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Lower leg injuries commonly occur in both automobile accidents and underbody explosive blasts, which can be experienced in war by mounted soldiers. These injuries are associated with high morbidity. Accurate methods to predict these injuries, especially in the foot and ankle, must be developed to facilitate the testing and improvement of vehicle safety systems. Anthropomorphic Test Devices (ATDs) are one of the tools used to assess injury risk. These mimic the behavior of the human body in a crash while recording data from sensors in the ATD. Injury criteria for the lower leg have been developed with testing of the leg in a neutral posture, but initial posture may affect the likelihood of lower leg injury. In this thesis, the influence of initial posture on key injury assessment criteria used in crash testing with ATDs was examined. It was determined that these criteria are influenced by ATD leg posture, but further work is necessary to determine if the changes in outcome correspond to altered injury risk in humans when the ankle is in the same postures. In order to better quantify the forces acting on various areas of the foot and correlate those with injury, allowing for development of new criteria, a purpose built force sensor was created. An array of these sensors was incorporated into a boot and used to instrument an ATD leg during impact testing. The sensors provided useful information regarding the force distribution across the sole of the foot during an impact. A numerical simulation of the active material in the sensor was also created to better understand the effect of shear loading on the sensor. This work furthers the understanding of lower leg injury prediction and develops a tool which may be useful in developing accurate injury criteria for the foot and lower leg. / Thesis / Master of Applied Science (MASc) / This work investigates how the posture of the lower leg of a crash test dummy can influence the interpretation of crash test results. A tool was created to measure forces acting on the foot during testing. The force measurement uses a material which changes resistance when it is compressed.

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Micromobility has a direct impact on the urban area, since it tries to make cities more liveable, o:ffering an alternative transport option that contributes to reduce air and noise pollution. Additionally, it promotes intennodality, promotes money savings, reduces parking space and helps to avoid road congestion in cities that have their own lanes for the use of micromobility vehicles such as bicycles, stand-up e-scooters (escooters) and other personal mobility vehicles (PMVs). In Spain, micromobility has significantly increased in recent years, through the increase in the supply and demand for bicycles and other PMVs, mainly e-scooters. There are many reasons that have motivated users to prioritize the bicycle and the other PMVs over other means of tra.nsport. In addition to the growing concern for health and the environment, the COVID-19 pandemic has also driven the growth in the use ofthe different PMVs in 2020. Accordmg to data from Global Public Transport Report, published by the mobility application Moovit, 31 % of Spanianis have used bicycles, scooters or e-scooters in 2020, increasing their use by 7% since 2019. However, in parallel and because of the increase in PMVs exposure, the number of crashes involving users of these vehicles has also increased in recent years. For this reason, among road safety researchers, interest and concern for the study of this kind of crashes have also increased The aim of this research is to characterize the crashes in Spain in which at least one PMV (bicycle, e-scooter or other PMV) is involved between the years 2016 and 2020.

Access management techniques such as raised median installation and driveway consolidation improve safety conditions for motorists. Several locations where these access management techniques have been installed in the state of Utah were selected for analysis of the safety impacts. Although crash rates were not necessarily reduced as a result of the access management techniques, other safety improvements were observed. The raised medians generally reduced the more serious types of collisions, which resulted in a decrease in the severity of crashes. The fatality rates generally decreased as crashes became less severe. Because fatalities and the overall severity of crashes decreased, the overall cost of crashes was reduced. The cost of installing the raised medians was easily recouped by this reduction in the cost of crashes.

