1. Spatiotemporal Pattern Mining For Travel Behavior Prediction UIC IGERT Seminar 02/14/2007 Chad Williams

2. Agenda What is the problem What is the goal Hypothesis and objectives Novelty of work Approach

3. Problem Accurately predicting and modeling individual daily traveler behavior – Number of trips – Source and destination – When trips are made and how – What factors affect these decisions Motivation – Regional planning – Intelligent Traveler's Assistant (ITA)

4. Current challenges Quality of dataset - paper based travel diaries – Tendency to underestimate actual travel time – Time events occurred is imprecise/unreliable – Locations/activities forgotten during entry Level of predictions Transferability of predictions unreliable/difficult

5. Goal Gain insight into travel decisions and behavior – Leverage richer datasets available through new technologies (GPS enabled PDA surveys) More accurate time information GPS coordinates of destinations and route choice Location characteristics/Mode alternatives Planned vs. actual travel behavior – Mine meaning from the combined stream  Enter spatiotemporal pattern mining!!

6. What is spatiotemporal mining? Data mining across the dimensions of space and time simultaneously Relation to transportation – Spatial characteristics of transportation network, and accessibility also known to affect choices – Travel behavior is known to change over time (ie. throughout the day) – Traveler profile known to influence mode choice, activity choice Thus it is really multi-dimensional mining, but spatiotemporal mining is the catch word right now.

7. Hypothesis Patterns across travelers that take all of these dimensions into account will improve the transferability of mined patterns; resulting in improved traveler behavior prediction

8. Research Objectives Algorithms for mining patterns that span non- aligned information streams ( more about this later ) – Spatial – location, route – Temporal – activity timing, congestion fluctuation – Decision behavior – planned vs. actual Formal models – Destination and mode choice prediction – Formal model for prediction of trip chaining Formal measure of “interestingness” in spatiotemporal travel patterns

9. Prior work Activity & travel behavior modeling – Move travel behavior prediction to individual level – Survey data used to model activity patterns – Past focus largely on activity order and frequency to understand traveler behavior ( Jones et al. 1990 & Axhausen and T. Grling 1992 ) – Recent work has shifted focus to the scheduling process and factors which affect decisions( Mohhamadian and Doherty 2005, 2006 )

10. Prior work (cont.) Destination, traveler movement prediction – Short term prediction for cellular networks General traveler movement ( Liu and Maguire 1995 ) Mobile user travel profiles ( Bhattacharya and Das 1999 ) – Aggregate activity based travel ( Wang and Cheng 2001 ) – GPS log history for individual travel prediction ( Ashbrook and Starner 2002, 2003 ) Information stream alignment ( A Marascu and F Masseglia 2005, Kleinberg 2007 )

11. Novelty of Work Key differences from prior work – Intraday temporal analysis of patterns – Transference of spatial patterns – Integration of these aspects for insight into traveler decision making and behavior – Collaborative approach to behavior prediction

12. Research Objectives Algorithms for mining patterns that span non- aligned information streams ( more about this later ) – Spatial – location, route – Temporal – activity timing, congestion fluctuation – Decision behavior – planned vs. actual Formal models – Destination and mode choice prediction – Formal model for prediction of trip chaining Formal measure of “interestingness” in spatiotemporal travel patterns

13. Why is the integration significant? Traditional data mining techniques focus on a single plane – Classification algorithms (ex. Amazon/NetFlix) Many “spatial dimensions” – User characteristics (income, family size, etc) – Buying/Rent history – Movie preferences/ratings But values fixed rather than range over time – Temporal mining Examines single item or set of items as it changes over time Lose context of role “spatial” characteristics play in influencing these changes

14. Spatial transferability Consider 2 working adults – Similar family profiles – Each work similar hours – But live and work in different areas of town or even different towns. – Which behaviors are transferable which aren’t? Key is mining spatial similarity in conjunction with behavior similarity – Accessibility, density of wanted attractions, travel time between locations, trip chaining tolerances

15. Research Objectives Algorithms for mining patterns that span non- aligned information streams ( more about this later ) – Spatial – location, route – Temporal – activity timing, congestion fluctuation – Decision behavior – planned vs. actual Formal models – Destination and mode choice prediction – Formal model for prediction of trip chaining Formal measure of “interestingness” in spatiotemporal travel patterns

16. Formal Models Destination and mode choice prediction – ITA benefits – Further understanding for planners Transit routes that are likely to get used Factors most likely to influence mode decision Formal model for prediction of trip chaining – Optimize individual’s schedule – Suggest routes or additional stops – Key is using insight into decision making to only suggest changes traveler actually might consider

17. Research Objectives Algorithms for mining patterns that span non- aligned information streams ( more about this later ) – Spatial – location, route – Temporal – activity timing, congestion fluctuation – Decision behavior – planned vs. actual Formal models – Destination and mode choice prediction – Formal model for prediction of trip chaining Formal measure of “interestingness” in spatiotemporal travel patterns

18. “Interestingness” Because of size of information space need to reduce the amount of data that is actually looked at Metric for identifying what rules or relations are likely to be of interest and which are not – Ex. text mining -> stop words Introduce metric for reducing these types of rules in the transportation domain

19. Caveat Not a silver bullet – Unlikely to solve all of these problems General idea is that by integrating multiple views of information that influences traveler’s behavior, we will be able to get a richer model for prediction.