Doctor of Philosophy / Department of Industrial & Manufacturing Systems Engineering / Malgorzata J. Rys / This dissertation is the result of studying different methods of increasing guide sign visibility and legibility to drivers during nighttime, to increase safety on roadways. It also studies intersection lighting to indicate the lighting benefits on nighttime crash frequency reduction. From a survey conducted, practices related to overhead guide sign illumination and retroreflectivity in United States were summarized. A laboratory experiment was conducted to compare light distribution of five light sources: Metal Halide, Mercury Vapor, High Pressure Sodium, induction lighting, and Light Emitting Diode (LED). Cost analysis of the five light sources was performed. Combining results of the laboratory experiment and the cost analysis, induction lighting was recommended for states that want to continue external sign illumination. A retroreflectivity experiment was conducted to compare three types of retroreflective sheeting: Engineering Grade (type I), Diamond Grade (type XI), and High Intensity (type IV), to determine the sheeting that best increases visibility and legibility. Diamond Grade (type XI) was found to be the optimal sheeting that increases visibility and legibility to drivers during nighttime. A glare experiment was conducted to expand the retroreflectivity experiment results. Four sheeting-font combinations of High Intensity (type IV) and Diamond Grade (type XI) materials and Series E (Modified) and Clearview fonts were compared. Results revealed an optimal sheeting-font combination of Diamond Grade (type XI) sheeting and Clearview font which increases the visibility and legibility of guide signs to drivers under presence of oncoming glare source. The Highway Safety Information System (HSIS) database was used to study the effect of intersection lighting on the expected crash frequency. Illuminated intersections showed 3.61% and 6.54% decrease in the expected nighttime crash frequency as compared to dark intersections in Minnesota and California, respectively. In addition, partial lighting at intersections decreases the expected nighttime crash frequency by 4.72% compared to continuous lighting in Minnesota. The recommended sheeting-font combination for Departments of Transportation was Diamond Grade (type XI) and Clearview. This combination will increase signs’ visibility and legibility to drivers, and consequently increase safety on roadways. Adding partial lighting at intersections will reduce the expected nighttime crash frequency, and increase safety on roadways.

Road traffic signs provide regulatory, warning, guidance, and other important information to road users to prevent hazards and road accidents. Therefore, the traffic signs must be detectable, legible, and visible both in day and nighttime to fulfill their purpose. The nighttime visibility is critical to safe driving on the roads at night. The state of the art gives clear evidence that the retroreflectivity improves the nighttime visibility (detectability and legibility) of the road traffic signs and that the nighttime visibility can be improved by using an adequate level of retroreflectivity. Furthermore, nighttime visibility can be affected by human, sign, vehicle, environmental, and design factors.  The retroreflectivity and colors of the road signs deteriorate over time and thus the visibility worsens, therefore, maintaining the road signs is one of the important issues to improve the safety on the roads.  Thus, it is important to judge whether the retroreflectivity and colors of the road sign are within the accepted levels for visibility and the status of the signs are accepted or not and need to be replaced.  This thesis aims to use machine learning algorithms to predict the status of road signs in Sweden. To achieve this aim, three classifiers were invoked: Artificial Neural Network (ANN), Support Vector Machines (SVM), and Random Forest (RF). The data which was collected in Sweden by The Road and Transport Research Institute (VTI) was used to build the prediction models. High accuracy was achieved using the three algorithms (ANN, SVM, and RF) of 0.84.3, 0.93, and 0.98, respectively. Scaling the data was found to improve the accuracy of the prediction for all three models and better accuracy is achieved when the data was scaled using standardization compared with normalization. Additionally using principal component analysis (PCA) has a different impact on the accuracy of the prediction for each algorithm. Another aim was to build prediction models to predict the retroreflectivity performance of the in-use road signs without the need to use instruments to measure the retroreflectivity or color. Experiments using linear and logarithmic regression models were conducted in this thesis to predict the retroreflectivity performance. Two datasets were used, VTI data and another data which was collected in Denmark by voluntary Nordic research cooperation (NMF group). The age of the road traffic sign, the chromaticity coordinate X for colors, and the class of retroreflectivity were found significant to the retroreflectivity in both datasets.  The logarithmic regression models were able to predict the retroreflectivity with higher accuracy than linear models. Two suggested logarithmic regression models provided high accuracy for predicting the retroreflectivity (R2 of 0.50 on VTI data and 0.95 on NMF data) by using color, age, class, GPS position, and direction as predictors. Nearly the same accuracy (R2 of 0.57 on VTI data and 0.95 on NMF data) was achieved by using all parameters in the data as predictors (including chromaticity coordinates X, Y for colors). As a conclusion, omitting chromaticity coordinates X, Y for colors from the logarithmic regression models does not affect the accuracy of the prediction.