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Stevens-Goldsberry | Fixed-interval Segmentation for Travel-time Estimations in Traffic Maps
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Stevens-Goldsberry | Fixed-interval Segmentation for Travel-time Estimations in Traffic Maps

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This presentation introduces research on the cartographic influence of segmentation schemes used within traffic map design. Two novel design approaches are discussed along with empirical evaluation.

This presentation introduces research on the cartographic influence of segmentation schemes used within traffic map design. Two novel design approaches are discussed along with empirical evaluation.

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  • Congestion introduces environment uncertainty…we know where we are going, but not how long it will take!
  • 4.8 billion hours >= 1,400 days of Americans playing Angry BirdsNew studies emerging linking traffic to heart disease and other illnesses (due to emissions, not rage!)Largest sectornot shipping and freight, but light-duty commuters
  • Recession = less money, less excess travel. Better ways to get people driving less -> make their drives more efficient
  • Signs along highway ‘tune into AM 410…”… but we can also map this info!
  • Based on static speed limit informationWe can improve this with real-time traffic conditions
  • Recall the conventional design. What does slow mean? Fast? Can you estimate route distance using linear scale bar?
  • Has precedent…sort of. Dashes used to establish hierarchy between road types.On 1:24k map, dash size is 240 feet, but this isn’t explicitly stated on map, nor designed for estimation.
  • Unreasonable to expect perfect accuracyArithmetic not a problem…poor travel-time estimations then relate to map interpretation
  • Clearly segmentation has a positive influence on distance estimation
  • Directional bias with conventional map (11.77 minutes underestimation) conditions look better than they areSlight over-estimation with fixed-minute map….91 seconds
  • Fixed-minute design the only significant difference p = .007).

