types. Thus the models developed in this thesis can be used to identify the specific problems at an intersection, and identify the factors that should be changed to improve its safety

Each year about 400,000 trucks are involved in motor vehicle crashes. Crashes involving a car and truck have always been a major concern due to the heavy fatality rates. These types of crashes result in about 60 percent of all fatal truck crashes and two-thirds of all police-reportable truck crashes. Car-truck crashes need to be analyzed further to study the trends for a car-truck crash and develop some countermeasures to lower these crashes. Various types of car-truck crashes are analyzed in this study and the effects of various roadway/environment factors and variables related to driver characteristics in these car-truck crashes are investigated. To examine the crash characteristics and to investigate the significant factors related to a car-truck crash, this study analyzed five years of data (2000-2004) of the General estimates system of National Sampling System (GES) and the Fatality Analysis Reporting system database (FARS). All two vehicle crashes including either a car or truck (truck-truck cases excluded because of their low percentage composition) were obtained from these databases. Based on the five year data (GES/FARS) the percentage of car-truck angle collisions constituted the highest percent of frequency of all types of car-truck collisions. Furthermore, based on the 2004 GES data there is a clear trend that the frequency of angle collision increases with the increase in driver injury severity. When analyzing the GES data it was observed that the percentage of angle collisions was the highest followed by the rear end and sideswipe (same direction) collisions respectively. When the fatalities were considered (FARS database used), the percentage of angle collisions was the highest followed by head-on and rear-end collisions. The nominal multinomial logit model and logistic regression models were utilized for this analysis. Divided section, alcohol involvement, adverse weather conditions, dark lighting condition and old age of drivers had a significant effect on the car-truck crashes and were likely to increase the likelihood of a car-truck crash. Whereas dark but light conditions, young aged drivers showed a less likelihood of involving in a car-truck crash. This research is significant in providing an insight into various car-truck crash types and provides with results, which have impacted the car-truck crashes. A better understanding of the factors impacting these crashes will help in providing better countermeasures, which would result in reducing the car-truck crashes.

No description available.

Strobe lights have been used successfully in many transportation applications to increase conspicuity. It was hoped that a strobe signal could also be applied to more effectively warn distracted drivers of an unexpected rear end conflict. This "proof of concept study" used a 2 x 2 between-subjects design using thirty-three subjects (16 subjects in the strobe condition, 17 subjects in the no strobe condition) who were divided into two age groups: younger (25-35) and older (60-70). The driver unexpectedly encountered a stopped "surrogate" vehicle in the roadway (with or without a rear-facing strobe light) in a controlled on-road study at the Smart Road located at the Virginia Tech Transportation Institute (VTTI). Results suggested that younger subjects' perception times improved as a result of being exposed to the strobe signal. Faster perception of the situation allowed more time to initiate a brake response. Older subjects perception and response times remained unchanged by the strobe signal. More severe initial steering rate and subjective responses indicated that the strobe conveyed a sense of urgency irrespective of age. Visual distraction of subjects proved difficult. Hence, the impact of the strobe on attracting the attention of a visually distracted driver to the stimulus could not be as fully investigated as originally hoped. The formulation of a more difficult distraction task was suggested for future research to truly assess the ability of the strobe light at alerting visually distracted drivers. / Master of Science

The purpose of the present study was to examine effects of driver, vehicle, and environment characteristics on Collision Warning System (CWS) design. One hypothesis was made that the capability of collision avoidance would not be same among a driver, vehicle, and environment group with different characteristics. Accident analysis and quantitative analysis was used to examine this hypothesis in terms of ‘risk’ and ‘safety margin’ respectively. Rear-end collision had a stronger focus in the present study. As a result of accident analysis, heavy truck showed a higher susceptibility of the fatal rear-end accidents than car and light truck. Also, dry road surface compared to wet or snow, dark condition compared to daylight condition, straight road compared to curved road, level road compared to grade, crest or sag, roadway having more than 5 travel lanes compared to roadway having 2, 3 or 4 travel lanes showed a higher susceptibility of the fatal rear-end accidents. Relative rear-end accidents involvement proportion compared to the other types of collision was used as a measure of susceptibility. As a result of quantitative analysis, a significant difference in terms of Required Minimum Warning Distance (RMWD) was made among a different vehicle type and braking system group. However, relatively small difference was made among a different age, gender group in terms of RMWD. Based on the result, breaking performance of vehicle should be regarded as an input variable in the design of CWS, specifically warning timing criteria, was concluded.

