TY - GEN
T1 - Understanding daily mobility patterns in urban road networks using traffic flow analytics
AU - Laña, Ibai
AU - Del Ser, Javier
AU - Olabarrieta, Ignacio Iñaki
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/30
Y1 - 2016/6/30
N2 - The MoveUs project funded by the European Commission aims to foster sustainable eco-friendly mobility habits in cities. In this context predicting the traffic flow is useful for managers to optimize the configuration of the road network towards reducing the congestions and ultimately, the pollution. With the explosion of the so-called Big Data concept and its application to traffic data, a wide range of traffic flow prediction methods has been reported in the related literature. However, most of the efforts in this field have been hitherto focused on short-term prediction models. This paper analyzes how to properly characterize traffic flow in urban road scenarios with an emphasis on the long term. To this end a clustering stage is utilized to discover typicalities or patterns within the traffic flow data registered by each road sensor, which permits building prediction models for each of such discovered patterns. These individual prediction models are intended to become part of the MoveUs platform, which will provide the technical means 1) for traffic managers to analyze in depth the status of the road network, and 2) for road users to better plan their trips.
AB - The MoveUs project funded by the European Commission aims to foster sustainable eco-friendly mobility habits in cities. In this context predicting the traffic flow is useful for managers to optimize the configuration of the road network towards reducing the congestions and ultimately, the pollution. With the explosion of the so-called Big Data concept and its application to traffic data, a wide range of traffic flow prediction methods has been reported in the related literature. However, most of the efforts in this field have been hitherto focused on short-term prediction models. This paper analyzes how to properly characterize traffic flow in urban road scenarios with an emphasis on the long term. To this end a clustering stage is utilized to discover typicalities or patterns within the traffic flow data registered by each road sensor, which permits building prediction models for each of such discovered patterns. These individual prediction models are intended to become part of the MoveUs platform, which will provide the technical means 1) for traffic managers to analyze in depth the status of the road network, and 2) for road users to better plan their trips.
KW - Long-term traffic flow prediction
KW - machine learning
KW - mobility patterns
UR - http://www.scopus.com/inward/record.url?scp=84979742597&partnerID=8YFLogxK
U2 - 10.1109/NOMS.2016.7502980
DO - 10.1109/NOMS.2016.7502980
M3 - Conference contribution
AN - SCOPUS:84979742597
T3 - Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium
SP - 1157
EP - 1162
BT - Proceedings of the NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium
A2 - Badonnel, Sema Oktug
A2 - Ulema, Mehmet
A2 - Cavdar, Cicek
A2 - Granville, Lisandro Zambenedetti
A2 - dos Santos, Carlos Raniery P.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE/IFIP Network Operations and Management Symposium, NOMS 2016
Y2 - 25 April 2016 through 29 April 2016
ER -
