Evidence map›Paper›PMID 41937926›Full record

ArticleEnvironment and planning. B, urban analytics and city science2025

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

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

Abstract read
In one paragraph

Article in Environment and planning. B, urban analytics and city science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hussein MahfouzInstitute for Transport Studies, University of Leeds, Leeds, UK.ORCID https://orcid.org/0000-0002-6043-8616
Sam F GreenburyThe Alan Turing Institute, London, UK.ORCID https://orcid.org/0000-0003-4452-2006
Bowen ZhangThe Alan Turing Institute, London, UK.
Stuart LynnThe Alan Turing Institute, London, UK.
Tao ChengThe Alan Turing Institute, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

activity-based travel demandopen-sourcereproduciblesynthetic populations

Identifiers

PMID41937926
PMCPMC13044431

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.