1. Introduction. PEDRO CAMARGO Senior Model Developer Veitch Lister Consulting

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1 PEDRO CAMARGO Senior Model Developer Veitch Lister Consulting LAUREN WALKER Transport Planner Veitch Lister Consulting FREIGHT MODELLING IN AUSTRALIA: WHAT WE VE GOT, WHAT DO WE NEED AND HOW ARE WE GETITNG THERE? With the Bureau of Infrastructure, Transport and Regional Economics (BITRE) projecting that the road freight task will grow by 76% between 2008 and 2030, it is imperative that engineers, planners and policymakers have appropriate data and tools available in order to make evidence-based strategies and investment decisions on how to best manage growing freight demands. Currently, there are limited freight modelling tools available to support these decisions. This paper describes how freight modelling frameworks have evolved in Europe and the US over the past several years, and evaluates their appropriateness for application in an Australian context. Further to this, this paper examines the availability and suitability of existing freight data in Australia to support the development of similar freight models for use in our cities. While US modelling frameworks could be readily adapted, there are a number of data gaps that remain, presenting a hurdle for their immediate development for Australian cities and regions. The collection of a commercial goods, vehicles and establishments survey, as well as the development of more extensive general freight datasets, would enable higher quality freight models to be developed in Australia. 1. Introduction According to the Bureau of Infrastructure, Transport and Regional Economics (BITRE), the total road freight task in Australia grew close to 54% in the ten year period from 2008 to 2007 (BITRE 2010). It is expected to grow a further 76% in the period from 2008 and 2030, with growth in capital cities of around 66% and interstate flows over 125%. Rapid growth in freight movements has resulted in an increased need for robust analytical tools that enable engineers, planners and policymakers to understand the expected impacts of increased freight demands and make evidence based policies to mitigate any negative impacts. Despite this, many road agencies have made limited improvements to their freight modelling capabilities, creating a void in the transportation policy space that will need to be filled if we intend to plan appropriate Australian infrastructure for the next 30 years. The objective of this paper is to discuss the current Australian freight modelling context and to suggest potential ways to develop a new freight model for Australia. This is done by providing: An overview of international freight modelling benchmarks An analysis of data availability in Australia The results of exploratory research performed with the available data.

2 2. The international benchmarks In the last ten years, a series of new freight models has been developed in both Europe and the United States (US). Most of the freight models developed in Europe are still based on 4-step model frameworks, with only a small number of models including some sort of logistics chain component (De Jong et al. 2013). There is also evidence to suggest that, where an understanding of logistics chains is required, microsimulated models are the most appropriate tool currently available (De Jong et al. 2013). The American framework has evolved much faster than in Europe, which is partly described in Pourabdollahi et al The freight modelling framework being used in the US was first formalised in a model implemented for the Chicago Metropolitan Agency for Planning (CMAP), and consisted of a three layer framework economic, logistics, and transport. These three layers correspond to the macroscale, mesoscale, and microscale models in a supply chain and logistics-based setting, as depicted in below Table 1Figure 1Figure 1 below. 1 Figure 1 Chicago model framework This framework has since been further developed to consolidate the demand modelling portion of the model, going down to the individual interaction between firms. This allows for the consideration of the logistics requirements of each individual industry and market. This more detailed framework is depicted in Figure 2. As a segue to the demand model, the tour-based truck model depicted in Figure 3 is capable not only of correctly modelling freight transportation, but also of treating the movement of urban goods and service vehicles, which have a growing impact on urban traffic congestion and have historically been poorly represented by transportation models. Finally, the result of the truck tour model is a time-stamped list of truck trips by truck type, which can be consolidated into assignable matrices and integrated with the person travel model for the same region. This framework has recently been implemented in several regions as part of a federal grants program called SHRP2, and is well documented in the technical literature (Pourabdollahi et al. 2017; Camargo 1 Cambridge Systematics, Inc., A Working Demonstration of a Mesoscale Freight Model for the Chicago Region Final Report and User s Guide, prepared for Chicago Metropolitan Agency for Planning, June 2011.

3 et al. 2017; Ravulaparthy et al. 2017; Kuppam et al. 2014; Bernardin et al. 2015; Smith et al. 2013; Shabani et al. 2014). 2 2 Strategic Highway Research Program 2

