Data-driven activity scheduler for agent-based mobility models
Authored by Jan Drchal, Michal Certicky, Michal Jakob
Date Published: 2019
DOI: 10.1016/j.trc.2018.12.002
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Abstract
Activity-based modelling is a modern agent-based approach to travel
demand modelling, in which the transport demand is derived from the
agent's needs to perform certain activities at specific places and
times. The agent's mobility is considered in a broader context, which
allows the activity-based models to produce more realistic trip chains,
compared to traditional trip based models. The core of any
activity-based model is an activity scheduler - a software component
producing sequences of agent's daily activities interconnected by trips,
called activity schedules. Traditionally, activity schedulers used to
rely heavily on hard-coded knowledge of transport behaviour experts. We
introduce the concept of a Data-Driven Activity Scheduler (DDAS), which
replaces numerous expert-designed components and their intricately
engineered interactions with a collection of machine learning models.
Its architecture is significantly simpler, making it easier to deploy
and maintain. This shift towards data-driven, machine learning based
approach is possible due to increased availability of mobility-related
data. We demonstrate DDAS concept using our own proof-of-concept
implementation, perform a rigorous analysis and compare the validity of
the resulting model to one of the rule-based alternatives using the
Validation Framework for Activity-Based Models (VALFRAM).
Tags
Agent-based model
Machine learning
model validation
Travel behavior
activity-based model
Decisions
Framework
System
Travel demand
model
Population modelling
Purpose