Statistics show that signalized intersections are among the most dangerous locations of a roadway network. Different approaches including crash frequency and severity models have been used to establish the relationship between crash occurrence and intersection characteristics. In order to model crash occurrence at signalized intersections more efficiently and eventually to better identify the significant factors contributing to crashes, this dissertation investigated the temporal, spatial, and site correlations for total, rear-end, right-angle and left-turn crashes. Using the basic regression model for correlated crash data leads to invalid statistical inference, due to incorrect test statistics and standard errors based on the misspecified variance. In this dissertation, the Generalized Estimating Equations (GEEs) were applied, which provide an extension of generalized linear models to the analysis of longitudinal or clustered data. A series of frequency models are presented by using the GEE with a Negative Binomial as the link function. The GEE models for the crash frequency per year (using four correlation structures) were fitted for longitudinal data; the GEE models for the crash frequency per intersection (using three correlation structures) were fitted for the signalized intersections along corridors; the GEE models were applied for the rear-end crash data with temporal or spatial correlation separately. For right-angle crash frequency, models at intersection, roadway, and approach levels were fitted and the roadway and approach level models were estimated by using the GEE to account for the "site correlation"; and for left-turn crashes, the approach level crash frequencies were modeled by using the GEE with a Negative Binomial link function for most patterns and using a binomial logit link function for the pattern having a higher proportion of zeros and ones in crash frequencies. All intersection geometry design features, traffic control and operational features, traffic flows, and crashes were obtained for selected intersections. Massive data collection work has been done. The autoregression structure is found to be the most appropriate correlation structure for both intersection temporal and spatial analyses, which indicates that the correlation between the multiple observations for a certain intersection will decrease as the time-gap increase and for spatially correlated signalized intersections along corridors the correlation between intersections decreases as spacing increases. The unstructured correlation structure was applied for roadway and approach level right-angle crashes and also for different patterns of left-turn crashes at the approach level. Usually two approaches at the same roadway have a higher correlation. At signalized intersections, differences exist in traffic volumes, site geometry, and signal operations, as well as safety performance on various approaches of intersections. Therefore, modeling the total number of left-turn crashes at intersections may obscure the real relationship between the crash causes and their effects. The dissertation modeled crashes at different levels. Particularly, intersection, roadway, and approach level models were compared for right-angle crashes, and different crash assignment criteria of "at-fault driver" or "near-side" were applied for disaggregated models. It shows that for the roadway and approach level models, the "near-side" models outperformed the "at-fault driver" models. Variables in traffic characteristics, geometric design features, traffic control and operational features, corridor level factor, and location type have been identified to be significant in crash occurrence. In specific, the safety relationship between crash occurrence and traffic volume has been investigated extensively at different studies. It has been found that the logarithm of traffic volumes per lane for the entire intersection is the best functional form for the total crashes in both the temporal and spatial analyses. The studies of right-angle and left-turn crashes confirm the assumption that the frequency of collisions is related to the traffic flows to which the colliding vehicles belong and not to the sum of the entering flows; the logarithm of the product of conflicting flows is usually the most significant functional form in the model. This study found that the left-turn protection on the minor roadway will increase rear-end crash occurrence, while the left-turn protection on the major roadway will reduce rear-end crashes. In addition, left-turn protection reduces Pattern 5 left-turn crashes (left-turning traffic collides with on-coming through traffic) specifically, but it increases Pattern 8 left-turn crashes (left-turning traffic collides with near-side crossing through traffic), and it has no significant effect on other patterns of left-turn crashes. This dissertation also investigated some other factors which have not been considered before. The safety effectiveness of many variables identified in this dissertation is consistent with previous studies. Some variables have unexpected signs and a justification is provided. Injury severity also has been studied for Patterns 5 left-turn crashes. Crashes were located to the approach with left-turning vehicles. The "site correlation" among the crashes occurred at the same approach was considered since these crashes may have similar propensity in crash severity. Many methodologies and applications have been attempted in this dissertation. Therefore, the study has both theoretical and implementational contribution in safety analysis at signalized intersections.

