<p> Existing railroad crossing crash prediction and hazard index equations are analyzed and found to inadequately measure safety at light rail crossings. The operational characteristics of common carrier freight and commuter railroads are different enough from the operational characteristics of light rail to affect the ability of existing railroad equations to accurately predict the number of crashes that occur at light rail crossings. These operational differences require light rail specific crash prediction equations to better predict crash numbers at light rail crossings. The goal of this research is to develop a method to measure safety at light rail crossings. </p><p> Through review of the literature describing different statistical methodologies that have been used to develop railroad crossing crash prediction and hazard index equations, the use of a nonlinear regression method to predict initial crash values with an Empirical Bayes Method adjustment to account for the actual crash history at the light crossing is determined to be the optimum model development method. </p><p> Operational alignment and configuration of light rail crossings are analyzed, and each is found to have some effect on the prediction of the number of crashes that occur at light rail crossings in addition to light rail vehicle volume, motor vehicle volume, sight obstructions, presence of a residential area near the light rail crossing, and the number of motor vehicle lanes crossing the crossing. Statistically valid models are developed to predict crashes based on light rail crossing alignment type, configuration type, and method of crossing control including traffic signals, flashing lights with gates, and passive signing. Sufficient data to develop a prediction equation for flashing light control is not available for this study. </p><p> The use of Geographic Information Systems (GIS) models is determined to be a benefit in use of application of the light rail specific crash number prediction equations. GIS models can be used not only to predict the number of crashes expected to occur at a light rail crossing, but also can be used to identify and analyze light rail crossing crash trends.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:3621825 |
| Date | 26 July 2014 |
| Creators | Fischhaber, Pamela Marie |
| Publisher | University of Colorado at Denver |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
Page generated in 0.0016 seconds