Real-time network-wide traffic signal optimization considering long-term green ratios based on expected route flows

Yang, Inchul; Jayakrishnan, R.

doi:10.1016/j.trc.2015.09.003

Cited by 16 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…More importantly, as many cities are growing by size and population, urban traffic studies have gained more popularity through multiple practical needs, policy reforms, regulatory perspectives, businessoriented analysis, impact assessment, and planning strategies. These studies have significantly contributed to major factors of efficient traffic modeling (Borg and Scerri, 2015), real-time system optimization (Yang and Jayakrishnan, 2015), recognition of various climate impacts by traffic (Golkhale and Pandian, 2007;Xie et al, 2013;Chang et al, 2019), and urban traffic congestion patterns (Karim et al, 2017), or those that suggest urban planning strategies and performative solutions. It is important to note there is growing research in developing countries or associated with cities facing rapid urbanization or urbanizing trends.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…() optimized minimum and maximum green durations, phase sequence, and actual green durations in a central bilevel dynamic programming model. Yang and Jayakrishnan () optimized green splits based on estimations of the optimal network flow for a 15‐minute period. Clempner and Poznyak () proposed a modeling approach for multi‐traffic signal‐control synchronization problem using noncooperative game‐theory approach, and solved it using Nash equilibrium based on the extraproximal method.…”

Section: Literature Reviewmentioning

confidence: 99%

A Cell‐Based Distributed‐Coordinated Approach for Network‐Level Signal Timing Optimization

Mehrabipour

Hajbabaie

2017

Computer aided Civil Eng

View full text Add to dashboard Cite

This article develops an efficient methodology to optimize the timing of signalized intersections in urban street networks. Our approach distributes a network‐level mixed‐integer linear program (MILP) to intersection level. This distribution significantly reduces the complexity of the MILP and makes it real‐time and scalable. We create coordination between MILPs to reduce the probability of finding locally optimal solutions. The formulation accounts for oversaturated conditions by using an appropriate objective function and explicit constraints on queue length. We develop a rolling‐horizon solution algorithm and apply it to several case‐study networks under various demand patterns. The objective function of the optimization program is to maximize intersection throughput. The comparison of the obtained solutions to an optimal solution found by a central optimization approach (whenever possible) shows a maximum of 1% gap on a number of performance measures over different conditions.

show abstract

“…To lower the size of space states, El-Tantaway and Abdulhai [30] introduced the decentralised design to the signal control system to decrease the computationally expensive. A realtime traffic signal control scheme was developed by Yang and Jayakrishnan [31] which is capable of considering expected route flows in the form of long-term green time ratios for intersection movement. The method used the ratio of the cumulative green time to the desired green time to update the queue weights and to find proper signal timing for minimum total delay.…”

Section: Introductionmentioning

confidence: 99%

Real‐time traffic signal control for intersections based on dynamic O–D estimation and multi‐objective optimisation: combined model and algorithm

Jiao

Sun

Dong-yue

et al. 2018

IET Intelligent Transport Systems

View full text Add to dashboard Cite

Real-time network-wide traffic signal optimization considering long-term green ratios based on expected route flows

Cited by 16 publications

References 28 publications

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

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

A Cell‐Based Distributed‐Coordinated Approach for Network‐Level Signal Timing Optimization

Real‐time traffic signal control for intersections based on dynamic O–D estimation and multi‐objective optimisation: combined model and algorithm

Contact Info

Product

Resources

About