Highways UK 2020 Starts in

4/5 November 2020, NEC, Birmingham





Congestion Category Shortlist

01 Atkins

Redeploying the SVD radar network to monitor traffic density and congestion 

What is your proposal?

The project proposes using existing selectable vehicle detection (SVD) radar sites installed on the all lane running J23-27 section of the M25 and reconfiguring them for monitoring traffic density and congestion. This traffic information would then be relayed either to the Regional Control Centre or directly to connected vehicles.

What does it do?

The proposal is to demonstrate that SVD radars are suitable for existing Motorway Incident Detection and Automatic Signalling (MIDAS) requirements. The SVD radars provide near 100% road coverage and, if configured for this purpose, could potentially replace the existing MIDAS sensors, improving safety and significantly reducing the infrastructure costs of implementing future smart motorway schemes.

How does it meet the challenge?

The solution is a cost-effective alternative to achieving and potentially improving the safety benefits currently provided by MIDAS. When a motorway incident occurs, the resulting traffic queue has to reach a sensor, currently positioned every 500m, for the MIDAS system to trigger speed restrictions and lane closures via the variable message signs. By deploying SVD radar units, which effectively provide 100% coverage, the response would be immediate. Connected vehicles could receive the information directly, potentially over time removing the need for variable message signs.

Kas Stucinskas




02 Neural Mind

Artificial intelligence powered camera system offers analysis based on actual vehicle data

What is your proposal?

Neural Mind is an artificial intelligence powered camera system combined with a cloud based big data analytics platform. The system allows the management of a traffic network by providing granular vehicle by vehicle data. This information empowers road operators to make informed decisions about improvement schemes whilst automating congestion alleviation measures. The camera system uses computer vision to recognise a vehicle down to make and model level without the requirement of referencing a database. Each vehicle pass is recorded in the cloud platform which is infinitely scalable allowing the connection of devices across networks to provide intelligence on vehicular profile and movement.

What does it do?

The Neural Mind system analytics are presented in an easy and flexible manner and do not require any specialist training to interpret, realising a significant cost benefit. Schemes can be implemented based on true individual vehicle metrics and unique classifications such as 'electric vehicle', enabling the easy implementation of clean air zones or planning electric vehicle charging locations exactly where they are required.


How does it meet the challenge?

The use of open protocols allows automated outputs to variable message signs for queue protection or user defined metrics. Root cause analysis of congestion provides insight from genuine vehicle data rather than inferred from existing data capture techniques.


Chris Stretton  



Stand E21: Please visit Mway Comms for a live demonstrations of the system



03 Cleverciti Systems GmbH

Informed parking decisions based on real time data

What is your proposal?

Around 30% of traffic and subsequent pollution is caused by drivers looking for parking. Studies show that real time detection of available on-street parking spaces can decrease traffic, pollution, time and distance to park by 30% or more.

What does it do?

Cleverciti sensors monitor the availability of parking spaces in real time. This data is then integrated into existing apps, unique bespoke apps, navigation devices, parking guidance systems, variable message screens, parking management or smart city platforms.

How does it meet the challenge?

Providing parking users with accurate real time information will allow more informed travel decisions based on real time data rather than luck, thus reducing congestion. As cities develop this data will prove vital to allow all technologies to work together in order to create a Smart City environment. This environment will then allow future technologies to be seamlessly integrated, such as the availability of electric vehicle charging points and spaces for autonomous vehicles.

Chris Heddle


www. cleverciti.com


04 1.21GigaWatts

Applying the concept of an air traffic control system to the road network

What is your proposal?

The Responsive Traffic Controller (RTC) is a unique situational awareness module to be embedded in the road infrastructure that enables the traffic control system to intelligently understand what is happening on the road, preform centralized scene analysis and actively guide vehicles, like the air traffic control system guides planes.

What does it do?

The RTC intercepts and analyzes the vehicles' radio frequency (RF) footprints and generates a live dynamic map of the area and the road users. This unique layer of data is used to actively guide vehicles and optimize the traffic flow by communicating directly with the vehicle's sensors.

How does it meet the challenge?

We materialize the notion that better driving decisions can and should be taken outside of the vehicle, by an intelligent external observer. The RTC will enhance safety, optimize the flow and improve the user experience. Due to the unique approach and cutting-edge sensing capabilities, we enable the transformation of traffic flow from liquid dynamics to slot based mechanism to improve congestion by minimizing unnecessary stops, optimizing the flow and enhancing road safety for all road users. The RTC will also support better and real time management and control capabilities for cities, road owners and operators.

Roni Dulberg




05 TransPix

Use of actual traffic patterns improves effectiveness of traffic management systems

What is your proposal?

SmartFlo uses artificial intelligence to analyse road traffic-patterns in real-time using only video cameras. It collects enriched and granular data for intelligent transportation systems. Actual not estimated data optimizes urban networks by 15-20%.

What does it do?

SmartFlo offers intelligent data analysis in real-time for optimizing real-time urban transport congestion management. Videos are collected from internet of things cameras, processed in real-time in-situ or the cloud. The system is able to detect variables including turning-movement, lane occupancy, queue length, vehicle classification, lane-merging, lane-changes, congestion, accident, road-works, stopped vehicles, and traffic patterns such as the interaction of freight in port cities.

How does it meet the challenge?

SmartFLo analyses complex driver behaviour. At present the only data that can be collected in real-time is the number of vehicles on the road, together with their type and speed; all other data used by urban traffic management systems is estimated. SmartFlo collects intelligent data on real traffic patterns. Research suggests that traffic management systems using this data instead of estimated data, respond better to the network perturbations in real-time and give an improvement in travel time of between 10-20%

Aparna Garg



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