Transcript

  • 1. Fixed-interval Segmentation for Travel-time Estimations in Traffic Maps JoshuaStevens KirkGoldsberryFeb. 25 | AAG 2012 NY Spatiotemporal Thinking, Computing, and Applications IV
  • 2. Introduction Traffic Maps Prototypes Results Conclusions • Outline – Traffic, congestion, uncertainty – Maps as part of the solution – Trouble w/ existing designs – Prototypes: Considering segmentation – Empirical evaluation – Findings – Conclusions – Limitations
  • 3. Introduction Traffic Maps Prototypes Results Conclusions • Congestion is a geographic hindrance, affecting millions of drivers every day
  • 4. Introduction Traffic Maps Prototypes Results Conclusions • More than an inconvenience – Congestion = costs – 4.8 billion hours in delays – 1.9 billion gallons in wasted fuel – $101 billion in expenses paid by commuters 2011 Urban Mobility Report, Texas Transportation Institute
  • 5. Introduction Traffic Maps Prototypes Results Conclusions • Congestion in other terms – $713 per urban commuter in 2010 – Experienced delay = 34 hours/yr • Or…4 vacation days
  • 6. Introduction Traffic Maps Prototypes Results Conclusions • Systems exist to enhance efficiency – ITS: Intelligent Transportation Systems – ATIS: Advanced Traveler Information Systems • Usually broadcast by radio and in-vehicle units “ATIS-equipped drivers make better decisions and fewer late route diversions, which increases network efficiency.” - Al-Deek and Khattak (1998)
  • 7. Introduction Traffic Maps Prototypes Results Conclusions • Decisions can be aided by travel-times Rand McNally Road Atlas
  • 8. Introduction Traffic Maps Prototypes Results Conclusions
  • 9. Introduction Traffic Maps Prototypes Results Conclusions
  • 10. Introduction Traffic Maps Prototypes Results Conclusions
  • 11. Introduction Traffic Maps Prototypes Results Conclusions • Variations of a single design approach
  • 12. Introduction Traffic Maps Prototypes Results Conclusions • Typical traffic maps don‟t provide answers ….they pose questions • To determine travel-time: – Estimate distance(s) – Guess velocities – Perform mental arithmetic
  • 13. Introduction Traffic Maps Prototypes Results Conclusions
  • 14. Introduction Traffic Maps Prototypes Results Conclusions • Conventional traffic maps lack explicit segmentation – Multiple and varying segment lengths – Ambiguous velocities • We can address these limitations through map design
  • 15. Introduction Traffic Maps Prototypes Results Conclusions • We designed two prototypes: – Improve distance estimation – Provide more detailed velocities – Or…represent travel-times directly
  • 16. Introduction Traffic Maps Prototypes Results Conclusions • Prototype 1: Equal-interval method – Fixed segment lengths used elsewhere Source: USGS
  • 17. Introduction Traffic Maps Prototypes Results Conclusions
  • 18. Introduction Traffic Maps Prototypes Results Conclusions • Prototype 2: Fixed-minute method – Each segment has temporal length – Reminiscent of isochrone approach • But…not restricted to a single origin or destination
  • 19. Introduction Traffic Maps Prototypes Results Conclusions
  • 20. Introduction Traffic Maps Prototypes Results Conclusions • Can map-readers estimate travel-times? – Do our designs improve these estimations? • We designed an experiment – 60 questions over 3 categories • Arithmetic • Distance estimation (control vs segmented) • Travel-time estimation (conventional vs our proposed alternatives)
  • 21. Introduction Traffic Maps Prototypes Results Conclusions • About our participants – n = 49 – Ages 18 – 33 (mode 20) – All had driving experience – All were MSU students, solicited across campus – Given $10 for participation
  • 22. Introduction Traffic Maps Prototypes Results Conclusions • Arithmetic
  • 23. Introduction Traffic Maps Prototypes Results Conclusions • Arithmetic – Considered „correct‟ if within 15% • 74.08 % correct, 87.14% confident 100 80 60 40 20 0 Correct Confident t (mean > n Min Max Median Mean % Error p threshold) 490 0.00 316.70 4.17 16.47 1.01 0.31
  • 24. Introduction Traffic Maps Prototypes Results Conclusions • Distance Estimation
  • 25. Introduction Traffic Maps Prototypes Results Conclusions • Distance Estimation
  • 26. Introduction Traffic Maps Prototypes Results Conclusions • Distance Estimation
  • 27. Introduction Traffic Maps Prototypes Results Conclusions • Distance Estimation
  • 28. Introduction Traffic Maps Prototypes Results Conclusions • Travel-time Estimation
  • 29. Introduction Traffic Maps Prototypes Results Conclusions • Travel-time Estimation
  • 30. Introduction Traffic Maps Prototypes Results Conclusions • Travel-time Estimation
  • 31. Introduction Traffic Maps Prototypes Results Conclusions • Travel-time Estimation % Correct Conventional 27.55% Equal-Interval 26.33% Fixed-minute 79.39% Mean Error ANOVA p-Matrix: Error Equal-interval Fixed-minute Conventional 19.77 minutes Fixed-minute 0.011 - Equal-Interval 13.57 minutes Fixed-minute 5.52 minutes Conventional 0.044 < .001
  • 32. Introduction Traffic Maps Prototypes Results Conclusions • Travel-time Estimation Mean Response Time Conventional 26.03 s Equal-Interval 28.07 s Fixed-minute 18.64* s
  • 33. Introduction Traffic Maps Prototypes Results Conclusions • User Preference
  • 34. Introduction Traffic Maps Prototypes Results Conclusions • User Feedback “I hated the [Google and equal-interval] maps because you really had to think, and do math in your head, which I did not enjoy.” “The [fixed-minute] map was easiest...The other maps were confusing.” “I really hope they never start using the type of maps that had 3 different colors for slow, fast, etc. I‟d never get anywhere on time.”
  • 35. Introduction Traffic Maps Prototypes Results Conclusions • Conclusions – Design matters: Segmentation can significantly influence estimation – Conventional designs are not sufficient • Most popular design makes conditions appear less severe than they are – These differences are likely to affect route decisions – Not limited to traffic maps • Great opportunities for rail/subway in particular
  • 36. Introduction Traffic Maps Prototypes Results Conclusions • Limitations – Suitable for in-vehicle navigation units? • Oblique view not evaluated – Difficult symbology • Requires some manual adjustments • Presents challenge for real-time implementation – University students should be good w/ math • Same results with general sample?
  • 37. Introduction Traffic Maps Prototypes Results Conclusions • Acknowledgements – My M.S. committee at MSU • Dr. Kirk Goldsberry, Dr. Judy Olson, Dr. Ashton Shortridge – California Dept. of Transportation – The MSU department of Geography – Many at Penn State for advice, critique, and new ideas
  • 38. Introduction Traffic Maps Prototypes Results Conclusions • Questions & Contact Info Joshua Stevens | josh.stevens@psu.edu Kirk Goldsberry | kgoldsberry@fas.harvard.edu