Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings |
| Subtitle of host publication | DMS 2014 - 20th International Conference on Distributed Multimedia Systems |
| Pages | 357-364 |
| Number of pages | 8 |
| ISBN (electronic) | 1891706365 |
| Publication status | Published - 2014 |
| Event | 20th International Conference on Distributed Multimedia Systems, DMS 2014 - Pittsburgh, United States Duration: 27 Aug 2014 → 29 Aug 2014 |
Publication series
| 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.
Keywords
- Event analysis, Planned special event, Traffic prediction
ASJC Scopus subject areas
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Software
Sustainable Development Goals
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Proceedings: DMS 2014 - 20th International Conference on Distributed Multimedia Systems. 2014. p. 357-364 (Proceedings: DMS 2014 - 20th International Conference on Distributed Multimedia Systems).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › 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 -