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Enabling crowdsensing-based road condition monitoring service by intermediary

Author

Listed:
  • Kevin Laubis

    (Information Process Engineering (IPE))

  • Marcel Konstantinov

    (Information Process Engineering (IPE))

  • Viliam Simko

    (Information Process Engineering (IPE))

  • Alexander Gröschel

    (Information Process Engineering (IPE))

  • Christof Weinhardt

    (Karlsruhe Institute of Technology (KIT), Institute of Information Systems and Marketing (IISM))

Abstract

Constant monitoring of road conditions would be beneficial for road authorities as well as road users. However, this is currently not possible due to limited resources. This is because road condition monitoring is carried out by engineering companies using limited resources such as specialized vehicles and trained personnel. The ubiquity of smart devices carried by drivers, such as smartphones and the ever-increasing number of sensors installed in modern vehicles, makes it possible to provide information about the condition of the road on which the vehicle is driving. We develop a smart, crowd-based road condition monitoring service that establishes an intermediary between the crowd as data provider and the road authorities and road users as service customers. In addition to providing customers with accurate and frequent road condition information, subscribers can monetize their collected data. We prove the feasibility and usability of this smart service through analytical and descriptive evaluations.

Suggested Citation

  • Kevin Laubis & Marcel Konstantinov & Viliam Simko & Alexander Gröschel & Christof Weinhardt, 2019. "Enabling crowdsensing-based road condition monitoring service by intermediary," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(1), pages 125-140, March.
    • Handle: RePEc:spr:elmark:v:29:y:2019:i:1:d:10.1007_s12525-018-0292-7
    DOI: 10.1007/s12525-018-0292-7
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    References listed on IDEAS

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    1. Ravi Bapna & Anitesh Barua & Deepa Mani & Amit Mehra, 2010. "Research Commentary ---Cooperation, Coordination, and Governance in Multisourcing: An Agenda for Analytical and Empirical Research," Information Systems Research, INFORMS, vol. 21(4), pages 785-795, December.
    2. Pierre Goovaerts & Geoffrey M. Jacquez, 2005. "Detection of temporal changes in the spatial distribution of cancer rates using local Moran’s I and geostatistically simulated spatial neutral models," Journal of Geographical Systems, Springer, vol. 7(1), pages 137-159, October.
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    Cited by:

    1. Delgosha, Mohammad Soltani & Hajiheydari, Nastaran & Talafidaryani, Mojtaba, 2022. "Discovering IoT implications in business and management: A computational thematic analysis," Technovation, Elsevier, vol. 118(C).
    2. Rainer Alt & Haluk Demirkan & Jan Fabian Ehmke & Anne Moen & Alfred Winter, 2019. "Smart services: The move to customer orientation," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(1), pages 1-6, March.

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    More about this item

    Keywords

    Crowdsensing; Internet of things; Road condition monitoring; Multi-sourcing; Service integration; Hotspot analysis;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • H54 - Public Economics - - National Government Expenditures and Related Policies - - - Infrastructures
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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