Browsing by Subject "Learning (Artificial intelligence)"

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    Aggregating VMT within Predefined Geographic Zones by Cellular Assignment: A Non-GPS-Based Approach to Mileage- Based Road Use Charging
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-08) Davis, Brian; Donath, Max
    Currently, most of the costs associated with operating and maintaining the roadway infrastructure are paid for by revenue collected from the motor fuel use tax. As fuel efficiency and the use of alternative fuel vehicles increases, alternatives to this funding method must be considered. One such alternative is to assess mileage based user fees (MBUF) based on the vehicle miles traveled (VMT) aggregated within the predetermined geographic areas, or travel zones, in which the VMT is generated. Most of the systems capable of this use Global Positioning Systems (GPS). However, GPS has issues with public perception, commonly associated with unwanted monitoring or tracking and is thus considered an invasion of privacy. The method proposed here utilizes cellular assignment, which is capable of determining a vehicle’s current travel zone, but is incapable of determining a vehicle’s precise location, thus better preserving user privacy. This is accomplished with a k-nearest neighbors (KNN) machine learning algorithm focused on the boundary of such travel zones. The work described here focuses on the design and evaluation of algorithms and methods that when combined, would enable such a system. The primary experiment performed evaluates the accuracy of the algorithm at sample boundaries in and around the commercial business district of Minneapolis, Minnesota. The results show that with the training data available, the algorithm can correctly detect when a vehicle crosses a boundary to within ±2 city blocks, or roughly ±200 meters, and is thus capable of assigning the VMT to the appropriate zone. The findings imply that a cellular-based VMT system may successfully aggregate VMT by predetermined geographic travel zones without infringing on the drivers’ privacy.
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    Video Detection and Classification of Pedestrian Events at Roundabouts and Crosswalks
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, 2013-08) Morris, Ted; Li, Xinyan; Morellas, Vassilios; Papanikolopoulos, Nikos
    A well-established technique for studying pedestrian safety is based on reducing data from video-based in-situ observation. The extraction and cataloging from recorded video of pedestrian crossing events has largely been achieved manually. Although the manual methods are generally reliable, they are extremely time-consuming. As a result, more detailed, encompassing site studies are not practical unless the mining for these events can be automated. The study investigated such a tool based on utilizing a novel image processing algorithm recently developed for the extraction of human activities in complex scenes. No human intervention other than defining regions of interest for approaching vehicles and the pedestrian crossing areas was required. The output quantified general event indicators—such as pedestrian wait time, and crossing time and vehicle-pedestrian yield behaviors. Such data can then be used to guide more detailed analyses of the events to study potential vehicle-pedestrian conflicts and their causal effects. The evaluation was done using an extensive set of multi-camera video recordings collected at roundabouts. The tool can be used to support other pedestrian safety research where extracting potential pedestrian-vehicle conflicts from video are required, for example at crosswalks at urban signalized and uncontrolled intersections.