mlsanityData to 7 Oct 2026

A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications

arXiv 2210.14524 ↗First posted 26 Oct 2022cs.LGcited by 2

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Abstract (arXiv)

Transportation is the backbone of the economy and urban development. Improving the efficiency, sustainability, resilience, and intelligence of transportation systems is critical and also challenging. The constantly changing traffic conditions, the uncertain influence of external factors (e.g., weather, accidents), and the interactions among multiple travel modes and multi-type flows result in the dynamic and stochastic natures of transportation systems. The planning, operation, and control of transportation systems require flexible and adaptable strategies in order to deal with uncertainty, non-linearity, variability, and high complexity. In this context, Reinforcement Learning (RL) that enables autonomous decision-makers to interact with the complex environment, learn from the experiences, and select optimal actions has been rapidly emerging as one of the most useful approaches for smart transportation. This paper conducts a bibliometric analysis to identify the development of RL-based methods for transportation applications, typical journals/conferences, and leading topics in the field of intelligent transportation in recent ten years. Then, this paper presents a comprehensive literature review on applications of RL in transportation by categorizing different methods with respect to the specific application domains. The potential future research directions of RL applications and developments are also discussed.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 60 of its 211 references that are in the map. First posted on arXiv 26 Oct 2022.

Builds on

60 of 211 references.

References · 60

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38 more
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50 most cited of 60 All 60 (JSON)

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Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2023UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Controlcited by 12
  2. 2024Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learningcited by 3