Simulation-based design optimization and finite element method are used in this research to investigate weight reduction of car body structures made of metallic and composite materials under different design criteria. Besides crashworthiness in full frontal, offset frontal, and side impact scenarios, vibration frequencies, static stiffness, and joint rigidity are also considered. Energy absorption at the component level is used to study the effectiveness of carbon fiber reinforced polymer (CFRP) composite material with consideration of different failure criteria. A global-local design strategy is introduced and applied to multi-objective optimization of car body structures with CFRP components. Multiple example problems involving the analysis of full-vehicle crash and body-in-white models are used to examine the effect of material substitution and the choice of design criteria on weight reduction. The results of this study show that car body structures that are optimized for crashworthiness alone may not meet the vibration criterion. Moreover, optimized car body structures with CFRP components can be lighter with superior crashworthiness than the baseline and optimized metallic structures.

Current literature predicts that climate change may increase both the occurrence and severity of heavy rainfall events and winter precipitation in the Northeast United States. A potential increase in intense precipitation events related to climate change would theoretically also cause an increase in weather-related delays, increase in overall traffic disruptions, a substantive shift in travel behavior, and presumably a negative effect on safety and maintenance operations of highways. This current research study examines the existing impacts from both an operational and behavioral perspective of how weather events currently impact overall safety along routes in Massachusetts. A secondary objective of the research effort is to evaluate the extent to which this information is captured on the crash report form for subsequent use in safety analyses. Utilizing data from Massachusetts Department of Transportation, National Climatic Data Center (NCDC) and the University of Massachusetts Data Warehouse, crash statistics were examined during varied levels of weather events and compared with non-weather conditions. In addition, crash report forms were analyzed in comparison to NCDC weather data to determine the correlation between of the weather specific fields of the reports and to help determine if crashes were weather-related. The results from the investigation show how the character of precipitation events impact traffic safety including both occurrence and intensity levels and in conjunction with existing weather predictions the relationships developed in this study are useful in evaluating how changes in extreme precipitation events projected for the Northeast may impact drivers’ safety in the future.

The road transport in Kenya is the most common means oftransport for people living in both urban and rural areas. The use of bicycles for transport dates back in the pre-colonial time and has been used as a mode of transport until 2008 when the use of motorcycles became a popular mode of travel in the rural and urban areas. However, the use of bicycle as a means of travel has declined consistently over the years until now and many have shifted to the use of car, public transport and most commonly motorcycles which are popularly known as 'boda boda' in Kenya. This modal shift can be attributed to a number of factors identified as challenges in the use of bicycles as a common mode of transport in comparison to other emerging modes of transport both in rural and urban areas. However, despite this modal shift, there are a substantial number of road users who would still prefer to use the bicycle mode amid prevalence in road traffic fatalities and injuries in Kenya. The government of Kenya has established initiatives to provide safe and inclusive transport system by investing in transport infrastructure that includes cycle tracks especially for roads located in the urban cities. This has been enabled by innovation in design, mixed traffic composition, change of legislation and road design standards especially in regards to non-motorized transport in Kenya. Cycling is still low in cities in Kenya despite this effort to improve geometric design of roads. This paper explores these challenges and opportunities in cycling in Kenya focusing on Nairobi city as a case study. [From: Introduction]

Major crossroads are designed to facilitate the conflicting movements of numerous vehicles in a manner that is both safe and efficient. Accesses located within the functional areas of major crossroads add complication to intersection activity due to additional conflicts arising from ingressing and egressing movements at the accesses. In this research, the impact of accesses on crashes within major crossroad functional areas was analyzed. Specifically, the effects of access spacing within functional areas and access setback from intersections were addressed. In order to conduct the analysis, the functional areas of 159 signalized major arterial crossroads across the state of Utah were examined. A database was built containing the frequency, type, and severity of functional area crashes as well as the intersection and roadway characteristics within the functional area. Statistical analyses were conducted to determine the influence of accesses in intersection functional areas on functional area crashes. The statistical analyses show that the existence of accesses within the functional areas was correlated with increased crashes and crash severity costs. In particular, an increase in commercial access density was associated with increases in crash totals, crash rates, and rear end crashes in intersection functional areas. The analyses also showed that study site intersections meeting Utah Department of Transportation (UDOT) corner clearance standards exhibited fewer right angle crashes and lower crash severity costs. Finally, intersections that prohibited all unsignalized access had lower crash totals, crash rates, right angle crash totals, and rear end crash totals than intersections that allowed some unsignalized access.

