Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of Mid-term Symposium “3D Spatial Information Science – The Engine of Change” |
| Pages | 185-192 |
| Publication status | Published - 2018 |
| Event | ISPRS TC IV Mid-Term Symposium on 3D Spatial Information Science - The Engine of Change - Delft, Netherlands Duration: 1 Oct 2018 → 5 Oct 2018 |
Publication series
| Name | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
|---|---|
| Publisher | International Society for Photogrammetry and Remote Sensing |
| Volume | XLII-4 |
| ISSN (Print) | 1682-1750 |
Abstract
Accurate predictions of the characteristics of urban streets in particular with respect to the typical traffic situations are crucial for numerous real world applications such as navigation, scheduling of logistic and public transportation services as well as high-level planning of infrastructure which may include planning of construction sites or even changes of the road topology. However, this information may be hard to obtain, especially in complex urban road networks where interdependencies between roads are highly present. In addition, accurate and recent traffic data is not always available, especially for uncommon situations like large-scale public events, traffic accidents or construction sites. This work demonstrates how to employ historical traffic datasets in conjunction with other, infrastructure related data, to derive a deeper understanding of urban traffic behaviour. In particular this paper provides the following contributions: (1) the generation of meaningful features to describe the segments in urban road networks; (2) an unsupervised machine learning approach that identifies similar segments based on those features; (3) a supervised approach to predict unknown features of the segments and, finally, (4) an extensive evaluation of the extracted road characteristics and the proposed methods using real-world data. The resulting clusters reveal the similarities of the street segments and give a different perspective on the road network and the traffic situation, respectively. The experiments on the classification approach demonstrate that unknown features can be predicted with a good quality.
Keywords
- Clustering, Data integration, Data mining, Floating-car-data, Machine learning, Spatio-temporal data, Traffic analysis, Urban traffic
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
Sustainable Development Goals
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Proceedings of Mid-term Symposium “3D Spatial Information Science – The Engine of Change”. 2018. p. 185-192 (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives; Vol. XLII-4).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Identification of similarities and prediction of unknown features in an urban street network
AU - Feuerhake, Udo
AU - Wage, Oskar
AU - Sester, Monika
AU - Tempelmeier, Nicolas
AU - Nejdl, Wolfgang
AU - Demidova, Elena
N1 - Funding information: This work is partially funded by the research initiatives ”Mobiler Mensch” and ”Urbane Logistik Hannover” as well as the associated BMBF projects ”Data4UrbanMobility” (grant ID 02K15-A040) and ”USEfUL” (grant ID 03SF0547).
PY - 2018
Y1 - 2018
N2 - Accurate predictions of the characteristics of urban streets in particular with respect to the typical traffic situations are crucial for numerous real world applications such as navigation, scheduling of logistic and public transportation services as well as high-level planning of infrastructure which may include planning of construction sites or even changes of the road topology. However, this information may be hard to obtain, especially in complex urban road networks where interdependencies between roads are highly present. In addition, accurate and recent traffic data is not always available, especially for uncommon situations like large-scale public events, traffic accidents or construction sites. This work demonstrates how to employ historical traffic datasets in conjunction with other, infrastructure related data, to derive a deeper understanding of urban traffic behaviour. In particular this paper provides the following contributions: (1) the generation of meaningful features to describe the segments in urban road networks; (2) an unsupervised machine learning approach that identifies similar segments based on those features; (3) a supervised approach to predict unknown features of the segments and, finally, (4) an extensive evaluation of the extracted road characteristics and the proposed methods using real-world data. The resulting clusters reveal the similarities of the street segments and give a different perspective on the road network and the traffic situation, respectively. The experiments on the classification approach demonstrate that unknown features can be predicted with a good quality.
AB - Accurate predictions of the characteristics of urban streets in particular with respect to the typical traffic situations are crucial for numerous real world applications such as navigation, scheduling of logistic and public transportation services as well as high-level planning of infrastructure which may include planning of construction sites or even changes of the road topology. However, this information may be hard to obtain, especially in complex urban road networks where interdependencies between roads are highly present. In addition, accurate and recent traffic data is not always available, especially for uncommon situations like large-scale public events, traffic accidents or construction sites. This work demonstrates how to employ historical traffic datasets in conjunction with other, infrastructure related data, to derive a deeper understanding of urban traffic behaviour. In particular this paper provides the following contributions: (1) the generation of meaningful features to describe the segments in urban road networks; (2) an unsupervised machine learning approach that identifies similar segments based on those features; (3) a supervised approach to predict unknown features of the segments and, finally, (4) an extensive evaluation of the extracted road characteristics and the proposed methods using real-world data. The resulting clusters reveal the similarities of the street segments and give a different perspective on the road network and the traffic situation, respectively. The experiments on the classification approach demonstrate that unknown features can be predicted with a good quality.
KW - Clustering
KW - Data integration
KW - Data mining
KW - Floating-car-data
KW - Machine learning
KW - Spatio-temporal data
KW - Traffic analysis
KW - Urban traffic
UR - http://www.scopus.com/inward/record.url?scp=85056199997&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLII-4-185-2018
DO - 10.5194/isprs-archives-XLII-4-185-2018
M3 - Conference contribution
AN - SCOPUS:85056199997
T3 - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SP - 185
EP - 192
BT - Proceedings of Mid-term Symposium “3D Spatial Information Science – The Engine of Change”
T2 - ISPRS TC IV Mid-Term Symposium on 3D Spatial Information Science - The Engine of Change
Y2 - 1 October 2018 through 5 October 2018
ER -