A value driver model for network operations

Graham Jacoby

Network Operations Analysis

A Value Driver Model for Network Operations

Value Driver Modelling: Concept

2

A tool used extensively in operations based private

industries

allow a company to understand how business drivers

influence the supply chain and impact the bottom line

Example Application: Mining

3

Application to Road Network Operations

4

How can this concept be applied to road space?

Supply, Demand and Cost

Inputs could be either:

Factors controlled by Main Roads, or

Factors outside our control

Outputs are

Speed

Reliability

Safety

VDMs to be built and calibrated route by route

Example: VDM for Road Network Ops

5

Perth’s Mitchell Freeway

Southbound (inbound) direction

30km length (93 lane kms)

Heavily congested in AM peak


6

Performance(Cost)

Supply

Demand


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Performance(Cost)

Supply

Physical Road Space

Posted Speed Limit

Acceptable Headway

Road StandardDemand


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How to quantify supply at a route level?

Macroscopic Fundamental Diagram (MFD)

Relates density to speed


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How to quantify supply at a route level?

Macroscopic Fundamental Diagram (MFD)

Relates density to speed


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Why Van Aerde Equation?

Four free parameters


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𝑑𝑚𝑎𝑥 ~ physical road space

𝑉0 ~ posted speed

𝐶0 ~ max throughput

𝑘𝑠𝑡 ~ road standard


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𝑑𝑚𝑎𝑥 = 53.3 veh/km/lane

𝑉0 = 99.2 km/hr

𝐶0 = 127,650 VKT/hr

𝑘𝑠𝑡 = 0.0861


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𝑑𝑚𝑎𝑥 = 70 veh/km/lane

𝑉0 = 99.2 km/hr

𝐶0 = 127,650 VKT/hr

𝑘𝑠𝑡 = 0.0861


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𝑉0 = 99.2 km/hr

𝐶0 = 127,650 VKT/hr

𝑘𝑠𝑡 = 0.0861


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𝑉0 = 80 km/hr

𝐶0 = 127,650 VKT/hr

𝑘𝑠𝑡 = 0.0861


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𝑉0 = 99.2 km/hr

𝐶0 = 127,650 VKT/hr

𝑘𝑠𝑡 = 0.0861


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𝑉0 = 99.2 km/hr

𝐶0 = 100,000 VKT/hr

𝑘𝑠𝑡 = 0.0861


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𝑉0 = 99.2 km/hr

𝐶0 = 127,650 VKT/hr

𝑘𝑠𝑡 = 0.0861


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𝑉0 = 99.2 km/hr

𝐶0 = 127,650 VKT/hr

𝑘𝑠𝑡 = 0.01


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𝑉0 = 99.2 km/hr

𝐶0 = 67.7 VKT/hr

𝑘𝑠𝑡 = 0.0861


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Performance(Cost)

Supply

Max Density 𝑑𝑚𝑎𝑥 Physical Road Space

Max Speed 𝑉0 Posted Speed Limit

Max Throughput 𝐶0Accepted Headway

(visibility)

Shape Factor 𝑘𝑠𝑡 Road Standard

Demand


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Performance(Cost)

Supply

Demand


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Performance(Cost)

Supply

Demand

Peak Demand

Peak Time

Peak Spread

Trip Length


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How to quantify demand at a route level?

Much simpler


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𝑡𝑝𝑒𝑎𝑘 = 07:30

𝐴𝑝𝑒𝑎𝑘= 12,000 veh/hr

𝑝𝑓𝑎𝑐 = 12


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𝑡𝑝𝑒𝑎𝑘 = 07:30




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𝑡𝑝𝑒𝑎𝑘 = 08:00




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𝑡𝑝𝑒𝑎𝑘 = 07:30




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𝑡𝑝𝑒𝑎𝑘 = 07:30




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𝑡𝑝𝑒𝑎𝑘 = 07:30




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𝑡𝑝𝑒𝑎𝑘 = 07:30




32

𝑡𝑝𝑒𝑎𝑘 = 07:30




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Performance(Cost)

