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Providing adequate nighttime visibility to roadway users is an important consideration for state and local transportation agencies. Driving at night is less dangerous when pavement markings are easily discernable. Retroreflectivity is a measure of nighttime visibility. Transportation agencies could use estimates of the expected service life of pavement markings to plan restriping operations at a time when markings are near a minimum threshold level of retroreflectivity. The present study proposes the use of an artificial neural network to predict pavement marking retroreflectivity as a function of initial retroreflectivity, the age of the markings, traffic flow, pavement marking type, and route location information using data from North Carolina. The results show that many of the input variables have a nonlinear association with pavement marking retroreflectivity. Surface plots of the degradation pattern are provided to illustrate the relationship between input and output variables. Estimates of service life are provided to show how the output can be used to manage pavement marking systems. [ABSTRACT FROM AUTHOR]

Data from a controlled nighttime driving experiment along a complex, two-lane rural highway in Pennsylvania were used to investigate participant speed and lateral vehicle position. Additionally, the association of pavement marking visibility and the roadway geometry on speed and lateral vehicle position was investigated. The modeling methodology employed considered a variety of single equation and systems methods to estimate models for speed and lateral vehicle position. These include ordinary least-squares regression models, random effects panel data models, a three-stage least-squares technique, and seemingly unrelated regression. These methods were used to determine if there was an endogenous relationship between the speed and lateral vehicle position metrics, and to account for the repeat observations on the research participants in the experiment. The results suggest that endogeneity did not exist in the speed and lateral vehicle position measures in the present experiment. A single-equation random effects panel regression model was an improvement over the ordinary least-squares regression model for the change in speed metric. However, the ordinary least-squares regression model was appropriate for the lateral vehicle position metric. A seemingly unrelated regression model was also specified to account for contemporaneous correlation across the change in speed and lateral vehicle position models. It was found that the roadside hazard rating, horizontal curve direction, vertical grade, horizontal curve radius, upstream horizontal curve radius, and approach tangent length all were associated with vehicle speed on the complex, two-lane rural highway. The horizontal curve direction, roadside hazard rating, vertical grade, horizontal curve radius, and approach tangent length all were associated with lateral vehicle position in the present experiment. [ABSTRACT FROM AUTHOR]

Nighttime driving behavior differs from that during the day because of differences in the driver’s field of view. At night, drivers must rely on their vehicle headlamps to illuminate the roadway. It is essential then that the roadway delineation system provide the appropriate lane guidance to motorists when navigating a roadway, particularly one that is curvilinear. A nighttime driving experiment was conducted to collect user perception data of various pavement markings and markers applied to horizontal curves. The effectiveness of each pavement marking was rated using a subjective scale. A fuzzy inference system was used to analyze the subjective pavement marking and marker evaluation ratings provided by the research participants. Pavement marking effectiveness, horizontal curve sharpness, and driver age were used to develop a fuzzy index for nighttime driving condition (FIND). Based on the FIND, the results indicate that drivers prefer that a combination of treatments be applied to horizontal curves rather than only a single treatment. A bright centerline, bright edgeline, and bright retroreflective raised pavement maker combination treatment, and a bright centerline and bright edgeline combination treatment, resulted in the highest FIND score. A bright, 8-in. (20.3 cm) edgeline, applied alone to a horizontal curve, scored the lowest FIND. [ABSTRACT FROM AUTHOR]