urban arterial travel time variability

8/13/2019 Urban Arterial Travel Time Variability

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Urban Arterial Road

Travel Time VariabilityModelling

Susilawati, PhD

Research Presentation, February 14th2014


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Outline

Travel time variability

Travel time distribution

Travel time variability modelling

Discussion


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Day to day variation onJourney to Work travel times

Day to day variation in JTW travel times

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

50.00

2

8/02/2007

2

8/03/2007

2

8/04/2007

2

8/05/2007

2

8/06/2007

2

8/07/2007

2

8/08/2007

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8/09/2007

2

8/10/2007

2

8/11/2007

2

8/12/2007

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8/01/2008

2

8/02/2008

Traveltime(min)

t

incident

incidentincident


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Use the 95th percentile t ) of the travel timedistribution as the travel time reliabilitymetrics

Utilise normal distribution properties; and as

the parameters to measure travel time reliability



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Travel time distribution

Need for best fit travel time distribution Empirical travel time data are likely to have long tail and

positive skew

in some cases shows bimodality and multimodality

The Burr distributionProposed Travel time distribution


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The Travel time distribution

Normal, Log normal, Weibull, Gamma & Betadistribution

The modelling technique

Multiple regression technique

Linear and power function

The influence factors

Free flow travel time

Volume Capacity ratio Link length

Delay congestion index

Travel time variability modelling


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Travel time variability modelling cont.

Multiple linear regressions

the most commonly used technique

e.g. Herman and Lam in Detroit, Richardson and Taylor in Melbourne Australia andPolus in Michigan

Eliasson

Stockholm bypass road by utilising travel time data collected thorough anautomatic camera system

t=actual travel time

t0= free flow travel time

L =link length

TODand speedare dummy variables representing time of day and the speedlimit,

is a constant, and , and are estimated parameters


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Peer et al (2009)

15 minute interval travel time was derived from speed data collected by

loop detector

VCR = flow capacity ratio

L=link length

=estimated parameter

Black and Chin (2007) GPS data from individual vehicles at 34 routes in ten of the largest urban

areas in England

Travel time variability modelling cont.


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pdf

cdf

rthmoment will only exist if ck>r

Modal will only exist if c> 1.

[If c1, then the distribution is L-shaped.]

Percentile

)1(1

)1(),,(

kcc

xckxkcxf

kcxkcxF

)1(1),,(

)1(

)1()(

)(

k

cr

crkk

xE rr

c

m

ck

c

x

/1

1

1

c k

P Px 1)1( /1

Burr distribution


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1090

5090

tt

ttskew

50

5090var

t

tt

c k

P Px 1)1( /1

c kx 12 /150 c k

x 110 /1

90

c k

c kc k

12

12110

/1

/1/1var

kcxP )1(1

c

k

x 1910

/1

10

Travel Time Variability Metric


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Study area Selected links from the Glen Osmond

Road (GOR link 1 and 6) and South

Road (SR link 18, 20 and 22) were

selected as study area.

Longitudinal journey to work travel

time using GPS-equipped probe

vehicles were analysed.

Glen Osmond Road:

link lengths vary from 152m to

1146m

posted speed limits of either 50km/h

or 60km/h

consist of 180 runs

South Road corridor

comprising 22 links Link lengths vary from 135m to

4007m

posted speed limits between

60km/h and 80km/h

consist of 100 runs


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Burr regression

The shape parameter (c)is allowed to vary with ywhere yiscovariate (dimensional vector)(Beirlant et al, 1998)

)1(1)1(),,( kcc xckxkcxf

k=shape parameter

c=shape parameter

x= travel time

y = Degree of saturation


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SCATS

SCATS system generates control parameters:

Maximum flow;

Headway at the maximum flow

Occupancy time at maximum flow

Degree of saturation

DSthe ratio of effectively used green time tothe total available green time


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GOR 6

0 20 40 60 80 100

0.0

0

0.0

2

0.0

4

0.0

6

0.0

8

travel time

Empirical Travel Time

Burr regression DS=0.8Burr regression DS=0.9

Burr regression DS=1Burr regression DS=1.1


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SR 22

0 200 400 600 800

0.0

00

0.0

01

0.

