Australian Road Pricing Simulation

A simulation comparing seven road pricing regimes across Australia's road network, grounded in transport economics theory and calibrated with Australian data.

What This Does

Models the effects of different road pricing approaches on:

• Revenue generation — can the regime fund road infrastructure?
• Congestion — does it reduce peak-hour delays?
• Emissions — does it incentivise cleaner transport?
• Equity — who bears the cost (urban vs rural, cars vs trucks, ICE vs EV)?
• Feasibility — how complex and politically viable is it?

Pricing Regimes Compared

Regime	Theory	Key Feature
Status Quo	Cost-recovery (crude)	Fuel excise + registration + tolls
Flat Distance	Second-best / user-pays	Same $/km for all vehicles and times
Congestion	Vickrey / Pigouvian	Time-varying charge, peak multiplier
CBD Cordon	Second-best (London/Stockholm model)	Fixed charge to enter CBD zone
Weight-Distance	AASHTO fourth-power / Henry Review	Charge scaled by vehicle mass
Full Externality	First-best Pigouvian	Charge = marginal external cost
Hybrid Reform	Pragmatic second-best	Distance + congestion top-up + weight

Theoretical Foundations

Six bodies of economic theory inform the model:

• Pigouvian Taxation (Pigou 1920) — price the externality
• Ramsey Pricing (Ramsey 1927) — minimise deadweight loss for cost recovery
• Vickrey Congestion Pricing (Vickrey 1963/1969) — time-varying tolls
• Cost Recovery / User-Pays (Henry Tax Review 2010) — road damage charges
• Equity & Accessibility (Rawlsian) — distributional fairness
• Second-Best Theory (Lipsey & Lancaster 1956) — practical approximations

Data Sources

Calibrated using publicly available Australian data:

• ABS Motor Vehicle Census (fleet composition)
• BITRE (road expenditure, VKT, congestion costs)
• ATAP (value of time, externality costs)
• NTC (heavy vehicle charges)
• State transport surveys (VISTA, HTS)
• DCCEEW National Greenhouse Accounts (emission factors)

Quick Start

pip install -r requirements.txt

# Run the simulation (2026 fleet)
python main.py

# Run with 2030 projected fleet
python main.py --year 2030

# Include sensitivity analysis
python main.py --sensitivity

# Skip chart generation
python main.py --no-charts

Output

Results are saved to output/:

• report.txt — full narrative analysis
• simulation_results.csv — key metrics by regime
• sensitivity_results.csv — parameter sensitivity data
• 9 PNG charts showing trade-offs, revenue, emissions, equity, and more

Project Structure

├── main.py                    # Entry point
├── src/
│   ├── theory.py              # Theoretical foundations documentation
│   ├── australian_data.py     # Fleet, network, and externality data
│   ├── pricing_regimes.py     # Seven pricing regime implementations
│   ├── simulation.py          # Simulation engine
│   ├── visualisation.py       # Chart generation
│   └── report.py              # Narrative report generator
├── output/                    # Generated results and charts
└── requirements.txt