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Efficient urban mobility is a critical challenge in modern smart cities, requiring intelligent traffic forecasting and route optimization strategies. This study proposes an AI-driven system that integrates map matching, traffic prediction, and road quality assessment to enhance transportation management. The system utilizes Hidden Markov Models (HMM) for accurate vehicle localization, Long Short-Term Memory (LSTM) networks for real-time traffic forecasting, and Convolutional Neural Networks (CNN) for road condition classification. Additionally, sensor-based data from accelerometers and gyroscopes enable precise detection of road anomalies such as potholes and uneven surfaces.

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Survey Paper on AI-Driven Traffic Forecasting and Route Optimization System for Enhanced Navigation and Emergency Response

  • 会议论文

  • 发表日期：

    2026年

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  • Shailesh Galande,
  • Mihir Patil,
  • Siddhesh Patil,
  • Prerana Mhatre,
  • Pranav Patil

摘要

Efficient urban mobility is a critical challenge in modern smart cities, requiring intelligent traffic forecasting and route optimization strategies. This study proposes an AI-driven system that integrates map matching, traffic prediction, and road quality assessment to enhance transportation management. The system utilizes Hidden Markov Models (HMM) for accurate vehicle localization, Long Short-Term Memory (LSTM) networks for real-time traffic forecasting, and Convolutional Neural Networks (CNN) for road condition classification. Additionally, sensor-based data from accelerometers and gyroscopes enable precise detection of road anomalies such as potholes and uneven surfaces.