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

verfasst von

Simon Kwoczek, Sergio Di Martino, Wolfgang Nejdl

Abstract

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.

Organisationseinheit(en)

Forschungszentrum L3S

Externe Organisation(en)

Volkswagen AG

Typ

Artikel

Journal

Journal of Visual Languages and Computing

Band

25

Seiten

973-980

Anzahl der Seiten

8

ISSN

1045-926X

Publikationsdatum

07.11.2014

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Sprache und Linguistik, Mensch-Maschine-Interaktion, Angewandte Informatik

Ziele für nachhaltige Entwicklung

SDG 9 – Industrie, Innovation und Infrastruktur, SDG 11 – Nachhaltige Städte und Gemeinschaften

Elektronische Version(en)

https://doi.org/10.1016/j.jvlc.2014.10.028 (Zugang: Geschlossen)