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

Mahfouz, Hussein; Greenbury, Sam F; Zhang, Bowen; Lynn, Stuart; Cheng, Tao; (2025) A reproducible pipeline for activity-based travel demand generation in England. Environment and Planning B: Urban Analytics and City Science 10.1177/23998083251379620. (In press). Green open access

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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.

Type: Article
Title: A reproducible pipeline for activity-based travel demand generation in England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/23998083251379620
Publisher version: https://doi.org/10.1177/23998083251379620
Language: English
Additional information: © The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/).
Keywords: Science & Technology, Social Sciences, Life Sciences & Biomedicine, Environmental Studies, Geography, Regional & Urban Planning, Urban Studies, Environmental Sciences & Ecology, Public Administration, activity-based travel demand, synthetic populations, open-source, reproducible
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10215993
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