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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared Parking.

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  • 1Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan.

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Summary
This summary is machine-generated.

Urbanization increases traffic congestion. This study introduces a shared parking system using IoT and AI to optimize parking availability without disrupting personal use, generating new revenue.

Keywords:
intelligent transportation systempredictionrecurrent neural networkshared economyshared parkingsmart cities

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Area of Science:

  • Urban planning and intelligent transportation systems.
  • Artificial intelligence and machine learning applications.
  • Sustainable urban development and resource management.

Background:

  • Rapid urbanization leads to increased vehicle numbers, causing traffic congestion, accidents, and pollution.
  • Existing transportation infrastructure expansion is insufficient to solve urban mobility challenges.
  • Parking scarcity is a significant issue in densely populated urban areas.

Purpose of the Study:

  • To propose and evaluate a shared parking system for urban environments.
  • To enable the public use of 'open-to-specific-groups' parking lots without compromising designated user access.
  • To generate potential new income streams for parking management and owners.

Main Methods:

  • Utilized an Internet of Things (IoT) system for data collection and preprocessing.
  • Employed recurrent neural network (RNN) algorithms, specifically many-to-many Long Short-Term Memory (LSTM), for demand prediction.
  • Evaluated the system using artificial and real-world datasets and simulated various parking release scenarios.

Main Results:

  • The many-to-many LSTM RNN model demonstrated the highest prediction accuracy among tested algorithms.
  • Simulations confirmed the shared parking system effectively provides public parking access without overriding personal usage.
  • The system successfully balances public access with the needs of designated users.

Conclusions:

  • The proposed shared parking system offers a viable solution to urban parking challenges.
  • AI-driven demand prediction is crucial for optimizing shared parking resources.
  • This system can enhance urban mobility, reduce congestion, and create economic benefits.