A Data-Driven Method for Congestion Identification and Classification
Atousa Zarindast, Subhadipto Poddar, Anuj Sharma
Abstract
Congestion detection is one of the key steps in reducing delays and associated costs in traffic management. With the increasing use of global positioning system (GPS)-based navigation, promising speed data are now available. This study used extensive historical probe data (year 2016) in Des Moines, Iowa. We used Bayesian change point detection to segment the speed signal and detect temporal congestion. The detected congestion events were then classified as recurrent congestion (RC) or nonrecurrent congestion (NRC). This paper thus presents a robust statistical, big-data-driven expert system and a big-data-mining methodology for identifying both recurrent and nonrecurrent congestion.
Topics & Concepts
Computer scienceIdentification (biology)Global Positioning SystemData miningTraffic congestionBig dataNetwork congestionReal-time computingEngineeringTransport engineeringComputer networkTelecommunicationsBiologyBotanyNetwork packetTraffic Prediction and Management TechniquesTime Series Analysis and ForecastingHuman Mobility and Location-Based Analysis