000189759 001__ 189759
000189759 005__ 20181114182329.0
000189759 037__ $$aREP_WORK
000189759 245__ $$a A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures
000189759 269__ $$a2013
000189759 260__ $$aLausanne$$c2013
000189759 300__ $$a51
000189759 336__ $$aReports
000189759 500__ $$aPublished as: A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures, Transportation Research Part C: Emerging Technologies. 44 ():146 - 170 (2014). 
000189759 520__ $$a In this technical report, we propose a methodology to use the communication network infra- structure, in particular WiFi traces, to detect the sequence of activity episodes visited by pedestrians. Due to the poor quality of WiFi localization, a probabilistic method is proposed that infers activity-episode locations based on WiFi traces and calculates the likelihood of observing these traces in the pedestrian network, taking into account prior knowledge. The output of the method consists in generating candidates of activity-episodes sequences associated with the likelihood to be the true one. The methodology is validated on traces generated by a known sequence of activities. Results show that it is possible to predict the number of episodes and the activity-episodes locations and durations, by merging information about the activity locations on the map, WiFi measurements and prior information about schedules and the attractivity in pedestrian infrastructure. The ambiguity of each activity episode in the sequence is explicitly measured.
000189759 6531_ $$anetwork traces
000189759 6531_ $$apotential attractivity measure
000189759 6531_ $$aactivity-episode sequence
000189759 6531_ $$asemantically-enriched routing graph (SERG)
000189759 6531_ $$apedestrians
000189759 6531_ $$aactivity choice modeling
000189759 700__ $$0245395$$aDanalet, Antonin$$g161277
000189759 700__ $$0245136$$aFarooq, Bilal$$g207159
000189759 700__ $$0240563$$aBierlaire, Michel$$g118332
000189759 8564_ $$uhttp://dx.doi.org/10.5281/zenodo.8492$$zURL
000189759 8564_ $$uhttp://blogs.epfl.ch/article/38612$$zURL
000189759 8564_ $$uhttp://blogs.epfl.ch/article/38622$$zURL
000189759 8564_ $$uhttp://blogs.epfl.ch/article/39979$$zURL
000189759 8564_ $$uhttp://blogs.epfl.ch/article/41546$$zURL
000189759 8564_ $$s1447794$$uhttps://infoscience.epfl.ch/record/189759/files/A%20Bayesian%20Approach%20to%20Detect%20Pedestrian%20Destination-Sequences%20from%20WiFi%20Signatures%20-%20Danalet%20Antonin_1.mobi$$yEbook version$$zEbook version
000189759 8564_ $$s8161010$$uhttps://infoscience.epfl.ch/record/189759/files/Danaletetal_WiFi.pdf$$yn/a$$zn/a
000189759 8564_ $$s23262385$$uhttps://infoscience.epfl.ch/record/189759/files/Data.zip$$yResearch data$$zResearch data
000189759 909C0 $$0252123$$pTRANSP-OR$$xU11418
000189759 909CO $$ooai:infoscience.tind.io:189759$$pGLOBAL_SET$$pENAC$$preport
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000189759 937__ $$aEPFL-REPORT-189759
000189759 970__ $$aREP-Danaletetal_WiFi/TRANSP-OR
000189759 973__ $$aEPFL
000189759 980__ $$aREPORT