Junction Modelling in a Strategic Transport Model

1
Junction Modelling in a Strategic Transport Model

• Wee Liang Lim
• Henry Le
• Land Transport Authority, Singapore

2
Outline

• Background
• Objective
• Overview of the LTA Strategic Transport Model
• Review of iterative junction modelling
• Revised junction modelling
• Comparison of performance results
• Conclusions

3
Background

• Singapore
• A city state
• 648 km2 area 4.1 mil population.
• 109 km rail lines (MRT/LRT), 150 km expressways
• 575 km major arterial roads, 1500 signalised
  junctions
• EMME/2 Strategic Transport Model
• Used widely to forecast travel demand for
  planning design of transport proposals, also
  calculate user benefits
• Enhanced over the years
• Incorporated iterative junction modelling in
  2000
• Recently revised junction modelling

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Objective

• To present a review of the iterative approach in
  junction modelling and its limitations.
• To present a revised simpler approach in
  junction modelling and its improvements in model
  convergence

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OVERVIEW OF LTA STRATEGIC TRANSPORT MODEL
Model Outputs
Model Inputs
Model Step
Land use data
Daily trip ends by purpose
- Planning Data population, employment,
school enrolment. - Car ownership, - Dwelling
types others
HBW, HBS, HBB HBL, NHB
Trip Generation
Trip rate data
Trip distribution matrices by trip purpose and
main mode
Trip distribution functions
HBW (highway, transit) HBS, HBB, HBL, NHB
Trip Distribution
HIS data
Skims of time and cost
Daily OD matrices by mode and trip purpose
From assignments - Car, m/c, Taxi - LRT/MRT/Bus
HBW (car, m/c, taxi, LRT, MRT, bus, c/o bus)
HBS (car, LRT, MRT, bus, school bus) HBB, HBL,
NHB
Mode Split
Mode split parameters
From HIS and SP survey
Peak hour matrices, AM, PM OP by mode
Peak hour factors by trip purpose, mode and area
Peak Hour Factors
- car, m/c, taxi - LRT/MRT - c/o bus, school
bus - bus
From HIS and traffic count data
Special trip matrices
- tourist trips, airport trips - goods vehicle
trips
Model outputs
- travel times - highway volumes - transit
volumes - other performance measures for
downstream analysis (e.g. financial, economic
analysis)
Network
Trip Assignment
- links, junctions - travel time, delay
functions - transit services
iteration
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Junction Modelling - Iterative Approach Review
Assignment Procedure
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Iterative Approach Review
Junction Coding

• Turn penalty (delay) function (tpf)
• User defined turn data
• UP1 6 digits to store
• 1 No. of lanes 2 No. of short lanes
• 3 Shared lane description 4 Signal
  control or not
• 5 Opposed information 6 unused
• UP2 unopposed green time opposed green time
• UP3 cycle time
• Extra user turn data effective green time
  capacity

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Iterative Approach Review
Delay Function for Signalised Movement
D(delay) c/2(1-u)2/(1-ux) 900(x-1
Sqr((x-1)2 4x/C))

• Delay function was based on SIDRA Formulae
• Delay uniform delay Overflow delay
• Function of cycle time, green split, arrival flow
  and movement capacity

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Iterative Approach Review
Movement Capacity

• Unopposed Movement
• Capacity Saturation flowgreen time/cycle time
• Opposed Movement
• Opposing movement flow
• Effective saturation flow
• Effective capacity for opposed movement
• Movement in a shared lane
• Capacity is proportioned to the ratio of its flow
  over total lane flow.

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Iterative Approach Review
Limitations

• Assignment convergence instability. Factors
  identified
• (i) Steep junction delay curve
• (ii) Iterative calculation of movement capacity

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REVISED JUNCTION MODELLING
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Revised Approach
Objectives

• To represent realistically the junction delay in
  a strategic network
• To improve model convergence and therefore
  assignment stability and accuracy

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Junction Modelling - Revised Approach
Assignment Procedure
Revised Approach
Start
Calculate movement capacity effective green time
Calculate link delay Calculate Junction delay
Assign Traffic
Check Convergence
No
Yes
END
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Revised Approach
Revised Delay Function
To reduce the steep gradient of the iterative
delay curve
Delay 0.25 0.25 (V/C)c-g for V/C lt1
0.5 1.5 (V/C-1)c-g 1 lt V/C lt 2
2 2 (V/C - 2) c-g 2 lt V/C
Source V/C lt 1 uniform delay V/C gt 1
calibration of the base model
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Revised Approach
Revised Improved Calculation of Movement
Capacity

• Different base saturation flow (veh/hour)
• Left Through Right
• 1700 1960 1800
• Simplified calculation for shared lane movements
• Saturation flow base saturation flow/no.
  movements
• Added calculation for short Lane
• Saturation flow storage length/(vehicle
  space mov. green time)
• (Capacity ? 400 veh/hr)
• Simplified calculation for opposed movement
• Saturation flow base saturation flow/3
• (Capacity ? 200 veh/hr)

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COMPARISON OF PERFORMANCE RESULTS
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Comparison of movement delays

• Left Movement

Iterative Ave 16.8 sec Revised Ave 22.2 sec 32
increase
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Comparison of movement delays

• Through Movement

Iterative Ave 30.0 sec Revised Ave 27.0 sec 10
reduction
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Comparison of movement delays

• Right Movement

Iterative Ave 38.4 sec Revised Ave 43.2
sec 12.5 Increase
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Comparison of network travel time
1999 Network - AM peak

• Observations
• Junction delay increased despite delay curve
  smoothened
• Link travel time reduction gt more efficient
  route choice, more converged assignment

21
Comparison between modelled and observed traffic
volumes
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Comparison between modelled and observed travel
time
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Improvement in model convergence
Comparison of model running time on the 2015
network
Note (38) number of iterations per highway
assignment
The revised approach has improved model
convergence through reducing number of iterations
running time.
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Conclusion

• Junction delay is a major contributor to a
  journey time in an urban network.
• Full incorporation of SIDRA to a strategic
  transport model may not suitable.
• Revised and simpler approach to calculation of
  junction delay was presented
• The revised model represents realistic movement
  delays, travel times and traffic demand in a
  network.
• Model converges faster and predicts stable travel
  time saving for transport schemes.