2021/11/19 by Alan Both, Dhirendra Singh, Both, Alan +7
Engineering · Social Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Transportation Planning and Optimization #Transportation and Mobility Innovations #Urban Transport and Accessibility
paper · pdf · doi:10.48550/arxiv.2111.10061
openalex publication_date 2021/11/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.