RBM Traffic Solutions Ltd
RBM RBM Traffic Solutions (RBMTS) is a transport modelling and research consultancy, which has supplied services to the University of Aberdeen, Vectos and the Transport Catapult (now the Connected Places Catapult). As well as working to deliver innovative courses on Transport Modelling and Simulation and Traffic Engineering (for the University of Aberdeen), the company is now focussed on product development in the form of a traffic computer simulation model which is a macroscopic model of traffic flow in road transport networks, based on the latest research , with the capacity to model signals, prices and queues. The director of RBMTS is Dr Richard Mounce. He has significant experience in the field of transport network modelling; as well as knowledge of AI, e.g. how pattern matching can be applied in both the transport and other sectors .
RBM Traffic Solutions Ltd is registered in England and Wales under number 09915181.
Links:
Academic Website: https://www.abdn.ac.uk/geosciences/people/profiles/r.mounce/
News
2023
RBM Traffic Solutions Ltd awarded a BridgeAI award as the lead partner from Innovate UK for a 6 month PointsAI project to utilise evolutionary artificial intelligence (MHS Ltd) to address the problem of optimising prices and signals in road transport networks. A road traffic network model developed by RBM TS will be utilised for this purpose. This model can quickly find the user equilibrium flows, which makes it ideal for the large succession of model runs which will be needed when implementing a genetic algorithm optimisation approach.
2023
RBM Traffic Solutions Ltd named as one of the top 100 businesses in York in 2023 by York business school in association with York St John University.
2023
RBM Traffic Solutions Ltd (as sub-contractor to Mounce Hydrosmart Ltd) named as one of the finalists of the Ofwat Discovery competition in July 2023 on the ACQUIRE project sharing in £1million to demonstrate bold solutions to some of the water sector’s biggest challenges. The ACQUIRE Project will analyse drinking water quality incident report data from water companies using the latest AI techniques (including Generative AI and Large Language Models) to develop an interactive management tool and open source portal benefitting the industry and its customers.
Providing bespoke data driven analytics and machine learning applications for the water sector
