Work

A reproducible pipeline for activity-based travel demand generation in England

Year: 2025

Type: article

Abstract: Agent-based transport models are gaining popularity due to their ability to model features such as heterogenous individual behaviour, household dependencies, and new dynamic modes of travel. Such models require as input disaggregate population datasets with detailed daily activity diaries (activity-based travel demand). While there is extensive literature on activity-based travel demand generation, few open-source tools are available for producing such datasets, and those that do exist are often difficult to adapt to different study areas. In this work, we present an open-source modular pipeline for generating activity-based travel demand for any region in England, producing individuals with household structures and geographically and temporally explicit daily activity plans. The framework includes activity scheduling and location assignment for a synthetic population, as well as self-consistency and validation frameworks to help fine-tune parameters.

Source: Environment and Planning B Urban Analytics and City Science

DOI: 10.1177/23998083251379620

Authors: Hussein Mahfouz, Sam F. Greenbury, Bowen Zhang, Stuart Lynn, Tao Cheng

Institutions: University of Leeds, The Alan Turing Institute, University College London

Language: English

Topic: Transportation Planning and Optimization

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $3,650