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REVIEW 3 major objections 5 minor 52 references

Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A fine-tuned large language model can act as an urban intersection traffic controller, detecting conflicts with 83% accuracy and emitting guidance that matches rule-based decisions.

desk verdict A plausible feasibility study of fine-tuned GPT-mini for synthetic intersection conflict detection, but the headline metrics measure agreement with the authors' own simulator, and the paradigm-level claims outrun the evidence. read the letter →

arxiv 2411.10869 v1 pith:LQCYWOWH submitted 2024-11-16 cs.CL cs.CEcs.CYcs.HC

Sari Masri , Huthaifa I. Ashqar , Mohammed Elhenawy This is my paper · ORCID
classification cs.CLcs.CEcs.CYcs.HC
keywords UrbanintersectionTrafficcontrolsystemsLargelanguagemodelsConflictdetectionChain-of-thoughtpromptingFine-tuningRight-of-wayrulesROUGE-L
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

The paper tries to establish that a large language model, after fine-tuning, can act as the decision-making core of an urban intersection traffic controller. On a synthetic multi-lane four-leg intersection, the fine-tuned GPT-mini detects potential vehicle conflicts with 83% accuracy and an F1 score of 0.84, and its text reports for conflict summaries, priority assignments, and waiting times closely match a Python simulator's rule-based outputs (ROUGE-L from 0.91 to 0.95). The model also issues driver-facing instructions such as yield, slow, or continue, so the proposal is not only classification but closed-loop guidance. If the claim holds, LLMs could centralize the detect, decide, disseminate, and deploy functions that conventional signal systems split across separate hardware and software.

What carries the argument

The load-bearing machinery is the pairing of chain-of-thought prompting with a Python-simulator ground truth: prompts force the LLM to explain the scene, identify path crossings, apply traffic rules, and produce structured text, while the simulator supplies the labels and reference text against which accuracy and ROUGE-L are measured. Around this sits the proposed 4D paradigm (Detect, Decide, Disseminate, Deploy), which imagines the LLM as a single node replacing disconnected detection, signal-timing, and feedback components.

What would settle it

Take the fine-tuned GPT-mini to a set of intersection scenarios that were not generated by the same template, with ground-truth conflict labels and right-of-way decisions produced by an independent traffic-engineering audit or by field observation at a real intersection, and compare labels and recommendations. If agreement on conflict detection falls near chance or the model assigns priority against the audited right-of-way rule in scenarios where the right-hand rule and straight-over-turn precedence disagree, the claimed controller capability would be refuted.

Watch

Extended reading notes

Core claim

On its own terms, the paper discovers that a fine-tuned GPT-mini reproduces the behavior of a rule-based intersection conflict resolver well enough to be proposed as a traffic controller. The authors build a synthetic dataset of 10,000 scenarios with two to eight vehicles at a multi-lane four-leg intersection, and a Python simulator applies standard right-of-way rules such as straight-over-turn precedence and the right-hand rule to label conflicts, priority order, and waiting times. Chain-of-thought prompts lead the model through context understanding and conflict resolution, and fine-tuning on the mixed-vehicle dataset yields 83% accuracy, 0.84 F1 for conflict detection, and high ROUGE-L scores for generated reports. The authors conclude that the approach is feasible, while noting that the Gemini and Llama versions they tested lag behind the fine-tuned GPT-mini.

Load-bearing premise

The Python simulator's conflict labels, priority decisions, and waiting times are treated as correct ground truth; the paper says traffic engineering experts validated them but provides no validation details, so every reported score measures agreement with that simulator.

Editorial extensions

If this is right

  • With mixed-vehicle fine-tuning, one model handles two-to-eight-vehicle scenarios at 83% accuracy, suggesting a single controller can cover varying traffic complexity.
  • High ROUGE-L scores on priorities and waiting times imply the model's text outputs are close to rule-based decisions, so it could generate driver instructions without a separate decision module.
  • Zero-shot performance is much lower, so any practical deployment depends on task-specific fine-tuning.
  • The 4D framing suggests the same model could absorb data from loop detectors, GPS, video, and vehicle-to-everything links and emit tailored instructions to human drivers, infrastructure, and autonomous vehicles.
  • Replacing or augmenting conventional signal controllers with such a model would make control outputs explainable in natural language.

