Ghazan Khan

Safety Evaluation of Horizontal Curves on Rural Undivided Roads

The objective of this research was to develop prediction models for total crashes and fatal or injury crashes for rural horizontal curves on undivided roads, with a focus on three distinct aspects. The first was an emphasis on assembling a large, high-quality data set. Crash prediction models were developed by using a data set of 11,427 rural horizontal curves on Wisconsin state trunk network roads with more than 13 parameters and four distinct types of crash data sets. The second focus area was to use regression tree analysis in creating a simple model of horizontal curve safety aimed at practitioners of systemic road safety management and creating subsets of data that warranted further analysis. Regression tree results identified the curve radius of approximately 2,500 ft as a significant point below which there is a marked increase in crashes on horizontal curves. The third focus area was to research the effect on horizontal curve crash prediction models of different selection criteria to assemble the crash data set. Models (total and fatal or injury) based on a crash data set with and without crashes in the proximity of intersections were compared. The results show that when crashes on horizontal curves are selected where crash report forms indicate the presence of a horizontal curve, crashes that occur in the proximity of intersections do not affect model results significantly; therefore, the inclusion of such crashes would increase the size of the data set and benefit model development.

Comprehensive Safety Evaluation of Roundabouts in Wisconsin

The modern roundabouts are proliferating rapidly in the United States and Wisconsin is no exception to this trend. The growing number of U.S.-specific research has played an important role in their acceptance in the United States. However, as new data become available, there is a need to continue the research to better understand roundabout safety in the United States. Moreover, the growing data sets also warrant the creation of localized models to better reflect ground conditions. The objectives of this research were to continue and enhance research efforts on the roundabout safety using current data sets. The aim was to analyze roundabout crash trend and patterns to further evaluate their performance under varying situations and develop crash prediction models. The results showed interesting observations as far as crash patterns at roundabouts were concerned. Even though crash severity was reduced, it is not the same situation for crash frequencies. Further research is required to assess the safety effectiveness of roundabouts in Wisconsin. The crash prediction models from this research would help in quantifying roundabout safety, especially when selecting which locations to be converted to roundabouts.

Development of Data Collection and Integration Framework for Road Inventory Data

The availability and quality of transportation data is a cornerstone of any data-driven program. There is a continuous need to identify and develop alternative, reliable, and inexpensive sources of data and efficient and robust integration techniques. This research presents an innovative cost-effective application to collect geographic information system (GIS)–compatible data from image-based databases. Road inventory data on guardrail end-type locations along with other road features on more than 8,000 mi of Wisconsin State Trunk Network highways were collected. Data collected from image-based sources with Global Positioning System coordinates presented the familiar problem of spatial mismatch. A framework was developed based on the principles of dynamic segmentation to integrate the data and resolve the spatial mismatch problem. The principles of dynamic segmentation and route calibration are well established in literature. However, there were no specific examples of a framework that created a workable program and addressed issues pertaining to practical solutions for statewide data. The framework developed presents an efficient and automated solution for data integration, which is applicable to any relevant data set. A quantitative assessment of the performance of the data collection and map-matching procedures was conducted to assess the results. The results showed that road features collected from the image-based data sets were located within an average distance of 6 to 7 m of their location on the Wisconsin Department of Transportation GIS base maps, which were highly accurate, given the limitations of the data sets.

Application and Integration of Lattice Data Analysis, Network K-Functions, and Geographic Information System Software to Study Ice-Related Crashes

Advances in geographic information system (GIS) software and exploratory spatial data analysis (ESDA) techniques give transportation safety engineers tools to observe and analyze safety-related data from a new perspective. This research takes the use of GIS software and ESDA techniques one step further by incorporating advanced statistical techniques for a more thorough and complex analysis of safety data. This is achieved by implementing a network-constrained cross K-function to analyze the relationship between bridges and the occurrences of ice-related crashes within a county. The counties in Wisconsin included in the analysis were selected through the use of the local Morans I statistic; this statistic allows for the selection of counties within the same geographical area, which have similar parameters (in this case, ice-related crash rates). The objective of this research is to explore the relationship between ice-related crashes and bridges in counties that display similar ice-related crash rates, to compare and analyze winter maintenance techniques. The results identify clustering of ice-related crashes around bridges in four counties with similar ice-related crash rates in southeast Wisconsin. Similarly, two of four counties show clustering of ice-related crashes around bridges in northwest Wisconsin. These results make a strong case to suggest that counties in these regions should focus additional winter maintenance efforts at bridge locations. In addition, this research shows how the use of advanced spatial statistical techniques, particularly network-based statistics applied within a GIS environment, can be used as a unique and innovative approach toward safety data analysis.

Incorporating safety into targeted pavement friction data collection and maintenance procedures

The objective of this research was to develop a methodology for targeted pavement friction data collection based on the analysis of weather-related crashes. Furthermore, the aim was to identify threshold values of pavement friction characteristics indicating a significant impact on safety prompting the need for maintenance and improvements. Spatial analysis using Local Moran’s I statistic identified hotspots where pavement friction data were collected. A master database was assembled including Wisconsin State Trunk Network (STN) road attributes, hotspots of weather-related crashes, and pavement friction data collected based on hotspot analysis. The analysis results provide evidence in support of hotspot analysis as a viable procedure for targeted pavement friction data collection to enable efficiency and cost reductions. Classification tree analysis using GUIDE (Generalized, Unbiased, Interaction Detection and Estimation) algorithm was used to further explore the relationship between pavement friction characteristics and safety. Statistically significant hotspots were observed below a pavement friction number of approximately 57 and very high hotspots below a pavement friction number of approximately 42. The results indicate that pavement friction thresholds identified in the literature between 20 and 32 may be too low and that safety may be impacted at friction numbers as high as in the forties. The results also show differences in friction and safety for various types of pavement surfaces. The use of weather-related crashes provides a data-driven and cost-effective method of prioritizing locations for pavement friction data collection and maintenance. Results from this research can be readily used in initial steps of systemic road safety management procedures by practitioners.

Spatial Analysis of Bicycle Crashes in Chile

In Chile, bicycle crashes yield more than 170 fatalities every year and these represent almost 12% of the total number of the yearly crashes. Cycling is the third cause of accident after vehicles and pedestrians. The focus of this study is on identifying high-risk bicycle crash locations and related factors at the district level that occurred during the 2008-2012 period using global and local spatial statistical analysis. The results suggest that the main contributing cause of bicycle crashes related to under the influence of alcohol or drugs tend to cluster in districts located in the central-south zone of Chile. Although the hotspots corresponding to the number of killed and severely injured cyclists has been reduced over the years of the study, the majority occur in the districts of Chillan and Rancagua. Additionally, high-risk zones of bicycle-vehicle collisions in the Metropolitan Region occur primarily due to the imprudence of the driver. The results pave the way for future investment and focus in these areas to improve bicycle safety and promote their use.