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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 1 | 29 Transportes, Inovação e Sistemas, S.A. Av. da Republica, 35, 6º 1050-186 Lisboa | Portugal | www.tis.pt Using @RISK for Traffic Forecast Analysis Case Study: Marão Tunnel Concession Palisade User Conference London, 22nd April 2008 Inês Teles Afonso

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 2 | 29 Table of Contents Case Study Presentation Context – What is a traffic study? What are the advantages of using @RISK? Traffic Modelling Model (VISUM) with @RISK Methodology @RISK Results Analysis The data presented in this presentation was modified so that we could ensure the privacy of our client

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 3 | 29 Case Study Presentation TUNNEL MARÃO CONCESSION National Politics Strategy Identical opportunities of Development Similar mobility conditions Traffic Forecast in concession sections Main Objective Traffic Study

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 4 | 29 Junction 3 Junction 5 Junction 1 Junction 4 IP4 A24/IP3 A4/IP4 Penaguião Amarante IP4 EN2 EN304 EN313-2 Vila Real EN15 A4/IP4 Carvalhais Campeã Quintã IP4 EN304 EN210 EN15 EN101-5 EN210 EN312 EN101 EN15 MARÃO TUNNEL CONCESSION CONCESSION Junction 2 A4/IP4 Main characteristics: Connection between cities of Amarante and Vila Real length → 30 km Cross-Section → 2x2 Free Flow Speed → 100 km/h Tolled motorway → open scheme with toll charge in road section → 3 - 4 Vila Real Case Study Presentation

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 5 | 29 45 Case Study Presentation Traffic Model was built on the VISUM platform The obtained results allow to predict the traffic demand on the studied sections over the period of analysis Note: These values doesn't correspond to the real project values 3 2 1

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 6 | 29 Context – What is a traffic study? INPUT Transport Demand Characteristics Traffic Study / Traffic Model Traffic Forecasts for the study infra-structure Finance Analysis (...income estimates) Environmental Impact Assessment Project Analysis OUTPUT Transport Supply Characteristics

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 7 | 29 Context – What is a traffic study? INPUT Traffic Study / Traffic Modal OUTPUT ANALYSIS It is a very important issue to know the expected evolution for the traffic (OUTPUT) and what are the associated risks. Usually we reflect the uncertainty of the model results in three scenarios such as “central”, “optimistic” and “pessimistic” allowing for a very limited deterministic analysis. Is it possible to present a clearer and stricter outcome?

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 8 | 29 What are the advantages of using @RISK? YES! Using @RISK, the OUTPUT ( traffic forecast) is represented by a probability distribution which improves the quality of decision- making. HOW? With simulation (Monte Carlo simulation). In each iteration @RISK tries all valid combinations of the values of INPUT to simulate all possible outcomes (OUTPUT). variable A variable B variable C Traffic Model Relations.. OUTPUT ANALYSIS

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 9 | 29 Traffic Modelling Model (VISUM) with @RISK 4 minutes for a traffic assignment 10.000 @RISK iterations iterations duration 833 hours! 35 days!!! SOLUTION Draw adjustment curves representing the relationship between OUTPUT (traffic forecast) and INPUT variables - Elasticity Curves PROBLEM Due the complexity of traffic model, it takes some time to get its outcome (traffic forecast). Therefore it is not feasible to do a traffic assignment (VISUM) for each @RISK iteration.

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 10 | 29 Traffic Modelling Model (VISUM) with @RISK Variable OUTPUT - AADT.km 2. Traffic Assignments 3. Elasticity curves adjustment 1. Change INPUT values

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 11 | 29 Methodology @RISK Application 1. OUTPUT Variable definition 2. INPUT Variables INPUT variables definition Definition of the probability distribution for each one Analysis of correlation between them 3. Interaction between INPUT and OUTPUT variables 4. @RISK Simulation 5. Results Analysis

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 12 | 29 OUTPUT Variable Definition OUTPUT Variable Traffic Forecast (AADT.km) for Tunnel Section (2011, 2020, 2030, 2040 and Accumulated Revenue)

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 13 | 29 INPUT Variables Definition INPUT Variables GDP Annual Variation Rate (after 2009) Toll value f(VAT) Value of Time (VoT) Variation Rate Fuel Cost Annual Variation Rate GC= l (length).Co (Operational Cost) + t (travel time).VOT + l.T(unit toll) Traffic growth factors Transport Demand Generalized Cost

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 14 | 29 Probability Distribution for INPUT Variables Around each input variable, there was a very deep discussion to decide which probability distribution should the variable assume. This discussion was based mainly on expert judgement.

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 15 | 29 Probability Distribution for INPUT Variables GDP Annual Variation Rate (after 2009) The source for GDP before 2009 was the Bank of Portugal After 2009 it is considered a stochastic variable Normal distribution Mean= 2,3% Standard Deviation = 0,5% To avoid to have negative values of traffic, which is a non sense, the distribution was truncated.

