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

Traffic simulation of connected and autonomous freight vehicles to increase traffic throughput via road tunnel networks

This paper simulates traffic at the Dartford-Thurrock Crossing Tunnel, Kent, UK. Using a traffic simulation model, Connected and Autonomous Freight Vehicles (CAV-F) are simulated alongside conventional light goods vehicles, to determine the feasibility of increasing the traffic throughput at the tunnel. The results show that with the use of CAV-F, the overall traffic flow is increased by --33% from current flow of --5,000 vehicles/hr. With the reduction in the headway and standstill distance and increase in scope of intelligent connectivity and traffic speed limit, the average congestion and travel time are reduced even at a higher traffic concentration. By analysing the results, it has thus been possible to highlight the benefits to traffic management and road utilisation by introducing CAV-F into our road network, in the long term

Multi-lane urban mmWave V2V networks : a path loss behaviour dependent coverage analysis

Vehicular cooperative autonomy characteristics such as adaptive platooning and collision avoidance are enabled only through the capability to reliably exchange, at multi-Gbps speeds, an ever growing quantity of data that are being generated by light detection and ranging (LIDAR), HD video, radar, and other sensors. Due to its high bandwidth availability, the mmWave communication channel is expected to act as the required, underpinning technological enabler. In this paper, a tractable analytical model for an in-lane routing scheme that approximates the coverage, rate coverage and an adaptation of area spectral efficiency of mmWave urban Vehicle-to-Vehicle networks is proposed. The analytical model is proposed for three different path loss behaviour scenarios, namely, Line-of-Sight, Non-Line-of-Sight, and Obstructed-Line-of-Sight. Each scenario is based upon corresponding, previously reported, experimental mmWave measurements and path loss models. It is shown that Non-Line-of-Sight behaviour provides the best performance in coverage, but the lowest reliability. Moreover, the careful choice of link distances, i.e. forcing communication to be limited to the nearest vehicle, removes the sensitivity of the system to interferences from increased vehicle density, which is an important result to be considered in dense urban networks. Additionally, it is found that narrowing the beamwidth significantly improves the performance, which is the result of eliminated interferences, rather than a corresponding increase in antenna gain. The results of this research will impact both communications systems infrastructure designers and vehicle manufacturers looking to balance system performance in the investigated scenarios