RBM Traffic Solutions Ltd
RBM Traffic Solutions Ltd* is a transport modelling and research consultancy, specialising in traffic signal control and optimisation. RBM Traffic Solutions has been developing a computer simulation model for modelling road transport networks; this model was used on the Innovate UK funded PointsAI project, which explored the feasibility of utilising evolutionary artificial intelligence to optimise signals and prices in road transport networks. RBM Traffic Solutions is also contributing to the ACQUIRE project (funded as part of the Ofwat Discovery Challenge), utilising case based reasoning to build a decision support tool for water quality incident management.
The director of RBMTS is Dr Richard Mounce. Richard has expertise in dynamic traffic assignment, traffic signal control and intelligent transport systems. With over 25 years’ experience in the modelling and optimisation of road traffic networks, he has worked on numerous transport research projects (including at the Universities of York, Cambridge and Queen’s University Belfast) and has taught MSc courses in Transport Modelling and Simulation and Traffic Engineering at the University of Aberdeen.
Professor Mike Smith is an Emeritus Professor of Mathematics at the University of York, has over 45 years' experience in the modelling and optimisation of road traffic networks. He has a distinguished record of publishing ground-breaking research. Some projects involved real life implementations with the City of York Council. Mike has been awarded 2 international prizes.
*Registered in England and Wales under number 09915181
Recent Publications
Smith, M. J., and Mounce, R. (2024) Backpressure or no backpressure? Two simple examples. Transportation Research Part C 161, April 2024, 104515.
Smith, M. J., Viti, F., Huang, W. and Mounce, R. (2023) With spatial queueing, the P0 responsive traffic signal control policy may fail to maximise network capacity even if queue storage capacities are very large. Transportation Research Part B 177 (2023) 102814.
Smith, M. J., Viti, F., Huang, W. and Mounce, R. (2023) Upstream-gating merge-control for maximising network capacity: With an application to urban traffic management. Transportation Research Part C 155, October 2023, 104287.
Smith M. J., Iryo, T., Mounce, R., Satsukawa, K. and Watling, D. (2022) Zero-queue traffic control, using green-times and prices together. Transportation Research Part C 138, 103630.
News
2024
RBM Traffic Solutions Ltd is a partner on the ACQUIRE project, which has been named as one of the Ofwat Discovery Challenge Winners! From a field of over 240 bids, 10 innovators are sharing in up to £4.5 million, to demonstrate how their bold solutions can help solve the biggest challenges facing the water sector, today and into the future. RBM Traffic Solutions Ltd will support project lead Mounce Hydrosmart Ltd and partner with the University of Sheffield on the ACQUIRE project, which will analyse drinking water quality incident reports from water companies and other sources using the latest AI techniques (including Generative AI, Large Language Models and Case Based Reasoning) to develop an interactive management tool and online portal benefiting the industry and helping keep customers safe.
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
