Crowd Density Estimation for Public Transport Vehicles Marcus Handte, Umer Iqbal, Stephan Wagner, Wolfgang Apolinarski, Pedro Marrón (UDE) Eva Munoz, Santiago Martinez (ETRA I+D) Sara Izquierdo, Mario González (EMT Madrid)

Motivation Existing information systems for public transport focus on timetables and routes This is probably the most important piece of information but … … in urban settings the same destination can be reached over multiple „similar“ connections e.g. same bus line leaving every 3 minutes e.g. two stops very close to destination Question: how to select most comfortable trip Idea: discriminate options based on number of passengers Focus: how to determine this crowd-level automatically

Design Goals Create a system to determine the crowd-level achieving Sufficient accuracy: provide a meaningful (i.e. representative) estimate, not necessarily perfect but good enough Full automation: no additional work for existing support personal (e.g. driver or guards) and no work for the passengers Low cost: hardware cost should be minimal to be feasible to deploy at a city scale (e.g. city of Madrid operates > 2000 busses) Low latency: to support real-time operation (what is the current crowd-level), the latency of the solution must be low (~ few minutes) Low privacy impact: non-intrusive system to ensure that solution is acceptable for passengers

Approach Use of passive monitoring of WIFI-enabled devices carried by the passengers (i.e. phones) Probe requests generated as part of active WIFI scans Deployment of low-cost monitoring devices in vehicles Real-time reporting of crowd-level via (existing) 3G connection Use of (existing) GPS tracking to determine bus location Attribution of crowd-level to specific route segment (i.e. from stop - to stop) using network information and GPS position

I‘ve seen this already, what‘s new? Passive WIFI monitoring has been done before, e.g. … A. B. M. Musa and J. Eriksson. Tracking unmodified smartphones using wi-fi monitors. In Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, SenSys ’12, pages 281–294, New York, NY, USA, 2012. ACM. But, crowd-level estimation in vehicles is different because the WIFI monitoring devices are moving through space we are only interested in devices that are in the vehicle Thus, we have to filter out WIFI devices of persons … … walking next to the bus … at the bus stop waiting for the next bus … sitting in their backyard …

Crowd Density Estimation Use of sliding window to count devices seen „consistently“ Experiment Installation of 1 WIFI monitor in a bus in the city of Madrid Collection of 384874 raw probe requests over 14 day period Results Almost 50% of the devices are only seen once Most devices are seen again after (at most) 3 minutes 3 min. window, 1 min initialization

Vehicle Tracking Goal: attribution of crowd-level to route segment (i.e. from stop – to stop) based on (imprecise) GPS position Basis: modeling of routes as polylines consisting of stops and intermediate points (e.g. road intersections) Approach: computation of shortest paths from GPS position to all polyline segments Attribution to route segment with shortest path

Implementation Development of inexpensive WIFI monitor using OpenWRT and TP-Link 3020 router (<50€) Computes and sends number of passengers every 30 seconds Development of 3 web services to get the routes of the bus network get the polyline for each route get the route and gps position of a vehicle Development of simple map-based crowd-level visualization { ”Id ”:4281 , ”LineId ”:17 , ”Loc”: { ”Lat”:−0.001534102118749, ”Lon”:−7.489303515333618 }, ”Route”:33342 }

Evaluation Discussion Experiments Full automation  no manual intervention required Low cost  less than 50€ per vehicle for the WIFI monitor Low privacy impact  local processing on the WIFI monitor, only crowd-level is stored (not the MAC addresses) Experiments Deployment of 3 WIFI monitors in 3 busses in Madrid for 3 weeks Low latency  3 minutes + transfer time (usually < 1 minute) Sufficient accuracy  comparison with manual counting shows a consistent ~20% detection rate

Conclusions Existing information systems for public transport focus on timetables and routes We argue that crowd-level information can enable passengers to optimize their trips in cities can be gathered with low cost in an automated and non-intrusive fashion using passive WIFI monitoring Next steps Integration with the bus navigator application developed by the GAMBAS FP7 project

Questions? average crowd level over 3 weeks thicker line  higher crowd level