Pavement markings, as a form of traffic control devices, play a crucial role in safely guiding drivers. Restriping pavement markings is an important task in the maintenance of traffic control devices. Every year state agencies spend a lot of money in maintaining pavement markings as the retroreflectivity or durability values of the markings fall below a minimum level. Currently, the most widely adopted method used to determine the presence conditions of pavement markings is by expert observation, a subjective technique that may not provide consistent and convinced results for agencies. Hence, a fast and accurate way to determine the presence conditions of pavement markings can lead to significant cost savings while ensuring driving safety. In this study, a systematic approach that can automatically determine the presence conditions of pavement markings using digital image processing techniques is presented. These techniques are used to correct the geometric deformity, detect colors of pavement markings, segment images, enhance images, detect edge lines of ideal pavement markings, and recognize the features of pavement markings appearing in the photographs. To better implement the aforementioned techniques, a software package has been developed by Graphic User Interface (GUI) as a platform to simultaneously evaluate the presence conditions of single or multiple pavement markings. The developed software package is able to do operations such as open files, calibrate camera calibration, clip, rotation, histogram display, and detection of edge lines of ideal pavement markings. The above system was tested and evaluated with the photograph datasets provided by the NTPEP Mississippi test deck. The empirical results (when compared with the manual method and expert observation) show that the developed system in this study is accurate and reliable. Additionally, the interactivity of the developed software package is satisfactory due to the feedback from ten volunteers. It is also concluded that the developed system, as an important reference, potentially helps agencies make a better decision in the maintenance of pavement markings with more accurate and speedy evaluation of the presence conditions of pavement markings.

This research effort focused on ascertaining the headlamp technology (of the eleven specified) that minimized the amount of discomfort glare and maximized the visibility of three types of pavement marking materials used in the study. Two baseline conditions, halogen low beam (HLB) and high-intensity discharge (HID) were measured both individually and in combination with three levels of UV-A. In addition, three other headlamp configurations were evaluated. Discomfort glare was measured subjectively for each headlamp configuration. Pavement marking visibility was directly measured via pavement marking detection distances. Thirty participants representing three age groups participated in this study: young (18-25 years old), middle (40-50 years old), and older (60 years and older). The headlamp technology and the pavement marking material needed to be beneficial for all age groups as all would potentially use the new technology if it were implemented in vehicles and roadways in the future. Participants evaluated discomfort glare at both a far and close distance using the nine-point DeBoer scale and evaluated pavement marking visibility by indicating when they could see the first and last pavement markings in each of the three sections. Overall, it was found that the HID configurations (HID, Middle UV-A + HID, High UV-A + HID) with a sharp cut-off beam pattern provided the least amount of discomfort glare. In contrast, the halogen configurations (HLB, Hybrid UV-A + HLB, Middle UV-A + HLB, High UV-A + HLB) and high output halogen with a straight-ahead beam pattern provided the longest detection distances. Two of the pavement markings: a two part liquid system (developed by 3M) and a fluorescent paint provided longer detection distances than a thermoplastic marking. / Master of Science

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This research provides the evaluation of the technical measurements of pavement marking materials from a two-year data collection on 2002 National Transportation Product Evaluation Program (NTPEP)?s Mississippi Test Deck from June, 2002 to June 2004. The materials studied in this research were divided into permanent and temporary material groups on two different pavement surfaces ---- asphalt and concrete. The retro reflectivity and durability of permanent marking materials among different surfaces, colors and groups were studied. Also the characteristics for temporary tapes, which include internal tape strength, adhesion, tackiness and dicernablity were compared and regressed. This correlation analysis is to see whether these ratings are correlated to each other. The results of this study can be used for estimating service lives of pavement marking materials. They also can be used by states to select appropriate pavement marking materials for different needs.

