Measuring and Predicting Departures from Routine in Human Mobility by Dirk Gorissen
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Measuring and Predicting Departures from Routine in Human Mobility by Dirk Gorissen

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Measuring and Predicting Departures from Routine in Human Mobility by Dirk Gorissen

Measuring and Predicting Departures from Routine in Human Mobility by Dirk Gorissen

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  • http://www.pnas.org/content/95/25/15145/F2.expansion.htmlhttps://cee.mit.edu/news/releases/2013/human-mobility-travel-configurations
  • http://ceur-ws.org/Vol-872/aum2012_paper_3.pdf

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  • 1. Measuring and Predicting Departures from Routine in Human Mobility Dirk Gorissen | @elazungu PyData London - 23 February 2014
  • 2. Me? www.rse.ac.uk
  • 3. Human Mobility - Credits  University of Southampton  James McInerney  Sebastian Stein  Alex Rogers  Nick Jennings  BAE Systems ATC  Dave Nicholson  Reference:  J. McInerney, S. Stein, A. Rogers, and N. R. Jennings (2013). Breaking the habit: measuring and predicting departures from routine in individual human mobility. Pervasive and Mobile Computing, 9, (6), 808-822.  Submitted KDD paper
  • 4.  Beijing Taxi rides  Nicholas Jing Yuan (Microsoft Research)
  • 5. Human Mobility  London in Motion - Jay Gordon (MIT)
  • 6. Human Mobility: Inference  Functional Regions of a city  Nicholas Jing Yuan (Microsoft Research)
  • 7. Human Mobility: Inference  Jay Gordon (MIT)
  • 8. Human Mobility: Inference  Cross cuts many fields: sociology, physics, network theory, computer science, epidemiology, … © PNAS © MIT
  • 9. Project InMind  Project InMind announced on 12 Feb  $10m Yahoo-CMU collaboration on predicting human needs and intentions
  • 10. Human Mobility  Human mobility is highly predictable  Average predictability in the next hour is 93% [Song 2010]  Distance little or no impact  High degree of spatial and temporal regularity  Spatial: centered around a small number of base locations  Temporal: e.g., workweek / weekend  “…we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.”
  • 11. Temporal Regularity  [Herder 2012] [Song 2010]
  • 12. Spatial Regularity  [Herder 2012] [Song 2010]
  • 13. Breaking the Habit  However, regular patterns not the full story  travelling to another city on a weekend break or while on sick leave  Breaks in regular patterns signal potentially interesting events  Being in an unfamiliar place at an unfamiliar time requires extra context aware assistance  E.g., higher demand for map & recommendation apps, mobile advertising more relevant, …  Predict future departures from routine?
  • 14. Applications  Optimize public transport  Insight into social behaviour  Spread of disease  (Predictive) Recommender systems  Based on user habits (e.g., Google Now, Sherpa)  Context aware advertising  Crime investigation  Urban planning  … Obvious privacy & de-anonymization concerns -> Eric Drass’ talk
  • 15. Human Mobility: Inference  London riots “commute”
  • 16. Modeling Mobility  Entropy measures typically used to determine regularity in fixed time slots  Well understood measures, wide applicability  Break down when considering prediction or higher level structure  Model based  Can consider different types of structure in mobility (i.e., sequential and temporal)  Can deal with heterogeneous data sources  Allows incorporation of domain knowledge (e.g., calendar information)  Can build extensions that deal with trust  Allows for prediction  Bayesian approach  distribution over locations  enables use as a generative model
  • 17. Bayes Theorem
  • 18. Bayesian Networks  Bottom up: Grass is wet, what is the most likely cause?  Top down: Its cloudy, what is the probability the grass is wet?
  • 19. Hidden Markov Model  Simple Dynamic Bayesian Network  Shaded nodes are observed
  • 20. Probabilistic Models  Model can be run forwards or backwards  Forwards (generation): parameters -> data  E.g., use a distribution over word pair frequencies to generate sentences
  • 21. Probabilistic Models  Model can be run backwards  Backwards (Inference): data -> parameters
  • 22. Building the model  We want to model departures from routine  Assume assignment of a person to a hidden location at all time steps (even when not observed)  Discrete latent locations  Correspond to “points of interest”  e.g., home, work, gym, train station, friend's house
  • 23. Latent Locations  Augment with temporal structure  Temporal and periodic assumption to behaviour  e.g., tend to be home each night at 1am  e.g., often in shopping district on Sat afternoon
  • 24. Add Sequential Structure  Added first-order Markov dynamics  e.g., usually go home after work  can extend to more complex sequential structures
  • 25. Add Departure from Routine  zn = 0 : routine  zn = 1 : departure from routine
  • 26. Sensors  Noisy sensors, e.g., cell tower observations  observed: latitude/longitude  inferred: variance (of locations)
  • 27. Reported Variance  E.g., GPS  observed: latitude/longitude, variance
  • 28. Trustworthiness  E.g., Eyewitness  observed: latitude/longitude, reported variance  inferred: trustworthiness of observation  single latent trust value(per time step & source)
  • 29. Full Model
  • 30. Inference
  • 31. Inference is Challenging  Exact inference intractable  Can perform approximate inference using:  Expectation maximisation algorithm  Fast  But point estimates of parameters  Gibbs sampling, or other Markov chain Monte Carlo  Full distributions (converges to exact)  But slow  Variational approximation  Full distributions based on induced factorisation of model  And fast
  • 32. Variational Approximation  Advantages  Straightforward parallelisation by user  Months of mobility data ~ hours  Updating previous day's parameters ~ minutes  Variational approximation amenable to fully online inference  M. Hoffman, D. Blei, C. Wang, and J. Paisley. Stochastic variational inference. arXiv:1206.7051, 2012
  • 33. Model enables  Inference  location  departures from routine  noise characteristics of observations  trust characteristics of sensors  Exploration/summarisation  parameters have intuitive interpretations  Prediction  Future mobility (given time context)  Future departures from routine
  • 34. Performance  Nokia Dataset (GPS only) [McInerney 2012]
  • 35. Performance
  • 36. Performance  Synthetic dataset with heterogeneous, untrustworthy observations.  Parameters of generating model learned from OpenPaths dataset
  • 37. Performance
  • 38. Implementation  Backend inference and data processing code all python  numpy  scipy  matplotlib  UI to explore model predictions & sanity check  flask  d3.js  leaflet.js  kockout.js  Future  Gensim, pymc, bayespy, …  Probabilistic programming
  • 39. Map View: Observed
  • 40. Map View: Inferred
  • 41. Departures from Routine: Temporal
  • 42. Departures from Routine: Spatial
  • 43. Departures from Routine: Combined
  • 44. Departures from Routine
  • 45. Conclusion & Future Work  Summary  Novel model for learning and predicting departures from routine  Limitations  Need better ground truth for validation  Finding ways to make the model explain why each departure from routine happened.  Needs more data (e.g., from people who know each other, using weather data, app usage data, …).  Future Work  Incorporating more advanced sequential structure into the model  e.g., hidden semi-Markov model, sequence memoizer  Supervised learning of what “interesting" mobility looks like  More data sources  Online inference  Taxi drivers
  • 46. Questions?  Thank you.  dirk.gorissen@baesystems.com | @elazungu  Reference:  J. McInerney, S. Stein, A. Rogers, and N. R. Jennings (2013). Breaking the habit: measuring and predicting departures from routine in individual human mobility. Pervasive and Mobile Computing, 9, (6), 808-822.