TY - JOUR
T1 - Investigating the Mobile Phone Data to Estimate the Origin Destination Flow and Analysis; Case Study
T2 - Paris Region
AU - Larijani, Anahid Nabavi
AU - Olteanu-Raimond, Ana Maria
AU - Perret, Julien
AU - Brédif, Mathieu
AU - Ziemlicki, Cezary
N1 - Publisher Copyright:
© 2015 The Authors.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - This paper is an output of a French national project called iSpace&Time aiming to provide a 4 dimensional platform of an urban dynamics. In order to express the urban traffic, we took an advantage of the mobile phone data to investigate the behavior of the origin destination flow within the Paris and its suburb aiming to explore the different mode of the transportation. Indeed the spatiotemporal heterogeneities of mobile phone data make the task of mode of transportation separation very challenging, sometimes even impossible. Thus, by exploring the OD matrix in order to revealing any probable continues trends or any dominant trace of the flow stating a specific mode of transportation, the commuter trains happened to be somehow detectable. Then an individual-based step-by-step approach is proposed to estimate mode of transportation from mobile phone data. Analyzing the individual trajectory, the decision is given to a segment level with respect to different measures. An early promising outcome consists of detection of the segments in which people would take the metro.
AB - This paper is an output of a French national project called iSpace&Time aiming to provide a 4 dimensional platform of an urban dynamics. In order to express the urban traffic, we took an advantage of the mobile phone data to investigate the behavior of the origin destination flow within the Paris and its suburb aiming to explore the different mode of the transportation. Indeed the spatiotemporal heterogeneities of mobile phone data make the task of mode of transportation separation very challenging, sometimes even impossible. Thus, by exploring the OD matrix in order to revealing any probable continues trends or any dominant trace of the flow stating a specific mode of transportation, the commuter trains happened to be somehow detectable. Then an individual-based step-by-step approach is proposed to estimate mode of transportation from mobile phone data. Analyzing the individual trajectory, the decision is given to a segment level with respect to different measures. An early promising outcome consists of detection of the segments in which people would take the metro.
KW - mobile phone data
KW - origin-destination matrix
KW - smart data processing
KW - transportation mode
U2 - 10.1016/j.trpro.2015.03.006
DO - 10.1016/j.trpro.2015.03.006
M3 - Article
AN - SCOPUS:84959353366
SN - 2352-1457
VL - 6
SP - 64
EP - 78
JO - Transportation Research Procedia
JF - Transportation Research Procedia
ER -
