2018 Data Visualization Award

Proactive Network Analysis to Improve Horizontal Curve Safety

Abstract:  Crashes on horizontal curves remain a key road safety challenge for road safety agencies across the globe. Statistics from the United States show that around 25 percent of fatal and serious crashes occur on horizontal curves. Traditional methods of detecting safety issues tend to involve geospatial analysis of crash data to highlight blackspots and reveal crash trends. Whilst these approaches enable horizontal curves with an established safety problem to be identified, they miss curves with an inherently high level of risk where few crashes have occurred in the past. This presentation introduces a geospatial risk prediction methodology that assesses the safety risk of horizontal curves based on curve approach speed and curve radii. Analysis is completed at a network level using Esri GIS software and allows all curves to be assessed quickly and at a fraction of the cost of manual assessments. The only data inputs required are a high-quality road center-line and speed limit information. Time consuming and expensive data collection is not required. The analysis utilizes several smart geospatial workflows to segment the network, identify curves, calculate curve radii, predict vehicle operating speeds along road corridors, and assess curve risk. The analytical method has thus far been applied to more than 70,000 miles of state and local roads. Validation of the results against crash data shows that injury crash rates on curves classified as high-risk are 100% higher than horizontal curves where modeled approach speed is consistent with curve design speed. These findings demonstrate that the horizontal curve risk assessment methodology is a strong indicator of underlying safety risk. As the methodology assesses risk independent of crash information, road agencies can now proactively target interventions at high-risk locations and have confidence that improved safety outcomes are an achievable outcome regardless of whether the location has an established crash problem or not. The methodology effectively bridges the gap between an awareness of a major safety issue on high-speed roads (crashes at horizontal curves) and detailed strategies to reduce the likelihood and consequence of crashes on horizontal curves. The results of the analysis are being used by road agencies to prioritize corridors for mass-action curve improvement programs, to provide an evidential basis for investment decisions, and to evaluate the benefit of speed management interventions in rural environments. This presentation will be of interest to everyone involved in the use of data, traffic records and new analytical techniques to improve safety outcomes on rural roads.

 

Carl O’Neil – Co-authored presentation with New Zealand Police Sergeant Dan Harker. Carl is a Senior Transportation Engineer at Abley Transportation Consultants. He has a Bachelor of Engineering degree from the University of Canterbury in New Zealand with First Class Honors in Civil Engineering. Carl has a strong analytical background and has particular expertise in the integration of data manipulation and GIS technologies to solve transportation and logistics problems. He has applied these skills to an ever-growing number of safety projects in New Zealand and Australia, bridging the gap between emerging analytical and technological methods and road safety applications.