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

With the increasing adoption of intelligent transportation systems and theupcoming era of autonomous vehicles, vehicular services (such as, remotedriving, cooperative awareness, and hazard warning) will face an ever changingand dynamic environment. Traffic flows on the roads is a critical condition forthese services and, therefore, it is of paramount importance to forecast howthey will evolve over time. By knowing future events (such as, traffic jams),vehicular services can be dimensioned in an on-demand fashion in order tominimize Service Level Agreements (SLAs) violations, thus reducing the chancesof car accidents. This research departs from an evaluation of traditionaltime-series techniques with recent Machine Learning (ML)-based solutions toforecast traffic flows in the roads of Torino (Italy). Given the accuracy ofthe selected forecasting techniques, a forecast-based scaling algorithm isproposed and evaluated over a set of dimensioning experiments of three distinctvehicular services with strict latency requirements. Results show that theproposed scaling algorithm enables resource savings of up to a 5% at the costof incurring in an increase of less than 0.4% of latency violations.

Dimensioning of V2X Services in 5G Networks through Forecast-based Scaling