Bhaven Naik

Weather impacts on single-vehicle truck crash injury severity.

INTRODUCTION The focus of this paper is on illustrating the feasibility of aggregating data from disparate sources to investigate the relationship between single-vehicle truck crash injury severity and detailed weather conditions. Specifically, this paper presents: (a) a methodology that combines detailed 15-min weather station data with crash and roadway data, and (b) an empirical investigation of the effects of weather on crash-related injury severities of single-vehicle truck crashes. METHOD Random parameters ordinal and multinomial regression models were used to investigate crash injury severity under different weather conditions, taking into account the individual unobserved heterogeneity. The adopted methodology allowed consideration of environmental, roadway, and climate-related variables in single-vehicle truck crash injury severity. RESULTS AND CONCLUSIONS Results showed that wind speed, rain, humidity, and air temperature were linked with single-vehicle truck crash injury severity. Greater recorded wind speed added to the severity of injuries in single-vehicle truck crashes in general. Rain and warmer air temperatures were linked to more severe crash injuries in single-vehicle truck crashes while higher levels of humidity were linked to less severe injuries. Random parameters ordered logit and multinomial logit, respectively, revealed some individual heterogeneity in the data and showed that integrating comprehensive weather data with crash data provided useful insights into factors associated with single-vehicle truck crash injury severity. PRACTICAL APPLICATIONS The research provided a practical method that combined comprehensive 15-min weather station data with crash and roadway data, thereby providing useful insights into crash injury severity of single-vehicle trucks. Those insights are useful for future truck driver educational programs and for truck safety in different weather conditions.

Safety effectiveness of actuated advance warning systems

Driver behavior within the dilemma zone can be a major safety concern at high-speed signalized intersections. The Nebraska Department of Roads (DOR) has developed and implemented an actuated advance warning dilemma zone protection system. This paper investigates the impact that system has had on safety at high-speed signalized intersections. The operating algorithm has been designed such that the system continually monitors an upstream detector, as well as traffic at the intersection, to predict the onset of the yellow signal indication. Flashing beacons are used to warn drivers of the impending end of the green indication. Although these systems have received positive reviews from the public—and commercial vehicle operators in particular—there has been no comprehensive analysis of their effect on safety. The focus of this research was to address this evaluative need and provide answers about the effectiveness of the Nebraska DOR system in improving safety. Crash records from before and after the implementation of the system at 26 intersections were compared. In addition, 29 control intersections were used to compare crash rates over time, and a fully Bayesian technique was employed to ensure that no exogenous variables affected the study. Results of the analysis were promising (an overall crash reduction rate of 8%) and suggested that the use of the system should be encouraged as an effective safety treatment for the dilemma zone problem at high-speed signalized intersections.

Estimating route travel time reliability from simultaneously collected link and route vehicle probe data and roadway sensor data

Route travel time variability estimates provide performance information related to the reliability of a trip and allow for confidence bands to be placed around the mean travel time estimates. The naïve (and sometimes used) method to estimate route travel time variance (reliability) is to assume independence of link travel times and consequently sum the individual link variances along the route. In this approach, correlation between links is assumed as zero and the approach is straightforward, but assuming independence is not realistic. This paper describes a post-processing procedure for providing improved route travel time mean and variance estimates, while taking into consideration the correlation existent between individual link travel times. Using automatic vehicle identification (AVI) and inductance loop detector (ILD) data from two separate routes in Texas, a practical application of the theory established by Fu and Rilett (1998. Expected shortest paths in dynamic and stochastic traffic networks. Transportation Research Part B: Methodological, 32(7), 499–516) is used to quantify link travel time correlation. The paper also provides an investigation on the usefulness of the loess statistical method and a polynomial regression model to estimate the distributional properties of link and route travel times. Another significant finding is that route reliability estimated from link ILD speeds (extrapolated to travel times) was not correlated to actual route travel time reliability measured by simultaneously operated probe vehicles. This work is unique in that instrumented probe vehicles were operated at exactly the same times as the roadway sensor data were collected, allowing direct comparison of the travel time estimates from all empirical data sources. The research presents valuable insight on how confidence intervals may be placed on travel time mean estimates for all traffic conditions. With the increased use of travel time data sources such as smartphones, connected vehicles, and private-sector data sources, the methods presented in this paper are invaluable for effective transportation system performance monitoring of both persons and freight movement.

