Local Authority teams face challenges using Bus, Active Travel and Roadworks data together. This presents an opportunity to improve day-to-day tactical decision making and month-to-month strategic investment decisions to ultimately benefit Net Zero targets. Current state-of-the-art to address this problem doesn't extract the causal insight and automatic alerting required to satisfy effective user decision making. This project co-creates a feasibility study to investigate overcoming these barriers, which stop shared data being used, stop data-driven decisions being made, and therefore adversely impact Net Zero targets.

Any hierarchical organisation or structure necessarily embeds "single point of failure" risks where a security breach at any point compromises everything below it. A problem at the top, whether accidental or malicious, leads to total system failure. Interconnected networks, by contrast, allow problems and attacks to be contained, without affecting the rest of the system. The internet uses this to keep the world wide web running, even when undersea cables need repairing. A web of trust delivers this crucial robustness, which is a critical requirement for any trust-reliant infrastructure. Furthermore, a web of trust can accurately quantify the level of trust for any given actor, to a degree which hierarchies can not. The deep tech at the heart of this project is a Zero Knowledge Web of Trust. Enabling the provider to maintain total privacy when sharing data, while simultaneously enabling the recipient to have trust in the data they receive. This breakthrough will remove a major barrier to data sharing between Electric Vehicles and the data aggregators, and Energy Market Participants who could use it. In doing so, it will unlock dramatically more data from the EV's and enable the Energy Market to operate more efficiently. It will reduce the need to increase electricity transmission and distribution infrastructure. And it will enable EV's, collectively the nation's largest battery, to act in a coordinated way to onboard renewables as fast and to the greatest extent possible.

A bus crossing a junction can take 1 second (on a green light) or 120 seconds (on a red light). Cities have buses which go through 5-15 junctions on a route, meaning buses can't avoid being 15 minutes late. This disrupts passengers' lives and discourages bus use. Millions of pounds are being spent to improve this situation via Bus Priority, however the setup and maintenance of this new infrastructure can benefit from significant productivity gains in both the manual processes and the pay-back provided by infrastructure on the network. Current state-of-the-art to address this problem is based on relatively simple monitoring software with significant downsides. We present innovations combining software and AI which allow Local Authorities to support more productive investment into bus networks in their regions. To realise the positive economic, environmental and societal impacts.

This innovative project focuses on developing a comprehensive platform to optimize routing and scheduling in public transportation. The solution will provide a data-driven AI platform to design and run an optimised end-to-end bus service for a region, served by a combination of Dynamic Responsive Transit (DRT) buses and traditional timetabled bus services. Importantly, this AI-powered optimisation of these two types of bus service delivery will provide a holistic bus solution which can serve a modern region's population: both in dense city and regional rural areas, providing inclusive and accessible transit for all. This will improve efficiency and decision-making for enhanced service delivery in a challenging environment where the balance of transit needs within cities and across less-densely populated regions is constantly in flux. Leveraging a digital twin and reinforcement learning, the solution will generate passenger demand using Reinforcement Learning (RL) based on historical data, road traffic, and event information to create a digital twin. The digital twin will closely mimic real-world demand by generating large-scale data and employing AI for training. The project also includes the implementation of a reinforcement learning-based optimal routing model. Continuous learning using demand prediction data and real operational data will drive ongoing model evolution. Additionally, the project focuses on achieving fast response performance and real-time optimal routing through model lightweighting, ultimately culminating in the development of a digital twin powered by these innovative techniques. The co-developed solution will be built on top of existing digital twin, bus routing and mobility platforms by project partners Ciel & Alchera. The project partners have established commercial success in productising advanced Machine Learning and already sell solutions to the target customers of this collaborative R&D, with a clear roadmap to commercialisation of this innovation.

