Real-time RL-based Matching with H3 Geohash Partitioning in Smart Freight Platform

This research presents a novel Deep Q-Learning (DQL) framework designed for efficient real-time matching of shipments and vehicles in the freight transportation sector. The framework utilizes the H3 geospatial indexing system for accurate positioning and employs a pre-filtering mechanism to streamli...

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Veröffentlicht in:IEEE Vehicular Technology Conference S. 1 - 7
Hauptverfasser: Shiri, Ali, YarAhmadi, Asad, Keivanpour, Samira, Lamghari, Amina
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 07.10.2024
Schlagworte:
Companies
Computational modeling
Costs
Deep Q-Learning
Freight Transportation
Geospatial analysis
Indexing
Learning-based algorithms
Logistics
Real-time Matching
Real-time systems
Reinforcement Learning
Smart Transportation
Transportation
Vehicle dynamics
Vehicular and wireless technologies
ISSN:2577-2465
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Beschreibung
Zusammenfassung:This research presents a novel Deep Q-Learning (DQL) framework designed for efficient real-time matching of shipments and vehicles in the freight transportation sector. The framework utilizes the H3 geospatial indexing system for accurate positioning and employs a pre-filtering mechanism to streamline the matching process. When evaluated on a simulated model of Montreal's transportation network, the framework demonstrates promising results in generating matches that reduce travel distance and prioritize timely service. Through extensive experimentation, a configuration utilizing ReLU activation was identified as particularly efficient, even under limited computational resources. This research contributes to the development of advanced, real-time matching algorithms in logistics and show-cases the potential of integrating reinforcement learning with geospatial analysis to address complex transportation challenges. These findings offer valuable insights for freight companies seeking to improve their matching processes, potentially leading to cost reductions and enhanced service quality.
ISSN:2577-2465
DOI:10.1109/VTC2024-Fall63153.2024.10757474