Home IssueData Economy 5 Q’s for Anna Jordan, Co-Founder of Alchera Technologies

5 Q’s for Anna Jordan, Co-Founder of Alchera Technologies

by Kir Nuthi
Anna Jordan

The Center for Data Innovation spoke with Anna Jordan, co-founder and operations lead of Alchera Technologies. Alchera Technologies is an AI-powered, cloud-based platform that analyzes real-time mobility data from vehicles and pedestrians to better plan smart cities. The company has partnered with multiple County Councils within the United Kingdom, the latest being Lanchasire City Council, to improve public transportation.

Kir Nuthi: How does Alchera Technologies use AI and machine learning to fight traffic congestion and promote smart city planning?

Anna Jordan: Alchera is a specialist in enterprise-grade mobility and infrastructure systems, providing software and machine learning tools to power data-driven infrastructure. The company specializes in helping operators of transport networks to maximize operational and commercial value using existing data.

Alchera’s suite of data-driven infrastructure (DDI) applications collects, analyzes, and makes recommendations to decision-makers with data in real-time from existing sensors across transport networks. These applications power specific, multi-million-pound mobility decisions, such as dynamically scheduling road space for operational efficiency and recommending traffic light changes to improve bus reliability.

The interesting thing is that we are actively using real-world data in real-time to power our machine learning models, meaning that the outputs are vastly more accurate and fit for purpose than more traditional methods. For example, smart city planning is often still done by transport models built in the 1980s. The world is a drastically different place now than it was then, from people’s behaviors to the transport options we have. We are providing the software platform and machine learning that can bring those approaches into the present—from powering modeling with up-to-date data to using new analytics methods to dynamically optimize how the city can self-optimize its own traffic lights.

Nuthi: Why does Alchera focus on traffic congestion, and why is this regulatory space a unique opportunity for data-driven solutions?

Jordan: Alchera’s DDI applications all focus on road space use cases—from urban traffic to motorway operation or airport surface access optimization. Unfortunately, where there is a road, there is often congestion, and so traffic naturally falls into our (steering) wheelhouse.

Alchera doesn’t just focus on traffic congestion but more so takes a holistic view of everything that impacts our roads. Our Pinch Point Analysis tool is just one of a suite of planned applications that seek to address the challenges of managing and optimizing road networks. Another example of a current live application is for dynamic road space booking—minimizing the impact of roadworks on a highway or urban road both for congestion but also for optimizing costs involved and the safety of the road workers.

Traffic congestion is a problem suffered in almost every urban area worldwide and has traditionally been a challenging environment to accommodate real-time, demand-based change.

However, the relatively recent boom in smart signals across transport networks in urban and peri-urban areas has given local and regional transport teams the ability to monitor and update traffic light phasing in real-time. These smart networks are an ideal environment for Alchera’s machine learning and AI to provide data-driven decision-making to improve journey times, promote consistent traffic flow and, as a result, reduce emissions.

It is a unique space as the move towards a more integrated data landscape means that our customers are seeking better ways of operating in real-time as a way to be more resilient to the fast-paced changes in behavior and more complex types of trips that their road users are taking. Data-driven solutions which can take into account the huge range of sensor types, latencies, and commercial providers and churn out useful answers are few and far between as it is a difficult technical undertaking, but one which has huge rewards when done well.

Nuthi: The use of mobility data in smart city planning is growing and is continuing to grow as policymakers look to data to plan sustainable solutions. Could you explain what is Alchera’s Pinch Point Analysis Tool as well as what are its current applications?

Jordan: Alchera has developed a Pinch Point Analysis Tool (PPAT) as an application specifically designed to analyze public transport systems focused on bus journeys. Finding and decreasing the impact of pinch points where buses waste time—and therefore making buses faster and reliable—is one of the biggest levers local authorities can pull to attract more ridership, improve wider network operation, and deliver on ambitious targets for improved bus services over time.

Alchera’s Bus Pinch Point Application aims to improve bus transport network operation in two ways: 1) Reducing variability in journey times to make buses more consistent; and 2) Increasing the average speed on routes to make bus journeys faster.

By taking a data-driven approach to understanding these two key variables, Alchera’s PPAT provides better visibility of pinch points for local authorities to make data-driven decisions, monitor and deliver new bus routes, and improve the passenger experience through better services. Monitoring how these bus services perform also gives a baseline to their fleet carbon emissions and allows local authorities to start to tune their infrastructure to prioritize the lowest emitting travelers—for example, speeding up busy buses. Optimizing these routes, as well as encouraging more people onto public transport, also serves to help local authorities meet their net zero carbon emissions policies.

Nuthi: What are some primary concerns you have encountered regarding the use of AI to reduce traffic congestion and vehicle pollution?

Jordan: One of the main concerns the transport market has around AI tools is related to the “black box” understanding the algorithms themselves. In many cases, once a machine learning model has been trained, there’s not a full understanding of how it actually works, exactly which data it’s using for any given decision, and what variables are being used to formulate a response.

Alchera avoids this issue by ensuring there’s full traceability back to the original data sources and model transparency for all recommendations and how a decision is made. This means that at every step, it is deliberately possible to sense check any decision or optimization to ensure that safe and reliable outcomes are achieved.

Nuthi: Now that Alchera has seen great success in partnering with Oxfordshire and Lancashire’s City Councils, what are the next steps for Alchera?

Jordan: Alchera’s DDI applications and Data Platform are in active use by both public and private sector operators, and we are excited to see which applications will take off next.

We are rapidly expanding our customer base for the Bus Pinch Point application across several other local authorities in the United Kingdom. We expect to make further public announcements related explicitly to those engagements in due course.

Some of our other DDI applications in use include a Dynamic Road Space application for managing highway operations that has been successfully tested and trialed by Connect Plus. Working on powering better data-driven decisions for the mobility infrastructure sector is our goal.

Alchera’s ambition is to continue to build out and release new DDI Applications in the next 12 months—we are always happy to speak to operators struggling to get answers from their data to see if we can help!

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