Alchera, in partnership with Wordnerds seek to explore collaboration of the most valuable qualitative and quantitative AI analytics in the field of transport, focused on taking action and improving peoples' lives. In this project, Alchera and Wordnerds will unlock data-driven decision making to support Local Authority businesses in planning, assessing and implementing mobility schemes. A huge challenge facing Local Authorities (LAs) is to juggle complex mobility infrastructure planning operations with rapidly evolving public opinion and changing behavioural patterns. Traditionally mobility operations data and customer sentiment data are available in isolation, however don't lend themselves easily to being fused into a single source of truth. This means LAs waste large amounts of time collating, comparing and interpreting these two disparate data sets to make crucial decisions about mobility infrastructure. Alongside our project partners, Wordnerds, we will help LAs to better understand the holistic impact of mobility schemes, such as Low Traffic Neighbourhoods (LTNs) and Intelligent Bus Priority (IBP) measures by fusing previously disparate data sets. This represents a step change in productivity, by pulling together two separate, complex work streams into one more streamlined workflow. This unique approach seeks to provide better allocation of public funds, reducing costs and enables targeted improvements in the areas most deserving. In turn this application supports positive environmental and societal impacts, in addition to addressing the Government's National Cyber Strategy (2022).

Buses running to schedule is a perennial problem faced by transport authorities. Major cities including Cambridge have struggled to gain insights into the root cause of bus delays, in order to better manage and operate wider transport services and encourage more sustainable use of public transport. However, step changes to road usage due to Covid-19, including increased pavement sizes, new cycle routes and changing travel behaviours mean that public transport operation is set to become more challenging than ever. While Covid-19 has temporarily reduced the occupancy of buses across the network, longer-term bus use remains a vital pillar in transport policy. Understanding how the bus network is performing can provide key insights into how it can be improved in an agile and responsive fashion, encouraging higher bus occupancy, and informing policy to getting more cars off the road. One of the underlying impacts of Covid is that it provides a unique opportunity to understand network dynamics as all buses have been operating more smoothly due to lockdown and fewer vehicles on the road. However as the lockdown continues to lift and more vehicles, pedestrians and cyclists return to our roads, pinch points are already beginning to occur again. In this project, Alchera will work closely with the Smart Cambridge programme; an initiative part of the Greater Cambridge Partnership (GCP) a body which includes, Cambridgeshire County Council, Cambridge City Council, South Cambridgeshire District Council and the University of Cambridge and was set up to deliver the Greater Cambridge City Deal. Working closely together with Smart Cambridge, Alchera will develop a fully integrated tool to identify when buses are running out of sync with the expected timetables. These data insights will identify pinch-points in order to inform operational decision making & wider transport policy for the better running of bus services in Cambridgeshire, in turn encouraging the adoption of sustainable travel. This work will build on Alchera's capabilities providing software tools and machine learning via our Intelligent Data Hub, and provide integration to existing workplace tools such as Power BI used by Local Authorities across the UK, to enable them to make data-driven decisions.

Alchera is developing a powerful software platform (‘Alpha’) that delivers superior traffic management capabilities in a faster and lower-cost way than existing solutions. It works by fusing and analysing data from networks of existing transport infrastructure, sensors, cameras and outputs from other 'connected' modes of transport and providing this new information as open APIs to digital transport applications and tools both for internal use and for 3rd party consumption. At its core, Alpha provides real-time quantification of vehicle and pedestrian movements in cities and on road networks, and supplements these with analytics and predictions of asset use to enable fast decision making and facilitate planning. Alpha has successfully been deployed in a number of projects across a variety of road infrastructure assets, but this will represent its first application to the UK’s strategic road network. Through interactions with Highways England (HE) and existing clients, Alchera has identified an opportunity to generate greater value from road data. For this project, we are proposing to build a data management platform that better exploits the value of Highways England’s data. The platform will consolidate asset data in real-time and enable internal and external data sharing with auditable access control and a development sandbox via an open API. The platform will become a source of innovation for new data-driven products and services as well as potentially provide HE with significant revenue opportunity through external licensing of data feeds.