Reading between the lines

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

  • Because all training and test scenarios come from one synthetic generator with templated text, the reported 83% and ROUGE-L scores mainly show the model can mimic that generator's rule outputs; whether it generalizes to real sensor data with noisy positions and speeds is an untested extension.
  • A natural next experiment is to swap the simulator for a different conflict-detection engine or real intersection video data and re-measure agreement; the paper's numbers would likely drop, revealing how much of the score is template memorization versus genuine geometric reasoning.
  • If the approach survives that test, the same fine-tuning recipe could be transferred to roundabouts, T-junctions, or mixed pedestrian-cyclist scenarios, since the prompt structure is geometry-agnostic.
  • Practical deployment would need latency, cost, and fail-safe guarantees for natural-language instructions; none of these are addressed by the accuracy numbers.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper proposes using large language models (LLMs) as traffic controllers at urban intersections, introducing a 4D paradigm (Detect, Decide, Disseminate, Deploy). The authors generate synthetic multi-lane, four-leg intersection scenarios with a Python simulator, use chain-of-thought prompts to query LLMs (GPT-4o-mini, Gemini 1.0/1.5, Llama-3.1) for conflict detection and decision-making, and fine-tune the GPT model. On a mixed-vehicle test set, they report 83% accuracy and F1 = 0.84 for conflict detection, plus ROUGE-L scores between 0.84 and 0.95 for conflict overview, decisions, priority assignment, and waiting times. The paper concludes that LLMs have transformative potential for real-world traffic control.

Significance. If the evaluation were externally grounded, the conflict-detection result would be a useful demonstration that a fine-tuned compact LLM can reproduce rule-based conflict identification on structured synthetic inputs. The availability of code and data on Zenodo is a concrete strength, as is the systematic comparison of several models under fine-tuned and zero-shot conditions. However, the decision-making and feedback evaluation rests on ROUGE-L against the authors' own templated simulator outputs, with no rule-based baseline, no safety or efficiency metrics, and no external validation details for the simulator. As it stands, the evidence supports a narrower claim: a fine-tuned LLM can imitate the simulator's text format and rule-based decisions on synthetic scenarios. The broader claims about transforming traffic control are not supported by the presented experiments.

major comments (3)
  1. [Analysis and Results (Decision-Making and Feedback Results)] The only stated validation of the ground-truth simulator is the sentence "The truth values were found using Python simulation and was validated by traffic engineering experts." No validation protocol, number of experts, inter-rater reliability measure, or specific conflict threshold is reported. In particular, the "close time interval" mentioned in the Methodology (Conflict Detection) is never defined, and the priority-rule details are not algorithmically specified. Because every accuracy, precision, recall, F1, and ROUGE-L score is computed against this simulator, the headline metrics are only as credible as the simulator's encoding of conflict and right-of-way rules. Please provide a precise specification of the conflict threshold and priority rules, and either a documented expert-validation procedure (e.g., number of reviewers, agreement statistics, adjudication process) or a comparison against an independent reference implementation.
  2. [Table 4 and Figure 7] The confusion matrices are inconsistent with the reported classification metrics. For the fine-tuned GPT-mini on the mixed-vehicle test set, Figure 7 shows TP=820, TN=849, FP=151, FN=180, which yields recall = 820/(820+180) = 0.82 and F1 ≈ 0.83, whereas Table 4 reports recall = 0.85 and F1 = 0.84. For the 4-vehicle model, TP=797, TN=830, FP=170, FN=203 implies recall ≈ 0.80 and F1 ≈ 0.81, while Table 4 reports recall = 0.83 and F1 = 0.82. Please reconcile these discrepancies and report the exact test-set sizes used for each condition, since the headline numbers are not self-consistent.
  3. [Analysis and Results (Decision-Making and Feedback Results)] The ROUGE-L evaluation measures textual overlap with the authors' own templated simulator outputs, not the quality or safety of the traffic decisions. The paper includes no rule-based or conventional traffic-signal baseline, no throughput, delay, or conflict-rate evaluation under the model's recommendations, and no human assessment of the decisions. A baseline that simply echoed the simulator's deterministic priority rules would likely achieve near-perfect ROUGE-L against the same reference templates, so the high scores do not demonstrate that the LLM controller improves on rule-based control. Consequently, the abstract's claim that the study "demonstrates LLMs transformative potential for traffic control" overstates what the experiments can support; the results should be framed as benchmark agreement with a synthetic simulator, not as evidence of real-world efficacy.
minor comments (5)
  1. [Abstract] Typo: "prformance" should be "performance"; also "delivere" in the Introduction should be "deliver".
  2. [Table 4 and throughout] The model is referred to inconsistently as "GPT-mini" in the text and "GPT-4o-mini" in Table 4; please standardize the naming.
  3. [Table 3] The JSON input example lists vehicle IDs V1151, V5173, V8617, V2618, while the adjacent "Input" text and JSON output refer to V7155, V6439, V5182, and V2432; please align the example.
  4. [Methodology (Conflict Detection)] The text says "as illustrated in Figure 4" when discussing conflicts and priority decisions, but Figure 4 is the intersection layout and Figure 5 is the conflict/non-conflict comparison; the cross-reference should be updated.
  5. [Model Selection and Fine-Tuning] The text says GPT-mini was fine-tuned on "three datasets" but then describes only the four-vehicle and mixed-vehicle datasets; the eight-vehicle dataset appears later in Table 4, so the description should be clarified.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the headline metrics are held-out evaluations against a Python simulator; the closed-loop synthetic benchmark is a validity limitation, not a derivation that reduces to its inputs.

