Do new Low Traffic Neighbourhoods (LTNs) in London lead to more walking and cycling?
| ISRCTN | ISRCTN13494066 |
|---|---|
| DOI | https://doi.org/10.1186/ISRCTN13494066 |
| ClinicalTrials.gov (NCT) | Nil known |
| Clinical Trials Information System (CTIS) | Nil known |
| Protocol serial number | NIHR133798 |
| Sponsor | University of Westminster |
| Funder | National Institute for Health Research |
- Submission date
- 26/03/2021
- Registration date
- 31/03/2021
- Last edited
- 24/10/2024
- Recruitment status
- No longer recruiting
- Overall study status
- Completed
- Condition category
- Other
Plain English summary of protocol
Background and study aims
In recent years, levels of motor traffic started increasing again after a period of decline. This is worrying for many reasons. Every year in the UK two thousand people die in road crashes, and tens of thousands are seriously injured. Tens of thousands die early each year because of air pollution or noise pollution, much of which comes from motor vehicles. If people walk or cycle more, and drive less, they are also much more likely to get the exercise they need to stay healthy.
The way we travel is not just a matter of personal choice. Our streets are often busy with cars, even smaller side streets. Streets are difficult to cross on foot, particularly for older people. They are scary to cycle on, particularly for children. This can mean that the easiest and most comfortable way to travel is by car. The pandemic can make walking even more difficult, especially for vulnerable groups, as it is hard to socially distance on narrow pavements.
For these reasons, local authorities have been implementing schemes that restrict motor traffic and make more space for walking or cycling. ‘Low Traffic Neighbourhoods’ are a type of traffic scheme that stop people in motor vehicles cutting through residential streets. This approach is widely used in the Netherlands but less common here. The aim is to make walking and cycling safer and more comfortable (as there are fewer cars), and make driving less convenient, encouraging people to walk or cycle instead. Because around 90% of people live on residential streets, if these schemes work they could become widely used. Already 4% of Londoners live in a Low Traffic Neighbourhood built from March to September 2020.
However, these schemes are controversial and many people object, although there is also strong support. It is important to know whether the goals – to get more people walking and cycling and reduce driving – are being met. We also need to know about any negative impacts on roads at the boundaries of these schemes, where there might be increases in motor traffic. But measuring these effects is difficult because travel patterns keep changing during the pandemic, so we need a scientific study.
This study will compare how volumes of walking, cycling, and driving change after new Low Traffic Neighbourhoods are built in six London boroughs. The researchers will examine the impacts both inside and on boundary roads.
Who can participate?
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.
What does the study involve?
The researchers will collect data using Vivacity cameras, which are mounted on lampposts and film the street, automatically counting the number of pedestrians, cyclists and vehicles that pass. This is anonymous, 24/7 data, so the researchers can look at shorter-term disruption and longer-term effects. They can also look at changes in speeds and where people walk or cycle (e.g. pavement or road). They will fit cameras in similar, nearby roads that are not getting a Low Traffic Neighbourhood (‘control sites’). This will mean that, for example, they can separate out increases in walking due to Low Traffic Neighbourhoods from increases in walking because lockdown is lifted or the weather improves. This will allow the researchers to reliably measure the effects of Low Traffic Neighbourhoods on how people travel, and any negative effects on scheme boundary roads.
What are the possible benefits and risks of participating?
The study will inform the debate about the impacts of LTNs and the potential disbenefits. In a contested policy area such evidence is important. However, the study is limited in only covering LTNs in London, although these are being chosen to ensure a mix of areas.
Where is the study run from?
Westminster University (UK)
When is the study starting and how long is it expected to run for?
December 2020 to May 2025
Who is funding the study?
National Institute for Health Research (UK)
Who is the main contact?
Prof. Rachel Aldred
r.aldred@westminster.ac.uk
Contact information
Scientific
School of Architecture and Cities
Westminster University
35 Marylebone Road
London
NW1 5LS
United Kingdom
 ORCID ID |
0000-0001-7413-7251 |
|---|---|
| Phone | +44 (0)20 7911 5021 |
| r.aldred@westminster.ac.uk |
Public
School of Architecture and Cities
Westminster University
35 Marylebone Road
London
NW1 5LS
United Kingdom
| ORCID ID |
0000-0001-7413-7251 |
|---|---|
| Phone | +44 (0)20 7911 5021 |
| r.aldred@westminster.ac.uk |
Study information
| Primary study design | Observational |
|---|---|
| Study design | Observational controlled before-and-after study |
| Secondary study design | Controlled before-and-after study |
| Study type | Participant information sheet |
| Scientific title | Low Traffic Neighbourhoods in London: baseline for a controlled before-and-after study |
| Study objectives | Research questions: 1. Do new Low Traffic Neighbourhoods (LTNs) lead to more, and more diverse, walking and cycling (active travel), compared to control areas? 2. Does traffic displacement of motor vehicles lead to unintended consequences on boundary roads? |
| Ethics approval(s) | Conditionally approved 22/03/2021, Westminster University Research Ethics Committee (309 Regent Street, London, W1B 2HW, UK; Tel: not available; H.Kelly01@westminster.ac.uk), ref: ETH2021-0902 |
| Health condition(s) or problem(s) studied | Changes in the use of different transport modes affect health through a variety of pathways and conditions (e.g. via increased physical activity) |
| Intervention | 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 same borough where no substantial transport intervention is planned. Machine learning cameras (Vivacity cameras) will be installed several months before interventions start, at control and intervention sites to record changes of use of all types of transport, including walking, and information such as speeds and paths taken. This will be supplemented by manual observations of the diversity of pedestrians and cyclists (apparent gender and adults/children). The analysis will initially stratify by LTN scheme and by inside LTN vs. boundary – i.e. comparing each site with its control site separately. Meta-analysis methods will then be used to pool results across LTNs if appropriate (but still stratifying inside LTN vs. boundary). Primary outcomes (change in active travel and car use) will also be stratified by time-of-day (peak/interpeak) and weekday/weekend. |
| Intervention type | Other |
| Primary outcome measure(s) | 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 |
| Key secondary outcome measure(s) | 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 |
| Completion date | 30/05/2025 |
Eligibility
| Participant type(s) | All |
|---|---|
| Age group | All |
| Sex | All |
| Target sample size at registration | 8000000 |
| Total final enrolment | 8000000 |
| Key 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. |
| Key exclusion criteria | There are no exclusion criteria |
| Date of first enrolment | 01/04/2021 |
| Date of final enrolment | 31/03/2023 |
Locations
Countries of recruitment
- United Kingdom
- England
Study participating centre
NW1 5LS
United Kingdom
Results and Publications
| Individual participant data (IPD) Intention to share | No |
|---|---|
| IPD sharing plan summary | Not expected to be made available |
| IPD sharing plan | Participant-level data is not being collected. |
Study outputs
| Output type | Details | Date created | Date added | Peer reviewed? | Patient-facing? |
|---|---|---|---|---|---|
| Participant information sheet | Participant information sheet | 11/11/2025 | 11/11/2025 | No | Yes |
Editorial Notes
24/10/2024: The overall end date was changed from 31/10/2024 to 30/05/2025.
23/08/2024: The overall end date was changed from 31/08/2024 to 31/10/2024.
26/03/2024: The following changes were made to the study record:
1. The overall study end date was changed from 31/03/2023 to 31/08/2024.
2. The intention to publish date was changed from 31/03/2024 to 30/06/2025.
3. Total final enrolment added.
21/09/2021: Internal review.
26/03/2021: Trial's existence confirmed by the NIHR.
