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Modelling Freight Transport

Prof Phil Greening

Heriot Watt University

TransiT

DARe

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Agenda

  • Context
  • The models
  • Some early insights
  • Conclusions

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Complex Adaptive System

Autonomous Actors

    • Companies
    • Vehicles
    • Drivers

Adaptive

    • All act in their own interest
    • Access to resources
    • Competitive advantage

Efficient

    • Little slack = tightly coupled

Dynamic

    • Energy system changing
    • Business models
    • Data
    • Computing power

​

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Models to digital Twins

Models

  • Historical data
  • New technology
  • Emergent operational models
  • Reference model

Design future system

  • Charging
  • New business models
  • New infrastructure
  • New operational models

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Models to digital Twins

New system implemented

Twins

Shadows

  • Historical data
  • New technology
  • Emergent operational models
  • Reference model
  • Operational decisions in information rich environment
  • Learning and re-training
  • Real/right time data
  • New technology
  • Emergent operational models
  • Reference model

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Decision Making Rules for Different Actors

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Impact of electrification on the Energy Network

interactive_charger_map_scaled_by_max_visitors.html

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Data & Scenarios

  • Data from 2 companies consisting of:
    • 5 depots (brown)
    • 851 destinations (red = JLP; orange = co-op)
  • 19 service stations (purple) where charging stations can be developed.
  • Scenario 1: 100% ICE truck
  • Scenario 2: 100% EV, 19 charging stations, ERS on M6, M62, M1, A14
  • Scenario 3: Like scenario 2 but with 12 hours electricity outage in the morning at Lofthouse interchange
  • Scenario 4: Like scenario 2 but the premium for ERS is set to 0
  • Scenario 5: Like scenario 3 but the premium for ERS is set to 0

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Charging & Depot Adaptation

Charging Location

Scenario 2

Scenario 3

Scenario 4

Scenario 5

Depot %

93.8%

94.65%

48.39%

52.41%

ERS %

3.81%

4.23%

47.7%

45.27%

Public Charging Stations %

2.39%

1.12%

3.91%

2.32%

Fleet Size All Operators

389

563

348

400

Total Number of Charger (Depot & Static)

76

93

72

77

Daily Energy Required (kWh)

283,707

293,477

269,662

278,760

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Current Outcomes from the Model

We can see how the journey times change with electrification and where agents choose to charge

We can see how the electrification of the fleet effects the volume of freight delivered each hour

The total energy charged each hour both in total and per charger

Locations of chargers that are used the most and their occupancy

Performance

gap

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The Multi Criteria Decision Analysis/Process

Cost

Future Proofing

Resilience

Economic growth

OPEX

Funding

Employment

CAPEX

LPI

Robustness

Recovery

Autonomy

War Footing

Cyber Security

Decision

0.3

0.2

0.2

0.3

0.3

0.4

0.3

0.6

0.4

0.6

0.4

0.3

0.3

0.4

Option 1: Static charging + Minimal ERS (no Premium)

Option 2: Static Charging + minimal ERS (premium)

​

OP1

OP2

8

7

7

9

8

8

5

7

8

7

8

8

7

8

8

6

7

8

6

8

OPTION 1 = 7.69

OPTION 2 = 7.19

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Conclusions

  • Freight is a complex adaptive system
  • Freight is changing
  • Agent based Models allow us to model this complex adaptive system
  • These models underpin digital twins
  • Digital twins are important in getting the most value from our freight system
  • We will still need humans involved in the decision-making process