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 16 | 29 Probability Distribution for INPUT Variables Fuel Cost Variation Rate It was considered that the likeliness of fuel prices reaching very high levels in the long or medium term is higher than that of regressing to lower levels Weibull distribution Percentile 5% = 0,8 Percentile 50% = 1 Percentile 95% = 1,5 The modelled variable consists of the Fuel Cost Variation until 2020.

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 17 | 29 Probability Distribution for INPUT Variables Value of Time (VoT) Variation Rate VoT is one of the most decisive parameters for the route choice model; Research on VoT growth over time indicates annual growth Triangular Distribution minimum = 0,3 Most likely = 0,7 Maximum = 1 ranging from 30% to 100% of annual GDP growth rate In the deterministic approach it was used 70%

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 18 | 29 Probability Distribution for INPUT Variables Toll value Toll = € 0,07.(1+VAT).(paid length) The toll value is changed when VAT changes. VAT is the INPUT variable; In 2007 Portuguese VAT was 21%; It is not likely that VAT can increase much more; The probability of simulating a lower VAT than the most likely is higher than getting a higher most likely value Weibull distribution Percentile 5% = 0,18 Percentile 50% = 0,21 Percentile 95% = 0,23

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 19 | 29 Correlation Analysis Between INPUT Variables The correlation matrix was constructed considering the following variable relations: Negative correlation between GDP and VAT, and GDP and Fuel Costs Positive correlation between GDP and VoT Positive correlation between VAT and Fuel Costs

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 20 | 29 Interaction between INPUT and OUTPUT variables GDP Annual Variation Rate and VoT Annual Variation rate have positive elasticity with the traffic forecast, which means that when they increase, the traffic demand on the Tunnel also increases Fuel Cost and Toll Annual Variation Rate have negative elasticity with the traffic forecast. Their growth implies a traffic demand decrease on the Tunnel

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 21 | 29 The data presented in this presentation was modified so that we could ensure the privacy of our client

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 22 | 29 Results Analysis – Output Distributions Graphs 1 2 3 4 Traffic Model

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 23 | 29 Results Analysis – Output Distributions Graphs The red line represents the deterministic output (traffic forecast.km) of the Traffic Model. The deterministic outcome is always on the right side of the mean value of the distribution. This means that traffic study may have assumed optimistic values

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 24 | 29 Results Analysis – Output Distributions Graphs The uncertainty of the model increases with time This evolution is an intuitive perception But the stochastic model allows to see that the uncertainty is bigger for the lower demand values

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 25 | 29 Results Analysis – Tornado Graphs 1 2 3 4

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 26 | 29 Results Analysis – Tornado Graphs These results shows the importance of the uncertainty of the INPUT variables on uncertainty of output outcome What factors cause higher uncertainty on the traffic forecast? GDP is the INPUT with more influence. Fuel Costs are the second most influential and become more relevant until 2020 where the variable value remains constant

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 27 | 29 What is the possibility of having the revenues 15% less than the deterministic forecast? The model allows to estimate that that outcome can occur with a probability of 13% Results Analysis - Revenues

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 28 | 29Conclusions With Deterministic model: The outcome sensitivity analysis is given by deterministic results by changing the values of the input variables; It is not possible to measure the probability of those results. With stochastic approach (@RISK) The outcome sensitivity analysis is based on a probabilistic distribution; It improves the deterministic analysis answering to the following questions: what are the expected variation for the traffic forecast results? what are the factors that cause higher uncertainty on the traffic forecast? What are the risks of having less revenue than the deterministic forecast? For all of these, the decision (expert and client) can obtain a more transparent and accurate approach of the outcome presented by traffic model using @RISK analysis software.

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 29 | 29Conclusions The undertaken analysis allow to identify the main RISKS associated with the Concession Traffic Forecast. Usually, on the deterministic model we assume the most likely values for the input variables. The @RISK results, in this case, allows to observe that the deterministic outcome could have been too optimistic The information supplied by @RISK analyses allows to add information to the traffic forecast results, improving the interpretations of the results In future analysis we remain with two main challenges: to accurately replicate the relevant relations of the traffic model in Excel (VISUM with @RISK) to improve the methodology for the setting of the probability distributions

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 30 | 29 Results Analysis – Tornado Graphs Thank You

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Using @RISK for Traffic Forecast Analysis, London | 22.04.2008 TIS.PT – Transportes Inovação e Sistemas, S.A. Slide 31 | 29 Transportes, Inovação e Sistemas, S.A. Av. da Republica, 35, 6º 1050-186 Lisboa | Portugal | www.tis.pt Using @RISK for Traffic Forecast Analysis Case Study: Marão Tunnel Concession Palisade User Conference London, 22nd April 2008 Inês Teles Afonso

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