Development of mathematical models to improve road freight movements for tunnel infrastructure using connected and autonomous vehicles

Road freight transportation is considered the backbone of country’s socio-economic framework and thus its vital to ensure it is working optimally. The research detailed in this thesis is focused on improving the movement of road freight, especially for hazardous goods vehicles via a road tunnel, with the help of Connected and Autonomous Freight Vehicles (CAV-F). The study analyses real-world Dartford Crossing tunnel data to identify the impact of existing check and allow procedures for Dangerous Goods Vehicles (DGVs) and Abnormal Load Vehicles (ALVs) at a tunnel. A near realistic traffic simulation model is developed as part of analysis and is validated against an independent Highways England’s Motorway Incident Detection and Automatic Signalling (MIDAS) data. The effectiveness of CAV-F in improving road traffic conditions is measured using different simulation scenarios involving mixed traffic (i.e. CAV-F and conventional vehicles alongside) and different real-world tunnel closure conditions. Once the effective performance of CAV-F is established, this research develops a novel mathematical model aimed at automating the check and allow procedures for DGVs at the tunnel. The mathematical model calculates the geo-reference locations for the placement of cooperative communications between the vehicles and road infrastructure to generate dynamic vehicular gaps. This will allow desired safety gaps between the platoon of DGVs and its preceding and following vehicles enabling isolated travel via the road tunnel to ensure safe and secure passage. The mathematical model is verified for different road layouts determined based on geo-referenced locations, approaching a road tunnel. Using traffic simulation, the results determine if the modulation of vehicles’ speeds at identified geo-referenced locations are suitable for desired gap generation. Finally, to conclude the research questions, the second mathematical model is developed to help uninterrupted traffic merging at the junctions, as was observed after the successful gap generation. This model could also be generalised to optimise the traffic merge sequence at a motorway junction. The approach is inspired by the noise cancellation technique which utilises destructive wave interference patterns, where vehicular flow on two merging roads is considered as traffic waves. By analysing the merge sequence of vehicles at the junction from fixed equidistant positions on separate roads, the dynamic phase shifting is applied by modulating the speeds of the identified vehicles which would otherwise approach at the junction simultaneously, leading to queue formation (or collision). The performance of the approach is then measured using a traffic simulation model and are determined against existing real-world traffic flow on motorways for improvements in travel time, and traffic throughput and reduction in congestion, with increasing traffic density