Evidence map›Paper›PMID 37456923›Full record

ArticleJournal of transport geography2023

Comparing travel behaviour characteristics and correlates between large and small Kenyan cities (Nairobi versus Kisumu).

Lambed Tatah, Louise Foley, Tolu Oni, Matthew Pearce, Charles Lwanga, Vincent Were, Felix Assah, Yves Wasnyo, Ebele Mogo, Gabriel Okello and 3 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of transport geography, 2023. 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
5.1field-weighted citation impact, top 6% of its field
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, 13 citations in OpenAlex.

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

13 authors at 6 institutions in 4 countries.

Lambed TatahMRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Louise FoleyMRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Tolu OniMRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Matthew PearceMRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Charles LwangaCenter for Global Health Research, Kenya Medical Research Institute (KEMRI), P.O. Box 1578, Kisumu 40100, Kenya.
Vincent WereCenter for Global Health Research, Kenya Medical Research Institute (KEMRI), P.O. Box 1578, Kisumu 40100, Kenya.
Felix AssahHealth of Populations in Transition (HoPiT) Research Group, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé I, Yaoundé, Cameroon.
Yves WasnyoHealth of Populations in Transition (HoPiT) Research Group, Faculty of Medicine and Biomedical Sciences, The University of Yaoundé I, Yaoundé, Cameroon.
Ebele MogoMRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
Gabriel OkelloInstitute of Sustainability Leadership, University of Cambridge, Cambridge, United Kingdom.
Stephen MogereJapan International Cooperation Agency (JICA), Britam Tower 22nd & 23rd Flrs, Upper Hill Road, P.O. Box 50572-00200, Nairobi, Kenya.
Charles ObonyoCenter for Global Health Research, Kenya Medical Research Institute (KEMRI), P.O. Box 1578, Kisumu 40100, Kenya.
James WoodcockMRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
MRC Epidemiology Unit · GBKenya Medical Research Institute · KEUniversité de Yaoundé I · CMUniversity of Cambridge · GBInstitute for Sustainability · GBJapan International Cooperation Agency · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding urban travel behaviour is crucial for planning healthy and sustainable cities. Africa is urbanising at one of the fastest rates in the world and urgently needs this knowledge. However, the data and literature on urban travel behaviour, their correlates, and their variation across African cities are limited. We aimed to describe and compare travel behaviour characteristics and correlates of two Kenyan cities (Nairobi and Kisumu). We analysed data from 16,793 participants (10,000 households) in a 2013 Japan International Cooperation Agency (JICA) household travel survey in Nairobi and 5790 participants (2760 households) in a 2016 Institute for Transportation and Development Policy (ITDP) household travel survey in Kisumu. We used the Heckman selection model to explore correlations of travel duration by trip mode. The proportion of individuals reporting no trips was far higher in Kisumu (47% vs 5%). For participants with trips, the mean number [lower - upper quartiles] of daily trips was similar (Kisumu (2.2 [2-2] versus 2.4 [2-2] trips), but total daily travel durations were lower in Kisumu (65 [30-80] versus 116 [60-150] minutes). Walking was the most common trip mode in both cities (61% in Kisumu and 42% in Nairobi), followed by motorcycles (17%), matatus (minibuses) (11%), and cars (5%) in Kisumu; and matatus (28%), cars (12%) and buses (12%) in Nairobi. In both cities, females were less likely to make trips, and when they did, they travelled for shorter durations; people living in households with higher incomes were more likely to travel and did so for longer durations. Gender, income, occupation, and household vehicle ownership were associated differently with trip making, use of transport modes and daily travel times in cities. These findings illustrate marked differences in reported travel behaviour characteristics and correlates within the same country, indicating setting-dependent influences on travel behaviour. More sub-national data collection and harmonisation are needed to build a more nuanced understanding of patterns and drivers of travel behaviour in African cities.

Indexed as

KenyaKisumuNairobiTravel behaviourUrban transport

Identifiers

PMID37456923
PMCPMC10345788
OpenAlexW4380854357

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.