The purpose of this thesis is to explore how the installations of traffic signals affect crash experience at intersections, to identify those factors which help predict crashes after a signal is installed, and to develop a crash prediction model. It is the intent of this thesis to supplement the Manual on Uniform Traffic Control Devices Signal Warrant procedure and aid the traffic engineer in the signal installation decision making process. Crash data, as well as operational and geometric factors were examined for 32 state road intersections in the northeast Florida area before and after signal installation. Signal warrant studies were used as sources for traffic volumes, geometric information and crash history, before signal installation. The Florida Department of Transportation's Crash Analysis Reporting System (CARS) was used to gather crash data for the time period after signal installation. On average, the 32 intersections experienced a 12% increase in the total number of crashes and a 26% reduction in crash rate after signals were installed. The change in the number of crashes was not significant, but the rate change was significant with 90% confidence. Angle crash frequency dropped by 60% and the angle crash rate dropped by 66%, both are significant. Left-turn crashes dropped by 8% and their rate by 16%, although neither was significant. Rear-end crashes increased by 86% and the rear-end crash rate decreased by 5%. Neither of these changes was statistically significant. When crash severity was examined, it was found that the number of injury crashes increased by 64.8% and the rate by only 0.02%. Neither change was significant. Both the number of fatal crashes and the rate decreased by 100% and were significant. Property Damage Only (PDO) crashes increased by 96%, after signalization, but this change was not significant. The PDO rate, however, decreased by 46.5% and is significant. Operational factors such as AADT, turning movement counts, and speed limits; and geometric factors such as medians, turn lanes and numbers of lanes were considered to determine their effect on crashes at signalized intersections. Smaller roads, with low AADT, fewer lanes, and a rural character were found to benefit from signalization more than busier urbanized roads, in terms of crash rate reduction. The AADT, roadway cross section, number of lanes, medians, speed limit and left turn volume were all found to be important factors influencing crash rates. This thesis recommends: 1) the use of crash prediction models to supplement the MUTCD Crash Warrant, 2) the addition of a left-turn warrant to the MUTCD signal warranting procedure, and 3) development of an intersection database containing crash data as well as operational and geometric information to aid in future research.

Recent research at the University of Central Florida involving crashes on Interstate-4 in Orlando, Florida has led to the creation of new statistical models capable of determining the crash risk on the freeway (Abdel-Aty et al., 2004; 2005, Pande and Abdel-Aty, 2006). These models are able to calculate the rear-end and lane-change crash risks along the freeway in real-time through the use of static information at various locations along the freeway as well as the real-time traffic data obtained by loop detectors. Since these models use real-time traffic data, they are capable of calculating rear-end and lane-change crash risk values as the traffic flow conditions are changing on the freeway. The objective of this study is to examine the potential benefits of variable speed limit implementation techniques for reducing the crash risk along the freeway. Variable speed limits is an ITS strategy that is typically used upstream of a queue in order to reduce the effects of congestion. By lowering the speeds of the vehicles approaching a queue, more time is given for the queue to dissipate from the front before it continues to grow from the back. This study uses variable speed limit strategies in a corridor-wide attempt to reduce rear-end and lane-change crash risks where speed differences between upstream and downstream vehicles are high. The idea of homogeneous speed zones was also introduced in this study to determine the distance over which variable speed limits should be implemented from a station of interest. This is unique since it is the first time a dynamic distance has been considered for variable speed limit implementation. Several VSL strategies were found to successfully reduce the rear-end and lane-change crash risks at low-volume traffic conditions (60% and 80% loading conditions). In every case, the most successful treatments involved the lowering of upstream speed limits by 5 mph and the raising of downstream speed limits by 5 mph. In the free-flow condition (60% loading), the best treatments involved the more liberal threshold for defining homogeneous speed zones (5 mph) and the more liberal implementation distance (entire speed zone), as well as a minimum time period of 10 minutes. This treatment was actually shown to significantly reduce the network travel time by 0.8%. It was also shown that this particular implementation strategy (lowering upstream, raising downstream) is wholly resistant to the effects of crash migration in the 60% loading scenario. In the condition approaching congestion (80% loading), the best treatment again involved the more liberal threshold for homogeneous speed zones (5 mph), yet the more conservative implementation distance (half the speed zone), along with a minimum time period of 5 minutes. This particular treatment arose as the best due to its unique capability to resist the increasing effects of crash migration in the 80% loading scenario. It was shown that the treatments implementing over half the speed zone were more robust against crash migration than other treatments. The best treatment exemplified the greatest benefit in reduced sections and the greatest resistance to crash migration in other sections. In the 80% loading scenario, the best treatment increased the network travel time by less than 0.4%, which is deemed acceptable. No treatment was found to successfully reduce the rear-end and lane-change crash risks in the congested traffic condition (90% loading). This is attributed to the fact that, in the congested state, the speed of vehicles is subject to the surrounding traffic conditions and not to the posted speed limit. Therefore, changing the posted speed limit does not affect the speed of vehicles in a desirable manner. These conclusions agree with Dilmore (2005).