Current Issues in Highway–Rail Grade Crossing Hazard-Ranking and Project Development

This paper reports on a comprehensive research study of hazard-ranking models for prioritization and selection of highway–rail grade crossing improvement projects. The U.S. Department of Transportation (DOT) accident prediction model is the most commonly used hazard-ranking model, although 11 states use state-specific models. Key variables not included in the U.S. DOT model but included in state-specific models may provide useful insight for identifying hazardous crossing locations. Emerging issues, such as the crash modeling approach used by the U.S. DOT model and the application of economic analysis principles in project prioritization, are also discussed. The findings reported in this paper provide a starting point for a broader discussion among all stakeholders in the grade crossing safety community about ways to improve hazard-ranking and project selection for highway–rail grade crossing investments.

Resampling Methods for Estimating Travel Time Uncertainty: Application of the Gap Bootstrap

To a large extent, methods of forecasting travel time have placed emphasis on the quality of the forecasted value—how close is the forecast point estimate of the mean travel time to its respective field value? However, understanding the reliability or uncertainty margin that exists around the forecasted point estimate is also important. Uncertainty about travel time is a fundamental factor as it leads end-users to change their routes and schedules even when the average travel time is low. Statistical resampling methods have been used previously for uncertainty modeling within the travel time prediction environment. This paper applies a recently developed nonparametric resampling method, the gap bootstrap, to the travel time uncertainty estimation problem, especially as it pertains to large (probe) data sets for which common resampling methods may not be practical because of the possible computational burden and complex patterns of inhomogeneity. The gap bootstrap partitions the original data into smaller groups of approximately uniform data sets and recombines individual group uncertainty estimates into a single estimate of uncertainty. Results of the gap bootstrap uncertainty estimates are compared with those of two popular resampling methods—the traditional bootstrap and the block bootstrap. The results suggest that, for the datasets used in this research, the gap bootstrap adequately captures the dependent structure when compared with the traditional and block bootstrap methods and may thus yield more credible estimates of uncertainty than either the block bootstrap method or the traditional bootstrap method.

Evaluation of Grade Crossing Hazard Ranking Models

Public agencies involved with highway-railroad grade crossing safety must allocate available funding to projects which are considered the most in need for improvements. Mathematical models provide a ranking of hazard risk at crossings and support the project selection process. This paper reports the results of a research study sponsored by the Ohio Rail Development Commission (ORDC) and the Ohio Department of Transportation (ODOT) examining hazard ranking models for grade crossing project selection. The goal of the research was to provide ORDC, ODOT, and other stakeholders with a better understanding of the grade crossing hazard ranking formulas and other methods used by States to evaluate grade crossing hazards and select locations for hazard elimination projects. A comprehensive literature review along with personal interviews of state DOT personnel from eight states yielded best practices for hazard ranking and project selection. The literature review found that more than three-quarters of states utilize some type of hazard ranking formula or other systematic method for project prioritization. The most commonly-used hazard ranking model in use is the U.S. DOT Accident Prediction Model; however, at least eleven states utilize state-specific hazard ranking models. Detailed evaluation of several different hazard ranking models determined that the existing hazard ranking model used in Ohio, the U.S. DOT Accident Prediction Model, should continue to be used. The research also recommends greater use of sight distance information at crossings and expanding the preliminary list of crossings to be considered in the annual program as enhancements to the existing project selection process used by the ORDC and ODOT.Copyright