Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Nils Breyer"'
Publikováno v:
Journal of Advanced Transportation, Vol 2020 (2020)
Data on travel patterns and travel demand are an important input to today’s traffic models used for traffic planning. Traditionally, travel demand is modelled using census data, travel surveys, and traffic counts. Problems arise from the fact that
Externí odkaz:
https://doaj.org/article/e7bcfc1a766049e1907d9d940d984f18
Autor:
Angelica Andersson, Maria Börjesson, Nils Breyer, Andrew Daly, Leonid Engelson, Ida Kristoffersson
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9570362f26e9734f2bd0771ba519320d
https://doi.org/10.2139/ssrn.4246865
https://doi.org/10.2139/ssrn.4246865
Autor:
Nils Breyer
Publikováno v:
Linköping Studies in Science and Technology. Dissertations ISBN: 9789179296650
Comprehensive knowledge of travel patterns is crucial to enable planning for a more efficient traffic system that accommodates human mobility demand. Currently, this knowledge is mainly based on tr ...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2efd9b330b1d78500f14c001e4e66dcc
https://doi.org/10.3384/diss.diva-175347
https://doi.org/10.3384/diss.diva-175347
Many applications in transport planning require an understanding of travel patterns separated by travel mode. To use cellular network data as observations of human mobility in these applications, classification by travel mode is needed. Existing clas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a8cc5259babcf145dcc2ebb1c463bec7
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-174245
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-174245
Good knowledge of travel patterns is essential in transportation planning. Cellular network data as a large-scale passive data source provides billions of daily location updates allowing us to observe human mobility with all travel modes. However, ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::694a5776770f4a5a933b5200aad120c1
Publikováno v:
Transportation Research Part C: Emerging Technologies. 131:103198
Disruptions in public transport are a major source of frustration for passengers and result in lower public transport usage. Previous studies on the effect of disruptions on passenger travel behaviour have mainly focused on shorter disruptions, while
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8254e49cf71d9a4cdfaf3462c3ba3343
https://doi.org/10.1016/j.trc.2021.103198
https://doi.org/10.1016/j.trc.2021.103198
Publikováno v:
Journal of Urban Technology. 25:85-104
The signaling data in cellular networks provide means for analyzing the use of transportation systems. We propose methods that aim to reconstruct the used route through a transportation network fro ...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6551f91a8a0e1ce8c3d8abdaa228e59f
https://doi.org/10.1080/10630732.2017.1386939
https://doi.org/10.1080/10630732.2017.1386939
Publikováno v:
Computer Communications. 95:29-42
Cellular networks signaling data provide means for analyzing the efficiency of an underlying transportation system and assisting the formulation of models to predict its future use. This paper describes how signaling data can be processed and used in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e1227e7cf6f8ae22eeaf96b26a9b42b
https://doi.org/10.1016/j.comcom.2016.04.015
https://doi.org/10.1016/j.comcom.2016.04.015
Publikováno v:
MT-ITS
To get a better understanding of people's mobility, cellular network signalling data including location information, is a promising large-scale data source. In order to estimate travel demand and infrastructure usage from the data, it is necessary to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6bed92b3243f92a5e3dd4b849e33d7af
https://doi.org/10.1109/mtits.2017.8005688
https://doi.org/10.1109/mtits.2017.8005688