Supply

Demand

Peak Demand

Peak Time

Peak Spread

Trip Length


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Performance(Cost)

Supply

Demand

𝐴𝑝𝑒𝑎𝑘 Peak Demand

𝑡𝑝𝑒𝑎𝑘 Peak Time

𝑝𝑓𝑎𝑐 Peak Spread

12km Trip Length


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Performance(Cost)

Supply

Demand


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Performance(Cost)

Performance Model

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Simple queuing system

INPUT STORAGE OUTPUT

RESTRICTION

Performance Model

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Simple queuing system

1. Check # vehicles in storage

2. Calculate density (vehicles/road space)

3. Read speed from supply curve

4. Calculate TT (trip length/speed)

5. Read input from demand model

6. Add input to storage and hold for TT

7. Release output

8. Update # vehicles in storage

9. repeat INPUT STORAGE OUTPUT

RESTRICTION

Performance Model

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Results – queue model iterated for 18,000 seconds

Sys. speed over time

Queue length (nV) over time


RESTRICTION

Performance Model

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Results

Bulk metrics


RESTRICTION

Performance Model

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Results

Sys. speed over time

Queue (nV) over time


RESTRICTION


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Performance(Cost)

Supply

Max Density 𝑑𝑚𝑎𝑥 = 53

Max Speed 𝑉0 = 99

Max Throughput 𝐶0 = 128,000

Shape Factor 𝑘𝑠𝑡 = 0.086

Demand

Peak Demand 𝐴𝑝𝑒𝑎𝑘 = 12,000

Peak Time 𝑡𝑝𝑒𝑎𝑘 = 07: 30

Peak Spread Fac 𝑝𝑓𝑎𝑐 = 12

Trip Length = 12km


43

Performance(Cost)

Supply

Max Density 𝑑𝑚𝑎𝑥 = 53

Max Speed 𝑉0 = 99

Max Throughput 𝐶0 = 128,000 ±5%

Shape Factor 𝑘𝑠𝑡 = 0.086

Demand

Peak Demand 𝐴𝑝𝑒𝑎𝑘 = 12,000 ±5%

Peak Time 𝑡𝑝𝑒𝑎𝑘 = 07: 30 ±5%

Peak Spread Fac 𝑝𝑓𝑎𝑐 = 12

Trip Length = 12km ±5%

Perturb inputs

Multirun

Prototype: VDM for Road Network Ops

44

Perth’s Mitchell Freeway SB

Models a full year of

operations minute by minute

14 day types

Rainfall + lighting conditions

Crashes, breakdowns, debris

Road maintenance schedule

Events calendar

Heavy vehicle traffic

Prototype: VDM for Road Network Ops

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Perth’s Mitchell Freeway SB

Significant calibration effort

Forecasts

Delay

VoC

Reliability

Emissions

Crashes

absolute and assoc. $ costs

Results: Dashboard Summary

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Use Case Examples: Scenario Modelling

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Maintenance Scheduling:

Close one lane for three nights or three lanes for one

night?

Incident Response:

Benefits of expanding the IRS fleet, investing in new

capabilities (e.g. road rakes), or optimal allocation of the

existing fleet.

Impact of Events:

How will demand for events at the new Perth Stadium

influence freeway performance? Should we anticipate

increased incidents on opening?

END

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What the VDM is

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Route level model for rapid scenario testing

Complementary to conventional modelling tools, i.e.

Strategic models

Mesoscopic models

Microsimulation models

Traffic engineering tools

Designed to forecast reliability and safety

Designed to model peak or off-peak, rain or shine

Designed to model incidents and environmental

factors

What the VDM is not

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Not designed to replace traditional modelling tools

Not designed to replace traditional modelling tools

Not suitable for modelling highly detailed/local

treatments

Not deterministic

Value Driver Model

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Environmental Model

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Supply Model

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Demand Model

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Incident Model

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Performance Model

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Education

A value driver model for network operations