<p>The purpose of the present study was to examine effects of driver, vehicle, and environment characteristics on Collision Warning System (CWS) design. One hypothesis was made that the capability of collision avoidance would not be same among a driver, vehicle, and environment group with different characteristics. Accident analysis and quantitative analysis was used to examine this hypothesis in terms of ‘risk’ and ‘safety margin’ respectively. Rear-end collision had a stronger focus in the present study. </p><p>As a result of accident analysis, heavy truck showed a higher susceptibility of the fatal rear-end accidents than car and light truck. Also, dry road surface compared to wet or snow, dark condition compared to daylight condition, straight road compared to curved road, level road compared to grade, crest or sag, roadway having more than 5 travel lanes compared to roadway having 2, 3 or 4 travel lanes showed a higher susceptibility of the fatal rear-end accidents. Relative rear-end accidents involvement proportion compared to the other types of collision was used as a measure of susceptibility. </p><p>As a result of quantitative analysis, a significant difference in terms of Required Minimum Warning Distance (RMWD) was made among a different vehicle type and braking system group. However, relatively small difference was made among a different age, gender group in terms of RMWD. Based on the result, breaking performance of vehicle should be regarded as an input variable in the design of CWS, specifically warning timing criteria, was concluded.</p>

The current driving simulator study investigates the effect of 2 distinct levels of distraction on a drivers’ situational awareness and latent and inherent hazard anticipation. In this study, rear-end crashes were used as the primary crash configuration to target a specific category of crashes due to distraction. The two types of task load used in the experiment was a cognitive distraction (mock cell-phone task) & visual distraction (I-pad task). Forty-eight young participants aged 18-25 years navigated 8 scenarios each in a mixed subject design with task load (cognitive or visual distraction) as a between-subject variable and the presence/absence of distraction representing the within-subject variable. All participants drove 4 scenarios with a distraction and 4 scenarios without any distraction. Physiological variables in the form of Heart rate and heart rate variability was collected for each participant during the practice drives and after each of the 8 experimental drives. After the completion of each experimental drive, participants were asked to fill up a NASA TLX questionnaire which quantifies the overall task load experienced by giving it a score between 1 and 100, where higher scores translate to higher perceived task load. Eye-movements were also recorded for the proportion of latent and inherent hazards anticipated and mitigated for all participants. Standard vehicle data (velocity, acceleration & lane offset) were also collected from the simulator for each participants’ each drive. Analysis of data showed that there was a significant difference in velocity, lane offset and task load index scores across the 2 groups (between-subject factors). The vehicle data, heart rate data and TLX data was analyzed using Mixed subject ANOVA. There was also a logistic regression model devised which showed significant effects of velocity, lane offset, TLX scores and age on a participants’ hazard anticipation abilities. The findings have a major practical implication in reducing drivers’ risk of fatal, serious or near crashes.

In recent years the use of advanced driving simulators has increased in the transportation engineering field especially in evaluating safety countermeasures. The driving simulator at UCF is a high fidelity simulator with six degrees of freedom. This research aims at validating the simulator in terms of speed and safety with the intention of using it as a test bed for high risk locations and to use it in developing traffic safety countermeasures. The Simulator replicates a real world signalized intersection (Alafaya trail (SR-434) and Colonial Drive (SR-50)). A total of sixty one subjects of age ranging from sixteen to sixty years were recruited to drive the simulator for the experiment, which consists of eight scenarios. This research validates the driving simulator for speed, safety and visual aspects. Based on the overall comparisons of speed between the simulated results and the real world, it was concluded that the UCF driving simulator is a valid tool for traffic studies related to driving speed behavior. Based on statistical analysis conducted on the experiment results, it is concluded that SR-434 northbound right turn lane and SR-50 eastbound through lanes have a higher rear-end crash risk than that at SR-50 westbound right turn lane and SR-434 northbound through lanes, respectively. This conforms to the risk of rear-end crashes observed at the actual intersection. Therefore, the simulator is validated for using it as an effective tool for traffic safety studies to test high-risk intersection locations. The driving simulator is also validated for physical and visual aspects of the intersection as 87.10% of the subjects recognized the intersection and were of the opinion that the replicated intersection was good enough or realistic. A binary logistic regression model was estimated and was used to quantify the relative rear-end crash risk at through lanes. It was found that in terms of rear-end crash risk SR50 east- bound approach is 23.67% riskier than the SR434 north-bound approach.