002

0.0

03

0.0

04

density

Empirical Travel Time

Burr regression DS=0.8Burr regression DS=0.9

Burr regression DS=1

Burr regression DS=1.1


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Link No

Burr Parameter for Empirical Data Percentile

Var

c k scale 10th 50th 90th

1 GOR 8.9 0.5 100.9 85.1 113 165 0.7

6 GOR 63.7 0 28.4 21.3 26 44.4 0.9

Link NoBurr Parameter for Estimated Data Percentile Var

DS c k scale 10th 50th 90th

1 GOR 1.9 0.6 3.1 1 120.1 59.3 119.1 237.7 1.5

0.7 3.8 67.1 119.2 211.1 1.2

0.8 4.6 74.2 119.4 191.4 1

0.9 5.6 80.7 119.5 176.5 0.8

1 6.7 86.5 119.6 165.1 0.7

1.1 8.2 91.5 119.7 156.2 0.5

6 GOR 3.1 0.6 6.5 1 37.8 20.2 34.9 114.1 2.7

0.7 8.8 20.5 30.3 71.2 1.7

0.8 12 20.6 27.4 50.7 1.10.9 16.4 20.8 25.5 39.7 0.7

1 22.4 20.9 24.2 33.3 0.5

1.1 30.6 20.9 23.3 29.3 0.4

1.2 41.8 21 22.6 26.7 0.3


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Link NoBurr Parameter for Empirical Data Percentile

Var

c k scale 10th 50th 90th

18 SR 3 2 317.2 119.2 235.1 407.6 1.2

20 SR 3.4 4.8 200.6 64.6 115.3 173.7 0.9

22 SR 5.4 0.7 261.4 186.5 286.5 470.3 1

Link NoBurr Parameter for Estimated Data Percentile Var

DS c k scale 10th 50th 90th

18 SR 1.2 0.6 2.1 3.7 420.4 78.8 199.9 394.2 1.6

0.7 2.4 95.9 218.1 397.2 1.4

0.8 2.7 114 235.5 399.9 1.2

0.9 3.1 132.9 252.1 402.2 1.1

1 3.5 152.1 267.6 404.3 0.9

1.1 3.9 171.3 282.2 406.2 0.8

20 SR 1.3 0.6 2.2 5.4 213.8 34.7 85.2 159.5 1.5

0.7 2.5 43.2 95.1 165.2 1.3

0.8 2.8 52.4 104.9 170.4 1.1

0.9 3.2 62.1 114.3 175.1 1

1 3.6 72.1 123.3 179.4 0.91.1 4.1 82.2 131.7 183.2 0.8

22 SR 1.4 0.6 2.3 1.3 331.8 114.9 288 669 1.9

0.7 2.7 132 293.4 610.5 1.6

0.8 3.1 148.8 298.1 563.8 1.4

0.9 3.5 165.2 302.3 526.1 1.2

1 4 181 306 495.4 1

1.1 4.7 195.9 309.2 470.1 0.9


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Conclusion

The value of c, k and scale parameters are close to the value of the

empirical c, k and scale parameter and the pdf functions for empirical

and estimated data have similar shape

The estimated percentiles are similar to the empirical data percentiles.

The 10th, 50th and 90th percentiles resulting from 0.6 degree of

saturation are lower than 0.9 degree of saturation.

The 10th and 50th percentiles increase as the degree of saturation

increases while the 90thpercentile decreases .

Higher degree of saturation is likely to have less variability since the 10th

percentile is close


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Conclusion

Burr regression technique allowing us to explore the role of SCATS

degree of saturation in determining the value of the Burr parameter c,

and leading to different shapes of the distribution and different values of

the travel time variability metrics. Thus it is possible to estimate the travel

time variability at varying levels of the SCATS degree of saturation


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Further Research Burr regression technique for other traffic

parameters

Cost Benefit analysis in relation to travel timereliability modelling

Data collection method

Online data collection, incident management

GIS and GPS integration in relation to ESRI cloud

environment which called ArcGIS online fororganisation

urban arterial travel time variability

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