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Department of Transport

Policy & Communications Modelling & Mapping Branch

Victorian Integrated Transport Model Parameters for the Reference Case

DRAFT COPY

Version Version Date Details

1 January 2012 Authors Reviewed

Department of Transport SX1 Building, 121 Exhibition Street, Melbourne Victoria3000

Tel +61 3 9655 6666 Fax +61 3 9095 4096 www.transport.vic.gov.au

s33(1): personal affairs s33(1)

Department of Transport VITM Parameters for the Reference Case


CONTENTS Page

1. INTRODUCTION 1

2. FORECASTING WITH THE VITM 2 2.1 Model Structure 2 2.2 The Reference Case 2

2.2.1 Forecast Years 2 2.2.2 Demographic and Land-use Projections 2 2.2.3 Highway Provision 2 2.2.4 Public Transport Service Provision 2 2.2.5 Generalised Cost Parameters 2

3. VEHICLE OPERATING COST (VOC) 4 3.1 Projected Fuel Price 4 3.2 Forecast Vehicle Efficiency 6

4. PARKING COST 8

5. PUBLIC TRANSPORT RELIABILITY 10

6. PUBLIC TRANSPORT FARES 10

4. APPENDIX A – FUEL PRICE DISCUSSION 11

5. APPENDIX B – PARKING 13 6.1.1 Policy 13 6.1.2 Supply 13

6. REFERENCES 15



1. INTRODUCTION The Victorian Integrated Transport Model (VITM) is the Department of Transport’s (DOT) in-house strategic transport demand model. The VITM is a four-step model which can be used to forecast how travel patterns will shift into the future as a result of factors such as changes to demographics/land-use and to the perceived cost of travel.

For the purposes of the Reference Case, the DOT specifies a set of inputs relating to the demographics/land-use, freight, the transport network and generalised cost parameters. These inputs represent a likely scenario assuming a continuation of current trends in urban form/land use patterns, infrastructure investment, travel behaviour/attitudes and government policies. The Reference Case can be used as a basis for estimation of future demand and shaping of other scenarios relating to particular project and policy assessments and/or sensitivity tests.

In terms of demographics and land-use, the DOT adopts the Victoria in Future (VIF) population and household projections generated by the Department of Planning and Community Development (DPCD). The DOT also maintains a set of employment and school enrolment projections which are developed in-line with the VIF population estimates and current trends in urban form/land-use patterns and policies.

The transport networks for the Reference Case are developed by VicRoads and the Public Transport Victoria (PTV). VicRoads and PTV maintain lists of future year road and public transport projects respectively.

The majority of the generalised cost parameters used in the VITM are held constant in the future year Reference Case definitions. However, data and literature indicate that some of the cost parameters demonstrate historic trends of growth above inflation. Therefore, for the purposes of the VITM modelling, the DOT has a recommended set of future year values for these parameters.

This document discusses the generalised cost parameters which change into the future.



2. FORECASTING WITH THE VITM

2.1 Model Structure The VITM is a four-step model which estimates how travel patterns will shift into the future as a result of factors such as changes to demographics/land-use and to the perceived cost of travel.

Forecasts of the general volume of travel are derived during the trip generation step of the model and require estimates of future year demographics and land-use such as population, employment and enrolments. These estimates are derived independently of the model and input explicitly.

Forecast travel patterns (such as the trip origin and destination, the chosen mode of travel and the route) are estimated during the distribution, mode-choice and assignment steps of the model. These choices are based on the perceived cost of travel which in turn is based on the transport network supply and the assumed generalised cost parameters.

While some of the factors affecting the perceived cost of travel are directly input to the model (such as vehicle operating costs, parking charges, public transport fares), there are a number of factors implicitly dealt with in the VITM.

Road congestion is reflected in decreased travel speeds and increased travel times; these flow through to the destination choice and mode choice components of the model resulting in changes in modelled travel behaviour such as changes to trip distance or mode-choice.

Changes to public transport service provision are modelled in the VITM via changes to the bus, tram and train service plans, either via service frequency changes, addition/removal of routes or changes to route coverage. These changes affect the cost of travel by public transport which is fed back to the destination choice and mode-choice models.

