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.
Smart card in public transportation: Designing a analysis system at the human scale
2016-11-01
645356 byte
Conference paper
Electronic Resource
English
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