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.

1. Fixed-interval Segmentation for Travel-time Estimations
in Traffic Maps
JoshuaStevens
KirkGoldsberry
Feb. 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