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Integrated land use and transport modelling

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The QLD Department of Transport and Main Roads (DTMR) are currently investigating developing the capability within DTMR to implement an integrated land use transport model, with the ability to test different land use scenarios in response to key infrastructure projects, such as Cross River Rail.

This proposed framework and subsequent model would give DTMR the ability to influence land use planning from both a state and local government perspective to better utilise and account for changes in the future transport network capacity. This model would also greatly improve the relationship and linkages between transport and land use planning.

This project will be unique by drawing upon best practice methodologies from a range of different academic disciplines including; demographic projection methods, land use planning, transport demand modelling, economic and econometric modelling. The case study for this project will be South East Queensland in its practical application but the framework, methods and findings from the research component will be far-reaching and could be utilised for any major urban area.

Currently, demographic projections and the land use plans which underpin them do not have the ability to incorporate how future changes in the transport network will influence where people choose to reside, seek employment and educational opportunities. This project will seek to address this shortcoming in the current methods in land use planning and the demographic projections used for infrastructure planning and business case submissions.

Participants

Project background

Transport infrastructure investment decisions are made with the help of transport model outputs. However, contemporary transport models focus extensively on day-to-day decisions at various levels of aggregation, and generally use macroeconomic projections of population, residential location and activity locations as exogenous.

That is, the relations between population projections, long-term decisions (e.g., residential location, workplace location, car ownership) and mobility decisions (e.g., mode and route choices) are generally ignored in their endogenous nature. In particular, the behavioural dimensions of the decision-making processes by populations are not currently fully accounted for.

For example, highly congested urban networks likely influence household residential location choice, and household composition growth probably affects both residential location, workforce participation and mobility decisions. Model outputs are affected by the shortcoming of ignoring the endogenous nature of these relations and are likely unrealistic in their predictions. These have consequences in the allocation of resources, planning environments and funding priorities. In addition, given future mobility solutions that are likely to change the concept of commute, it becomes even more relevant to consider more integrated dynamic models.

This project will be conducted over a three-year time frame and will comprise of three interlinked stages:

  • Stage 1 will be concerned with the compilation of a knowledge base capturing international best practice that will inform Stage 2.
  • Stage 2 will develop an integrated land use and transport modelling framework.
  • Stage 3 will be to implement and operationalise a prototype model in an SEQ context.

The overarching aim of this research will be to assemble a new modelling framework with the capacity to analyse and model population-land use and transport interactions. The case study context for this project is the South East Queensland region.

Project objectives

  • Support the planning and design of future transport networks with new services and CAVs.
  • Models that use new sources of data and intermodal information to support network, planning, infrastructure design and the development of traffic management systems. The two main project objectives are:
  1. Define and understand the relations between accessibility, cost and travel time and how these factors affect people’s choices of where they live, work and seek educational opportunities.
  2. Develop housing unit demographic projection modelling framework that embeds the key factors and relationships as determined in the above point (1).

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