@article{feuerhake2018identification,
  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.},
  added-at = {2019-01-29T14:41:25.000+0100},
  author = {Feuerhake, U. and Wage, O. and Sester, M. and Tempelmeier, N. and Nejdl, W. and Demidova, E.},
  biburl = {https://www.bibsonomy.org/bibtex/2d9b7a16d786b95b0ad41b98d90fe2195/ntempelmeier},
  interhash = {f7fbf533006880081e8b805a5086a327},
  intrahash = {d9b7a16d786b95b0ad41b98d90fe2195},
  journal = {International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences},
  keywords = {myown},
  number = 4,
  pages = {185 -192},
  timestamp = {2019-01-29T14:41:25.000+0100},
  title = {Identification of Similarities and Prediction of Unknown Features in an Urban Street Network},
  volume = 42,
  year = 2018
}

@article{feuerhake2018identification,
  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.},
  added-at = {2018-10-15T17:57:23.000+0200},
  author = {Feuerhake, U. and Wage, O. and Sester, M. and Tempelmeier, N. and Nejdl, W. and Demidova, E.},
  biburl = {https://www.bibsonomy.org/bibtex/2d9b7a16d786b95b0ad41b98d90fe2195/demidova},
  interhash = {f7fbf533006880081e8b805a5086a327},
  intrahash = {d9b7a16d786b95b0ad41b98d90fe2195},
  journal = {International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences},
  keywords = {data4urbanmobility myown tempelmeier},
  number = 4,
  pages = {185 -192},
  timestamp = {2019-05-20T13:43:35.000+0200},
  title = {Identification of Similarities and Prediction of Unknown Features in an Urban Street Network},
  volume = 42,
  year = 2018
}