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<p>In this work, the design of a robust route recommendation system is proposed based on crowdsourcing. The prevalent research challenge of crowdsourcing, that is, biased or unreliable user opinions has been addressed in the work through a multi-phased data validation framework. The client-server-based multi-tier architecture proposed in the work ensures scalable performance. Data collected for the routes have been applied to their component subparts by the system. Thus, conditions of other routes sharing the roads can also be predicted. The work emphasizes the use of a regression model that is deployed in the cloud for the prediction of the best route. The analysis has been carried out on both synthetic and collected datasets. The work can be easily extended to any crowdsourced recommendation systems that are based on numeric user reviews. The system is implemented as a working prototype and the working is shown for real data collected from the users for the city of Kolkata.</p>

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Design of an end-to-end recommendation system for crowdsourced road monitoring applications based on machine learning

  • 期刊论文

  • 发表日期：

    2025年2月8日

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  • Rusan Ahsan,
  • Ayan Kumar Panja,
  • Moumita Roy,
  • Chandreyee Chowdhury

摘要

In this work, the design of a robust route recommendation system is proposed based on crowdsourcing. The prevalent research challenge of crowdsourcing, that is, biased or unreliable user opinions has been addressed in the work through a multi-phased data validation framework. The client-server-based multi-tier architecture proposed in the work ensures scalable performance. Data collected for the routes have been applied to their component subparts by the system. Thus, conditions of other routes sharing the roads can also be predicted. The work emphasizes the use of a regression model that is deployed in the cloud for the prediction of the best route. The analysis has been carried out on both synthetic and collected datasets. The work can be easily extended to any crowdsourced recommendation systems that are based on numeric user reviews. The system is implemented as a working prototype and the working is shown for real data collected from the users for the city of Kolkata.