Traffic simulation of connected and autonomous freight vehicles (CAV-F) using a data-driven traffic model of a real-world road tunnel

Abstract

The study is based on the Dartford-Thurrock Crossing tunnel, Kent, UK. It analyses the impact of the tunnel closures necessary for monitoring the flow of Dangerous Goods Vehicles and Abnormal Load Vehicles as per The European Agreement concerning the International Carriage of Dangerous Goods by Road regulations. A traffic simulation model is developed using PTV Vissim software, based on real-world Dartford Crossing traffic data and validated against independent Motorway Incident Detection and Automatic Signalling data. The autonomous driving implementations of Dangerous Goods Vehicles and Abnormal Load Vehicles, defined as per CoEXist project in the PTV Vissim software, are compared against the conventional vehicles traffic simulations. The results show that if the tunnel closures are reduced to two or less per hour then significant improvements in road congestion and travel time are observed. Furthermore, the benefits of autonomous Dangerous Goods Vehicles and Abnormal Load Vehicles are observed in improving traffic queues and travel times, given that the Dartford Crossing tunnel is appropriately equipped with intelligent communication technologies. The study shows that even with a small proportion of Connected and Autonomous Vehicles, the movement of road traffic can largely be influenced.