Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions

Doaa Mahmud; Hadeel Hajmohamed; Lameya Aldhaheri; Nasir Saeed; Ruhul Amin Khalil; Shamma Almentheri; Shamma Alqaydi

REVIEW 4 major objections 5 minor 1 cited by

Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This review argues that large language models can serve as the reasoning layer of intelligent transportation systems, improving prediction, signal control, and driving assistance while facing real-time and ethical barriers.

desk verdict A broad but uneven survey whose central LLM-specific claim is undercut by its own evidence, much of which is about transformer architectures rather than language models. read the letter →

arxiv 2501.04437 v1 pith:RGRVYENM submitted 2025-01-08 eess.SY cs.AIcs.ETcs.SY

Doaa Mahmud , Hadeel Hajmohamed , Shamma Almentheri , Shamma Alqaydi , Lameya Aldhaheri , Ruhul Amin Khalil , Nasir Saeed This is my paper · ORCID

classification eess.SYcs.AIcs.ETcs.SY

keywords intelligenttransportationsystemslargelanguagemodelstrafficpredictionsignalcontrolautonomousdrivingV2XcommunicationdecentralizedLLMsedgecomputing

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review sets out to establish that large language models, though built for text, can serve as the reasoning layer of intelligent transportation systems: predicting traffic, timing signals, planning routes, assisting autonomous vehicles, scheduling transit, and enabling V2X communication. It treats the transformer's self-attention mechanism as the bridge, since the same machinery that captures word dependencies can capture spatiotemporal dependencies in traffic data, and the same model that generates language can generate alerts, explanations, and control suggestions. The paper's own evidence is a set of research prototypes in which LLM-based or transformer-based systems match or beat classical baselines for traffic forecasting and signal control. If the claim holds, transportation agencies would integrate LLMs into traffic management rather than treating them as isolated chatbots, while meeting data quality, latency, privacy, and network reliability constraints.

What carries the argument

The central object is the transformer's multi-head self-attention mechanism, generalized beyond language so it can encode both temporal and spatial dependencies in traffic data; the paper also relies on pre-training and fine-tuning, and on decentralized or edge deployment, as the machinery for making LLMs practical in ITS. The attention mechanism lets a model weigh every relevant input at once, which is what makes it suited to forecasting traffic from many heterogeneous streams and to explaining its own control choices. Pre-training supplies general world knowledge that can be adapted to traffic with limited in-domain data, while decentralization distributes inference across vehicles, roadside units, and edge nodes to cut latency and preserve data locality.

What would settle it

A field deployment that measures end-to-end latency of an LLM-driven traffic signal controller against the signal phase cycle would settle the practical claim: if median decision latency during peak traffic exceeds the phase-change interval, or if the controller causes a safety-critical failure, the real-time viability thesis is falsified. A second observation would be a benchmark where an LLM-based traffic forecaster, trained on the same historical data, fails to beat a standard graph neural network on held-out incident days.

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Extended reading notes

Core claim

The paper argues that LLMs are broadly applicable to transportation management and should be integrated into future ITS: they can interpret, predict, and respond to complex scenarios within transportation networks, improving decision-making and traffic management. Its central claim is that the transformer's attention mechanism can be transferred from language to traffic data, letting the same models that understand text also model spatiotemporal traffic patterns and generate human-readable explanations for control actions. The paper also claims that decentralized and edge-deployed LLM architectures are needed to make this practical in connected vehicle environments, and that data quality, computational cost, bias, privacy, and network latency are the barriers that future research must remove.

Load-bearing premise

The load-bearing premise is that LLM-based systems can be run in real time in safety-critical transportation settings with acceptable latency, reliability, and resource use, and that decentralized LLM architectures can meet the communication and computational constraints of vehicular networks.

