Do new Low Traffic Neighbourhoods (LTNs) in London lead to more walking and cycling?

Low Traffic Neighbourhoods in London: baseline for a controlled before-and-after study

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ISRCTN

Registry ID

ISRCTN13494066

Enrollment

8000000

Registered

2021-03-31

Start date

2021-04-01

Completion date

Unknown

Last updated

2024-11-04

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Changes in the use of different transport modes affect health through a variety of pathways and conditions (e.g. via increased physical activity) Not Applicable

Interventions

The intervention group will be streets that are (a) key travel desire lines inside Low Traffic Neighbourhoods (LTNs) or (b) boundary roads. The comparison group will be similar matched streets in the

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: The study uses anonymous data on usage of street locations gathered from machine learning cameras, supplemented by observations of user characteristics (e.g. apparent gender) from which only anonymous aggregated data will be recorded and stored. Note therefore that the dates given below relate to the planned study length.

Exclusion criteria

Exclusion criteria: There are no exclusion criteria

Design outcomes

Primary

Measure	Time frame
Measured using machine learning cameras (Vivacity cameras) for 2 years: 1. Number of (i) pedestrians, (ii) cyclists, (iii) e-scooters 2. Number of (i) cars/taxis, (ii) motorcycles, (iii) vans, (iv) trucks 3. Motor vehicle congestion, defined in terms of a count of vehicles within a given zone	—

Secondary

Measure	Time frame
Measured using manual observations for 12 hours of footage per LTN and matched control site in June 2021, covering the week-day morning peak, interpeak, after school, evening peak, weekend morning, weekend afternoon: 1. Active travel diversity 1: % pedestrians using wheelchairs 2. Active travel diversity 2: % pedestrians with pushchairs; % bicycles that are cargo bikes 3. Active travel diversity 3: % (i) pedestrians and (ii) cyclists who are children 4. Active travel diversity 4: % female cyclists Measured using machine learning cameras (Vivacity cameras) for 2 years: 5. % footway versus carriageway use by pedestrians, e-scooters, and cyclists 6. Average motor vehicle speeds and % exceeding speed limit	—

Measure

Time frame

Measured using manual observations for 12 hours of footage per LTN and matched control site in June 2021, covering the week-day morning peak, interpeak, after school, evening peak, weekend morning, weekend afternoon: 1. Active travel diversity 1: % pedestrians using wheelchairs 2. Active travel diversity 2: % pedestrians with pushchairs; % bicycles that are cargo bikes 3. Active travel diversity 3: % (i) pedestrians and (ii) cyclists who are children 4. Active travel diversity 4: % female cyclists Measured using machine learning cameras (Vivacity cameras) for 2 years: 5. % footway versus carriageway use by pedestrians, e-scooters, and cyclists 6. Average motor vehicle speeds and % exceeding speed limit

—

Countries

England, United Kingdom

Contacts

Public ContactRachel Aldred

r.aldred@westminster.ac.uk+44 (0)20 7911 5021

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Feb 4, 2026