2.2 The Reference Case 2.2.1 Forecast Years

The DOT has developed a set of Reference Case inputs for the VITM at 5 year intervals corresponding to the ABS Census years. The modelled years range from 2011 to 2046.

2.2.2 Demographic and Land-use Projections

For the purposes of the Reference Case, the DOT adopts the Victoria in Future (VIF) population and household projections generated by the DPCD. The DOT also maintains a set of employment and school enrolment projections which are developed in-line with VIF population estimates and assume a continuation of current trends in urban form/land use patterns and policies.

2.2.3 Highway Provision

VicRoads maintains a list of future year road projects ranging from the construction of new roads to alteration of existing roads such as duplications or closures. The project list is coded into the VITM highway network so that appropriate highway networks can be generated for each of the forecast years.

2.2.4 Public Transport Service Provision

Similarly, the PTD maintains a specification of public transport services for each forecast year. The specification includes details of alterations to trains, trams and buses including changed frequencies, stopping patterns, alignments, speeds and new routes or stations. The information is used to generate a set of public transport service files for the VITM.

2.2.5 Generalised Cost Parameters

Future year travel supply conditions also require estimates of the parameters that are adopted in the generalised cost equations. Key parameter values are summarised in Table 1. These parameters include:

• Values of time • Vehicle occupancy • Vehicle operating costs (fuel price, vehicle efficiency, maintenance charges)



• Parking costs • Tolls and other road charges • Perceptions of journey time relating to public transport travel • Public transport fares.

The UK’s Transport Analysis Guidance (WebTAG) recommends that for the purpose of modelling the Reference Case, projections of these factors should represent a reasonable expectation of future values (DfT 2009). There should be a reasonable evidence base to support any assumptions of changes to future values.

The majority of the generalised cost parameters used in the VITM are held constant in the future year Reference Case definition. However, data and literature indicate that some of the cost parameters demonstrate historic trends of real growth above inflation. Therefore, for the purposes of the VITM modelling, the DOT has a recommended set of future year values for these parameters.

The following sections of this document relate to parameters which are altered into the future.

Table 1 VITM Cost Parameters

Cost Parameters Future Assumption

Value of Time (VOT) unchanged

Vehicle Occupancy unchanged

Vehicle Operating Costs (VOC) * 2.0% CAGR btw 2006-2021

1.4% CAGR btw 2021-2031

1.1% CAGR btw 2031-2041

0.5% CAGR btw 2041-2046

Parking Costs change, work purpose 2.3% CAGR btw 2008-2021

2.1% CAGR btw 2021-2031

1.8% CAGR btw 2031-2041

1.6% CAGR btw 2041-2046

Parking Costs change, non-work purpose 3.9% CAGR btw 2008-2021

2.1% CAGR btw 2021-2031

1.8% CAGR btw 2031-2041

1.6% CAGR btw 2041-2046

Tolls / Road Charges unchanged

Public Transport Fares +5.0% 2012

+5.0% 2013

Unchanged beyond 2013

Perceptions of public transport travel -0.2% CAGR btw 2011-2026

Unchanged beyond 2026 Note: Growth is real growth. * Includes a combination of petrol price change and vehicle efficiency improvements



3. VEHICLE OPERATING COST (VOC) Changes to the vehicle operating cost have been derived by projecting changes in fuel price and vehicle efficiency.

Fuel price and vehicle efficiency trends are combined to derive projected vehicle operating cost growth rates, these are summarised in Table 1.

3.1 Projected Fuel Price Table 2 summarises the fuel price growth rates currently used in the VITM.

Table 2 Assumed VITM Fuel Price Growth (above real prices)

Year CAGR

2006-2021 2.4%

2021-2031 1.8%

2031-2041 1.6%

2041-2046 1.4%

Numerous sources have been investigated to establish whether there is reasonable evidence for the assumption of a fuel price growth rate above inflation in the VITM. Medium to long term fuel price forecasts vary widely depending on the assumptions upon which they are based and the organisations by which they are produced.

The discussion in Appendix A demonstrates there is no clear consensus as to the outlook for petrol prices but there is a general belief that prices will rise. The steepness and shape of the price rise is a matter for debate. Many believe the “age of cheap fuel is over” but acknowledge that various forms of policy action could have an influence on the level of price increase. Prices paid in Melbourne are obviously influenced by local factors, such as the strength of the Australian dollar and taxation levels.

