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Big Data for Travel Demand Modeling

Big Data for Travel Demand Modeling

DataPalooza 6/4/2014 : Analyzing Integrated Data Part 2

GTMA_USDOT_DataPalooza

June 27, 2014
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  1. What is Big Data? Big Data for Travel Demand Modeling

    • Opportunities • Strengths • Examples • Weaknesses • Threats Conclusions and Discussion
  2. USE OF DIGITAL SERVICES mobile phones Web services ONLINE INFORMATION

    social media newspapers, e-commerce transactions PHYSICAL SENSORS satellite imagery nightlights data CROWDSOURCING Volunteered Geographic Information Structured Versus Unstructured Data Varying Levels of spatial and temporal variation Repurposed Data Sources Data Characteristics
  3. • Noise • Incompleteness • Biased • Anomalies/outliers • Uncertainty

    VERACITY Trustworthiness of the data Wikipedia.com Apollo.lsc.vsc.edu Baldscientist.wordpress.com
  4.  Human/Societal Behavior  Social Networks  Proliferation of Unstructured

    Data (text, video, photos)  Digitization of documents and content  Sentiment/Preferences  Crowdsourcing What’s New? Ripe for Travel Demand Analysis (And Transportation In General) What’s It For Us?
  5. 0 9 29 172 0 50 100 150 200 2011

    2012 2013 2014 TRB Papers Associated with "Big Data" Source: Shanjiang Zhu, George Mason University
  6. Innovations In Travel Modeling, Baltimore, 04/27-04/30 Innovations in Data Workshop

    Dynamic Models and Dynamic Data Old Data with a New Twist GPS and Emerging Data in Travel Forecasting International Transport Forum 2014 Summit, Leipzig •Big Data in Transport: Applications, Implications, Limitations Exploratory Advanced Research •Video feature extraction automation •Real time data for connected highway-vehicle systems •FY 2014 Solicitation, Data for safety analysis and freight Source: Shanjiang Zhu, George Mason University …And of course Transportation Datapalooza!
  7. What are we trying to understand? …And why? • When

    and why do people travel? • What mode of transport do they use? • What routes do they take? • Do they change their travel behavior under certain circumstances? • Do they travel with others? • How do activities drive travel behavior? • How does travel behavior vary depending on characteristics of individuals? • What are the preferences of travelers? • Capacity Planning/Expansions • Transportation Demand Management • Strategies to Enhance Accessibility (Including Disadvantaged Segments of the population) • Livable/Sustainable Solutions • Democratization of Transport Planning/Policy Process
  8. TRADITIONAL DATA BIG DATA  Limited geographic and temporal coverage

     Lack of consistency across regions  Lack information on socialization/social ties  Small samples, margin of error  Short-Distance Travel  Information on Residents  Costly, Timely to Collect  Very fine-grained levels of resolution  Available for many, if not all parts of the globe  Social networks  Large Datasets  Long-Distance  Information on Visitors (In addition to Residents)  Costly, Timely to Process
  9. Sources: c2.staticflickr.com More refined locations can be defined using information

    on: • Speed of Travel • Stops • Development Patterns
  10. From: Cheng et al. (2011) Foursquare Checkins (October 2010 –

    February 2011) Foursquare, Gowalla, Brightkite Data: “checkins”, social networks
  11. Phx.homeguide.com • Internet clicks/searche s • Use of mobile apps

    • Consumer Purchases • Text Messages Collecting Massive Amounts of Data About Us!
  12. Flowingcity.com Taxi Movements in London Taxis in NYC Source: Ferraira,

    Poco, Freire, Silva (2013), Visual Exploration of Big Spatio-Temporal Urban Data: A Study of NY Taxi Trips
  13. Source: Gonzalez M, Hidalgo C, Barabasi AL (2008) Understanding individual

    human mobility patterns. Nature Letters. 5: 779-782 Cell Phone Traces Within a Region
  14.  Anonymous Cell Phone Records and Mobile Phone traces, “Human

    mobility and resulting contacts between people have a high degree of predictability (Song et al., 2010)”  Regular patterns across population, despite the different socio-economic characteristics Of Individuals and travel context  Scaling in distances travelled, time spent at locations and popularity of places  Artefact of Underlying Development or Do We Care? Example of Scaling Behavior Source: brenocom.com
  15. When People Travel Within a Day How Far People Travel

    Source: Cheng et al. (2011), Exploring Millions of Footprints in Location Sharing Services, Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media
  16. “Socio-Spatio” Network approach 1 set of vertices represent space, the

    other set represent individuals The full dataset contains 4,491,143 “check-ins” made by Brightkite users over the period April 2008-October 2010. The undirected social graph is comprised of 58,228 individuals and 214, 078 social ties (Cho et al. 2011). We extracted “check-ins for only t hose locations in the contiguous United States, and assigned “check-ins” to counties. There were almost 3 million “check- ins, 21,001 unique users and 2706 counties with at least one “check-in.” Schintler, L.A., Kulkarni, R. Haynes, K. and R. Stough (2014). Sensing “Socio-Spatio” Interaction and Accessibility from Location-Sharing Services Data. eds. Margerida, A. et al. Accessibility and Spatial Interaction
  17.  How does spatial proximity and travel behavior affect probability

    of friendship ?  How do social ties influence travel behavior?
  18.  Not random: bias  Missing data (and noise!) 

    Observations not independent  Issues related to temporal/spatial data  Traditional Methods Do Not Apply  Inequities  Computational Complexity www.thesalesblog.com
  19. Chen and Schintler, forthcoming More research is needed to assess

    the statistical accuracy of big data, what types of bias are present, how can they be corrected for, and what analytical methods are best-suited for the data!!
  20. • How far can we go in the use of

    big data for understanding/modeling travel behavior? • How do we safeguard individual’s privacy? www.publicpolicy.telfonica.co m www.cbsnews.com