Details
| Original language | English |
|---|---|
| Pages (from-to) | 973-980 |
| Number of pages | 8 |
| Journal | Journal of Visual Languages and Computing |
| Volume | 25 |
| Issue number | 6 |
| Publication status | Published - 7 Nov 2014 |
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.
Keywords
- Geographic information systems, Machine learning, Mobility, Traffic prediction, Visualization
ASJC Scopus subject areas
- Arts and Humanities(all)
- Language and Linguistics
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Computer Science Applications
Sustainable Development Goals
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In: Journal of Visual Languages and Computing, Vol. 25, No. 6, 07.11.2014, p. 973-980.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Predicting and visualizing traffic congestion in the presence of planned special events
AU - Kwoczek, Simon
AU - Di Martino, Sergio
AU - Nejdl, Wolfgang
N1 - Funding information: The research leading to these results has been partly funded by the European Community?s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 610990 – Project COMPANION. The authors wish also to thank TomTom NV for providing the traffic data used in this research.
PY - 2014/11/7
Y1 - 2014/11/7
N2 - 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.
AB - 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.
KW - Geographic information systems
KW - Machine learning
KW - Mobility
KW - Traffic prediction
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=85027939394&partnerID=8YFLogxK
U2 - 10.1016/j.jvlc.2014.10.028
DO - 10.1016/j.jvlc.2014.10.028
M3 - Article
AN - SCOPUS:85027939394
VL - 25
SP - 973
EP - 980
JO - Journal of Visual Languages and Computing
JF - Journal of Visual Languages and Computing
SN - 1045-926X
IS - 6
ER -