Editorial extensions

If this is right

Traffic prediction can move from task-specific deep networks to pre-trained LLMs that integrate text, weather, and sensor data with limited labeled data, reducing the dependence on large historical traffic datasets.
Traffic signal control can become human-mimetic and explainable: LLM-based agents can justify each signal phase change, which helps operators trust and audit decisions in complex urban intersections.
Autonomous driving and V2X communication can gain a linguistic layer that interprets instructions, road signs, and alerts, and generates context-aware responses, improving situational awareness and hazard detection.
Public transit can use LLM-driven analysis of traffic patterns, sensor data, and rider information to adjust schedules in real time and provide passengers with useful, up-to-date travel information.
Realizing these applications at scale requires decentralized and edge deployment, model compression, and new network architectures, because cloud-only LLMs are too slow and bandwidth-hungry for safety-critical traffic loops.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If LLMs enter live traffic systems, their first safe role is likely advisory: producing explainable signal recommendations, incident summaries, and passenger alerts, with a human or a verified controller retaining final authority, because current latency and verification gaps make direct control risky.
The case studies reviewed are mostly research prototypes and simulation results, so the natural next step is shared, standardized benchmarks that measure not only forecast accuracy but also inference latency, bandwidth, and safety outcomes across LLM-based and conventional ITS controllers.
The paper's emphasis on edge computing suggests a concrete testable route: small fine-tuned LLMs running at roadside units could meet real-time constraints while large cloud models handle non-critical planning, a hybrid architecture the review points toward but does not itself test.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

First: this is a review, not a research contribution, and it reads like one. Its value is as a wide map of LLM-related work in ITS. For a newcomer, it does a decent job of assembling model names, application areas, and open problems. The challenge and future-directions sections (data, compute, privacy, edge, 6G, quantum) are broad and would help a student scope a thesis. That is the honest upside.

The soft spots are not minor. The central claim that LLMs can 'revolutionize' ITS is carried by examples that mostly are not LLMs. TrafficBERT is a BERT-style time-series transformer pre-trained on traffic data, not a language model; STPT and TransTTE are general transformer architectures; BERT4ITS adapts BERT for sensor and time-series data. Those are transformers, not LLMs. The 'decentralized LLMs' section lists GPT-NeoX, Colossal-AI, Mesh TensorFlow, and Petals, which are training and distribution frameworks, not LLMs themselves. Once you remove those, the direct evidence for LLM-specific gains is thin, mostly prototype results like LA-Light in synthetic settings. The survey needs to either reframe its claim as covering transformer architectures broadly or tighten the evidence to actual LLMs.

There are also internal errors a referee would catch immediately. Section II-A calls GPT-3 the 'latest iteration' while later describing GPT-4. Section II-E contains an incoherent passage about 'Sora's LLMs' and the 'torrent protocol' that belongs nowhere and looks like a paste artifact. Table II labels LLaMA as encoder-decoder when it is decoder-only. These undermine trust in the synthesis.

What is genuinely new: not much. The paper explicitly positions itself against several earlier surveys and acknowledges them in Table I. That is honest, but the contribution is organizational rather than novel.

Who is this for? Graduate students and practitioners wanting a broad, non-systematic tour of the area. A corrected version would be genuinely useful. In its current form, I would not cite it, and I would not trust its specific claims without checking the original sources.

Recommendation: a serious editor could send this to peer review, because the topic is timely and a corrected survey would be useful. But it needs major revision first: fix the factual errors, remove the incoherent passage, and audit whether the cited systems are LLMs or general transformers. I would ask for that audit before accepting.

Referee Report

4 major / 5 minor

Summary. This manuscript is a survey of the role of large language models in intelligent transportation systems. It motivates the topic with market projections, reviews centralized LLM families (GPT, T5, BERT, LLaMA, Falcon) and decentralized training/inference frameworks (GPT-NeoX, OpenFlamingo, BLOOM, Colossal-AI, Mesh TensorFlow, Petals), and then surveys uses in traffic prediction, signal control, route planning, autonomous driving, public transport, V2X, ADAS, traffic control centers, smart cities, pedestrian flow, and multimodal transport. It also presents five case studies and a discussion of challenges and future directions including data, computation, ethics, integration, latency, scalability, edge computing, and 6G/quantum computing. The paper's central claim is that LLMs can potentially revolutionize user interaction with transportation systems and substantially improve ITS functions.