This thesis documents the results of the calibration of the Highway Safety Manual (HSM) safety performance function (SPF) for rural two-lane two-way roadway segments in Utah and the development of new SPFs using negative binomial and hierarchical Bayesian modeling techniques. SPFs estimate the safety of a roadway entity, such as a segment or intersection, in terms of number of crashes. The new SPFs were developed for comparison to the calibrated HSM SPF. This research was performed for the Utah Department of Transportation (UDOT).The study area was the state of Utah. Crash data from 2005-2007 on 157 selected study segments provided a 3-year observed crash frequency to obtain a calibration factor for the HSM SPF and develop new SPFs. The calibration factor for the HSM SPF for rural two-lane two-way roads in Utah is 1.16. This indicates that the HSM underpredicts the number of crashes on rural two-lane two-way roads in Utah by sixteen percent. The new SPFs were developed from the same data that were collected for the HSM calibration, with the addition of new data variables that were hypothesized to have a significant effect on crash frequencies. Negative binomial regression was used to develop four new SPFs, and one additional SPF was developed using hierarchical (or full) Bayesian techniques. The empirical Bayes (EB) method can be applied with each negative binomial SPF because the models include an overdispersion parameter used with the EB method. The hierarchical Bayesian technique is a newer, more mathematically-intense method that accounts for high levels of uncertainty often present in crash modeling. Because the hierarchical Bayesian SPF produces a density function of a predicted crash frequency, a comparison of this density function with an observed crash frequency can help identify segments with significant safety concerns. Each SPF has its own strengths and weaknesses, which include its data requirements and predicting capability. This thesis recommends that UDOT use Equation 5-11 (a new negative binomial SPF) for predicting crashes, because it predicts crashes with reasonable accuracy while requiring much less data than other models. The hierarchical Bayesian process should be used for evaluating observed crash frequencies to identify segments that may benefit from roadway safety improvements.

This thesis contains the results of the development of crash prediction models for curved segments of rural two-lane two-way highways in the state of Utah. The modeling effort included the calibration of the predictive model found in the Highway Safety Manual (HSM) as well as the development of Utah-specific models developed using negative binomial regression. The data for these models came from randomly sampled curved segments in Utah, with crash data coming from years 2008-2012. The total number of randomly sampled curved segments was 1,495. The HSM predictive model for rural two-lane two-way highways consists of a safety performance function (SPF), crash modification factors (CMFs), and a jurisdiction-specific calibration factor. For this research, two sample periods were used: a three-year period from 2010 to 2012 and a five-year period from 2008 to 2012. The calibration factor for the HSM predictive model was determined to be 1.50 for the three-year period and 1.60 for the five-year period. These factors are to be used in conjunction with the HSM SPF and all applicable CMFs. A negative binomial model was used to develop Utah-specific crash prediction models based on both the three-year and five-year sample periods. A backward stepwise regression technique was used to isolate the variables that would significantly affect highway safety. The independent variables used for negative binomial regression included the same set of variables used in the HSM predictive model along with other variables such as speed limit and truck traffic that were considered to have a significant effect on potential crash occurrence. The significant variables at the 95 percent confidence level were found to be average annual daily traffic, segment length, total truck percentage, and curve radius. The main benefit of the Utah-specific crash prediction models is that they provide a reasonable level of accuracy for crash prediction yet only require four variables, thus requiring much less effort in data collection compared to using the HSM predictive model.

Two way left turn lanes (TWLTL) and raised medians are common median treatments on roadways. This research focused on evaluating the safety effectiveness of conversion of TWLTLs into raised medians using Before-After and Cross Sectional Studies. In the Before-After Studies, we evaluated the effect of this treatment using the Naive, Before-After with Comparison Group (CG), and Before-After with Empirical Bayes (EB) Methods. In order to apply these methods, a total of 33 segments of a treated group and 109 segments of a comparison group have been collected. Also, safety performance functions (SPFs) have been developed using the negative binomial model in order to calibrate crash modification factors (CMF) using the Before-After with Empirical Bayes Method. This research also evaluated the safety effectiveness of this treatment on four and six lane roads using Before-After with CG and Before-After with EB. The type of raised medians was further evaluated using Before-After with CG and EB. In sum, the results from this study show that applying the before-After and Cross Sectional studies have proved that the conversion from a TWLTL to a raised median helped to reduce total, fatal and injury, head on, angle, and left turn crashes. It significantly reduces crashes for head-on and left turn crashes, by restricting turning maneuvers. Also, this study has proved that the treatment is more effective on four rather than six lane roads. Furthermore, two types of raised medians, concrete and lawn curb, were evaluated after the conversion from TWLTLs. It was found that both medians have similar effects due to the conversion, and both median types helped in reducing the number of crashes.

