Influence of Multiple Freeway Design Features on Freight Traffic Safety

Juneyoung Park,Gunwoo Lee,Mohamed Abdel-Aty,Jungyeol Hong,Ling Wang

doi:10.1155/2019/5739496

Abstract

Since various freeway design features are simultaneously installed on roadways, it is important to assess their combined safety effects correctly. This study investigated associations between multiple roadway cross-section design features on freeways and traffic safety. In order to consider the interaction impact of multiple design features and nonlinearity of predictors concurrently, multivariate adaptive regression splines (MARS) models were developed for all types and freight vehicle crashes. In MARS models, a series of basis functions is applied to represent the space of predictors and the combined safety effectiveness of multiple design features can be interpreted by the interaction terms. The generalized linear regression models (GLMs) with negative binomial (NB) distribution were also evaluated for comparison purposes. The results determine that the MARS models show better model fitness than the NB models due to its strength to reflect the nonlinearity of crash predictors and interaction impacts among variables under different ranges. Various interaction impacts among parameters under different ranges based on knot values were found from the MARS models, whereas two interaction terms were found in the NB models. The results also showed that the combined safety effects of multiple treatments from the NB models over-estimated the real combined safety effects when using the simple multiplication approach suggested by the HSM (Highway Safety Manual). Therefore, it can be recommended that the MARS is applied to evaluate the safety impacts of multiple treatments to consider both the interaction impacts among treatments and nonlinearity issues simultaneously.

Highlights

Tra c safety has become one of the serious global concerns and many countries have taken safety plans and initiatives towards safer roadways
The estimated basis functions are statistically signi cant at a 95% con dence level except for three cases (i.e., Basis 19 function in multivariate adaptive regression splines (MARS) model for large truck-involved crashes and, Basis 3 and Basis 16 functions in MARS model for total crashes)
In the MARS model for large truck-involved crashes, the rst basis function, Basis 1, is MAX(Ln(AADT)−11.608,0) and where the knot value is 11.608. e Basis 1 function can be included in the model when the logarithm of AADT is greater than 11.608 and the Basis 1 function is 0 for otherwise

Summary

Introduction

Tra c safety has become one of the serious global concerns and many countries have taken safety plans and initiatives towards safer roadways. Erefore, designing roadways with appropriate facilities contributes to tra c safety to prevent death and injury from crashes. It is generally known that large trucks (i.e., commercial or freight vehicles) are substantial contributors to the roadway fatalities and injuries [1]. According to the National Highway Tra c Safety Administration (NHTSA) [2], a 10-percent increase was found in 2017 in large-trucks involved in fatal crashes from 2016 in the United States. From 2016 to 2017, large truck fatalities per 100 million vehicle miles traveled increased by 6 percent. The number of largetruck involved injury crashes increased from 102,000 in 2016 to 107,000 in 2017. The number of large trucks involved in property damage (only crashes) increased by 3 percent, from 351,000 in 2016 to 363,000 in 2017

Objectives

Methods

Results