Paper Info
Title
Smart Card In Public Transportation: Designing A Analysis System At The Human Scale
Abstract
In the 20th century, most mobility studies were based on costly surveys with few samples; nowadays, the data from static and mobile sensors allow to track the habits of a massive number of citizens. However, the counterpart of sensors data is that they generally provide noisy and partial signals lacking semantic information: the purpose of each human activity captured by the sensor is unknown. Extracting this latent semantic information from raw sensors data is a challenging and crucial task. In this paper, a novel algorithm based on non negative matrix factorization (NMF) is proposed in order to extract precise and meaningful user temporal profiles from logs of smart card data in a transportation system. The proposed NMF based algorithm allows a natural and informative clustering of the profiles which can lead to semantic information on the mobility of the users. The approach is compared to 4 others algorithms and focuses on the human scale, indeed, individual profiles differ quite substantially from group profiles. Experiments are conducted on a 3 months dataset supplied by the STIF, the Parisian public transport authority.
Year
Venue
Field
2016
2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
Data mining,Human scale,Computer science,Smart card,Public transport,Artificial intelligence,Cluster analysis,Matching pursuit algorithms,Computer vision,Simulation,Semantic information,Non-negative matrix factorization,Machine learning,Semantics
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Emeric Tonnelier151.97
Nicolas Baskiotis211911.73
Vincent Guigue300.68
Patrick Gallinari41856187.19