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A force directed approach for offline gps trajectory map

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SIGSPATIAL 2018 paper

A Force-Directed Approach for Offline GPS Trajectory Map Matching
Efstratios Rappos (University of Applied Sciences of Western Switzerland (HES-SO)),
Stephan Robert (University of Applied Sciences of Western Switzerland (HES-SO)),
Philippe Cudré-Mauroux (University of Fribourg)

Published in: Technology
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A force directed approach for offline gps trajectory map

  1. 1. A Force-Directed Approach for Offline GPS Trajectory Map Matching Dr Efstratios Rappos HEIG-VD, Switzerland 9 November 2018 ACM SIGSPATIAL, Seattle, USA
  2. 2. Background – what is Map-matching? • Placing a trajectory obtained from GPS sensors onto a real map • Need two things: • the trajectory (sequence of points) • the map • Online versus offline: • Real-time (online) case: we can only use info about the trajectory up to now (focus on ‘speed’) • Offline case: the future is also available (focus on ‘accuracy’) • In this work we considered the offline case
  3. 3. Traditional methods • Routing-based (using routing algorithms for matching) • Probabilistic – Hidden Markov Chains • Similarity based (combining data from many trajectories) • Speed versus accuracy (ACM SIGSPATIAL 2012 competition) • Matching can be very easy or very hard!
  4. 4. A force directed approach • Force directed methods are common on graph / network visualization • Video:
  5. 5. Force directed for Map-matching • “Have the road attract the path” : novel idea • In every road of the map, an ‘electric current’ passes though. • Each GPS point is attracted or repelled by the road. • Trajectory can extend or contract as necessary, within reason. • Forces decrease with distance and over time. • Points are allowed to move slightly under the total forces, producing a new trajectory ‘closer to reality’, and easier to work with. • => Trajectories converge towards the road
  6. 6. Computational experimentation • Maps from OpenStreetMap • Data for Taxis in Rome: Advantage: very good dataset, because • High road density, non-grid-like • GPS points recorded every 10+ secs (not too often) • Thousands of trajectories Disadvantage • Incompleteness in the Map data (taxis can drive to many more places than OpenStreetMap says)
  7. 7. Map limitations
  8. 8. Computational results • Compared the map matching produced by GraphHopper • on the original trajectories • with the trajectories modified according to the Force-Directed algorithm • Two evaluation metrics found in the literature for the case of the absence of ground truth • Length index (comparison of trajectory lengths): consistently better, up to 5% improvement • Average lateral error: an average of 16% improvement • It works no matter what the map matching algorithm is! • Just ‘correct’ the GPS points by perturbing them slightly (pre- processing) and improve the accuracy.
  9. 9. Video
  10. 10. Ongoing work • Test with other datasets • Test with other matching algorithms • Calibrate / optimize the parameters of the algorithm • Consider cases with constellations, flyovers, multi-lane matching • Use the timestamp information

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