This ‘intelligent mobility’ is fundamentally about optimising the movements of people and goods, and I believe ‘big data’ is the lifeblood of this vision, for both the good of our health and the planet.
What is big data in mobility?
Big data is very big data sets that are produced by people using the internet, and that can only be stored, understood, and used with the help of special tools and methods[1]. In mobility, the most common forms of big data include:
- People movement: mobile phone network data and GPS.
- ITS transport: traffic signals, traditional traffic count sites, Wi-Fi/Bluetooth vehicle counters, journey time calculations, CCTV and data of incidents, collisions and congestion.
- Transit: public transport timetables, live vehicle locations, predicted and actual arrival/departure times.
- Infrastructure status: roadworks and road closures, bridge health, ground movement, EV charge point status, road ride quality.
- Air quality and carbon emissions: Measured pollution levels, emissions data , data sets from emissions and dispersion modelling tools
- Weather: temperature, precipitation, wind, road conditions (ice, flooding, etc).
- Static: road geometry and layout, signage and signal locations, parking regulations, traffic regulations orders.
- Socio-economic: mosaic geodemographic data, behavioural analysis
- Future developments modelling: Future road layout and supporting infrastructure, projected traffic flow and transit routes/occupancy/demand
Independently, these data sources only paint part of the picture of how a transport network is operating. When these data sets are combined, they create a rich tapestry of mobility services and infrastructure, where interrelationships can be easily identified. Combining and analysing this data enables us to create accurate models of effective transport networks and make predictions about future trends in transport at a local, regional and national level. Proposed interventions in the transport model, like making a road one way, or introducing EV only lanes, can be played out and combined with other interventions to identify the best mix of solutions.
Understanding trends in mobility
Anecdotal evidence suggests that the rise of flexible working, and flexible job roles are contributing to a shift in people movement patterns. In March 2020, Covid-19 meant UK road travel fell by 73%, to levels not seen since 1955.[2] However, prior to the pandemic the average number of car journeys increased, compared to 2009[3]. The picture is confusing, and more data is required at a localised level to ensure decision makers are informed.
We will be pioneering work in this area as part of the Staffordshire ADEPT Smart Places Live Lab programme. The study will seek to determine the best location for mobility hubs within Staffordshire. This ‘demand analysis’ will focus on two elements; local transport patterns and the development of personas to understand the customer journeys behind changes in travel patterns.
The former involves sourcing people movement data, analysed by mode, time, origin/destination and purpose. The latter will centre on using mosaic data to understand the socio-demographic factors behind people’s transport patterns. This will result in the development of several target personas with associated customer journeys who could be targeted with alternative transport modes. Without these analysed data sources, it is almost impossible to develop meaningful mobility services which reflect the needs of communities.
A data driven response to Covid 19
COVID-19 has had a negative impact on the capacity of public transport due to social distancing. This ‘new normal’ is rapidly reducing the commercially viability of transport. TfL saw fares and other revenues drop by 90% in March 2020.[4]Companies are looking at ways to make these services sustainable again under current restrictions, and data has an important role.
One UK SME we are working with are deploying a mix of e-bikes and e-scooters to encourage car free travel. Each e-bike/e-scooter is ‘Internet of Things’ connected, enabling users to find and rent a bike using their phone, and operators to analyse the details of each use. This data can be fed into a mobility platform to manage movements locally. Other companies are adapting Demand Responsive Transport (DRT) to traditional fixed route buses and passengers ‘check-in’ via their phone, enabling operators to monitor passenger numbers and manage bus capacity. Connected devices and their data is critical to ensuring future operators can manage capacity.
Micromobility: new data sources
Prior to COVID-19, we saw a rise in the number of people using micromobility solutions[5] as they are quicker than cars in most cities[6]. Analysis is available because micromobility solutions are highly sensorized, virtually all of them have GPS and 3G/4G connectivity. This presents transport planners with a new source of data.
We should use this data intelligently to support the move towards micromobility. Our project for Liverpool City Council used mobile network data to understand mobility patterns in the city to improve traffic flows. The results showed a greater proportion of walking trips have originated inside the City Centre and a greater proportion of motorised trips have originated outside. This analysis helps the Council understand how journeys to the city by car could be pushed away from the city centre and complimented with micromobility solutions to encourage the last-mile or so to be completed without using a car. This would reduce congestion in the city centre and have a positive impact on air quality.
Ditching the car and the climate crisis
In 2018 domestic transport made up 28% of greenhouse gas emissions in the UK, with 55% coming from cars and vans[7]. This is worrying considering even pre-Covid 19, 68% of all commuting journeys were taken by car.
Tackling petrol car ownership and encouraging greener alternatives is one of the greatest challenges facing the transport industry in tackling climate change. There is a cultural problem in convincing people of the merits of shared transport versus owning their own motor, which Covid-19 has not helped. Data is key here in enabling people to adopt these alternatives and encouraging car owners to mix up their modal journeys.
Crucial to this is longer term investments such as electrical vehicle charging, blending green technologies with traditional consumer appetites.
We are delivering the data driven design and build aspects of the Greater Manchester Electric Vehicle Network, the aim of which is to take data relating to local preference for EV adoption and ensure there are enough chargers to support EV take up. Though not discouraging people from using cars, the challenge is to encourage greener alternatives and data is central to enabling this shift.
If we are to successfully tackle the twin challenges of COVID-19 and climate crisis we must use data. Data allows us to better understand the challenges we face. It allows us to model the impact of potential solutions and support key decision makers when deploying the right solutions. It also allows us to analyse our impact, ensuring we are delivering the best results for our people and our planet.
[1] https://dictionary.cambridge.org/dictionary/english/big-data
[2] https://www.theguardian.com/uk-news/2020/apr/03/uk-road-travel-falls-to-1955-levels-as-covid-19-lockdown-takes-hold-coronavirus-traffic
[3] https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/870647/tsgb-2019.pdf
[4] https://railway-news.com/tfl-receives-government-funds-to-avoid-bankruptcy/
[5] https://inrix.com/press-releases/micromobility-study-uk-2019/
[6] https://inrix.com/press-releases/micromobility-study-uk-2019/
[7] Decarbonising transport, Bob Moran, Deputy Director, Head of Environment Strategy, Department for Transport, 4 June 2020