full rationale

The claimed results are empirical measurements, not derivations. Fine-tuned GPT-mini is evaluated on test scenarios generated by the same Python simulator that produced the training labels and the ROUGE-L reference text; this is a supervised learning benchmark on a synthetic distribution. The 83% accuracy and F1=0.84 are computed on held-out test labels, so they are not fitted values renamed as predictions. ROUGE-L compares the model's generated text to simulator templates on held-out scenarios; that measures template imitation, but the score is not equivalent to the input by construction. The only external-validation sentence ('The truth values were found using Python simulation and was validated by traffic engineering experts', Decision-Making and Feedback Results) is vague, and the absence of an independent reference or a rule-based baseline limits the real-world claims; these are correctness/completeness concerns, not circularity. Self-citations appear in background statements about LLM capabilities (e.g., refs [21], [28], [29]) but are not load-bearing for the headline metrics, and no uniqueness theorem or ansatz is imported from the authors' prior work. The '4D' framing is a conceptual relabeling of standard control-loop components, but it is not used as evidence for the empirical results. No circular step is exhibited.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

All ground-truth labels come from the authors' Python simulator, so the simulator's assumptions carry the evaluation. No external field data or independent traffic model is used, and the decision-quality metric is ROUGE-L against the simulator's own templated outputs.

free parameters (2)
  • conflict arrival-time threshold
    Used to decide whether vehicles arriving at the intersection are in conflict; no numeric value or sensitivity analysis is provided in the Conflict Detection section.
  • waiting-time computation parameters
    The example waiting times (0, 1, 2, 3 seconds) imply an undocumented scheduling rule; no formula is given in the Conflict Detection section or Table 3.
assumptions (4)
  • domain assumption The Python simulation correctly encodes real-world intersection conflicts and right-of-way rules.
    Used to generate all ground-truth labels and ROUGE-L reference text; no quantitative validation against field data or external simulators is provided. Stated in Conflict Detection and Decision-Making and Feedback Results.
  • domain assumption Standard traffic priority rules (straight-over-turn, right-hand rule) are complete and correctly applied in the simulator.
    These rules determine which vehicles yield and the waiting times; any incompleteness changes the labels. Stated in the Conflict Detection section.
  • ad hoc to paper ROUGE-L is an appropriate measure of traffic decision quality.
    The paper uses ROUGE-L to score conflict identification, decisions, priorities, and waiting times, but ROUGE-L measures lexical overlap with templated references, not safety or feasibility. Introduced in Decision-Making and Feedback Results.
  • domain assumption Synthetic scenarios with 2 to 8 vehicles are representative of real-world urban intersection traffic.
    The paper generalizes to real-world applications from these scenarios; no real-world or calibrated traffic data is used. Stated in Dataset Creation and Initialization and the Conclusion.

