Management of traffic congestion in adaptive traffic signals using a novel classification-based approach. Issue 9 (2nd September 2019)

• Record Type: Journal Article
• Authors: Sadollah, Ali; Gao, Kaizhou; Zhang, Yicheng; Zhang, Yi; Su, Rong
• Is Part Of: Engineering optimization. Volume 51:Issue 9(2019)
• Page Start: 1509
• Page End: 1528
• Publication Date: 2019-09-02
• Subjects: Traffic signal scheduling -- traffic congestion -- classification -- support vector machine -- extreme learning machine; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2018.1525708
• Languages: English
• ISSNs: 0305-215X

Abstract

ABSTRACT: Traffic congestion is a critical problem which makes roads busy. Traffic congestion challenges traffic flow in urban areas. A growing urban area creates complex traffic problems in daily life. Congestion phenomena cannot be resolved only by applying physical constructs such as building bridges and motorways and increasing road capacity. It is necessary to build technological systems for transportation management to control the traffic phenomenon. In this article, a new idea is proposed to tackle traffic congestion with the aid of machine learning approaches. A new strategy based on a tree-like configuration ( i.e. a decision-making model) is suggested to handle traffic congestion at intersections using adaptive traffic signals. Different traffic networks with different sizes, varying from nine to 400 intersections, are examined. Numerical results and discussion are presented to prove the efficiency and application of the proposed strategy to alleviate traffic congestion.