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Full-Text Articles in Urban Studies and Planning
Road Work Ahead: Using Deep Neural Networks To Estimate The Impacts Of Work Zones, Abbas Rashidi, Ali Hassandokht Mashhadi
Road Work Ahead: Using Deep Neural Networks To Estimate The Impacts Of Work Zones, Abbas Rashidi, Ali Hassandokht Mashhadi
TREC Project Briefs
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 neural network model for analyzing the automobile traffic impacts of construction zones.
Evaluating Mobility Impacts Of Construction Work Zones On Utah Transportation System Using Machine Learning Techniques, Ali Hassandokht Mashhadi, Abbas Rashidi
Evaluating Mobility Impacts Of Construction Work Zones On Utah Transportation System Using Machine Learning Techniques, Ali Hassandokht Mashhadi, Abbas Rashidi
TREC Final Reports
Construction work zones are inevitable parts of daily operations at roadway systems. They have a significant impact on traffic conditions and the mobility of roadway systems. The traffic impacts of work zones could significantly vary due to several interacting factors such as work zone factors (work zone location and layout, length of the closure, work zone speed, intensity, and daily active hours); traffic factors (percentage of heavy vehicles, highway speed limit, capacity, mobility, flow, density, congestion, and occupancy); road factors (number of total lanes, number of open lanes, and pavement grade and condition); temporal factors (e.g., year, season, month, weekday, …
Data Files: Green Waves, Machine Learning, And Predictive Analytics: Making Streets Better For People On Bikes, Stephen Fickas
Data Files: Green Waves, Machine Learning, And Predictive Analytics: Making Streets Better For People On Bikes, Stephen Fickas
TREC Datasets and Databases
The project builds on a prior app that was designed for Green Light Optimized Speed Advisory (GLOSA). This is more colloquially known as keeping a vehicle in the green wave: you are at a location and moving at a speed that will allow you to (theoretically) have a green light at each intersection you encounter along a corridor. Our long-term goal is to extend the FastTrack app described in the Background section to include actuated signals along a corridor. This project takes a first step by evaluating the effectiveness of machine-learning algorithms to predict the next phase of an actuated …
Green Waves, Machine Learning, And Predictive Analytics: Making Streets Better For People On Bikes, Stephen Fickas
Green Waves, Machine Learning, And Predictive Analytics: Making Streets Better For People On Bikes, Stephen Fickas
TREC Final Reports
This project focuses on giving bicyclists a safer and more efficient path through a city’s signalized intersections. It builds on a prior NITC project that tested an app for a fixed-time corridor. The goal of this project is to lay the groundwork for extending this earlier app to include actuated signals. Two machine-learning algorithms are introduced that have a good track record with time-series forecasting: LSTM and 1D CNN. The algorithms are tested on data captured from a busy bike corridor on the south end of the University of Oregon campus. A specific actuated intersection is identified on this corridor …