Preliminary Exploration of Pedestrian Destinations using Traces from WiFi Infrastructures

Transcription

1 Preliminary Exploration of Pedestrian Destinations using Traces from WiFi Infrastructures Antonin Danalet, Michel Bierlaire, Bilal Farooq STRC

2 Presentation Outline 1. Motivation 2. Data collections 3. Discriminating destinations from signals 4. Future works 2

3 1. Motivation 3

4 Destinations of pedestrians Input for destination choice analysis Input for route choice analysis Input for pedestrian OD matrix 4

5 Pedestrian data collections Depends on scale and information you re interested in: Dedicated GPS Smartphones Manual counting Single-row laser-range Pedometer Eye-tracking Cameras GSM scanners (LD-A) 5

6 How to measure pedestrian destinations? Cameras Privacy issues Need of a large coverage Smartphones Mode detection Acceptance by the user 6

7 Approach Everybody has a smartphone in the pocket Device-centric Communication infrastructure 7

8 Traces from WiFi infrastructures Available in most campuses, transportation hubs, shopping centers and city centers Mode is mostly walking in these contexts No additional costs required 8

9 Literature Traces from communication infrastructure used with cell towers (Calabrese et al., 2011) With WiFi, destinations are APs or aggregation of APs (Aschenbruck et al., 2011) 9

10 2. Data collections 10

11 EPFL campus Access to WiFi infrastructures Most people on campus are pedestrians Precise map of campus available 11

12 Data collections 2 data collections using WiFi infrastructures at EPFL: 1. Access points data: localization of the AP to which a user is connecting (anonymously for all users) 2. Cisco Context Aware API: triangulation based on signal strength (for 12 known participants) 12

13 789 APs 13

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15 Number of APs by floor Number of APs Floors 15

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17 Video not available in PDF format Please visit: 17

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19 Pedestrian network 4 levels of path (major, inter-, intra-building, access to rooms) edges, vertices public points of interest rooms 19

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21 Points of interest on campus 21

22 Points of interest by floor Total Public Offices Classrooms

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25 3. Discriminating destinations from signals 25

26 Probabilistic measurement model Goal: Extract possible lists of destinations visited by pedestrians (and their likelihood) using: Traces from WiFi infrastructure Pedestrian network 26

27 Definitions and goal Measurement: State variable: ŝ =(ˆx, ˆt) d =(x, t,t + ) Goal: Associate a likelihood to each list of destinations with arrival and departure times P (ŝ 1,...,ŝ n d 1,...,d n ) 27

28 Generation of x For each signal ŝ, define a domain of data relevance (Bierlaire and Frejinger, 2008) and consider all destinations x in it For AP data: a 50-meter radius circle around each AP For Cisco data: a square with a 95% confidence interval 28

29 Number of points of interest for each signal Number of points of interest Signals (in chronological order) 29

30 Generation of t,t + t i [ˆt i 1 + tt xi 1,x i, ˆt i ] t + i [ˆt i, ˆt i+1 tt xi,x i+1 ] 30

31 position ˆx i+1 ˆx i tt xi 1,x i t t + tt xi,x i+1 ˆx i 1 ˆt ˆt ˆt i 1 i i+1 time 31

32 position ˆx i+1 tt xi 1,x i ˆx i tt xi,x i+1 ˆx i 1 ˆt ˆt ˆt i 1 i i+1 time 32

33 Travel time tt xi,x i+1 = dist(x i,x i+1 ) v Chen, 2012: Speed distribution for pedestrians from smartphone data f(v) =ωλe λv 1 +(1 ω) v 2πτ 2 e (ln v µ) 2 2τ 2 33

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35 Measurement model: P (ˆx x) Friis law for free-space environment: 1 dist(p, ˆp) 2 In case of absorption by obstacles, reflection, scattering, refraction: more complicated. 2 solutions: fingerprinting relate RSS to distance Wang et al. (2003), Cisco: 1 dist(p, ˆp) 3 35

36 Future works Develop the probabilistic model Explore the outcome for route choice Explore the outcome for OD matrix estimation 36

37 Slides and contact information: 37