English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Platooning of connected and autonomous vehicles (CAVs) is an emergingtechnology with a strong potential for throughput improvement and fuelreduction. Adequate macroscopic models are critical for system-level efficiencyand reliability of platooning. In this paper, we consider a hybrid queuingmodel for a mixed-autonomy highway section and develop an easy-to-use trainingalgorithm. The model predicts CAV and non-CAV counts according to the trafficdemand as well as key parameters of the highway section. The training algorithmlearns the highway parameters from observed data in real time. We test themodel and the algorithm in Simulation of Urban Mobility (SUMO) and show thatthe prediction error is around 15% in a stationary setting and around 25% in anon-stationary setting. We also show that the trained model leads to a platoonheadway regulation policy very close to the simulated optimum. The proposedmodel and algorithm can directly support model-predictive decision-making forplatooning in mixed autonomy.

A Hybrid Queuing Model for Coordinated Vehicle Platooning on Mixed-Autonomy Highways: Training and Validation