Journal of Transportation Engineering, Part A: Systems

This paper summarizes the results of Phase II of a research project developing a novel design of directional rumble strips (DRSs) to deter wrong-way driving (WWD) onto freeway off-ramps. In Phase I of this project, five concept designs of the DRS (named from A to E) were developed to generate elevated sound and vibration to warn against WWD and to generate normal sound and vibration to slow down the right-way (RW) traffic. After initial field tests in Phase I, three final conceptual DRS designs were selected for further field verification, specifically, Patterns C, D3, and E1. Pattern C was designed based on traditional transverse rumble strips (TRSs), but the spacing between the strips was changed to generate different rhythms of sound and vibration. Pattern D3, which was modified based on the advance warning markings for speed humps, increases in thickness and length in each strip. Pattern E1 has a right-angled triangle cross section that can produce the most recognizable sound and vibration from the WWD direction among the three patterns. The objective of this paper was to document the field verification results of the final three patterns and to develop implementation recommendations for applying them on off-ramps. Overall, the field verification results indicated that the tested DRS can generate recognizable interior sound and a moderate amount of vibration to alert wrong-way drivers. Recommendations for implementation were also developed for transportation agencies to identify proper locations for installing them to achieve the best performance based on type and length of off-ramp.

The establishment of work zones along roadways is considered a necessity for the construction of new roadways, the maintenance of deteriorating structures, and to provide access for the installation and maintenance of utilities. This study attempts to investigate the risk factors contributing to driver’s injury severity in the different areas that constitute the formation of roadway work zones. The injury severity outcomes of a crash have a natural and discrete ordering, and therefore this study has adopted the mixed generalized ordered response probit (MGORP) model. As compared to the standard ordered response probit model (ORP), which is widely utilized in the injury severity literature, the MGORP framework has the ability to recognize not only the ordering of the injury severity categories, but also allow for the investigation of unobserved effects of risk factors, known in the literature as unobserved heterogeneity. The empirical analysis was conducted using a database that consisted of 10 years of work zone crashes. This database was available through the Highway Safety Information System (HSIS). Elasticity analysis suggests that airbag deployment, alcohol involvement, ejection, seatbelt use, and partial control of access are key factors contributing to the likelihood of severe outcomes. Additionally, the effects of several covariates were found to vary across the different work zone component areas where crashes have occurred.

In this paper, train-vehicle crash risk at highway–rail grade crossings (HRGCs) is analyzed with a neural network (NN) model to return meaningful rankings of crash-contributory-variable importance based on different criteria, but also to produce dependent nonlinear contributor-crash curves with all other contributors considered for a specific contributor variable. Historical crash data for North Dakota public HRGCs from 1996 to 2014 were used for the study. Several principal findings were observed: (1) 22 input variables describing traffic characteristics and crossing characteristics are related to crashes at public HRGCs; (2) a mean-square error–based NN model and a connection weights–based NN model represent two relative contributory-variable importance lists for different application purposes; (3) the effect of different variables on crash likelihood is different when all other contributors are set at different levels, and the relationship between contributors and crash likelihood is dynamic nonlinear; and (4) in predictive and explanatory power, the neural network model outperforms the decision tree approach for the considered case study.

Drivers’ speed choice on a curve depends on their perception of the safety offered by that particular geometric element. Perception is a latent variable and is difficult to measure directly from the field. This study assumed that perception is reflected in the speed and path radius adopted by drivers on curves. Preliminary analysis of the speed variability and the difference between path radius and curve radius showed that the curve direction and superimposed horizontal and vertical alignments significantly influence the driver’s perception. The study suggests a clustering-based approach to deal with drivers’ perception of a complex geometry. Curve direction and type of superimposed vertical curve were considered as the criteria for clustering. Based on those two criteria, the curves were clustered into eight groups. For each cluster, the geometric variables influencing the operating speed were identified through correlation analysis. The study found a significant improvement in the correlation between geometric parameters and operating speed after clustering. The results support the assumption that driver perception plays a significant role in operating speed decisions.