Arguably the greatest issue facing the transportation profession is the ability to provide social equity with regards to both safety and mobility given the aging population. Given the overall dominance of the automobile within the transportation system, the ability to provide feasible alternatives is daunting. This fact, when coupled with the well-documented challenges of older drivers, underscores the need for improved safety features and system-wide safety approaches with a focus on the older driver. This paper describes an application of spatial crash analysis and road safety investigations that were employed in Massachusetts with a direct focus on the older driver. Specifically, the paper outlines an approach for identifying high crash locations for older drivers and presents the results of older driver focused road safety investigations for selected locations. The research approach targets both intersections and roadway segments identifying locations where older drivers are overrepresented in crashes. The road safety investigations resulted in recommended countermeasures aimed at mitigating the older driver crash problem at the identified locations. Although the resulting countermeasures, which were based upon established literature such as the Older Driver Design Handbook, included a full spectrum of recommendations, a specific emphasis was placed upon short-term and low cost measures that could be readily employed. Techniques to identify relationships between high crash location identification methods and the recommended countermeasures for the identified locations are considered. Ultimately the application of these techniques may provide transportation professionals with a means to associate specific older driver focused countermeasures with the results of particular methods of high crash location identification.

Examine the Prevalence and Geography of Road CollisionsSpatial Analysis Methods of Road Traffic Collisions centers on the geographical nature of road crashes, and uses spatial methods to provide a greater understanding of the patterns and processes that cause them. Written by internationally known experts in the field of transport geography, the bo

The percentage of the U.S. population aged 65 years or older is increasing rapidly. Statistics also show this age group was 14.9 percent of the population in 2015 and is expected to be 20.7 to 21.4 percent for the years 2030-2050. Kansas has similar statewide trends with its aging population. Therefore, identifying issues, concerns, and factors associated with severity of older-driver crashes in Kansas is necessary. The Kansas Crash Analysis and Reporting System (KCARS) database maintained by Kansas Department of Transportation was used in this study to identify older-driver crash characteristics, compare older drivers with all drivers, and develop crash severity models. According to KCARS data, older drivers were involved in more than one in five fatal injuries out of all drivers in Kansas from 2010 to 2014. When compared with all drivers, older drivers were overly represented in fatal and incapacitating injuries. The percentage of older-driver fatal injuries was more than the twice that of all drivers. When compared with all drivers, older drivers were involved more often in crashes at four-way intersections, on straight and level roads, in daylight hours, and at a stop or yield signs. An in-depth crash severity analysis was carried out for the older drivers involved in crashes. Three separate binary logistic regression models were developed for single-vehicle crashes where only the older driver was present (Model A), single-vehicle crashes involving an older driver with at least one passenger (Model B), and multi-vehicle crashes involving at least one older driver (Model C). From the crash severity analysis, it was found that left turns were significant in changing the crash severity for Model A, but it was not significant in model B, meaning that older drivers may be safer with passengers. For Model B, none of the passenger attributes were significant, though it was originally developed to identify passenger attributes. Gender of the older driver was not significant in any model. For all models, variables such as safety equipment use, crash location, weather conditions, driver ejected or trapped, and light conditions distinguished crash severity. Furthermore, for Model A, variables such as day of the week, speed, accident class, and maneuver, distinguished crash severity. Moreover, accident class, surface type, and vehicle type changed crash severity in Model B. Number of vehicles, speed, collision type, maneuver, and two-lane roads were significant in Model C.A road-user survey was also conducted to identify habits, needs, and concerns of Kansas' aging road users since it was not advisable to conclude safety factors solely on crash data. The probability of occurrence was calculated by taking the weighted average of answers to a question. Then a contingency table analysis was carried out to identify relationships among variables. For older drivers, seatbelt use as a driver had the highest probability of occurrence. Driving in heavy traffic, merging into traffic, moving away from traffic, and judging gaps were dependent on age group. Findings of this research gave an understanding of older-driver crashes and associated factors. Since more than 85 percent of crash contributory causes were related to drivers, driver awareness programs, driver licensing restrictions, providing public transportation, and law enforcement can be used as countermeasures. Accordingly, results of this study can be used to enhance older-driver safety and awareness programs.

This book examines driver, vehicle, roadway and environmental characteristics associated with increased crash involvement by older drivers. Project activities were designed to prioritise the situations causing problems for older drivers based on the magnitude of the crash problem, older driver's degree of over-representation, the likelihood of serious injury, or other criteria of interest. The resulting list of the most problematic situations frame further discussions of how age-related functional decline can mediate increased crash risk for older drivers, and hopefully, point to potential countermeasures for lowering this risk.

