This use case is from South London Partnership – InnOvaTe “IoT” Project.
Outcome
Road users and air quality monitoring has provided detailed insights into vehicular, cycling, and pedestrian patterns which has not only improved traffic management and road safety, but provided insights into air policy implications in residential areas, supported strategic planning and helped respond to Freedom Of Information requests and council member enquiries.
Sectors
Local authority, Highways, Transport, Air Quality, Public Realm, Public Sector.
Key Stakeholders
Highways Team, Parks Team, Air Quality, Strategic Transport Team, Data Team, Environment Team, GLA, TfL.
Overview
Data captured by IoT Sensors from across the urban landscape about vehicles, pedestrians, cyclists, buses, journey times, air quality, cycle congestion and footfall across the public realm, has helped generate new insights. Previously siloed data was ingested from various systems and applications into a centralised data platform where it was overlaid with other data sources and transformed to provide actionable insights for urban planning, traffic regulation, and environmental protection.
About the Author
The InnOvaTe Programme is an Internet of Things initiative by South London Partnership (SLP) to “pilot and research” IoT across the 5 London boroughs of Croydon, Merton, Richmond upon Thames, Sutton, and the Royal Borough of Kingston upon Thames. The programme looks at ways to generate economic growth, support local businesses, help people live better, healthier lives and assist with addressing the climate emergency. The project assessed 150 IoT ideas for the councils concerned, implementing 48 of them successfully over 18 months. The programme was formally completed in March 2023.
Case Study Challenge
Confronted with challenges like rising traffic congestion, deteriorating air quality, and the need for safer pedestrian pathways, a number of boroughs were willing to deploy sensors to capture data points relating to these aspects so that new insights could be generated. The goal was to employ a number of IoT solutions to gather nuanced insights into urban dynamics, thereby enabling smarter, data-driven governance to support decision-making at a strategic level. All use cases started with a problem statement, rather than focussing on the technology – this allowed for suitable solutions to be designed to resolve challenges, rather than focussing on the technology itself.
Use Case Design Objectives
The project aimed to use IoT for real-time monitoring of traffic flow, various road users including cyclists and pedestrians, air quality (near schools), and footfall in parks and busy streets. The objective was to gain insights for improving road safety, reducing pollution, and enhancing public space utilisation. The following types of questions were asked:
The goal of each use case eas to ascertain if there is indeed a problem and then subsequently:
Commissioning (budget/procurement)
A number of competitive tender processes took place in 2021 and 2022 for various services related to this case study, with the primary procurement vehicle being the Crown Commercial Services (CSS) Spark framework. Use cases ranged from £10,000 to £300,000 typical supplier cost per use case.
Deployment (what / who / where / how long)
Deployment involved the installation of various types of sensors in key locations by specialised teams. Traffic sensors were typically installed by Vivacity Labs on lamp columns across the boroughs once Street Lighting teams provision power via commando sockets. Air quality monitors were placed near schools to gauge pollution levels and devise health-protective strategies. Footfall sensors in parks provided data on public space usage. The deployment spanned several months, involving local councils and technology providers.
Technology Implemented
Traffic sensors offered detailed analytics on vehicle, cycle, and pedestrian movement (VivaCity Labs). Air quality monitors assessed pollutants to inform health-focused urban policies (Breathe London). Footfall sensors captured park and street usage patterns, influencing planning and maintenance (North Tech).
Results / Key Findings
Traffic sensors provided detailed data on movement patterns, leading to more improvements made to traffic management – this has specifically supported journey time improvements on high congestion routes, baselines for future changes, and utilising road user path data for decision making when considering new pedestrian crossings. Air quality monitors revealed insights that were used to understand the real impact of policy changes, especially around schools and cycle lanes – this combined with traffic data provided evidence to support schemes and policy changes. Insights obtained provided to Community Safety new facts to guide changes and improvements to traffic flow. Following the lifting of restrictions, evidence was obtained using footfall data to identify if there has been a ‘bounce back’ in the months following. Footfall sensors highlighted usage patterns in parks and streets to assist in optimising cleaning routine and prioritise high usage areas. Kingston had very limited data about the use of its cycle infrastructure – data was generated successfully to confirm the number of cyclists using these pathways and when they are travelling, which direction they are going, possible routes for expansion, how congested the routes were and if there was any unauthorised usage of the spaces by mopeds and other vehicles. These insights have significant implications to obtaining new investment in cycling infrastructure and are very beneficial to those who need to make investment decisions.
Benefits / Usefulness of Data
Traffic data led to optimised road safety measures and congestion management. Air quality data informed policies for health protection in school areas – this was especially innovative as the overlaying of air quality data with traffic data gave unique insights into understanding the impact on pedestrians at various times of the day. Data captured has been invaluable for understanding pollution at a very detailed level, i.e. number of pedestrians who were present outside a specific school, with accompanying traffic and air quality data to match. We could also see how those levels change in response to traffic volumes, vehicle types and interventions made by the council, helping to influence and inform future decision making. Public space usage data aided in better park management and urban planning. Data was captured during Covid Lockdowns which assisted in preparing for people returning to the public realm. Large volumes of data provided new insights into longer time-frame patterns for footfall, vehicle types/counts, journey paths and journey times.
Lessons Learned
Emphasised the importance of multi-disciplinary collaboration, the value of real-time data for proactive management, and the necessity for ongoing technological adaptation. Council staff confirmed that the quantity of information was beneficial, especially as this data would take hours to manually capture and would not be as accurate. The challenge experienced was around reporting – generating outputs that specifically met business needs was difficult and often involved a lot of data manipulation. Data anomalies did often occur and needed investigation, e.g. a mobility scooter driving through a pedestrian underpass under the A3 was mistaken for black cab, however these occurrences did reduce over time as the system learned and improved its visual recognition. Data generated was very useful to support ideas for developing new schemes. Data is also used for defending schemes and if TfL requests justifications for road layouts, and helps explain road changes, and supports with funding bids. Interestingly, overhanging trees were often a cause for sensors not capturing data, and this required sensor adjustment or a team to be sent to remove the offending branches. Finally, lack of engagement from data users typically meant the data was not fully utilised – preventing positive outcomes from being captured, despite data being made available to achieve this.
Conclusion
This case study demonstrates the significant role that IoT can play in capturing data pertaining to modern urban environments. The insights gained from this project have provided data insights to a number of different council teams which has aided decision-making at a strategic level.
Photos from the use case
Contact
For further information, please contact the service leads involved in this project, listed below.
Chris Smith
Team Leader – Design and Delivery, Highways
Royal Borough of Kingston
chris.smith@kingston.gov.uk
Paul Garside
Senior Travel Planner
London Borough of Sutton
paul.garside@sutton.gov.uk
Jason Andrews
EH Pollution Manager (Air Quality)
London Boroughs of Merton
Jason.Andrews@merton.gov.uk
Helen Millier
Senior Sustainable Transport Officer
Royal Borough of Kingston
helen.millier@kingston.gov.uk
Mark Dalzell
Head of Neighbourhood Services
London Borough of Sutton
mark.dalzell@sutton.gov.uk
Liam Swaffield
Community Safety
London Borough of Sutton
liam.swaffield@sutton.gov.uk
Sean Gillen
Corporate Head of Employment, Skills and Enterprise
Royal Borough of Kingston
sean.gillen@kingston.gov.uk
Heather Evans
Economic Renewal and Regeneration
London Borough of Sutton
heather.evans@sutton.gov.uk
Pierre Venter
IoT Delivery Manager
Royal Borough of Kingston and London Borough of Sutton
pierre.venter@sutton.gov.uk
