Activity-based and agent-based Transport model of Melbourne (AToM): an open multi-modal transport simulation model for Greater Melbourne

TL;DR

The paper presents an open, multi-modal activity-based and agent-based transport workflow for Greater Melbourne, integrating a synthetic population, a city-wide road-and-PT network, and a calibrated mode-choice model for driving, public transport, cycling, and walking. It employs MATSim with a detailed network and a 10% synthetic sample, calibrating utilities against Census MTW and travel surveys, and validating outputs against road counts and station passenger flows. The resulting system demonstrates realistic mode shares, peak car volumes, and travel times, and offers a reusable pipeline for policy analysis and scenario testing, including potential health and environmental impact assessments. While the framework is open and generalizable, limitations include mode-choice being scoped to work trips and simplified PT-road interactions, with future work aimed at extending discretionary travel, incorporating active-mode infrastructure effects, and integrating health impact tools.

Abstract

Agent-based and activity-based models for simulating transportation systems have attracted significant attention in recent years. Few studies, however, include a detailed representation of active modes of transportation - such as walking and cycling - at a city-wide level, where dominating motorised modes are often of primary concern. This paper presents an open workflow for creating a multi-modal agent-based and activity-based transport simulation model, focusing on Greater Melbourne, and including the process of mode choice calibration for the four main travel modes of driving, public transport, cycling and walking. The synthetic population generated and used as an input for the simulation model represented Melbourne's population based on Census 2016, with daily activities and trips based on the Victoria's 2016-18 travel survey data. The road network used in the simulation model includes all public roads accessible via the included travel modes. We compared the output of the simulation model with observations from the real world in terms of mode share, road volume, travel time, and travel distance. Through these comparisons, we showed that our model is suitable for studying mode choice and road usage behaviour of travellers.

Figures (13)

• Figure 1: Selecting next region for a cycling trip from home (circle) to work (triangle) showing: region selection probability (Pr) for local and global distance distributions (a and b), region selection probability (Pr) for local and global destination attraction (c and d), number of trips (hop count) that would be reasonably required to reach home (e), and combined region likelihood (f) (source: both2021activity).
• Figure 2: The MATSim process loop
• Figure 3: The model development workflow overview
• Figure 4: A schematic illustration of a car traveller entering traffic from the link's start node in MATSim with and without 500m access points
• Figure 5: Generated road network and network for the study area