Traffic crashes have been identified as one of the main causes of death in the US, making road safety a high priority issue that needs urgent attention. Recognizing the fact that more and effective research has to be done in this area, this thesis aims mainly at developing different statistical models related to the road safety. The thesis includes three main sections: 1) overall crash frequency analysis using negative binomial models, 2) seemingly unrelated negative binomial (SUNB) models for different categories of crashes divided based on type of crash, or condition in which they occur, 3) safety models to determine the probability of crash occurrence, including a rainfall index that has been estimated using a logistic regression model. The study corridor is a 36.25 mile stretch of Interstate 4 in Central Florida. For the first two sections, crash cases from 1999 through 2002 were considered. Conventionally most of the crash frequency analysis model all crashes, instead of dividing them based on type of crash, peaking conditions, availability of light, severity, or pavement condition, etc. Also researchers traditionally used AADT to represent traffic volumes in their models. These two cases are examples of macroscopic crash frequency modeling. To investigate the microscopic models, and to identify the significant factors related to crash occurrence, a preliminary study (first analysis) explored the use of microscopic traffic volumes related to crash occurrence by comparing AADT/VMT with five to twenty minute volumes immediately preceding the crash. It was found that the volumes just before the time of crash occurrence proved to be a better predictor of crash frequency than AADT. The results also showed that road curvature, median type, number of lanes, pavement surface type and presence of on/off-ramps are among the significant factors that contribute to crash occurrence. In the second analysis various possible crash categories were prepared to exactly identify the factors related to them, using various roadway, geometric, and microscopic traffic variables. Five different categories are prepared based on a common platform, e.g. type of crash. They are: 1) Multiple and Single vehicle crashes, 2) Peak and Off-peak crashes, 3) Dry and Wet pavement crashes, 4) Daytime and Dark hour crashes, and 5) Property Damage Only (PDO) and Injury crashes. Each of the above mentioned models in each category are estimated separately. To account for the correlation between the disturbance terms arising from omitted variables between any two models in a category, seemingly unrelated negative binomial (SUNB) regression was used, and then the models in each category were estimated simultaneously. SUNB estimation proved to be advantageous for two categories: Category 1, and Category 4. Road curvature and presence of On-ramps/Off-ramps were found to be the important factors, which can be related to every crash category. AADT was also found to be significant in all the models except for the single vehicle crash model. Median type and pavement surface type were among the other important factors causing crashes. It can be stated that the group of factors found in the model considering all crashes is a superset of the factors that were found in individual crash categories. The third analysis dealt with the development of a logistic regression model to obtain the weather condition at a given time and location on I-4 in Central Florida so that this information can be used in traffic safety analyses, because of the lack of weather monitoring stations in the study area. To prove the worthiness of the weather information obtained form the analysis, the same weather information was used in a safety model developed by Abdel-Aty et al., 2004. It was also proved that the inclusion of weather information actually improved the safety model with better prediction accuracy.

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.

Passive safety systems like airbags have significantly improved road safety. These occupant safety systems help in reducing the severity of injuries, and save lives in the event of a road accident. The airbag systems must be configured correctly to minimize the impact of collision and protect the occupants. To configure the airbag, test crashes are performed and data is recorded. This data is simulated to find out appropriate parameters for the airbag deployment. The airbag simulation results are stored into databases. Airbag application tools are used to handle the data stored in databases. The airbag simulation results must be extracted efficiently and required computations needs to be performed. This data is then stored to reports. RSDBnext is an airbag application tool, it stands for Result Database next generation. This tool is used for extraction of data from the database. The RSDBnext tool should be adapted to generate Standard Customer Reports. These reports are to be generated based on customer requirements. The existing methodology to generate Standard Customer Reports used Excel macros, which took a lot of time to generate the reports. This method was complex and unstable. Hence, a new methodology was proposed without using macros. In the proposed method, an XML file and XSLT StyleSheet were used to generate the report in Excel using C# with Visual Studio. This approach reduces report generation time, and overcomes the drawbacks of the previous approach. From the results, this methodology to generate reports is faster, easier, and more reliable.