How can connected and automated vehicles improve merging efficiency at freeway on-ramps?

Abstract

Traffic bottleneck is frequently generated near the freeway on-ramp due to the merging behaviour of vehicles. Given the technology advance of connected and automated vehicles (CAVs), this study aims to investigate the performance of CAVs on reducing the merging time at real-world freeway on-ramp via a comparison study based on both empirical and simulation data. First, real merging scenarios composed of empirical trajectory data were extracted from the Next Generation Simulation (NGSIM) dataset. Each merging scenario contains three manually driven vehicles (MDVs): the leading MDV and following MDV on the mainline, and the merging MDV from the on-ramp. Then the merging MDV is replaced by a CAV whose trajectory is optimised by a proposed optimisation model to minimise its merging time. The results indicate that under the same merging scenarios, CAVs can merge more efficiently and smoothly than MDVs while ensuring safety. Finally, sensitivity analyses were conducted to test the robustness and transferability of the proposed model.

KEYWORDS:

• Merge efficiency
• freeway on-ramp
• connected and automated vehicles
• trajectory optimisation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was sponsored by the National Natural Science Foundation of China [71901223], the Foundation of Central South University [No. 202045010], Shanghai Pujiang Program [2020PJC086], and the Joint Laboratory for Internet of Vehicles, Ministry of Education-China Mobile Communications Corporation [2020109], the Postgraduate Research Innovation Project of Central South University [1053320215544].