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Power function-based signal recovery transition optimization model of emergency traffic

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Abstract

In view of the disturbance of the emergency signal preemption to the normal traffic flow, a scientific and reasonable signal transition optimization model was proposed in this paper, in which fair and efficiency are both considered. Since the emergency signal preemption may lead to longer vehicle queuing and greater vehicle delay at intersections, the difference of queue length and the vehicle delay were selected as the objectives taking into account the fair benefit, and a multi-objective signal recovery transition optimization model for emergency rescue was established based on the power function method. The queue length was calculated by the 95% queue length model of the SYNCHRO, and the average vehicle delay was calculated with HCM2000 delay model; furthermore, parameters’ weight in the model was calibrated with the variation coefficient method, and model was solved by genetic algorithm. Moreover, power function method was used to score the signal recovery transition schemes of emergency traffic. Finally, based on the survey data from the typical route network in the Lion Mountain area of Suzhou City, the model proposed in this study was compared with three classical smooth transition schemes (immediate transition scheme, two-cycle transition scheme and three-cycle transition scheme) at the scenario of evening peak. Simulation results showed that the model in this paper can reduce the vehicle delay and queue length compared with the above three smooth transition schemes; the average reduction ratio of queue length delay was 13.82%, and this number of queue length was 13.65%, from which we can conclude that the model proposed in this has better performance than other three classical transition schemes in emergency signal transition applicability.

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Funding

This study was funded by MOE (Ministry of Education in China) Project of Humanities and Social Sciences (17YJCZH225) ” Emergency oriented multi-objective traffic management and control model at urban area in the environment of big data”. It was also funded by the humanistic and social science climbing program funding of University of Shanghai for Science and Technology (SK18PB03), and by the humanistic and social science research funding of University of Shanghai for Science and Technology (SK17YB05). Prof. Jin Wan g is the corresponding author.

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Authors and Affiliations

  1. Business School, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Jiao Yao, Kaimin Zhang & Yaxuan Dai

  2. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410004, China

    Jin Wang

Authors
  1. Jiao Yao
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  2. Kaimin Zhang
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  3. Yaxuan Dai
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  4. Jin Wang
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Correspondence to Jin Wang.

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Jiao Yao and Jin Wang declare no conflict of interest. Kaimin Zhang and Yaxuan Dai declare no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Informed consent was obtained from all individual participants included in the study.

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Cite this article

Yao, J., Zhang, K., Dai, Y. et al. Power function-based signal recovery transition optimization model of emergency traffic. J Supercomput 74, 7003–7023 (2018). https://doi.org/10.1007/s11227-018-2596-y

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  • DOI: https://doi.org/10.1007/s11227-018-2596-y

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