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

Owing to the rapid growth number of vehicles, urban traffic congestion hasbecome more and more severe in the last decades. As an effective approach,Model Predictive Control (MPC) has been applied to urban traffic signal controlsystem. However, the potentially high online computation burden may limit itsfurther application for real scenarios. In this paper, a new approach based ononline active set strategy is proposed to improve the real-time performance ofMPC-based traffic controller by reducing the online computing time. Thisapproach divides one control cycle into several sequential sampling intervals.In each interval, online active set method is applied to solve quadraticprogramming (QP) of traffic signal control model, by searching the optimalsolution starting at the optimal solution of previous interval in the feasibleregion. The most appealing property of this approach lies in that it candistribute the computational complexity into several sample intervals, insteadof imposing heavy computation burden at each end of control cycle. Thesimulation experiments show that this breakthrough approach can obviouslyreduce the online computational complexity, and increase the applicability ofthe MPC in real-life traffic networks.

Computation Load Balancing Real-Time Model Predictive Control in Urban Traffic Networks