Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings |
| Untertitel | DMS 2014 - 20th International Conference on Distributed Multimedia Systems |
| Seiten | 357-364 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 1891706365 |
| Publikationsstatus | Veröffentlicht - 2014 |
| Veranstaltung | 20th International Conference on Distributed Multimedia Systems, DMS 2014 - Pittsburgh, USA / Vereinigte Staaten Dauer: 27 Aug. 2014 → 29 Aug. 2014 |
Publikationsreihe
| Name | Proceedings: DMS 2014 - 20th International Conference on Distributed Multimedia Systems |
|---|
Abstract
The recent availability of datasets on transportation networks with high spatial and temporal resolution is enabling new research activities in the fields of Territorial Intelligence and Smart Cities. Within these domains, in this paper we focus on the problem of predicting traffic congestion in urban environments caused by attendees leaving a Planned Special Events (PSE), such as a soccer game or a concert. The proposed approach consists of two steps. In the first one, we use the K-Nearest Neighbor algorithm to predict congestions within the vicinity of the venue (e.g. a Stadion) based on the knowledge from past observed events. In the second step, we identify the road segments that are likely to show congestion due to PSEs and map our prediction to these road segments. To visualize the traffic trends and congestion behavior we learned and to allow Domain Experts to evaluate the situation we also provide a Google Earthbased GUI. The proposed solution has been experimentally proven to outperform current state of the art solutions by about 35% and thus it can successfully serve to reliably predict congestions due to PSEs.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computergrafik und computergestütztes Design
- Informatik (insg.)
- Mensch-Maschine-Interaktion
- Informatik (insg.)
- Software
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Proceedings: DMS 2014 - 20th International Conference on Distributed Multimedia Systems. 2014. S. 357-364 (Proceedings: DMS 2014 - 20th International Conference on Distributed Multimedia Systems).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Predicting Traffic Congestion in Presence of Planned Special Events
AU - Kwoczek, Simon
AU - Di Martino, Sergio
AU - Nejdl, Wolfgang
PY - 2014
Y1 - 2014
N2 - The recent availability of datasets on transportation networks with high spatial and temporal resolution is enabling new research activities in the fields of Territorial Intelligence and Smart Cities. Within these domains, in this paper we focus on the problem of predicting traffic congestion in urban environments caused by attendees leaving a Planned Special Events (PSE), such as a soccer game or a concert. The proposed approach consists of two steps. In the first one, we use the K-Nearest Neighbor algorithm to predict congestions within the vicinity of the venue (e.g. a Stadion) based on the knowledge from past observed events. In the second step, we identify the road segments that are likely to show congestion due to PSEs and map our prediction to these road segments. To visualize the traffic trends and congestion behavior we learned and to allow Domain Experts to evaluate the situation we also provide a Google Earthbased GUI. The proposed solution has been experimentally proven to outperform current state of the art solutions by about 35% and thus it can successfully serve to reliably predict congestions due to PSEs.
AB - The recent availability of datasets on transportation networks with high spatial and temporal resolution is enabling new research activities in the fields of Territorial Intelligence and Smart Cities. Within these domains, in this paper we focus on the problem of predicting traffic congestion in urban environments caused by attendees leaving a Planned Special Events (PSE), such as a soccer game or a concert. The proposed approach consists of two steps. In the first one, we use the K-Nearest Neighbor algorithm to predict congestions within the vicinity of the venue (e.g. a Stadion) based on the knowledge from past observed events. In the second step, we identify the road segments that are likely to show congestion due to PSEs and map our prediction to these road segments. To visualize the traffic trends and congestion behavior we learned and to allow Domain Experts to evaluate the situation we also provide a Google Earthbased GUI. The proposed solution has been experimentally proven to outperform current state of the art solutions by about 35% and thus it can successfully serve to reliably predict congestions due to PSEs.
KW - Event analysis
KW - Planned special event
KW - Traffic prediction
UR - http://www.scopus.com/inward/record.url?scp=84923885010&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84923885010
T3 - Proceedings: DMS 2014 - 20th International Conference on Distributed Multimedia Systems
SP - 357
EP - 364
BT - Proceedings
T2 - 20th International Conference on Distributed Multimedia Systems, DMS 2014
Y2 - 27 August 2014 through 29 August 2014
ER -