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Congestion Adaptive Traffic Light Control and Notification Architecture Using Google Maps APIs

Author

Listed:
  • Sumit Mishra

    (Research Consultant, Learnogether Technologies Pvt. Ltd., Ghaziabad 201014, India)

  • Devanjan Bhattacharya

    (Nova Information Management School, Universidade Nova de Lisboa, 1070-312 Lisbon, Portugal)

  • Ankit Gupta

    (Department of Civil Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi 221005, India)

Abstract

Traffic jams can be avoided by controlling traffic signals according to quickly building congestion with steep gradients on short temporal and small spatial scales. With the rising standards of computational technology, single-board computers, software packages, platforms, and APIs (Application Program Interfaces), it has become relatively easy for developers to create systems for controlling signals and informative systems. Hence, for enhancing the power of Intelligent Transport Systems in automotive telematics, in this study, we used crowdsourced traffic congestion data from Google to adjust traffic light cycle times with a system that is adaptable to congestion. One aim of the system proposed here is to inform drivers about the status of the upcoming traffic light on their route. Since crowdsourced data are used, the system does not entail the high infrastructure cost associated with sensing networks. A full system module-level analysis is presented for implementation. The system proposed is fail-safe against temporal communication failure. Along with a case study for examining congestion levels, generic information processing for the cycle time decision and status delivery system was tested and confirmed to be viable and quick for a restricted prototype model. The information required was delivered correctly over sustained trials, with an average time delay of 1.5 s and a maximum of 3 s.

Suggested Citation

  • Sumit Mishra & Devanjan Bhattacharya & Ankit Gupta, 2018. "Congestion Adaptive Traffic Light Control and Notification Architecture Using Google Maps APIs," Data, MDPI, vol. 3(4), pages 1-19, December.
    • Handle: RePEc:gam:jdataj:v:3:y:2018:i:4:p:67-:d:190652
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    References listed on IDEAS

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    1. Liu, Henry X. & Oh, Jun-Seok & Recker, Will, 2002. "Adaptive Signal Control System with On-line Performance Measure for Single Intersection," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3ks1w9qc, Institute of Transportation Studies, UC Berkeley.
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    Cited by:

    1. Suhono H. Supangkat & Rohullah Ragajaya & Agustinus Bambang Setyadji, 2023. "Implementation of Digital Geotwin-Based Mobile Crowdsensing to Support Monitoring System in Smart City," Sustainability, MDPI, vol. 15(5), pages 1-27, February.
    2. Promporn Sornsoongnern & Suthatip Pueboobpaphan & Rattaphol Pueboobpaphan, 2023. "Innovative Dynamic Queue-Length Estimation Using Google Maps Color-Code Data," Sustainability, MDPI, vol. 15(4), pages 1-15, February.

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