Study on Simulation Optimization of Dynamic Traffic Signal Based on Complex Networks

Li, Shubin; Yang, Li; Fu, Bai-Bai; Wen-xiu, Dang

doi:10.1016/j.proeng.2016.01.228

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…As part of urban traffic simulation studies, urban traffic modeling is a common analytical approach to assess and optimize vehiclebased studies (Amadio et al, 2018), urban traffic flow (Ledoux, 1997), urban traffic control system (Boillot et al, 2006) or prediction (De Oliveira and Camponogara, 2007), mode choice behavior (Garcia-Aunon et al, 2019), urban traffic networks (Li et al, 2016), etc. Some studies also utilize combined modeling analyses, such as the development of a nexus between traffic flow and urban networks (Gartner and Stamatiadis, 2002), or mathematical methods (Wang et al, 2006), multi-agent methods (Ou et al, 2000), multi-objective methods (Tang and Wang, 2007), prediction methods (Nigarnjanagool and Dia, 2005), network-based studies (Schadschneider et al, 2005), integrated methods (Li and Zhao, 2008), etc.…”

Section: State Of the Artmentioning

confidence: 99%

Urban Traffic Optimization Based on Modeling Analysis of Sector-Based Time Variable: The Case of Simulated Ningbo, China

Cheshmehzangi¹,

Ardakani²

2021

Front. Sustain. Cities

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This paper studies the important factor of sector-based time variable, which is critical to urban mobility patterns in an urban environment. In particular, this study analyzes urban traffic optimization based on modeling analysis of a simulated urban environment. In doing so, we develop and assess the urban traffic model based on three key components of agents, urban map, and mobility pattern. We develop an urban sample based on a medium-to-large city in China, which is represented by Manhattan grid pattern layout. By developing a homogeneous urban layout, we distribute the urban blocks of various sectors across the sample model. Through simulation studies, we model urban traffic based on “conventional operation hours” and “proposed operation hours” of all sectors. This urban traffic model is used to study the impact of the proposed approach on urban traffic based on two measured metrics of end-to-end delay (ETE) and Agent queue count (AQC). By suggesting a new sector-based time variable, we then evaluate the urban traffic model based on multiple active agent ratio. The findings from this simulation uncover the importance of sector-based time variable in optimizing urban traffic.

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