An Activity-Based Model of Transport Demand for Greater Melbourne

TL;DR

The paper tackles generating open, replicable activity-based transport demand for Greater Melbourne by integrating cohort-based activity scheduling, balance-driven destination attraction, and a hop-count constraint to ensure feasible returns. The authors develop a full pipeline that transforms Vista Trip Table data into Vista-like activity chains, samples census-like populations, matches cohorts, and spatially assigns locations and timings for MATSim-ready travel diaries. Validation shows that the synthetic outputs closely reproduceVista-derived distributions of start/end times, distances, destinations, and mode shares across regions, with guidance on appropriate sample sizes to maintain accuracy. The work advances practical application of open data to city-scale agent-based transport modelling, offering a flexible framework for scenario analysis and policy evaluation, while acknowledging limitations such as baseline-pattern generation and lack of household-level trip coupling.

Abstract

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.

Figures (13)

• Figure 1: Overview of the activity-based transport demand generation method (Section \ref{['sec:method']}).
• Figure 2: Ordered sequence of activities (circles) and trips (arrows) for anonymous person Y12H0000104P02 in the vista Trip Table
• Figure 3: Structure of matrix $\mathcal{D}$ for storing start(end) time distributions for $\vert\mathcal{A}\vert$ activities against $\mathcal{T}$ time bins of the day
• Figure 4: Simplified vista Trip Table derived activities in $\mathcal{T}$$=48$ discrete time bins
• Figure 5: Dendrogram of hierarchical cluster analysis. Grey rectangles indicate cluster membership by age and sex based on probabilities of engaging in Work, Study, Shop, Personal and Social/Recreational activities.