Browsing by Author "Donaldson, David"
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Item ADVANCED DATA ANALYTICS AND MESOSCOPIC DYNAMIC TRAFFIC ASSIGNMENT SIMULATION FOR TRAFFIC IMPACT ANALYSIS OF MARYLAND CASINOS(2019) Donaldson, David; Zhang, Lei; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Three full-service casinos recently opened in Maryland: Live! Casino at the Arundel Mills Mall (June 2012), Horseshoe in Downtown Baltimore (August 2014), and MGM at the National Harbor (December 2016). The increased travel demand associated with such large entertainment complexes prompted an effort to quantify each facility’s impact on regional and local traffic patterns; therefore, a three-pronged analysis was conducted. First, historic vehicle probe data were analyzed to quantify and visualize the observed, local traffic impact for selected months before and after each casino became operational. Subsequently, an open-source mesoscopic DTA simulator named DTALite modeled the regional impact of the before/after scenarios as well as a special event scenario (e.g. Baltimore Ravens’ football game). The paper’s final component explored two innovative trip generation estimation methods to supplement the ITE Manual’s data limitation for casinos by utilizing aggregated mobile device trip data and an origin-demand adjustment system imbedded within DTALite. Ultimately, the data analytics and simulation-based modeling revealed no major traffic impact was generated by any casino. Moreover, upon comparison with ground truth count data, the origin-demand estimation technique out-performed both the ITE-based and location-based trip estimation methods.Item Visualizing Recreational Trails: Montgomery County, Maryland(Partnership for Action Learning in Sustainability (PALS), 2018) Bondarenko, Iryna; Gibson, Hunter; Pepe, Lauren; Donaldson, David; Sun, Jane; Zhou, Weiyi; Liu, ChaoAt the request of the Montgomery County Department of Parks, eight county recreation trails were visualized via 360° photos and video, to help develop a state-of-the-art bike stress index tailored toward mountain bike trails. Using the Google Street View platform to publish photos online as well as several GIS open-source datasets and analytic tools, approximately 60 miles of trail were successfully visualized, and four mountain biking trails were stress-indexed. With the images and stress analysis methodology in hand, the department has the opportunity to lead the nation in visualizing recreation trails by integrating the data products into their online trail web-map. The project deliverables also provide a resource for trail planners and managers who strive to convey variable trail conditions to new and local trail users from anywhere in the world.