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The Indiana Department of Transportation (INDOT) manages and maintains over 3,000 miles of interstates across the state. Assessing lane marking quality is an important part of agency asset tracking and typically occurs annually. The current process requires agency staff to travel the road and collect representative measurements. This is quite challenging for high volume multi-lane facilities. Furthermore, it does not scale well to the additional 5,200 centerline miles of non-interstate routes. <div><br></div><div>Modern vehicles now have technology on them called “Lane Keep Assist” or LKA, that monitor lane markings and notify the driver if they are deviating from the lane. This thesis evaluates the feasibility of monitoring when the LKA systems can and cannot detect lane markings as an alternative to traditional pavement marking asset management techniques. This information could also provide guidance on what corridors are prepared for level 3 autonomous vehicle travel and which locations need additional attention. </div><div><br></div><div>In this study, a 2019 Subaru Legacy with LKA technology was utilized to detect pavement markings in both directions along Interstates I-64, I-65, I-69, I-70, I-74, I90, I-94 and I-465 in Indiana during the summer of 2020. The data was collected in the right most lane for all interstates except for work zones that required temporary lane changes. The data was collected utilizing two go-pro cameras, one facing the dashboard collecting LKA information and one facing the roadway collecting photos of the user’s experience. Images were taken at 0.5 second frequency and were GPS tagged. Data collection occurred on over 2,500 miles and approximately 280,000 images were analyzed. The data provided outputs of: No Data, Excluded, Both Lanes Not Detected, Right Lane Not Detected, Left Lane Not Detected, and Both Lanes Detected. </div><div><br></div><div>The data was processed and analyzed to create spatial plots signifying locations where markings were detectable and locations where markings were undetected. Overall, across 2,500 miles of travel (right lane only), 77.6% of the pavement markings were classified as both detected. The study found</div><div><br></div><div>• 2.6% the lane miles were not detected on both the left and right side </div><div>• 5.2% the lane miles were not detected on the left side </div><div>• 2.0% the lane miles were not detected on the right side 8 </div><div><br></div><div>Lane changes, inclement weather, and congestion caused 12.5% of the right travel lane miles to be excluded. The methodology utilized in this study provides an opportunity to complement the current methods of evaluating pavement marking quality by transportation agencies. </div><div><br></div><div>The thesis concludes by recommending large scale harvesting of LKA from a variety of vendors so that complete lane coverage during all weather and light conditions can be collected so agencies have an accurate assessment of how their pavement markings perform with modern LKA technology. Not only will this assist in identifying areas in need of pavement marking maintenance, but it will also provide a framework for agencies and vehicle OEM’s to initiate dialog on best practices for marking lines and exchanging information.</div>

This research explores the red light running phenomena and offer a better understanding of the factors associated with it. The red light running is a type of traffic violation that can lead to angle crash and the most common counter measure is installing a red light running cameras. Red light running cameras some time can reduce the rates of red light running but because of the increased worry of the public towards crossing the intersection it can cause an increase in rear end crashes. Also the public opinion of the red light running cameras is that they are a revenue generator for the local counties and not a concern of public safety. Further more, they consider this type of enforcement as violation of privacy. There was two ways to collect the data needed for the research. One way is through a tripod cameras setup temporarily placed at the intersection. This setup can collect individual vehicles caught in the change phase with specific information about their reactions and conditions. This required extensive manual analysis for the recorded videos plus data could not be collected during adverse weather conditions. The second way was using traffic monitoring cameras permanently located at the site to collect red light running information and the simultaneous traffic conditions. This system offered more extensive information since the cameras monitor the traffic 24/7 collecting data directly. On the other hand this system lacked the ability to identify the circumstances associated with individual red light running incidents. The research team finally decided to use the two methods to study the red light running phenomena aiming to combine the benefits of the two systems. During the research the team conducted an experiment to test a red light running countermeasure in the field and evaluate the public reaction and usage of this countermeasure. The marking was previously tested in a driving simulator and proved to be successful in helping the drivers make better stop/go decisions thus reducing red light running rates without increasing the rear-end crashes. The experiment was divided into three phases; before marking installation called "before", after marking installation called "after", and following a media campaign designed to inform the public about the use of the marking the third phase called "after media" The behavior study that aimed at analyzing the motorist reactions toward the signal change interval identified factors which contributed to red light running. There important factors were: distance from the stop bar, speed of traffic, leading or following in the traffic, vehicle type. It was found that a driver is more likely to run red light following another vehicle in the intersection. Also the speeding vehicles can clear the intersection faster thus got less involved in red light running violations. The proposed "Signal Ahead" marking was found to have a very good potential as a red light running counter measure. The red light running rates in the test intersection dropped from 53 RLR/hr/1000veh for the "before" phase, to 24 RLR/hr/1000veh for the "after media" phase. The marking after media analysis period found that the marking can help the driver make stop/go decision as the dilemma zone decreased by 50 ft between the "before" and the "after media" periods. Analysis of the traffic condition associated with the red light running it revealed that relation between the traffic conditions and the red light running is non-linear, with some interactions between factors. The most important factors included in the model were: traffic volume, average speed of traffic, the percentage of green time, the percentage of heavy vehicles, the interaction between traffic volume and percentage of heavy vehicles. The most interesting finding was the interaction between the volume and the percent of heavy vehicles. As the volume increased the effect of the heavy vehicles reversed from reducing the red light running to increasing the red light. This finding may be attributed to the sight blocking that happens when a driver of a passenger car follows a larger heavy vehicle, and can be also explained by the potential frustration experienced by the motorist resulting from driving behind a bigger vehicle.

