Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical...

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Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning Applications Conference

Transcript of Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical...

Page 1: Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning.

Estimating Intrazonal Impedances in

Macroscopic Travel Demand Models

Matthew Bediako OkrahTechnical University of Munich

15th Transportation Planning Applications Conference

20 May 2015

Page 2: Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning.

Contents• Background

• Proposed Method

• Application and Suitability Checks

• Significant Observations

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Page 3: Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning.

Background

• Existing approximate methods - Nearest Neighbour Techniques- Functions of Area Size

• Questions- Good estimates of intrazonal impedances?- Good estimates of intrazonal flows?

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Page 4: Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning.

Intrazonal Trips

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Page 5: Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning.

Intrazonal Trips

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Page 6: Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning.

Proposed Method

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Select Nodes

Calculate Weight Matrix Weighted Distance Matrix

Assign Node Weights

Compute Intrazonal Distance

Calculate Distance Matrix

Page 7: Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning.

Proposed Method

• Variants of Method- Unweighted Nodes- Nodes Weighted by Closeness Centrality- Nodes Weighted by Degree Centrality

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Page 8: Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning.

Application of Method

• Dachau with

102 TAZ

• Area= 13 sq.mi.

• Pop= 46,000

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Page 9: Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning.

Suitability Check 1

• Aggregate 102 Zones into:- 25 Zones- 10 Zones- 5 Zones- 2 Zones

• Intrazonal distance with different methods

• Compare estimated intrazonal distances

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Page 10: Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning.

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25-Zone10-Zone5-Zone2-Zone

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

RM

SE (km

)

UnweightedDegreeClosenessArea-BasedNearest Neighbor

RMSE in Estimated Intrazonal Distances

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Suitability Check 2

• Trip Distribution with the different methods - Home-Work Trips - Non-Home-Based Trips- Home-Shopping Trips

• Between modeled and observed trips;- Compare TLFDs (Coincidence Ratio)- Compare Total Number of Intrazonal Trips

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Page 12: Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning.

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10987654321

25

20

15

10

5

0

Distance (km)

Perc

ent

Observed (ATL = 1.6 km)Estimated (ATL = 1.5 km)

Coincidence Ratio = 0.83

TLFD of NHB Trips (Unweighted)

Estimated Intrazonal Trips = 1215

Page 13: Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning.

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10987654321

25

20

15

10

5

0

Distance (km)

Perc

ent

Observed (ATL = 1.6 km)Estimated (ATL = 1.5 km)

Coincidence Ratio = 0.83

TLFD of NHB Trips (Degree)

Estimated Intrazonal Trips = 1195

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10987654321

20

15

10

5

0

Distance (km)

Perc

ent

Observed (ATL = 1.6 km)Estimated (ATL = 1.5 km)

Coincidence Ratio = 0.82

TLFD of NHB Trips (Closeness)

Estimated Intrazonal Trips = 1384

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10987654321

20

15

10

5

0

Distance (km)

Perc

ent

Observed (ATL = 2.6 km)Estimated (ATL = 3.4 km)

Coincidence Ratio = 0.55

TLFD of NHB Trips (Area-Based)

Estimated Intrazonal Trips = 2136

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10987654321

25

20

15

10

5

0

Distance (km)

Perc

ent

Observed (ATL = 1.6 km)Estimated (ATL = 1.5 km)

Coincidence Ratio = 0.82

TLFD of NHB Trips (Nearest Neighbor)

Estimated Intrazonal Trips = 1548

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Home-Shopping(700)Non-Home-Based(1280)Home-Work(280)

60

40

20

0

-20

-40

-60

Perc

ent

UnweightedDegreeClosenessArea-BasedNearest Neighbor

Errors in Estimated Number of Intrazonal Trips

Page 18: Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning.

Conclusions

• Unweighted provides better estimates of

intrazonal impedances

• Better intrazonal impedance no guarantee for

better estimates of intrazonal flows

• Nearest neighbour method sufficient given

ease of measurement

• Need for smaller zones to avoid intrazonal

trips18

Page 19: Estimating Intrazonal Impedances in Macroscopic Travel Demand Models Matthew Bediako Okrah Technical University of Munich 15 th Transportation Planning.

Thanks for your Attention

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