Anita Graser, Maximilian Leodolter and Hannes Koller Towards Better Urban Travel Time Estimates Using Street Network Centrality http://eurocarto.org/wp-content/uploads/2015/10/2-13.pdf

1. Towards Better Urban Travel Time
Estimates Using Street Network Centrality
Anita Graser, Maximilian Leodolter & Hannes Koller

2. Problem Statement
We want to:
1. Predict travel time / speed on a certain road at a certain time
2. Where we don’t have measurements
3. Using only street network data – NO travel demand data
2

3. Travel Time Prediction
Usual approaches:
 Sensor measurements
 Traffic model
 Fallback: Speed limit ×correction factor
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4. 4
Our Basic Prediction Model
MAPE
Explanatory
Variables:
- Time of day
- Road class
- Speed limit

5. Centrality
1. Closeness: “Central”  “Peripheral”
How far is it to all other network nodes?
2. Betweenness: “Important”  “Unimportant”
How often is this location on the shortest paths between network nodes?
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6. 6
Global Closeness

7. 7
Local Closeness

8. 8
Global Betweenness

9. 9
Local Betweenness

10. Linear Regression Model
Standard Linear OLS (ordinary least square) Regression model
Y = X +  (1)
where
 𝛽 = (𝑋 𝑇
𝑋)−1
𝑋 𝑇
𝑌 (2)
Estimators:
 𝒚𝒕,𝒇 = 𝜷 𝒕 + 𝜷 𝒇,𝒔 ∙ 𝒔 + 𝜷 𝒇,𝒃 ∙ 𝒃 + 𝜷 𝒇,𝒄 ∙ 𝒄 + 𝜷 𝒇,𝒃𝒄 ∙ 𝒃 ∙ 𝒄 (3)
Where
 Y … Speed records
 X … Model matrix ( daytime t, frc f, betweenness b, closeness c, speed limit s)
 𝛽 … stacked vector of
OLS estimates 𝛽𝑡, 𝛽𝑓,𝑠, …
 b … betweenness
 c … closeness
 s … speed limit
  … Residual
1013.11.2015

11. Centrality Model Parameters

1113.11.2015
Central
Peripheral
Unimportant Important
Secondary & tertiary roads Local roads & residential streets

12. Result1: Improved Coverage of Estimators
1213.11.2015
Secondary & tertiary roads with speed limit 50 kph

13. Interpretation of Results
1. Base model
2. 1st extension using global centrality
3. 2nd extension using local centrality
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14. 14
Mean Percentage Error
Base Model
MAPE

15. 15
Mean Percentage Error
2nd Extended Model
MAPE

16. MAPE
16
Mean Percentage Error
Base Model

17. MAPE
17
Mean Percentage Error
2nd Extended Model

18. 18
Improvements using
Global Centrality
MAPE Difference
8.5%

19. 19
Improvements using
Local Centrality
MAPE Difference
14%

20. 20
Difference between
Global & Local Centrality
MAPE Difference
6%

21. Model Performance Summary
2113.11.2015

22. Contact
Anita Graser
anita.graser@ait.ac.at
@underdarkGIS