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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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CRAWLING: a crowdsourcing algorithm on wheels for smart parking.

Émiland Garrabé1, Giovanni Russo2

  • 1Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, 84084, Fisciano, Italy. egarrabe@unisa.it.

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Summary
This summary is machine-generated.

We introduce CROWDSOURCING ALGORITHM ON WHEELS for smart parking (CRAWLING), an in-car service that uses connected cars to crowdsource data for optimal routing. This system effectively orchestrates vehicles to adapt to real-time road conditions and minimize costs.

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Area of Science:

  • Intelligent Transportation Systems
  • Control Theory
  • Data Science

Background:

  • Connected vehicles offer new opportunities for data collection and intelligent routing.
  • Existing routing algorithms often struggle with dynamic, heterogeneous data streams and stochastic behaviors.

Purpose of the Study:

  • To present the design and effectiveness of CROWDSOURCING ALGORITHM ON WHEELS for smart parking (CRAWLING).
  • To enable connected cars to crowdsource data for optimized routing tasks.
  • To develop a control-theoretical framework for data-driven routing in dynamic environments.

Main Methods:

  • Developed a control-theoretical formulation for an optimal data-driven control problem.
  • Designed a general-purpose, modular architecture for the CRAWLING service.
  • Incorporated heterogeneous data streams and stochastic behaviors into the routing model.

Main Results:

  • Simulations demonstrated that CRAWLING effectively orchestrates vehicles for real-time adaptation to road conditions.
  • The system enables cars to minimize their cost functions by maximizing environmental rewards.
  • The proposed architecture proved effective in various illustrative scenarios.

Conclusions:

  • CRAWLING provides a principled approach to crowdsourced in-car routing for connected vehicles.
  • The system's ability to handle heterogeneous data and stochasticity enhances routing efficiency.
  • The open-source architecture facilitates further research and replication of results.