TY - GEN
T1 - Stuck Around the Stadium? An Approach to Identify Road Segments Affected by Planned Special Events
AU - Kwoczek, Simon
AU - Martino, Sergio Di
AU - Nejdl, Wolfgang
PY - 2015/11
Y1 - 2015/11
N2 - The recent availability of large amount of mobility data has fostered many research efforts to improve mobility prediction. Lots of these studies are focused on learning the impact of influencing factors on traffic, such as rush hour or accidents. Nevertheless, only very few have investigated the impact of Planned Special Events (PSEs), such as concerts, soccer games, etc., despite their well-known influence on traffic. In this paper we present an automatic solution to model the impact of PSEs on traffic around the venue of the events. In particular, we answer the question of 'which road segments are affected by PSEs?' by identifying which roads show an event specific behavior that can identify the happening of a PSE reliably. For that, we propose a solution based on an Artificial Neural Network (ANN) classifier that is trained on traffic data on event and non-event days for each road. The proposed approach has been evaluated on two different venues in Germany with a leave-one-out cross-validation performed on all the soccer matches played in those locations during the season 2013/14 of the German First League. Results show that the approach can reliably identify road segments affected by PSEs, with an F-Measure up to 0.97.
AB - The recent availability of large amount of mobility data has fostered many research efforts to improve mobility prediction. Lots of these studies are focused on learning the impact of influencing factors on traffic, such as rush hour or accidents. Nevertheless, only very few have investigated the impact of Planned Special Events (PSEs), such as concerts, soccer games, etc., despite their well-known influence on traffic. In this paper we present an automatic solution to model the impact of PSEs on traffic around the venue of the events. In particular, we answer the question of 'which road segments are affected by PSEs?' by identifying which roads show an event specific behavior that can identify the happening of a PSE reliably. For that, we propose a solution based on an Artificial Neural Network (ANN) classifier that is trained on traffic data on event and non-event days for each road. The proposed approach has been evaluated on two different venues in Germany with a leave-one-out cross-validation performed on all the soccer matches played in those locations during the season 2013/14 of the German First League. Results show that the approach can reliably identify road segments affected by PSEs, with an F-Measure up to 0.97.
UR - https://www.scopus.com/pages/publications/84950252763
U2 - 10.1109/itsc.2015.206
DO - 10.1109/itsc.2015.206
M3 - Conference contribution
AN - SCOPUS:84950252763
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1255
EP - 1260
BT - Proceedings - 2015 IEEE 18th International Conference on Intelligent Transportation Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Conference on Intelligent Transportation Systems, ITSC 2015
Y2 - 15 September 2015 through 18 September 2015
ER -