Measuring and Predicting Departures from Routine in Human Mobility

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Understanding human mobility patterns is a significant research endeavor that has recently received considerable attention. Developing the science to describe and predict how people move from one place to another during their daily lives promises to address a wide range of societal challenges: from predicting the spread of infectious diseases, improving urban planning, to devising effective emergency response strategies. This presentation will discuss a Bayesian framework to analyse an individual’s mobility patterns and identify departures from routine. It is able to detect both spatial and temporal departures from routine based on heterogeneous sensor data (GPS, Cell Tower, social media, ..) and outperforms existing state-of-the-art predictors. Applications include mobile digital assistants (e.g., Google Now), mobile advertising (e.g., LivingSocial), and crowdsourcing physical tasks (e.g., TaskRabbit).

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Measuring and Predicting Departures from Routine in Human Mobility

  1. 1. Measuring and Predicting Departures from Routine in Human Mobility Dirk Gorissen | @elazungu PyData London - 23 February 2014
  2. 2. Me? www.rse.ac.uk
  3. 3. Human Mobility - Credits  University of Southampton      BAE Systems ATC   James McInerney Sebastian Stein Alex Rogers Nick Jennings 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. 4.  Beijing Taxi rides  Nicholas Jing Yuan (Microsoft Research)
  5. 5. Human Mobility  London in Motion - Jay Gordon (MIT)
  6. 6. Human Mobility: Inference  Functional Regions of a city  Nicholas Jing Yuan (Microsoft Research)
  7. 7. Human Mobility: Inference  Jay Gordon (MIT)
  8. 8. Human Mobility: Inference  Cross cuts many fields: sociology, physics, network theory, computer science, epidemiology, … © MIT © PNAS
  9. 9. Project InMind  Project InMind announced on 12 Feb  $10m Yahoo-CMU collaboration on predicting human needs and intentions
  10. 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. 11. Temporal Regularity  [Herder 2012] [Song 2010]
  12. 12. Spatial Regularity  [Herder 2012] [Song 2010]
  13. 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. 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. 15. Human Mobility: Inference  London riots “commute”
  16. 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. 17. Bayes Theorem
  18. 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. 19. Hidden Markov Model   Simple Dynamic Bayesian Network Shaded nodes are observed
  20. 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. 21. Probabilistic Models   Model can be run backwards Backwards (Inference): data -> parameters
  22. 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. 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. 24. Add Sequential Structure  Added first-order Markov dynamics   e.g., usually go home after work can extend to more complex sequential structures
  25. 25. Add Departure from Routine   zn = 0 : routine zn = 1 : departure from routine
  26. 26. Sensors  Noisy sensors, e.g., cell tower observations   observed: latitude/longitude inferred: variance (of locations)
  27. 27. Reported Variance  E.g., GPS  observed: latitude/longitude, variance
  28. 28. Trustworthiness  E.g., Eyewitness   observed: latitude/longitude, reported variance inferred: trustworthiness of observation  single latent trust value(per time step & source)
  29. 29. Full Model
  30. 30. Inference
  31. 31. Inference is Challenging   Exact inference intractable Can perform approximate inference using:  Expectation maximisation algorithm    Gibbs sampling, or other Markov chain Monte Carlo    Fast But point estimates of parameters Full distributions (converges to exact) But slow Variational approximation   Full distributions based on induced factorisation of model And fast
  32. 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. 33. Model enables  Inference      Exploration/summarisation   location departures from routine noise characteristics of observations trust characteristics of sensors parameters have intuitive interpretations Prediction   Future mobility (given time context) Future departures from routine
  34. 34. Performance  Nokia Dataset (GPS only) [McInerney 2012]
  35. 35. Performance
  36. 36. Performance   Synthetic dataset with heterogeneous, untrustworthy observations. Parameters of generating model learned from OpenPaths dataset
  37. 37. Performance
  38. 38. Implementation  Backend inference and data processing code all python     UI to explore model predictions & sanity check      numpy scipy matplotlib flask d3.js leaflet.js kockout.js Future   Gensim, pymc, bayespy, … Probabilistic programming
  39. 39. Map View: Observed
  40. 40. Map View: Inferred
  41. 41. Departures from Routine: Temporal
  42. 42. Departures from Routine: Spatial
  43. 43. Departures from Routine: Combined
  44. 44. Departures from Routine
  45. 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. 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.

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