In the absence of a clear consensus and in order to maintain consistency with existing year assumptions for fuel price it has been decided to derive a future year fuel price by trending the historical Australian unleaded fuel price.



Figure 1 shows the variation in petrol price in metropolitan Australia from 1998 to 2009 (Gargett 2010).

Figure 1: Australian Price of Petrol at the Pump, 1998-2009, Aus$

In Figure 2, the impact of inflation has been removed from the prices. A simple linear regression has been applied in order to calculate a real growth rate of petrol prices over time. While the linear regression model has a modest R-squared value of 0.7, it does provide a basis for forecasting.

Figure 2: Australian Price of Petrol at the Pump, 1998-2009, 2011 Aus$

This type of trend does assume a linear relationship for petrol price into the future. Advocates for the “peak oil” position would suggest that with the growing demand (global population growth, increased demand from the developing world, etc) and limited supply, price rises are likely to more closely follow a curve into the future based on a compounding growth rate. However the steepness of any potential curve will also be impacted by other external factors, such as political instability in oil producing regions, currency fluctuations and Government policy.

The forecast that results from this linear trend falls between the more conservative IEA forecasts and the more optimistic predictions.

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4. PARKING COST In the absence of long term historical data, the future trend of parking prices has been calculated from the trend of commercial parking prices in the last five years. This data has been collected by the Department of Treasury and Finance on the parking operator websites and is been summarised in Figure 5 below.

Figure 5: Average Car Parking Rates in Congestion Levy Area ($)

In Figure 6, the impact of inflation has been removed from the prices and simple linear regressions have been applied in order to calculate the growth rate of parking prices.

Figure 6: Linear Regression of Melbourne Levy Area Averaged Real Parking Price, 2005-2010

The two linear regressions cross in 2015 suggesting that hourly rates would become more expensive than early bird rates. This is considered an unrealistic trend which reflects the discussion in the previous section that operators are more likely to increase short term rates.

Assuming the trends continue until the two rates match, it seems realistic to imagine that operators will then either favour hourly parking spaces and change the supply, or stop the dual rate system. This is encouraged by all congestion policies.

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On this basis, an average of the two rates has been used to calculate a single linear equation to base the forecasting on after 2015. This linear regression model has an R-squared value of 0.97.

The forecasting produces the CAGR for parking prices listed in Table 1.

The difference in price between Work and Other trip purposes is due to the difference in the average duration of stay as reported in VISTA.

Further discussion of factors influencing the price of parking is contained in Appendix B.



5. PUBLIC TRANSPORT RELIABILITY Improvements to on-time-running has been identified as a key objective to be achieved as part of the public transport strategy and forward investment program. VITM does not include in its generalised cost equation the cost of unreliability of public transport services. However, most surveys suggest that it is a major consideration in the level of customer satisfaction and is commonly cited as a reason why public transport users switch to other modes.

The initiatives to improve reliability are expected to provide improvements across the entire public transport network. These projects are included in the Reference Case. Key initiatives include:

• Addressing the maintenance deficit and implementation of infrastructure to enable the metropolitan rail network to operate as separate and independent services. Services operating independently will eliminate delays on one service propagating delays onto other services

• Continual improvements in on-road priority for tram and bus services

• Reduction in delays and dwell times at stops and stations as a result of introducing rolling stock with better layouts, such as more doors and vestibules designed for high volume loading and unloading and platform stops

• Improved co-ordination across modes that will result in transfers being more reliably made.

It is proposed to model this benefit by applying a progressive reduction of 0.2% per annum CAGR in public transport generalised cost across the network from 2011 to 2026 in the Reference Case. This equates to an overall reduction of 3% in 2026. This reduction is proposed to be held constant from 2026 onwards as all the relevant initiatives will have been implemented by this time.

6. PUBLIC TRANSPORT FARES Public transport fares increased by 5% above inflation on 1 January 2012 and will increase by a further 5% on 1 January 2013. Fares are then expected to remain at real prices for the forecast period.

