Key Takeaways & Executive Findings
- •• TransRAG integrates retrieval-augmented generation and chain-of-thought prompting to mitigate hallucinations and black-box issues in foundational models for parallel transportation. • A three-layer architecture (storage, management, execution) enables personalized, diverse, and adaptive traffic services in dynamic environments. • Continuous real-virtual interaction optimizes decisions and incrementally updates knowledge, enhancing system reliability and trustworthiness. • Integration of blockchain, smart contracts, and caching addresses critical challenges (e.g., single point of failure, privacy breaches, data access delays), advancing the transition to 6S Transportation 5.0.
Abstract
Parallel transportation serves as a holistic paradigm for achieving intelligent traffic management and control, focusing on addressing the complexity of human and social factors. Recently, the emergence and development of foundational models (FMs) have ushered in a new era for the realization of parallel transportation. However, the inherent issues of “hallucinations,” outdated knowledge, and the “black-box” nature of FMs render their generated decisions unreliable and untrustworthy. To address these issues, we propose a TransRAG framework for parallel transportation based on retrieval-augmented generation and chain-of-thought (CoT) prompting. TransRAG is composed of three interacting layers, storage, management, and execution, which work together to deliver personalized and diverse traffic services to users. The external knowledge from the storage layer is incorporated to augment the FM in management layers for computational experiments. The real–virtual interaction between artificial and actual transportation systems is used to continuously optimize the decisions from the management layer. Therefore, TransRAG can incrementally update knowledge and adjust strategies to adapt to the evolving and dynamic traffic environment. Additionally, the integration of blockchain, smart contracts, and caching into TransRAG is expected to address a range of challenges, such as single point of failure, potential privacy breaches, and delays in data access, thereby advancing the transition to “6S” Transportation 5.0.
1. Introduction
A transportation system is a complex cyber–physical–social system (CPSS), seamlessly integrating physical infrastructure, information processing, and social behaviors, all of which are intricately interconnected and interact with each other. However, existing models address each transportation subproblem (e.g., path planning, traffic flow prediction, and decision recommendation) independently from different perspectives, and no universal model offers comprehensive management of the entire transportation system. Additionally, the uncertainty, diversity, and complexity of human and social factors further exacerbate the difficulty of effective system management.
To address these issues, the concept and framework of parallel transportation were proposed to enhance the agility, focus, and convergence in system management and control. Its core principle is artificial transportation systems, computational experiments, and parallel execution (ACP); that is, the extensive real traffic data collected by ubiquitous terminals alongside virtual data generated by artificial models are used to build artificial transportation systems, where computational experiments are conducted to analyze traffic patterns and their underlying causes, ultimately enabling the implementation of optimal management solutions in real-world systems.
Recently, the advancement of FMs, such as ChatGPT, GPT-4, and Sora, has elevated the implementation of parallel transportation systems to a new level because of their exceptional understanding, generation, and reasoning abilities. However, FMs’ inherent flaws hinder their application in actual transportation systems and could even jeopardize the safety of passengers and drivers as follows: (1) “Hallucination” phenomena and outdated knowledge within FMs contribute to the unreliability of their inferences; (2) The fact that FMs are neural network based “black-box” models makes their generation results inexplicable and untrustworthy.
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Jing Yang, Xingyuan Dai, Yisheng Lv, Levente Kovács, Fei-Yue Wang (2025). TransRAG for parallel transportation: toward reliable and trustworthy transportation systems via retrieval-augmented generation. Frontiers of Information Technology & Electronic Engineering. https://doi.org/10.1631/FITEE_2400800
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Frequently Asked Questions
What is TransRAG in parallel transportation?
TransRAG is a novel framework that integrates retrieval-augmented generation (RAG) and chain-of-thought (CoT) prompting to enhance the reliability and trustworthiness of foundational models (FMs) in intelligent transportation systems. It consists of storage, management, and execution layers to deliver personalized traffic services.
How does TransRAG address the hallucination problem of foundational models?
TransRAG mitigates hallucinations by incorporating external knowledge from its storage layer into prompts, grounding FM outputs in real-time and authoritative information. Additionally, CoT prompting improves reasoning transparency.
What are the three interacting layers of TransRAG?
The three layers are storage (external knowledge base), management (computational experiments with FM augmentation), and execution (real-virtual interaction and strategy adjustment), which collectively enable adaptive and reliable traffic management.
How does TransRAG contribute to Transportation 5.0?
By integrating blockchain, smart contracts, and caching, TransRAG addresses challenges such as single point of failure, privacy breaches, and data access delays, thereby advancing the transition to '6S' Transportation 5.0.
What makes TransRAG's decisions trustworthy?
TransRAG combines retrieval-augmented generation with chain-of-thought reasoning, ensuring that decisions are based on current external knowledge and that reasoning steps are interpretable, while continuous real-virtual interaction optimizes strategies over time.
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