This project analyzes the characteristics and spatial distributions of motor vehicle crash types in order to evaluate the degree and scale of their spatial clustering. Crashes occur as the result of a variety of vehicle, roadway, and human factors and thus vary in their clustering behavior. Clustering can occur at a variety of scales, from the intersection level, to the corridor level, to the area level. Conversely, other crash types are less linked to geographic factors and are more spatially "random." The degree and scale of clustering have implications for the use of strategies to promote transportation safety. In this project, Iowa's crash database, geographic information systems, and recent advances in spatial statistics methodologies and software tools were used to analyze the degree and spatial scale of clustering for several crash types within the counties of the Iowa Northland Regional Council of Governments. A statistical measure called the K function was used to analyze the clustering behavior of crashes. Several methodological issues, related to the application of this spatial statistical technique in the context of motor vehicle crashes on a road network, were identified and addressed. These methods facilitated the identification of crash clusters at appropriate scales of analysis for each crash type. This clustering information is useful for improving transportation safety through focused countermeasures directly linked to crash causes and the spatial extent of identified problem locations, as well as through the identification of less location-based crash types better suited to non-spatial countermeasures. The results of the K function analysis point to the usefulness of the procedure in identifying the degree and scale at which crashes cluster, or do not cluster, relative to each other. Moreover, for many individual crash types, different patterns and processes and potentially different countermeasures appeared at different scales of analysis. This finding highlights the importance of scale consideration in problem identification and countermeasure formulation.

The research conducted in this thesis consists of a Geographic Information Systems (GIS) based safety study and a spatial analysis of vehicle crashes in the State of Florida. The GIS safety study is comprised of a County and Roadway Level GIS analysis of multilane corridors. The spatial analysis investigated the use of county-level vehicle crash models, taking spatial effects into account. The GIS safety study examines the locations of high trends of severe crashes (includes incapacitating and fatal crashes) on multilane corridors in the State of Florida at two levels, county level and roadway level. The GIS tool, which is used frequently in traffic safety research, was utilized to visually display those locations. At the county level, several maps of crash trends were generated. It was found that counties with high population and large metropolitan areas tend to have more crash occurrences. It was also found that the most severe crashes occurred in counties with more urban than rural roads. The neighboring counties of Pasco, Pinellas and Hillsborough had high severe crash rate per mile. At the roadway level, seven counties were chosen for the analysis based on their high severe crash trends, metropolitan size and geographical location. Several GIS maps displaying the safety level of multilane corridors in the seven counties were generated. The GIS maps were based on a ranking methodology that was developed in research that evaluated the safety condition of road segments and signalized intersections separately. The GIS maps were supported by Excel tables which provided details on the most hazardous locations on the roadways. The results of the roadway level analysis found that the worst corridors were located in Pasco, Pinellas and Hillsborough Counties. Also, a sliding window approach was developed and performed on the ten most hazardous corridors of the seven counties. The results were graphs locating the most dangerous 0.5 miles on a corridor. For the spatial analysis of crashes, the exploratory Moran's I statistic test revealed that crash related spatial clustering existed at the county level. For crash modeling, a full Bayesian (FB) hierarchical model is proposed to account for the possible spatial correlation among crash occurrence of adjacent counties. The spatial correlation is realized by specifying a Conditional Auto-regressive prior to the residual term of the link function in standard Poisson regression. Two FB models were developed, one for total crashes and one for severe crashes. The variables used include traffic related factors and socio-economic factors. Counties with higher road congestion levels, higher densities of arterials and intersections, higher percentage of population in the 15-24 age group and higher income levels have increased crash risk. Road congestion and higher education levels, however, were negatively correlated with the risk of severe crashes. The analysis revealed that crash related spatial correlation existed among the counties. The FB models were found to fit the data better than traditional methods such as Negative Binomial and that is primarily due to the existence of spatial correlation. Overall, this study provides the Transportation Agencies with specific information on where improvements must be implemented to have better safety conditions on the roads of Florida. The study also proves that neighboring counties are more likely to have similar crash trends than the more distant ones.

PennDOT engaged Gannett Fleming to conduct research into best practices in the use of geospatial analysis tools for highway safety analyses. The goals of the effort were to define a methodology for PennDOT to follow in identifying the best candidate locations for highway safety improvements, and to develop a Proof of Concept to test the proposed methodology. After conducting interviews and workshops involving more than 35 of PennDOT's stakeholders in highway safety processes, Gannett Fleming interviewed highway safety managers in five other state and federal highway agencies to determine what innovative tools and practices are currently being used. Gannett Fleming's research also included a review of literature related to the study from more than 80 sources. Based on Gannett Fleming's research and analysis, PennDOT selected the "Highway Safety Data Relationships Knowledge Base" for further research. The knowledge base is an information repository based on concepts in data mining and expert systems. It uses advanced statistical analysis methods and expert business knowledge rules to discover data patterns based on correlation and other forms of relationships in the data. The knowledge base can be applied to diagnosing specific combinations of data attributes and features that may indicate the causative factors among homogeneous populations of crashes. Most highway safety data analyses involve studying correlations among multiple data sets. The knowledge base is an innovative and compreh3nsive tool for such an application. It provides a framework for identifying and managing relationships among many combinations of data sets that are useful in highway safety analyses. Gannett Fleming proceeded to develop a prototype as a proof of concept. Gannett Fleming demonstrated the prototype using actual PennDOT crash data. Three analysis scenarios were demonstrated" evaluating safety programming alternatives for alcohol involved crashes, diagnosing data patterns of crashes at a selected highway location, identifying potential sites for system-wide deployment of a selected countermeasure