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5. APPENDIX B – PARKING Apart from inflation, two drivers may alter the cost of parking, the policies and the supply-demand balance. To investigate these drivers we have looked at the ways to park in Inner Melbourne:

• In an ‘On-street’ space, with fees set by the municipality,

• In a private car park, which the employer may include in the commuter salary package

• In a commercial car park with an ‘Early Bird’ fee,

• In a commercial car park with an ‘Hourly’ fee.

6.1.1 Policy

In 2006, the Victorian Government introduced the Congestion Levy, with a rate of $400 per parking space. This levy rate was doubled to $800 in 2007. According to the Review of the effectiveness of the Congestion Levy, ‘Parking operators are passing a significant portion of the Levy to the car park users. However, [… they] may be passing some of the cost of the Levy on to short-stay parking users’. Commuters using private car parks may also be affected by policies like the Congestion Levy if it reduces the number of employer-paid parking arrangements.

With Inner Melbourne Action Plan, the municipalities investigate the way to reduce traffic congestion in the future. Actions to archive this are the ‘use of pricing mechanisms’ as well as ‘limiting car spaces in new developments’. In that direction, one of the city of Melbourne policies, as per its CBD and Docklands Parking Plan 2008-2013, is to ‘convert long-term commuter parking into affordable short-stay parking’.

These policies may result in an increase in parking prices above inflation and a change in the parking supply-demand balance.

6.1.2 Supply

Supply-demand change may affect all types of car parking. It can drive the commercial operator to increase the parking price. It may also increase the share of payers of parking fees. It increases the time spent looking for an on-street space, which can be translated into a cost.

Over a long period, Table 3 shows a decline of on-street car parking spaces in the CBD while the overall supply of car parks continues to rise.

Table 3: Parking Spaces in the CBD

Year On-street total spaces Off-street total spaces % total of on-street

1964 9,500 22,500 42%

1977 9,300 35,300 26%

1984 8,000 43,500 20%

2007 4,200 64,000 7%



Figure 9 shows the percentage of floor space taken up by car parks. The figure indicates that the growth in car parking supply is slower than other construction in terms of space used. The growth in car parking supply is also slower than the supply of jobs.

Figure 9: Footprint and Supply/Demand of parking in the CBD (CLUE 2008)

The trends identified in Table 3 and Figure 9 may be accentuated by a change in the proportion of short term and long term spaces due to policy changes and to the fact that short term car park users are more likely to accept price increases. Moreover, one can argue the validity of the ‘early bird’ rate continuing in the future. Many other cities don’t have an ‘early bird’ rate.

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6. REFERENCES Department for Transport, Transport Analysis Guidance – Forecasting Using Transport Model Unit 3.15.1, U.K., 2009.

Australian Bureau of Statistics, Survey of Motor Vehicles Use (12 months ended 31 Oct 2007), Australia, 2007.

Australian Institute of Petroleum, Annual Retail Price data, http://www.aip.com.au/pricing/retail.htm, 2011.

Austroads, Guide to Project Evaluation Part 4: Project Evaluation Data, Australia, 2008.

Gargett, Petrol prices in Australia, BITRE, Canberra, 2010.

International Energy Agency, World Energy Outlook, 2010.

Australian Automobile Association, FUELtrac database, http://www.aaa.asn.au, 2011.

Odell, Why carbon fuels will dominate the 21st century’s global energy economy, Multi-Science Publishing, Essex, U.K., 2003.

Laherrere, Uncertainty of data and forecasts for fossil fuels, paper given at the Universidad de Castilla-La Mancha, 2007.

CSIRO, Fuel for thought – The future of transport fuels: challenges and opportunities, Future Fuels Forum, Australia, 2008

BITRE, Fuel consumption by new passenger vehicles in Australia 1979-2008, Information sheet 30, Australia, 2009.

Department of Treasury and Finance. May 2010. Review of the effectiveness of the Congestion Levy.

City of Melbourne. 2008. CBD and Docklands Parking Plan 2008-2013.

Documents

VITM Parameters Reference Case EastWest Metro Version...Figure 1 shows the variation in petrol price in metropolitan Australia from 1998 to 2009 (Gargett 2010). Figure 1: Australian