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CAVIE - Connected and Autonomous Vehicles in an Infrastructure Environment

EPSRC iCASE with Costain Ltd

Student: Kushagra Bhargava (WMG and Costain Ltd.) Industrial Supervisor: Dr Kum Wah Choy (Costain Ltd.)

Academic Supervisors: Dr Matthew Higgins, Dr Stewart Birrell.

Challenge: Congestion has been an age-old problem which is mainly caused by rise in travel demand with the existing capacity of transportation infrastructure. Many previous studies have provided ways to ease congestion by proposing an increase or upgrade to the existing road infrastructures, by use of Intelligent Transportation Systems (ITS), change in work and logistic patterns to avoid peak hours or to implement a congestion or work parking charges to make people use public transportation instead of driving themselves. These solutions are either too difficult to implement or are short-lived due to continuous increase in traffic demand. It is also often challenging to justify the cost of building new roads, bridges, tunnel or implementing driving charges to the long-term benefits of easing congestion and travel time. It is also difficult to implement monetary penalties for using vehicles to commute and can have decremental effects on businesses and work environments. With CAV becoming mainstream, it’s time to explore new venues to find a solution to the congestion problem.

Objectives: With this study we are aiming to understanding the congestion pattern by looking at individual flow of vehicles, especially Dangerous Good Vehicles (DGVs), in and around the tunnel subsystems. Special international laws and regulations are required in dealing with DGVs on roads. For the tunnels, the ADR (The European Agreement concerning the International Carriage of Dangerous Goods by Road (ADR) codes are required, which based on its grading, permit the transit of certain types of DGVs via a tunnel. To ensure the safety of the tunnel subsystem, checking methods such as manual checks at the tolls or automatic detection of DGVs (at Dartford Crossing Tunnel) are deployed to stop unauthorised entry of DGVs. From the traffic data obtained from Highways England and local City Councils, the study will remodel the existing traffic and congestion scenarios. Then, CAV enabled traffic will be simulated for different scenarios such as mixed traffic with autonomous vehicles, fully autonomous traffic, autonomous DGVs but rest traffic being non-autonomous, etc. The results will then be analysed to conclude the performance of CAV enabled DGVs in easing congestion. The study does not aim at proposing new ways of improving transportation infrastructure nor introduction of new monetary penalties but working with existing network of traffic and CAV how can be better improve the congestion. The study might be extended to study other Heavy Good Vehicles or Abnormal Load vehicles in dealing with traffic congestion.

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