abayesianapproachtodetect pedestriandesnaonsequences ...more and more available in airports, malls,...
TRANSCRIPT
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A Bayesian Approach to Detect Pedestrian Des3na3on-‐Sequences from WiFi Signatures Antonin Danalet Bilal Farooq Michel Bierlaire
TRISTAN VIII, San Pedro de Atacama June 10th, 2013
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● Motivation ● Data requirement ● Methodology ● A case study on EPFL campus ● Conclusion ● Future work
Presenta3on outline
A. Danalet TRANSP-‐OR ENAC EPFL 2
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MOTIVATION
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Walking is the key for efficient mul3modal transport systems
A. Danalet TRANSP-‐OR ENAC EPFL 4
Crowd in a railway station in Mumbai, India Photo: National Geographic
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Lake Geneva region
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By 2030, 100ʼ000 "passengers per day "between Geneva "and Lausanne"
travellers/day between Geneva and Lausanne*"
travellers/day between Geneva and Lausanne*"
> 100ʼ000 travellers/day between Geneva and Lausanne*"
= 2000 travelers/day"
> 50ʼ000 travellers/day between Geneva and Lausanne*"
> 25ʼ000 travellers/day between Geneva and Lausanne*"
* Forecast by Swiss Railways for the maximum scenario"
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Understand pedestrian ac3vi3es
What we are doing: Campus What we want to do: Station
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Challenges
● Detect pedestrian destinations
● Model pedestrian activity scheduling behavior
● Forecast the impact of changes in the infrastructure
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Carlstein, T. (1978)
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DATA REQUIREMENT
A. Danalet TRANSP-‐OR ENAC EPFL 8
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Data requirement
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● Required - Localization data with full coverage of the facility - Semantically-enriched routing graph for pedestrians
● Not really required but often available information - Prior potential attractivity
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Data requirement: Localiza3on
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● Data from communication network infrastructure - GSM traces (Calabrese et al. 2011, Bekhor et al. 2011) - WiFi traces
● Data processing is needed (Rieser-Schüsseler 2012) - Detection of stop points - Activity purpose detection through land-use
information and spatial matching
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Data requirement: Pedestrian network
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● We need maps - With points of interests (space) - With shortest path (time)
● More and more available in airports, malls, museums, campuses, hospitals - Nokia: 214 shopping malls in 2011, 4605 indoor maps
in July 2012, 5100 in December 2012 - Microsoft: 2700 indoor maps - Google: > 10’000 indoor maps - Start-ups: Wifarer, Meridian, Point Insider, ByteLight
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Data requirement: Poten3al aQrac3vity
● Potential attractivity C(x,t) depends on - destination x
● Classroom, platform, scene, … - time t
● class schedules, train schedules, opening hours, … ● Examples: - 1500 passengers on platform 4 arriving at 16h04 - 32 students in a classroom from 8h15 to 10h - 400 seats in a restaurant open from 11h to 14h30
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METHODOLOGY
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● Goal: extract the possible activity-episodes performed by pedestrians from digital traces from communication networks
Methodology
• Localization measurement • Potential attractivity • Semantically-enriched routing
graph
Input
• set of candidate activity-episodes sequences associated with the likelihood to be the true one
Output
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Defini3ons / Nota3ons
● Measurement:
● Activity-episode:
● Episode location, start time and end time
● Activity-episode sequence:
● Activity:
● Activity pattern:
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s = (x, t)
a = (x, t−, t+)
(a1, ..., am) = a1:m
A(a)
(A1, ..., Am) = A1:m
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● Probabilistic measurement model: A Bayesian approach - Measurement equation - Prior
● Generation of activity-episode sequences - Episode location - Episode start and end times
Methodology
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P (a1:m|s1:n) ∝ P (s1:n|a1:m) · P (a1:m)
Probabilis3c measurement model
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Activity model
Measurement likelihood Prior
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Measurement error
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Independence between activities
Independence between signals
No time measurement error
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Prior
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P (a1:m) =m�
j=1
P (aj) (1)
=m�
j=1
P (xj , t−j , t
+j ) (2)
=m�
j=1
Cxj (t−j , t
+j )�
x∈X Cx(t−j , t
+j )
(3)
=m�
j=1
� t+j
tj=t−j
Cxj (tj)�x∈X Cx(tj)
dt (4)
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Genera3on of ac3vity-‐episode sequences
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Genera3on of ac3vity-‐episode sequences
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t+i ∼ U (ti , ti+1 − ttxi ,xi+1 )
t−i+1 ∼ U (t+i + ttxi ,xi+1 , ti+1 )
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Genera3on of ac3vity-‐episode sequences
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Intermediary signals
● Eliminate intermediary signal if since we generate an activity episode at each signal.
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E(t+)− E(t−) < Tmin
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Sequence elimina3on
A. Danalet TRANSP-‐OR ENAC EPFL 24
E(t+)− E(t−) < Tmin
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A CASE STUDY ON EPFL CAMPUS
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EPFL data: Localiza3on
● 8 participants for 2 months with known ID
● Non-participants: 46 days, but only 10 with courses - 200 students in 6 different
classes - 317 employees - 700 students from
University of Lausanne ● For 151 CE students,
152’598 observations ● Precision: 191 meters
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EPFL data: Pedestrian network
● Source: map.epfl.ch ● 56’655 edges ● 4 different levels of path
- Major (« highway ») - Inter-building - Intra-building - Access to offices
● Shortest path ● All offices, restaurants,
classrooms and other points of interest are coded: X
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EPFL data: Poten3al aQrac3vity C(x,t) ● Class schedules with
- Number of students - Name of the classroom
● Number of employees per office - Name of the office - Sum of percent of work
(e.g, 3 full times = 300%) ● Number of seats in
restaurants - Localization - Opening hours
● Number of seats in library
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Results
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SENSITIVITY ANALYSIS
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● With flat prior - # destinations / Start and end time: OK - Distance / category of destination: Not OK
● Attractivity of visited destinations should be 3x bigger than of non-visited destinations
● Global capacity creates bias
Sensi3vity analysis: prior
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CONCLUSION
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Conclusion
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● Prior needed to overcome low precision ● Localization data brings dynamics in the model ● Pedestrian map gives: - Spatial information - Temporal information
● Our methodology is merging these different types of data
● Robust for low density data
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FUTURE WORK
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Future work
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● Binary choice model for attendance of scheduled activity
● Actual start and end times of scheduled activity-episodes
● Analysis of the access to the facility - First and last destination of the sequence - Arrival times, departure times
● Based on class attendance and on available time budget: activity scheduling
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THANK YOU
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References ● Bekhor, S., Cohen, Y. and Solomon, C. (2011), Evaluating long-distance travel
patterns in Israel by tracking cellular phone positions. J. Adv. Transp.. doi: 10.1002/atr.170
● Calabrese, F.; Di Lorenzo, G.; Liang Liu; Ratti, C., "Estimating Origin-Destination Flows Using Mobile Phone Location Data," Pervasive Computing, IEEE , vol.10, no.4, pp.36,44, April 2011 doi: 10.1109/MPRV.2011.41
● Rieser-Schüssler, N. (2012). Capitalising Modern Data Sources for Observing and Modelling Transport Behaviour, Transportation Letters: the International Journal of Transportation Research (2): 115–128. doi: 10.3328/TL.2012.04.02.115-128
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