Dynamic lane configuration and cooperative autonomous vehicles for improving travel time on smart roads: a case study

Abstract To reduce congestion on urban and suburban road networks, specific capacity-enhancing strategies must be implemented, either through physical and structural interventions or by adopting appropriate traffic regulation systems. The introduction of smart roads and autonomous vehicles (CAVs) offers new opportunities to enhance road functionality by adjusting capacity to fluctuating traffic demand over time. Among these strategies is the Dynamic Lane Configuration (DLC), which enables the dynamic adjustment of the number of lanes by modifying their original number and width, without compromising road safety in traffic flows composed of CAVs. This article presents the results of a study conducted on an urban arterial road in Italy, which was reconfigured by increasing the number of lanes and simultaneously reducing their width. Travel time and space mean speed were derived through the analysis of TomTom big data, and macroscopic fundamental diagrams (MFDs) were obtained, along with travel time functions, in the case of manually driven vehicles. A simplified traffic engineering closed-form model was subsequently developed to evaluate the effects of implementing the DLC system on the selected road, with a view toward its potential future transformation into a smart road accommodating different market penetration levels of CAVs. The proposed approach is then tested through several traffic analyses, which highlight the potential benefits of the novel traffic control strategy.