Investigating effects of asphalt pavement conditions on traffic accidents in Tennessee based on the pavement management system (PMS)

Abstract

SUMMARYPavement maintenance is essential for ensuring good riding quality and avoiding traffic congestion, air pollution, and accidents. Improving road safety is one of the most important objectives for pavement management systems. This study utilized the Tennessee Pavement Management System (PMS) and Accident History Database (AHD) to investigate the relationship between accident frequency and pavement distress variables. Focusing on four urban interstates with asphalt pavements, divided median types, and 55 mph speed limits, 21 Negative Binomial Regression models were developed for predicting various types of traffic accident frequencies based on different pavement condition variables, including rut depth (RD), International Roughness Index (IRI), and Present Serviceability Index (PSI). The modeling results indicated that the RD models did not perform well, except for predicting accidents at night and accidents under rain weather conditions; whereas, IRI and PSI were always significant prediction variables in all types of accident models. Comparing the models goodness-of-fit results, it was found that the PSI models had a better performance in crash frequency prediction than the RD models and IRI models. This study suggests that the PSI accident prediction models should be considered as a comprehensive approach to integrate the highway safety factors into the pavement management system.

Analysis of Nonsevere Crashes on Two- and Four-Lane Urban and Rural Highways: Effects of Wet Pavement Surface Condition

“…Chan et al [9] used the 2006 Tennessee accident database collected from 55 mph posted speed limit divided interstates to calibrate regression models to predict the relationship between crash frequency and pavement conditions (mainly pavement distress) in the State of Tennessee for daytime, nighttime, different weather conditions (normal and rainy), peak hour, and nonpeak hour accidents. The variables used in the negative binomial regression model were rut depth (RD), present serviceability index (PSI), and IRI.…”

Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model

“…Despite the preponderance of literature in safety research, most studies are focused on crash predictions on aggregate levels based on yearly records [1,4,14,15,18,25]. Those studies employed highly aggregated data, thus being unable to provide guidance for proactive intervention.…”

“…Over the last decades, traffic safety researchers have spent tremendous effort and time to gain a better understanding of the contributory factors towards motor vehicle crash [1][2][3][4][5][6]. Despite the progress, there are many knowledge gaps yet to be filled in safetyrelated studies.…”

Measuring Efficiency and Effectiveness of Highway Management in Sustainability

“…IRI has score ranges of "<60, 60-94, 95-170, 171-220, and >220," and a higher value of IRI means poorer quality. We divided the ranges into "good (IRI < 95)," "fair (95 ≤ IRI ≤ 170)," and "unacceptable (IRI > 170)" according to the literature [38][39][40] and coded them as 3, 2, and 1, respectively. Then, we computed the weighted mean of each state highway by using the ratio of the certain quality road miles to the total miles: [3 × good (miles) + 2 × fair (miles) + 1 × poor (miles)]/total (miles).…”