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 -