4 Figure 2 Phoenix behaviour-based freight model 3 3 Source: Cambridge Systematics

5 Figure 3 Phoenix tour-based truck model 4 4 Source: Cambridge Systematics

6 For a more in-depth description of the freight models recently developed abroad, (De Jong et al. 2013) and (Chow et al. 2010) provide a very comprehensive overview of the models developed until 2010, while (Maggi et al. 2016) provides a good insight into how freight modelling has evolved over the last five years, particularly in the US. 3. A feasible model structure for the Australian markets There are a number of reasons why the US model structures referenced in this paper are likely to be the best starting point for the model structure for major Australian cities. The implementation of the latest generation of freight models in the US has been largely successful, and a number of comparisons can be drawn between the size of the pilot cities in the US and the continental dimension of the two countries. There are, however, two main areas of concern that remain data availability and the treatment of imports and exports. The first issue is related to data availability. This issue is more thoroughly addressed in the next chapter of this paper, where major gaps between Australian datasets and data that is available or under active development in US jurisdictions are identified. However, it is worth noting here that the feasibility of using a US model structure in an Australian context is highly dependent on finding a suitable source for a large portion of the data that underpins the US model structures. While limited data availability could be addressed through transferring US model parameters to Australian models, this is not recommended and would need to be treated with extreme caution. Any model development that uses this approach should be subjected to considerable scrutiny and accompanied by appropriate caveats and documentation. The second issue that needs to be addressed is necessary refinements of port movements, which are considerably more important in all major Australian cities than in most US metropolitan areas. This is due to the greater dependence of the Australian market on imported products. To represent imports and exports, we need to assume that macro-economic forecasts for total international trade will be made available by the Australian Government, as this is outside of the scope of a transportation modelling exercise. Having the total expected import and export movements, we propose an import/export transportation model compatible with the modelling framework for which we are advocating (Pourabdollahi et al and Kuppam et al. 2014). The proposed import/export framework, described in Figure 4, closely resembles the model proposed by Pourabdollahi et al. 2017, but we must assume that the parameters for each mode component are likely to be distinct from those in the main model. This distinction is also likely to be true for the truck tour model portion of this framework, as loads coming from ports are more likely to be delivered/picked-up in a single destination/origin than domestic freight flows.

7 Figure 4 Import/export model framework 4. What we ve got: the data available in Australia As previously discussed, the data currently available in Australia is not likely to be sufficient to allow for the development of freight models similar to those in operation in the US, or even Europe. However, it is our understanding that the collection of such data might become possible if government agencies commit to developing a more advanced understanding of freight movements in order to better plan the country s infrastructure. To develop the model structure we propose for the Australian market, a few key data priorities have been identified, comprising specific surveys as well as more general datasets. 4.1 Surveys In the survey category, there are two datasets that are required in order to properly develop the proposed models. The first dataset in this category is a commercial goods, vehicles and establishments survey. This survey would need to provide information such as the truck tour patterns for companies in different economic sectors and of different sizes, the types of agents (e.g. households, other establishments, transportation facilities etc.) with which these companies trade, the types of vehicles used, and many other parameters. Geographic differences between distinct regions within the surveyed area (e.g. industrial zone vs. mixed used area) might also be detectable depending on the coverage and sample size for the survey. The second data set in this category is a broad freight movement survey, which would cover all the freight movements throughout the modelled region usually undertaken at a national level. A similar dataset was collected by the ABS in the mid-1990s, and was replaced with a version that covers only road freight in Despite covering only road freight, this survey provides very detailed information, including empty trucks, 20 commodity types, general cargo, unclassified cargo, plus information on the types of trucks transporting each type of commodity/good. Should information on

8 rail, air and water freight be available internally within other government departments, it would hypothetically be possible to build an excellent dataset to support freight model development. 4.2 General datasets Among the more general data sources to be developed, one of the most important is a directory of firms/companies. This usually plays a crucial role in advanced models, as the characteristics of truck tours (e.g. number, frequency, length of stop, shipment size) are often found to be highly dependent on company size and the sector of the economy in which they operate. In this sense, having good information on the active companies within the modelled area is the equivalent of having census information for personal travel models. As far as we are aware, such a database is not readily available to the public in Australia. However, government agencies may be able to gain access to the Australian Business Register, maintained by the Australian Tax Office. This database does not have information on company size or industry sector however, data fusion techniques can be applied to derive a proxy for aggregate industry sector from land-use data. Company sizes can be similarly derived from the aggregate employment information collected by agencies such as Queensland s Department of Transport and Main Roads. Therefore, to a large extent the data that is available in Australia can be converted to a more complete database through the development of a commercial establishment synthesiser. Another crucial piece of data for the development of advanced freight models, particularly truck tour models, is a sizeable amount of truck GPS data. Virtually all new truck tour models developed in the US, along with others developed in Russia and Europe, have been designed using the procedure described in (Kuppam et al. 2014). Further, the availability of truck GPS data also greatly reduces the sample size required for a vehicle and commercial establishment survey. Although not currently commercially available in Australia, truck GPS data has already been obtained by BITRE, which will apply the techniques described in Camargo et al to process its data. Until the end of 2016 only 10 companies had provided BITRE with truck GPS data, but the Bureau plans to expand that data collection in the 2017 calendar year. 5 Thus a partnership between agencies developing freight models and BITRE in seeking truck operators/fleet management companies willing to share their GPS data could prove fruitful. The absence of such data does not compromise the suggested model structure, as tour-based models can be estimated from the survey alone however, it would be preferable to supplement such surveys with these general datasets. Finally, a number of other critical datasets are already at a stage where they are suitable for use in model development. These include employment databases, the population census, the Motor Vehicle census and the Survey of Motor Vehicle Use. 5. Exploratory research In order to understand the quality of the data already available in Australia, we reproduced some of the earlier freight work developed in the US by authors including Camargo et al and Ranaiefar et al These consisted of building land-use datasets suitable for modelling and disaggregating preexisting freight demand matrices provided by the Australian Bureau of Statistics. 5 Personal communications with staff at BITRE

