Illicit heroin use is a worldwide problem, with significant health and social costs. Treatment is known to be effective in changing heroin use habits, but it often needs to be provided over a lifetime, with people cycling in and out of treatment. It is therefore important to

No abstract

This paper presents a methodology to construct travel related activity schedules for individuals in a synthetic population. The resulting list of activity schedules are designed as an input into a micro-simulator for urban transport dynamics analysis. The methodology involves two main steps. The first step generates a synthetic population based on census data sourced from the Australian Bureau of Statistics (ABS). The second step assigns individuals in the synthetic population activity schedules using Household Travel Survey (HTS) data related to the geographical area of interest (in this case, the Sydney Greater Metropolitan area). Each individual is assigned an ordered set of trips, travel purpose, travel mode, departure time and estimated trip time. The significance of the methodology is twofold in that it generates a synthetic population aligned with area demographics, as well as generating activity schedules that realistically represent how the population uses existing transport infrastructure. The methodology also preserves the inter-dependencies (in terms of the sequence, travel times and purpose of trips) of individual's daily trips, in contrast to many trip generators for transport micro-simulation purposes. A case study of Randwick area in southern Sydney is presented where the proposed methodology is applied. Case study data is validated against real world results and the scalability and applicability to other urban areas are discussed.

Modelling and analysis of large systems of infrastructure systems carries with it a number of challenges, in particular around the volume of data and the requisite complexity (and thus computing resources required) of models. In this paper we present an integrated land use-transportation model of a region in Sydney, and detail how we integrated an agent-based model of location and transport choice with a traffic micro-simulator. We also discuss both some novel architectures for scalability of modelling as well as for fusion and relevant visualisation of large data sets. We have a particular focus on geospatial infrastructure data visualisation.

When upgrading rail infrastructure ROI‐based decisions partly rely on the demonstration of immediate improvements from those upgrades to the system. The rail industry is beginning to suffer in some areas from diminishing ROI from improving parts of its infrastructure. To inform decisions and help justify large expenditures, modelling needs to be done to detect the likely improvement to dependent systems as well as the primary system. Sydney Trains (ST) has undertaken a pilot study to demonstrate how modelling can be used to propagate performance changes made in one sub‐system into its dependent neighbors. The trial examines how to model the effect of improvements to the air dryer on the reliability and lifecycle of pneumatic systems of the Tangara train which utilize the air: the doors and brakes. The very detailed level model in this case helps provide evidence of improved reliability in these dependent systems can then be used to further justify the ROI and to assist in assessing the safety of the system on an ongoing basis.