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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Published on: February 25, 2013

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Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning.

Syed Muhammad Asad1, Jawad Ahmad2, Sajjad Hussain1

  • 1James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.

Sensors (Basel, Switzerland)
|May 9, 2020
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) optimizes train passenger traffic flows using historical data and a novel encryption model. This approach enhances railway operations and infrastructure management for smart city planning.

Keywords:
5GRFID sensorsartificial intelligenceencryptionmachine learningmobility predictionsoptimisationpassenger pathwayssmart city planningtransportation

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Last Updated: Dec 22, 2025

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Published on: February 25, 2013

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Area of Science:

  • Computer Science, Artificial Intelligence, Transportation Systems Engineering

Background:

  • Smart City Planning (SCP) requires optimized train passenger traffic flows, traditionally addressed by reactive methods.
  • Existing mobility prediction and encryption techniques are insufficient for near real-time optimization.
  • Information and Communication Technology (ICT) integration is crucial for advancing transportation efficiency.

Purpose of the Study:

  • To develop proactive, Artificial Intelligence (AI)-driven solutions for optimizing train passenger traffic.
  • To enhance railway operational performance and infrastructure management using Machine Learning (ML) and a novel encryption model.
  • To analyze passenger traffic flows using an Access, Egress, and Interchange (AEI) framework for improved safety and efficiency.

Main Methods:

  • Development of mobility prediction models using historical passenger data from Radio Frequency Identification (RFID) sensors.
  • Application of Machine Learning (ML) algorithms for passenger flow prediction.
  • Implementation of a novel, lightweight encryption model for real-time traffic handling.
  • Analysis of passenger flow data from London Underground and Overground (LUO) using an AEI framework.

Main Results:

  • The proposed AEI framework achieved 91.17% prediction accuracy for passenger traffic flows.
  • The novel encryption scheme demonstrated high security efficacy with parameters like entropy (>7.70) and pixel change rate (>99%).
  • The integrated approach supports train infrastructure against congestion, accidents, and overloading.

Conclusions:

  • AI-driven passenger flow prediction and secure encryption offer a proactive solution for smart city transportation.
  • The AEI framework significantly improves railway operational performance and safety.
  • The developed methods are effective for handling heavy passenger traffic in real-time with robust security.