9 One of the matrices disaggregated was the general freight matrix, which is provided in an Statistical Area 3 zoning system and was disaggregated to the Statistical Area 1 level. As it is difficult to depict the results of matrix disaggregation exercises using traditional methods such as desire lines, we chose to use Delaunay Lines as depicted on Figure 5 (Camargo 2016). Figure 5 General freight matrix for Southeast Queensland disaggregated In disaggregating these matrices, we learned that the quality of the data available for Australia seems to be at least as good as data available in the US, but it also became clear that there is a considerable difference in data availability and data quality from state to state in Australia. As an example, the land use data used to disaggregate the Queensland-only matrix presents a land use classification that is completely different to the one used in Victoria and New South Wales, and no easily available data was found for other states. 6. Conclusion and next steps This paper presents the first two steps in the development of a new generation of freight models for Australia, which is the establishment of a freight modelling framework and a broad analysis of the data sources available in Australia to establish this modelling framework. Freight movements have grown rapidly over the past several years, and are projected to continue to rise into the future. In order to fully understand the impacts that additional freight demands are likely to have on Australia s urban and rural transport networks, and to develop appropriate policies to mitigate possible negative impacts, there is a compelling case for investing in a new generation of freight analysis tools. This paper advocates for a modelling framework that will provide an ideal platform for evidence-based future planning of freight movements in Australia.

10 In order to continue into a full model development exercise, however, first it is necessary to completely understand which of the data sources identified can be developed in Australia, as well as potential development time frames for such effort. In turn, this analysis will provide the basis for the consolidation of the modelling framework and allow for the creation of a roadmap for freight modelling in Australia in the coming years. In parallel it is also recommended that local, state and federal governments establish a set of agreed data standards to ensure consistency in the quality and detail of available data. In the current environment, inconsistency makes it difficult to develop a single modelling framework to be applied across the country. 7. References Bernardin, V.L. et al., Expanding Truck GPS-Based Passive Origin-Destination Data in Iowa and Tennessee. In p. 12p. Available at: Bureau of Infrastructure, Transport and Regional Economics, Road freight estiamtes and forecasts in Australia: interstate, capital cities and rest of state, Report 121, Canberra ACT. Camargo, P. et al., Expanding the Uses of Truck GPS Data in Freight Modeling and Planning Activities. In p. 16p. Available at: Camargo, P., Using Delaunay tringles to build desire lines. In Australasian Transport Research Forum. Melbourne, p. 9. Available at: Camargo, P. V, Tok, A.Y. & Ritchie, S.G., Spatial disaggregation of California freight demand for regional planning models. In ARRB, p. 14. Available at: [Accessed May 8, 2017]. Chow, J.Y.J., Yang, C.H. & Regan, A.C., State-of-the art of freight forecast modeling: lessons learned and the road ahead. Transportation, 37(6), pp Available at: De Jong, G. et al., Recent developments in national and international freight transport models within Europe. Transportation, 40(2), p.pp Available at: Kuppam, A.R. et al., Development of Tour-Based Truck Travel Demand Model Using Truck GPS Data. In Transportation Research Board, 93rd Annual meeting. Washington, DC, p. 27. Maggi, E. & Vallino, E., Understanding urban mobility and the impact of public policies: The role of the agent-based models. Research in Transportation Economics, 55, pp Available at: Pourabdollahi, Z. et al., An Agent-based Computational Economics Model for Supplier Selection Problem: Application for Phoenix-Tucson Regional Freight Model. In p. 16p. Available at: Pourabdollahi, Z. et al., An Agent-based Supply Chain and Freight Transportation Model: Case Study For Chicago Metropolitan Area. In p. 20p. Available at: Ranaiefar, F. et al., Geographic Scalability and Supply Chain Elasticity of a Structural Commodity Generation Model Using Public Data. In Transportation Research Board 92nd Annual Meeting. Available at: [Accessed January 18, 2014]. Ravulaparthy, S.K. et al., Spatial Firm Demographic Microsimulator: Development and Validation for Phoenix and Tucson Mega-Region. In p. 21p. Available at: Shabani, K. et al., Development of a Statewide Freight Trip Forecasting Model for Utah. In p. 15p. Available at: Smith, C. et al., Disaggregate Tour-Based Truck Model with Simulation of Shipment Allocation to Trucks. In p. 17p. Available at: