No-passing zone crashes constitute a sizable percentage of the total crashes on two-lane rural roads. A detection and warning system has been devised and implemented at a no-passing zone site on route 114 of Southwest Virginia to address this problem. The warning system aims at deterring drivers from illegally conducting a passing maneuver within the no-passing zone area. The violating driver is warned in real time to stop the illegal act. This system is currently operational and its main function is to warn the no-passing zone violator. The aim of this research is to extend the warning system to the opposing vehicle in the same lane of the persistent violator in order to avoid crashes caused by the illegal maneuver that is taking place at a crest vertical curve of the two-lane rural road. In order to test the new system prior to its physical installation, a computer simulation has been developed to represent the real world violation conditions so that a better understanding of the problem and its varying scenarios would be achieved. The new simulation, which is the focus of this thesis, takes advantage of an existing simulation developed earlier to replicate only the illegal maneuver without giving any warnings to the opposing vehicle. The new program simulates the outcome of deploying a warning sign to the opposing driver for crash avoidance purposes assuming that all violators persist to pass the vehicle ahead. More than 712,000 computer runs were conducted to simulate the various possible outcomes including the sensitivity analysis. A critical comparison was made between the previous system that warned only the violating vehicle and the current program that warns both the violator as well as the opposing vehicle. The results indicate that warning the opposing driver would reduce the rate of unavoidable crashes by approximately 11% in the east direction and 13.25% in the west direction. / Master of Science

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This thesis implements an automated system that counts pedestrians with 85% accuracy. Two approaches have been considered and evaluated in terms of count accuracy, cost and ease of deployment. The first approach employs the Autoscope Solo Terra, a traffic camera which is widely used to monitor vehicular traffic. The Solo Terra supports an image processing-based detector that counts the number of objects crossing user-defined areas in the captured image. The count is updated based on the amount of movement across the selected regions. Therefore, a second approach has been considered that uses a histogram of oriented gradients (HoG), an advanced vision based algorithm proposed by Dalal et al. which distinguishes a pedestrian from a non-pedestrian based on an omega shape formed by the head and shoulders of a human being. The implemented detection software processes video frames that are streamed from a low-cost digital camera. The frames are divided into sub-regions which are scanned for an omega shape whenever movement is detected in those regions. It has been found that the HoG-based approach degrades in performance due to occlusion under dense pedestrian traffic conditions whereas the Solo Terra approach appears to be more robust. Undercounts and overcounts were encountered using the Solo Terra approach. To combat the disadvantages of both the approaches, they were integrated to form a single system where count is incremented predominantly using the Solo Terra. The HoG-based approach corrects the obtained count under certain conditions. A preliminary prototype of the integrated system has been verified.