David Gargett Afzal Hossain 13 February 2007 PASSENGER MOVEMENTS BETWEEN AUSTRALIAN CITIES, 1970–71 TO 2030–31 

Background Arose from need to forecast light vehicle traffic on the national highways. But need to consider all modes. Thus aim is to model drivers of total passenger travel between cities. Only then can one predict highway car travel patterns.

Objectives Estimation of passenger movements by mode between Australian major cities from to What will growth be to in passenger movements by mode between Australian cities?

Ten city pairs Sydney-Melbourne Sydney-Brisbane Melbourne-Brisbane Melbourne-Adelaide Eastern States-Perth Sydney-Adelaide Melbourne and Sydney-Gold Coast Eastern States-Tasmania Canberra-Sydney Eastern States-Northern Territory

Modes Air Car Coach Rail Other

Time series Historical Data: to Projections: to Based on GDP (Treasury) Population Growth Rates (ABS) Changes in Fares (various sources)

Main data sources Designed around tourism data on inter- regional passenger movements. Because the data has been and continues to be measured by Tourism Research Australia (TRA), to the tune of $4M/year. TRA surveys both domestic and international travellers about their travel between the regions of Australia. This data has been assembled for the 10 city pairs by mode from to

Gravity Model Total passenger travel between any two cities (say i and j) can be calculated: Tij = (Pi x Pj x GDPc 2 ) / (Tc / CPI) where Tij - Total trips between regions i and j. Pi and Pj - Total population in region i and region j. GDPc - National gross domestic product per capita. Tc - Real generalised cost of travel. CPI - Consumer Price Index.

Logistic Substitution Model Forecasts of total travel were converted to forecasts for specific modes by using logistic substitution models of mode split. For each mode, a competitiveness index was estimated based on changes in the mode share over the last decade. A competitiveness index below 1.0 means the mode is expected to decline over time in share relative to air.

Logistic Substitution Model – Contd. For example, on the short Canberra-Sydney route, the competitiveness indices are: Air Car Coach Rail On the long Sydney-Brisbane route (more typical of the intercity routes), the competitiveness indices are: Air – 1.00 Car Coach Rail

Passenger Movements by Mode

Mode Share (per cent)

Average annual growth rates by mode, to City pairs(per cent) AirCarCoachRailOther Syd-Mel Syd-Bne Mel-Bne Mel-Adl ES-Per Syd-Adl Mel+Syd-GC ES-Tas Cbr-Syd ES-NT

Average annual growth rates comparison (all modes)

So what do we get ? 1.An understanding of the drivers of travel. 2.An understanding of the patterns of mode share change. 3.A link to a continuing and funded data source updated yearly. 4.1 to 3 provide the basis for the OZPASS interregional travel model. 5.Forecasts out of the OZPASS model are providing forecasts of car traffic along AUSLINK Corridors.

These forecasts are: 1.Systematic – based on transparent research. 2.Validated – Bruce Highways over 10 years. 3.Multi-modal – e.g. current airports project. 4.Open to scenarios – i.e. policy changes or changes in assumed conditions (e.g. fares).

Summary On all routes, except Canberra–Sydney route, air travel has been progressively taking mode share from car plus coach and rail. This effect will be less important in the future as the rate of mode share capture by air slows. Overall, total passenger travel growth is expected to continue to grow more quickly than GDP. The relationships found on the 10 corridors have been built into the OZPASS interregional travel model.

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