Srinivas R. Geedipally

Bio: Srinivas R. Geedipally is an academic researcher from Texas A&M Transportation Institute. The author has contributed to research in topics: Crash & Poison control. The author has an hindex of 14, co-authored 80 publications receiving 580 citations.

Effects of Geometry and Pavement Friction on Horizontal Curve Crash Frequency

Abstract: Horizontal curves tend to be associated with a disproportionate number of severe crashes. Many treatments have been proposed to reduce traffic crashes and fatalities occurring on horizontal curves. The application of high-friction surface treatments is one among many treatments that agencies have been considering. However, relatively less research has been conducted on the safety implications of pavement friction improvements caused by these treatments. Before–after case studies have been conducted on the effectiveness of high-friction surface treatments, but pavement variables like skid number (coefficient of friction multiplied by 100) have not yet been incorporated into safety prediction methodologies. The objective of this study is to develop a crash modification factor (CMF) for skid number for all crashes, wet-weather crashes, run-off-the-road crashes, and wet-weather run-off-the-road crashes. A horizontal curve database from a southern state in the United States was used to accomplish the s...

Methodology to predict the safety performance of rural multilane highways [Final Report for NCHRP Project 17-29]

Abstract: The objective of this research is to develop a methodology that predicts the safety performance of various elements considered in the planning, design, and operation of nonlimited- access rural multilane highways.

Vehicle Consumer Complaint Reports Involving Severe Incidents: Mining Large Contingency Tables:

Abstract: According to 2010–2014 Fatality Analysis Reporting System (FARS) data, nearly 6.35% of fatal crashes happened as a result of vehicles’ pre-existing manufacturing defects. The National Highway Traff...

Identifying Hot Spots by Modeling Single-Vehicle and Multivehicle Crashes Separately:

Abstract: Considerable research has been conducted on the development of statistical models for predicting motor vehicle crashes on highway facilities. These models often have been used to estimate the number of crashes per unit of time for an entire highway segment or intersection without distinguishing the influence that subgroups have on crash risk. The two most important subgroups identified in the literature are single- and multivehicle crashes. Recently, researchers have noted that two distinct models for these two categories of crashes provide better predicting performance than models that combine both crash categories to predict crashes for an entire facility. Thus, a study was done to determine whether any difference exists in the identification of hot spots when a single model is applied instead of two distinct models. A hot spot (or black spot) is a site with an accident frequency that is significantly higher than expected at some prescribed level of significance. The data used for the comparison analysis were collected on Texas multilane undivided highways for 1997 to 2001. The study shows that modeling single- and multivehicle crashes separately predicts slightly fewer false positives and negatives than modeling them together under a single aggregated model in the hot spot identification process. Thus, it is recommended that separate models be developed for single- and multivehicle crashes for predicting crashes and for identifying hot spots.