# An Activity-Based Model of Transport Demand for Greater Melbourne

arXiv: https://arxiv.org/abs/2111.10061 · HTML: https://mlsanity.com/paper/2111.10061/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 55 references that are in the map.
- First posted on arXiv 19 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [Building a large synthetic population from Australian census data](https://mlsanity.com/paper/2008.11660/) (2020)

## Built on by (most cited first, 2 of 2)

- [Activity-based and agent-based Transport model of Melbourne (AToM): an open multi-modal transport simulation model for Greater Melbourne](https://mlsanity.com/paper/2112.12071/) (2021)
- [Feasible and Novel Synthetic Population Generation with Tabular and Sequential Travel Attributes](https://mlsanity.com/paper/2608.15867/) (2026)

## Abstract (arXiv)

In this paper, we present an activity-based model for the Greater Melbourne area, using a combination of hierarchical clustering, probabilistic, and gravity-based approaches. The model outlines steps for generating a synthetic population-a list of agents with their demographic attributes-and for assigning activity patterns, schedules, as well as activity locations and modes of travel for each trip. In our model, individuals are assigned activity chains based on the probabilities of their respective demographic clusters, as informed by observed data. Tours and trips then emanate from these assigned activities. This is innovative compared to the common practice of creating trips or tours first and attaching activities thereafter. Furthermore, when selecting activity locations, our model incorporates both the distance-decay of trip lengths and the activity-based attraction of destination sites. This results in areas with higher attractiveness for various activities showing a greater likelihood of being selected. Additionally, when assigning the location for the next activity, we take into account the number of activities an agent has remaining to ensure they do not opt for a location that would be impractical for a return trip home. Our methodology is open and replicable, requiring only publicly available data and is designed to produce outcomes compatible with commonly used agent-based modeling software such as MATSim. Each sub-model is calibrated to match observed data in terms of activity types, start and end times, and durations.