%0 Journal Article %1 Kwoczek2014973 %A Kwoczek, Simon %A Martino, Sergio Di %A Nejdl, Wolfgang %D 2014 %J Journal of Visual Languages & Computing %K 2014 myown %N 6 %P 973 - 980 %R http://dx.doi.org/10.1016/j.jvlc.2014.10.028 %T Predicting and visualizing traffic congestion in the presence of planned special events %U http://www.sciencedirect.com/science/article/pii/S1045926X14001219 %V 25 %X 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.

@article{Kwoczek2014973, abstract = {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. }, added-at = {2014-12-09T13:03:45.000+0100}, author = {Kwoczek, Simon and Martino, Sergio Di and Nejdl, Wolfgang}, biburl = {https://www.bibsonomy.org/bibtex/2c7e3aee8904af5bc6284779f6f3c65ad/nejdl}, description = {Predicting and visualizing traffic congestion in the presence of planned special events}, doi = {http://dx.doi.org/10.1016/j.jvlc.2014.10.028}, interhash = {7517f55f0f1df9a0216d8a2fb439e42c}, intrahash = {c7e3aee8904af5bc6284779f6f3c65ad}, issn = {1045-926X}, journal = {Journal of Visual Languages & Computing }, keywords = {2014 myown}, note = {Distributed Multimedia Systems \{DMS2014\} Part I }, number = 6, pages = {973 - 980}, timestamp = {2014-12-09T13:03:45.000+0100}, title = {Predicting and visualizing traffic congestion in the presence of planned special events }, url = {http://www.sciencedirect.com/science/article/pii/S1045926X14001219}, volume = 25, year = 2014 }