In the United States, intersections are among the most frequent locations for crashes. One of the major problems at signalized intersection is the dilemma zone, which is caused by false driver behavior during the yellow interval. This research evaluated driver behavior during the yellow interval at signalized intersections and compared different dilemma zone countermeasures. The study was conducted through four stages. First, the driver behavior during the yellow interval were collected and analyzed. Eight variables, which are related to risky situations, are considered. The impact factors of drivers' stop/go decisions and the presence of the red-light running (RLR) violations were also analyzed. Second, based on the field data, a logistic model, which is a function of speed, distance to the stop line and the lead/follow position of the vehicle, was developed to predict drivers' stop/go decisions. Meanwhile, Cellular Automata (CA) models for the movement at the signalized intersection were developed. In this study, four different simulation scenarios were established, including the typical intersection signal, signal with flashing green phases, the intersection with pavement marking upstream of the approach, and the intersection with a new countermeasure: adding an auxiliary flashing indication next to the pavement marking. When vehicles are approaching the intersection with a speed lower than the speed limit of the intersection approach, the auxiliary flashing yellow indication will begin flashing before the yellow phase. If the vehicle that has not passed the pavement marking before the onset of the auxiliary flashing yellow indication and can see the flashing indication, the driver should choose to stop during the yellow interval. Otherwise, the driver should choose to go at the yellow duration. The CA model was employed to simulate the traffic flow, and the logistic model was applied as the stop/go decision rule. Dilemma situations that lead to rear-end crash risks and potential RLR risks were used to evaluate the different scenarios. According to the simulation results, the mean and standard deviation of the speed of the traffic flow play a significant role in rear-end crash risk situations, where a lower speed and standard deviation could lead to less rear-end risk situations at the same intersection. High difference in speed are more prone to cause rear-end crashes. With Respect to the RLR violations, the RLR risk analysis showed that the mean speed of the leading vehicle has important influence on the RLR risk in the typical intersection simulation scenarios as well as intersections with the flashing green phases' simulation scenario. Moreover, the findings indicated that the flashing green could not effectively reduce the risk probabilities. The pavement marking countermeasure had positive effects on reducing the risk probabilities if a platoon's mean speed was not under the speed used for designing the pavement marking. Otherwise, the risk probabilities for the intersection would not be reduced because of the increase in the RLR rate. The simulation results showed that the scenario with the pavement marking and an auxiliary indication countermeasure, which adds a flashing indication next to the pavement marking, had less risky situations than the other scenarios with the same speed distribution. These findings suggested the effectiveness of the pavement marking and an auxiliary indication countermeasure to reduce both rear-end collisions and RLR violations than other countermeasures.