Recent research has shown that incorporating roadway safety in transportation planning has been considered one of the active approaches to improve safety. Aggregate level analysis for predicting crash frequencies had been contemplated to be an important step in this process. As seen from the previous studies various categories of predictors at macro level (census blocks, traffic analysis zones, census tracts, wards, counties and states) have been exhausted to find appropriate correlation with crashes. This study contributes to this ongoing macro level road safety research by investigating various trip productions and attractions along with roadway characteristics within traffic analysis zones (TAZs) of four counties in the state of Florida. Crashes occurring in one thousand three hundred and forty-nine TAZs in Hillsborough, Citrus, Pasco, and Hernando counties during the years 2005 and 2006 were examined in this study. Selected counties were representative from both urban and rural environments. To understand the prevalence of various trip attraction and production rates per TAZ the Euclidian distances between the centroid of a TAZ containing a particular crash and the centroid of the ZIP area containing the at fault driver's home address for that particular crash was calculated. It was found that almost all crashes in Hernando and Citrus County for the years 2005-2006 took place in about 27 miles radius centering at the at-fault drivers' home. Also about sixty-two percent of crashes occurred approximately at a distance of between 2 and 10 miles from the homes of drivers who were at fault in those crashes. These results gave an indication that home based trips may be more associated with crashes and later trip related model estimates which were found significant at 95% confidence level complied with this hypothesized idea. Previous aggregate level road safety studies widely addressed negative binomial distribution of crashes. Properties like non-negative integer counts, non-normal distribution, over-dispersion in the data have increased suitability of applying the negative binomial technique and has been selected to build crash prediction models in this research. Four response variables which were aggregated at TAZ-level were total number of crashes, severe (fatal and severe injury) crashes, total crashes during peak hours, and pedestrian and bicycle related crashes. For each response separate models were estimated using four different sets of predictors which are i) various trip variables, ii) total trip production and total trip attraction, iii) road characteristics, and iv) finally considering all predictors into the model. It was found that the total crash model and peak hour crash model were best estimated by the total trip productions and total trip attractions. On the basis of log-likelihoods, deviance value/degree of freedom, and Pearson Chi-square value/degree of freedom, the severe crash model was best fit by the trip related variables only and pedestrian and bicycle related crash model was best fit by the road related variables only. The significant trip related variables in the severe crash models were home-based work attractions, home-based shop attractions, light truck productions, heavy truck productions, and external-internal attractions. Only two variables- sum of roadway segment lengths with 35 mph speed limit and number of intersections per TAZ were found significant for pedestrian and bicycle related crash model developed using road characteristics only. The 1349 TAZs were grouped into three different clusters based on the quartile distribution of the trip generations and were termed as less-tripped, moderately-tripped, and highly-tripped TAZs. It was hypothesized that separate models developed for these clusters would provide a better fit as the clustering process increases the homogeneity within a cluster. The cluster models were re-run using the significant predictors attained from the joint models and were compared with the previous sets of models. However, the differences in the model fits (in terms of Alkaike's Information Criterion values) were not significant. This study points to different approaches when predicting crashes at the zonal level. This research is thought to add to the literature on macro level crash modeling research by considering various trip related data into account as previous studies in zone level safety have not explicitly considered trip data as explanatory covariates.

Recognizing the distinct non-motorist injury severity profiles by crash location (segment or intersection), we propose a joint modeling framework to study crash location type and non-motorist injury severity as two dimensions of the severity process. We employ a copula-based joint framework that ties the crash location type (represented as a binary logit model) and injury severity (represented as a generalized ordered logit model) through a closed form flexible dependency structure to study the injury severity process. The data for our analysis is drawn from the Central Florida region for the years of 2015 to 2021. The model system explicitly accounts for temporal heterogeneity across the two dimensions. A comprehensive set of independent variables including non-motorist user characteristics, driver and vehicle characteristics, roadway attributes, weather and environmental factors, temporal and sociodemographic factors are considered for the analysis. We also conducted an elasticity analysis to show the actual magnitude of the independent variables on non-motorist injury severity at the two locations. The results highlight the importance of examining the effect of various independent variables on non-motorist injury severity outcome by different crash locations.