Significance. The topic is timely and the reference list is broad, covering 2023-2024 developments, and the paper offers a useful high-level taxonomy of challenges. The strongest contribution is the organization of applications and the enumeration of open problems rather than a quantitative comparison. However, several load-bearing examples in Sections IV and V are not language models, and the text contains internal inconsistencies; as a result, the manuscript does not currently establish the claimed LLM-specific advantages.

major comments (4)

[Sections IV-A, V-A, V-C, V-D, V-E] Several systems presented as evidence of LLM benefits are not large language models. TrafficBERT (V-A) is a BERT-style transformer pretrained on traffic time-series data; STransformer/STPT (V-C), TransTTE (V-D), and BERT4ITS/BERT-ITS (V-E) are general transformer architectures for spatiotemporal or time-series data without natural-language training. Including these as 'case studies of LLMs' conflates LLMs with transformer architectures generally. Since the central claim that LLMs improve ITS depends on these examples, the authors must reclassify each cited system as an LLM, a non-LLM transformer baseline, or a framework, and then reassess which remaining systems (e.g., LA-Light, TF-LLM) support the LLM-specific claim.
[Section II-E] The paragraph beginning 'Distributed architectures distribute...' introduces 'Sora's LLMs' without definition, describes data being stored 'within currently consigned data-protecting devices,' refers to a 'torrent protocol' for decentralized model updates, and ends with the incomplete sentence 'would not be just like that.' This passage is not coherent and cannot be verified against the cited literature. It should be removed or rewritten with proper technical definitions and citations before the survey can be considered reliable.
[Table II] Table II contains factual errors and category confusions. LLaMA is described as an 'Encoder-decoder Transformer,' but LLaMA models are decoder-only transformers. Colossal-AI, Mesh TensorFlow, and Petals are listed as 'LLM models' with architecture and training entries, although they are distributed training/inference frameworks. GPT-NeoX is also a library rather than a pretrained model, and the 'multi-query attention' attribution appears unsupported. The table should be corrected and reorganized to distinguish models from frameworks.
[Section III] This section repeatedly labels Colossal-AI, Mesh TensorFlow, Petals, and GPT-NeoX as 'decentralized LLMs' and says 'we discuss emerging DLLMs,' but these are tools for distributed computation rather than language models. This conflation affects the technical discussion of communication complexity and model updates, since the complexity of training infrastructure is not the same as the complexity of an LLM itself. The section should separate model architectures from systems/frameworks.

minor comments (5)

[Section II-A] GPT-3 is called 'the latest iteration' in the first paragraph of Section II-A, but GPT-4 is discussed later in the same subsection; the wording should be corrected to reflect the chronology.
[Sections I-C and III] There are typographical errors such as 'Colossoal-AI,' 'LlaMa,' 'pedestrain,' and 'logO(n log n)'; these should be corrected throughout.
[Table I] The row for reference [47] describes 'generative LLM models in traffic simulation' and mentions DriveGAN, but the cited work is a survey of vision-language models in autonomous driving and ITS; the table entry should match the cited source.
[Sections I and V] The model name is inconsistently written as 'LLMLight,' 'LLM-Light,' and 'LA-Light'; pick one notation and use it consistently.
[References [109]-[110]] The reference metadata in [109] and [110] appears inconsistent with the cited papers in author names and titles; check and update these entries.

Circularity Check

0 steps flagged · score 1.0 of 10

No substantive circularity; the survey's claims rest on external literature, with only minor non-load-bearing self-citations.

