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July 27, 2026Transportation Research Interdisciplinary PerspectivesOpen Access

Personalised urban tourist bus routing via multi-objective deep reinforcement learning: A hybrid PCA-double DQN approach

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Authors

PLPhan Gia Bao LeSMSara MoridpourMDMinh Ngoc Dinh

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Overview

Randomized trial reveals enhanced routing optimization in tourist buses, suggesting a balance between preferences and costs.

Key Points

  • This research aims to improve tourist bus routing by addressing operational needs and tourists' heterogeneous preferences through advanced routing algorithms.
  • Integrated a hybrid PCA-Double DQN approach for routing optimisation.
  • Utilized Principal Component Analysis and K-Means clustering to analyze tourist preferences.
  • Conducted simulation-based validation on Da Nang's road network.
  • Achieved approximately 91.2% preference alignment for tourists.
  • Reduced fuel consumption by around 25% compared to Dijkstra's method.
  • Outperformed conventional routing benchmarks like Standard DQN and Proximal Policy Optimization.

Cite This Study

Le et al. (2026) studied this question.

synapsesocial.com/papers/6a67004840bca442e0d4a04ahttps://doi.org/10.1016/j.trip.2026.102163
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Also Consider

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  1. 1Macroscopic impacts of optimised tourist bus routes on urban road networks: a spatiotemporal analysis2026
  2. 2Multi-Objective Optimization of Resilient, Sustainable, and Safe Urban Bus Routes for Tourism Promotion Using a Hybrid Reinforcement Learning Algorithm2024 · 7 citations
  3. 3Intelligent route optimization for agricultural heritage tourism using deep reinforcement learning2026
  4. 4DIRECT: Deep Reinforcement Learning for Tourist Route Generation2026
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