Model‐based control strategy for oversaturated traffic regimes based on the LWR‐IM traffic model

Ng, Kok Mun; Reaz, Mamun Bin Ibne; Ali, Mohd

doi:10.1049/iet-its.2018.5381

Cited by 6 publications

(8 citation statements)

References 21 publications

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“…Heuristics control methods were also compared relatively with other control methods to investigate their effects on the road networks. The MCS heuristics algorithm which was developed by the authors had been compared previously with fixed-time system (Ng et al, 2019). The MCS improved performance of average delays up to 24% when compared with existing fixed-time system and successfully regulates queue spillbacks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Figure 1 provides an overview of the closed loop behaviour of the model-based control strategy (MCS) introduced by Ng et al (2019). The MCS is an extension to traffic simulator called UTNSim developed by Ng et al (2015;.…”

Section: Model-based Control Strategymentioning

confidence: 99%

“…Recently, the authors proposed a heuristic modelbased control strategy (MCS) for urban networks. The MCS improved performance of average delays up to 24% when compared with existing fixed-time system and successfully regulates queue spillbacks (Ng et al, 2019).…”

Section: Introductionmentioning

confidence: 96%

“…Hence, the research question at hand is "Can heuristics traffic control strategies outperformed meta-heuristics in terms of performance and computation costs?" In view of this; a comparative study on the heuristics model-based control strategy (MCS) by Ng et al (2019) against meta-heuristics methods in Hajbabaie et al (2011) is presented in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…The performance of the MCS, GA and ES is evaluated based on average delays, network throughputs and computation time. This study is conducted via simulations using a traffic simulator namely UTNSim (Ng et al, 2018;Ng et al, 2019). The next section in this paper provides the literature review and the research gaps.…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of Heuristics and Meta-Heuristics Traffic Control Strategies Using the UTNSim Traffic Simulator

Ng¹,

Reaz²

2021

JST

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In the past few decades, intelligent traffic controllers have been developed to responsively cope with the increasing traffic demands and congestions in urban traffic networks. Various studies to compare and evaluate the performance of traffic controllers have been conducted to investigate its effect on traffic performances such as its ability to reduce delay time, stops, throughputs and queues within a traffic network. In this paper, the authors aim to present another comparative study on heuristics versus meta-heuristics traffic control methods. To our knowledge, such comparison has not been conducted and could provide insights into a purely heuristic controller compared to meta-heuristics. The study aims to answer the research question “Can heuristics traffic control strategies outperformed meta-heuristics in terms of performance and computational costs?” For this purpose, a heuristics model-based control strategy (MCS) which was previously developed by the authors is compared to genetic algorithms (GA) and evolution strategy (ES) respectively on a nine intersections symmetric network. These control strategies were implemented via simulations on a traffic simulator called UTNSim for three different types of traffic scenarios. Performance indices such as average delays, vehicle throughputs and the computational time of these controllers were evaluated. The results revealed that the heuristic MCS outperformed GA and ES with superior performance in average delays whereas vehicle throughputs were in close agreement. The computation time of the MCS is also feasible for real-time application compared to GA and ES that has longer convergent time.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Model-based Control Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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