full rationale

This is a review/survey paper, not a derivation. The central claim—that LLMs can enhance ITS in areas such as traffic prediction, signal optimization, and autonomous driving—is supported by citations to external systems and studies (e.g., TrafficBERT, LLM-Light, NavGPT, HighwayLLM). None of the paper's statements reduce by construction to its own inputs: there are no fitted parameters renamed as predictions, no self-referential definitions, no uniqueness theorems imported from the authors' prior work, and no ansatz smuggled in via citation. The only self-citations are [1] and [27] (author-group references in the introduction), used as general background support for 'ITS represent a transformative approach' and for the broadening of ITS potential with LLMs; these are not load-bearing because the surrounding claims are independently supported by a large set of other references. The paper does contain a scope problem—several systems treated as LLMs (TrafficBERT, STPT, TransTTE, BERT4ITS) are Transformer architectures for time-series or sensor data rather than language models, and Section III lists non-model frameworks (Colossal-AI, Mesh TensorFlow, Petals) as decentralized LLMs. This is a correctness/classification issue, not circularity. Similarly, the incoherent 'Sora's LLMs ... torrent protocol' passage in Section II-E is an artifact that undermines reliability but does not create a circular derivation. Under the circularity rubric, the honest finding is a very low score.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

This is a review, so there are no fitted parameters or invented entities. The central claim rests on the accuracy of cited literature and untested assumptions about real-time and decentralized deployment.

assumptions (3)

domain assumption The performance improvements reported in the cited studies are accurate and will transfer to real-world ITS deployments.
The review's central claim that LLMs enhance ITS is built on the empirical results of papers it cites; if those results are not reproducible or transferable, the claim weakens.
domain assumption LLMs can be integrated with real-time, safety-critical transportation systems without fundamental technical barriers such as unacceptable latency.
The paper speculates about applications like autonomous driving and traffic signal control that assume real-time feasibility, which is not established.
domain assumption Decentralized LLM training and inference frameworks can meet the communication and computational constraints of vehicular networks.
Section III discusses decentralized LLMs as a solution for ITS, assuming the distributed infrastructure is practical.

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Cite this review

Pith. "Pith review of Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions." pith.science (2026). https://pith.science/paper/RGRVYENM

@misc{pith2026250104437,
  author       = {Pith},
  title        = {Pith review of: Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RGRVYENM}},
  note         = {Machine review of arXiv:2501.04437}
}

read the original abstract

Intelligent Transportation Systems (ITS) are crucial for the development and operation of smart cities, addressing key challenges in efficiency, productivity, and environmental sustainability. This paper comprehensively reviews the transformative potential of Large Language Models (LLMs) in optimizing ITS. Initially, we provide an extensive overview of ITS, highlighting its components, operational principles, and overall effectiveness. We then delve into the theoretical background of various LLM techniques, such as GPT, T5, CTRL, and BERT, elucidating their relevance to ITS applications. Following this, we examine the wide-ranging applications of LLMs within ITS, including traffic flow prediction, vehicle detection and classification, autonomous driving, traffic sign recognition, and pedestrian detection. Our analysis reveals how these advanced models can significantly enhance traffic management and safety. Finally, we explore the challenges and limitations LLMs face in ITS, such as data availability, computational constraints, and ethical considerations. We also present several future research directions and potential innovations to address these challenges. This paper aims to guide researchers and practitioners through the complexities and opportunities of integrating LLMs in ITS, offering a roadmap to create more efficient, sustainable, and responsive next-generation transportation systems.

Figures

Figures reproduced from arXiv: 2501.04437 by the authors.

**Figure 2.** A generic illustration of the LLM transformer. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Different types of LLM models. the following, we briefly discuss these well-known LLMs. A. Generative Pre-Trained Transformer (GPT) GPT is a DL model that has been pre-trained on vast amounts of text data, enabling it to be fine-tuned for a variety of specific tasks, such as language generation, sentiment analysis, language modelling, machine translation, and text categorization [65]. The transformer architecture em… view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: Basic architecture of the LLaMA-2 model. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

**Figure 5.** Figure 5: A generic architecture of Falcon LLM. advanced language understanding and generative capabilities can achieve higher efficiency, safety, and user satisfaction levels [88], [89]. The Falcon LLM is notable for its outstanding scalability and performance among language m…

**Figure 6.** Figure 6: Illustration of the LA-light model for ITS. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]

**Figure 7.** Figure 7: Illustration of LLM-assisted optimization of public [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]

**Figure 8.** Figure 8: Illustration of traffic network, control, optimization, [PITH_FULL_IMAGE:figures/full_fig_p023_8.png]

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Forward citations

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