A methodology to control urban traffic noise under the constraint of environmental capacity: A case study of a double-decision optimization model. (December 2015)

• Record Type: Journal Article
• Authors: Huang, Baoxiang; Pan, Zhenkuan; Wang, Guodong
• Is Part Of: Transportation research. Volume 41(2015)
• Page Start: 257
• Page End: 270
• Publication Date: 2015-12
• Subjects: Traffic noise -- Environmental capacity -- Double decision optimization -- Noise control zone; Transportation -- Research -- Periodicals; Transportation -- Environmental aspects -- Periodicals; 354.76
• DOI: 10.1016/j.trd.2015.09.026
• Languages: English
• ISSNs: 1361-9209

Abstract

Highlights: A methodology to control traffic noise with noise capacity constraint was provided. Integrated modeling framework incorporated models based on a bi-level method. Genetic algorithm was used to calculate the proposed model. The proposed method was verified useful for urban traffic sustainable development. Abstract: This paper presents a novel methodology to control urban traffic noise under the constraint of environmental capacity. Considering the upper limits of noise control zones as the major bottleneck to control the maximum traffic flow is a new idea. The urban road network traffic is the mutual or joint behavior of public self-selection and management decisions, so is a typical double decision optimization problem. The proposed methodology incorporates theoretically model specifications. Traffic noise calculation model and traffic assignment model for O–D matrix are integrated based on bi-level programming method which follows an iterated process to obtain the optimal solution. The upper level resolves the question of how to sustain the maximum traffic flow with noise capacity threshold in a feasible road network. The user equilibrium method is adopted in the lower layer to resolve the O–D traffic assignment. The methodology has been applied to study area of QingDao, China. In this illustrative case, the noise pollution level values of optimal solution could satisfy the urban environmental noise capacity constraints. Moreover, the optimal solution was intelligently adjusted rather than simply reducing the value below a certain threshold. The results indicate that the proposed methodology is feasible and effective, and it can provide a reference for a sustainable development and noise control management of the urban traffic.