Predicting and visualizing traffic congestion in the presence of planned special events

The recent availability of datasets on transportation networks with higher spatial and temporal resolution is enabling new research activities in the fields of Territorial Intelligence and Smart Cities. Among these, many research efforts are aimed at predicting traffic congestions to alleviate their...

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Bibliographic Details
Published inJournal of visual languages and computing Vol. 25; no. 6; pp. 973 - 980
Main Authors Kwoczek, Simon, Di Martino, Sergio, Nejdl, Wolfgang
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.12.2014
Subjects
Geographic information systems
Machine Learning
Mobility
Traffic prediction
Visualization
Mobility
Visualization
Traffic prediction
Machine Learning
Geographic information systems
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ISSN1045-926X
1095-8533
DOI10.1016/j.jvlc.2014.10.028

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Summary:The recent availability of datasets on transportation networks with higher spatial and temporal resolution is enabling new research activities in the fields of Territorial Intelligence and Smart Cities. Among these, many research efforts are aimed at predicting traffic congestions to alleviate their negative effects on society, mainly by learning recurring mobility patterns. Within this field, in this paper we propose an integrated solution to predict and visualize non-recurring traffic congestion in urban environments caused by Planned Special Events (PSE), such as a soccer game or a concert. Predictions are done by means of two Machine Learning-based techniques. These have been proven to successfully outperform current state of the art predictions by 35% in an empirical assessment we conducted over a time frame of 7 months within the inner city of Cologne, Germany. The predicted congestions are fed into a specifically conceived visualization tool we designed to allow Decision Makers to evaluate the situation and take actions to improve mobility. •We analyze in detail the impact of Planned Special Events (PSEs) on traffic.•We propose two novel methods to predict upcoming congestions caused by PSEs.•Results show that our prediction methods outperform the state of the art by 35%.•We introduce a specifically designed tool for visualizing the prediction results.•Visualization tool allows experts to evaluate upcoming situations ahead of time.
ISSN:1045-926X
1095-8533
DOI:10.1016/j.jvlc.2014.10.028