Detecting traffic congestion propagation in urban environments – a case study with Floating Taxi Data (FTD) in Shanghai

Abstract

Traffic congestion in urban environments has severe influences on the daily life of people. Due to typical recurrent mobility patterns of commuters and transport fleets, we can detect traffic congestion events on selected hours of the day, so called rush hours. Besides the mentioned recurrent traffic congestion, there are non-recurrent events that may be caused by accidents or newly established building sites. We want to inspect this appearance using a massive Floating Taxi Data (FTD) set of Shanghai from 2007. We introduce a simple method for detecting and extracting congestion events on selected rush hours and for distinguishing between their recurrence and non-recurrence. By preselecting of similar velocity and driving direction values of the nearby situated FTD points, we provide the first part for the Shared Nearest Neighbour (SNN) clustering method, which follows with a density-based clustering. After the definition of our traffic congestion clusters, we try to connect ongoing events by quer...