1. Toll Road PPPs: Identifying, Mitigating and Managing Traffic Risk
OLC Webinar
31st October 2017

2. Forecasting and Traffic risk
Where does it come from?
How can it be reduced?
How can it be managed?

3. The Importance of Forecasting
Texas Toll-Road Operator Files for Bankruptcy
Wall Street Journal, March 2nd 2016
Forecasting is vital for all fields of science, social science and human endeavor
Forecasting is particularly vital for infrastructure – forecasts allow us to plan, design, dimension and finance infrastructure
BUT the world is becoming more complex and interdependent and the human skill of forecasting is becoming more difficult
This is a particular problem when forecasts are relied upon to raise scarce public and private capital for infrastructure as is often the case in PPP projects
Toll road PPPs are the enfant terrible in this respect with a history of project failures resulting from inaccurate financing
AUSTRALIA: ANOTHER TOLL ROAD GOES BANKRUPT
The Newspaper.com,
Spanish court rejects state bailout for bankrupt motorways
Reuters, February 26th 2015

4. What is Traffic Risk?
Where demand and revenue for an infrastructure project is over-estimated
Has led to financial distress through defaults, bankruptcies, renegotiations and government bail outs
Recurring issue in transport projects (particularly toll road PPPs)
Only 1 of 14 US toll roads studied by JP Morgan (1997) exceeded original revenue forecasts
On average, actual traffic was 60% of the forecast
Standard & Poors (2005) found actual traffic averaged 77% of forecast levels in a study of 104 international toll roads

5. The Traffic Forecasting Process
Forecasts can be produced by various project parties
Typically developed using a Travel Demand Model (TDM)
TDM is the forecasting base which replicates existing travel demand and road network
Consists of trip matrix, network and key behavioral parameters
New projects are inserted into this model to predict how travel changes into the future

6. Forecasting and traffic risk?
Where does it come from?
How can it be reduced?
How can it be managed?

7. Where does traffic risk come from?
Example of Traffic Risk in Road Traffic Forecasting
It’s part of the human condition – 3 key sources
Error: our technical limitations in understanding the current demand for travel
Uncertainty: our imperfect knowledge of the future
Bias: our pursuit of incentives
Optimism bias
Strategic Misrepresentation
The Winner’s Curse

8. Forecasting and traffic risk?
Where does it come from?
How can it be reduced?
How can it be managed?

9. BETTER PROJECT PREPARATION & STRUCTURING
How can it be reduced?
Error
Uncertainty
Bias
Better Modeling
Sensible Long-Range Forecasting and Stable Policy Environment
Better Alignment of Incentives
Deep data collection to understand the existing/base market for a project
Long-term monitoring of traffic trends
Adhere to industry standards in traffic modeling
Focus on willingness to pay, bring in specialist expertise
Realism in forecasting assumptions
Stability in policy environment so as to not threaten the competitive position of the project (e.g. toll/tariff policy)
Be explicit on ramp-up forecasts and time period
Sensitivity test key errors to understand size of risk
-Set a benchmark/reference case forecast by undertaking high quality public sector demand studies
-Independent review/ benchmarking of demand studies
-Provide base models to bidders
-Encourage due diligence
-Penalize aggressive forecasting in bid evaluation
-Ensure concession agreement is robust
BETTER PROJECT PREPARATION & STRUCTURING

10. Forecasting and traffic risk?
Where does it come from?
How can it be reduced?
How can it be managed?

11. How can it be managed?: Risk and Reward
Traffic risk can never be fully eliminated
The question of how to manage remaining risk depends on:
The underlying profitability/cost recovery of the project (e.g. NPV)
The impact of downside risk
Generally only government has the control and financial resources to manage high risk/low profitable projects
Options for managing demand risk in toll road projects

12. Measuring Risk and Reward
STEP 1 – BUILD A FINANCIAL MODEL OF THE PROJECT
STEP 2 – SCENARIO TEST TRAFFIC AND THE FINANCIAL IMPACT

13. Key Takeaways
Error can be reduced through better data collection and modeling
Uncertainty can be reduced by sensible long-range forecasting and creating a stable policy environment
Bias can be reduced through better alignment of incentives in the bidding process
Residual traffic risk can be managed through careful project structuring that considers risk/reward tradeoffs
Governments should hire experienced technical and transaction advisors to support the due diligence, structuring and procurement of projects

14. Thank you! Click Here for link to the new GIF/PPIAF Publication on Traffic Risk