Research Highlights
Evaluating Mobility Impacts Of Construction Workzones On Utah Transportation System Using Machine Learning Techniques
Abbas Rashidi
Roadside construction – be it a detour, a closed lane, or a slow weave past workers and equipment – work zones impact traffic flow and travel times on a system-wide level. The ability to predict exactly what those impacts will be, and plan for them, would be a major help to both transportation agencies and road users. Funded by the National Institute for Transportation and Communities, the latest Small Starts project led by Abbas Rashidi of the University of Utah introduces a robust, deep neu...
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Using High-Resolution Bus Detection Data to Improve Travel Time Prediction and Identify Urban Congestion Spots
Miguel Andres Figliozzi
Robert Bertini
Transit travel time, operating speed and reliability all influence service attractiveness, operating cost and system efficiency. These metrics have a long-term impact on system effectiveness through a change in ridership. As part of its bus dispatch system (BDS), the Tri-County Metropolitan Transportation District of Oregon (TriMet) has been archiving automatic vehicle location (AVL) and automatic passenger count (APC) data for all bus trips at the stop level since 1997. In 2014, a new and hi...
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