🤖 AI Summary
This paper addresses the challenge of constructing a reproducible, data-driven activity-based travel demand model for the Melbourne metropolitan area—specifically, how to generate high-fidelity synthetic populations and realistically simulate individual activity chains and travel behavior solely from publicly available data.
Method: We propose an “activity-first, trip-derived” paradigm: first generating activity chains (including type, sequence, and duration) via hierarchical clustering and probabilistic modeling; then allocating activity locations and travel modes using a gravity model integrated with spatial decay effects; and finally enforcing a constraint on remaining activities to ensure logically consistent return trips. All components are empirically calibrated against observed data and fully compatible with the MATSim simulation framework.
Contribution: We introduce the first activity-chain-oriented modeling framework that is entirely open-source, requires no proprietary data, and guarantees cross-platform reproducibility—thereby advancing transparency, accessibility, and scientific rigor in urban transport modeling.
📝 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.