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Cite this review

Pith. "Pith review of Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm." pith.science (2026). https://pith.science/paper/LQCYWOWH

@misc{pith2026241110869,
  author       = {Pith},
  title        = {Pith review of: Large Language Models (LLMs) as Traffic Control Systems at Urban Intersections: A New Paradigm},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LQCYWOWH}},
  note         = {Machine review of arXiv:2411.10869}
}
read the original abstract

This study introduces a novel approach for traffic control systems by using Large Language Models (LLMs) as traffic controllers. The study utilizes their logical reasoning, scene understanding, and decision-making capabilities to optimize throughput and provide feedback based on traffic conditions in real-time. LLMs centralize traditionally disconnected traffic control processes and can integrate traffic data from diverse sources to provide context-aware decisions. LLMs can also deliver tailored outputs using various means such as wireless signals and visuals to drivers, infrastructures, and autonomous vehicles. To evaluate LLMs ability as traffic controllers, this study proposed a four-stage methodology. The methodology includes data creation and environment initialization, prompt engineering, conflict identification, and fine-tuning. We simulated multi-lane four-leg intersection scenarios and generates detailed datasets to enable conflict detection using LLMs and Python simulation as a ground truth. We used chain-of-thought prompts to lead LLMs in understanding the context, detecting conflicts, resolving them using traffic rules, and delivering context-sensitive traffic management solutions. We evaluated the prformance GPT-mini, Gemini, and Llama as traffic controllers. Results showed that the fine-tuned GPT-mini achieved 83% accuracy and an F1-score of 0.84. GPT-mini model exhibited a promising performance in generating actionable traffic management insights, with high ROUGE-L scores across conflict identification of 0.95, decision-making of 0.91, priority assignment of 0.94, and waiting time optimization of 0.92. We demonstrated that LLMs can offer precise recommendations to drivers in real-time including yielding, slowing, or stopping based on vehicle dynamics.

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Reference graph

Works this paper leans on

52 extracted references · 29 canonical work pages

  1. [1]

    Human factors contributing to the road traffic accident occurrence,

    K. Bucsuházy, E. Matuchová, R. Zůvala, P. Moravcová, M. Kostíková, and R. Mikulec, “Human factors contributing to the road traffic accident occurrence,” Transportation research procedia, vol. 45, pp. 555–561, 2020

    work page 2020

  2. [2]

    Impact of risk factors on work zone crashes using logistic models and Random Forest,

    H. I. Ashqar, Q. H. Shaheen, S. A. Ashur, and H. A. Rakha, “Impact of risk factors on work zone crashes using logistic models and Random Forest,” 2021. doi: 10.1109/ITSC48978.2021.9564405

    arXiv 2021

  3. [3]

    Fuel consumption at signalized intersections: Investigating the impact of different signal indication settings,

    I. Albool et al., “Fuel consumption at signalized intersections: Investigating the impact of different signal indication settings,” Case Stud Transp Policy, vol. 13, p. 101022, 2023

    work page 2023

  4. [4]

    Joint Impact of Rain and Incidents on Traffic Stream Speeds,

    M. M. Elhenawy, H. A. Rakha, and H. I. Ashqar, “Joint Impact of Rain and Incidents on Traffic Stream Speeds,” J Adv Transp, vol. 2021, 2021, doi: 10.1155/2021/8812740

    work page doi:10.1155/2021/8812740 2021

  5. [5]

    Modeling driver behavior within a signalized intersection approach decision–dilemma zone,

    H. Rakha, A. Amer, and I. El -Shawarby, “Modeling driver behavior within a signalized intersection approach decision–dilemma zone,” Transp Res Rec, vol. 2069, no. 1, pp. 16 – 25, 2008

    work page 2008

  6. [6]

    Evaluating a signalized intersection performance using unmanned aerial Data,

    M. I. Ashqer et al. , “Evaluating a signalized intersection performance using unmanned aerial Data,” Transportation Letters , vol. 16, no. 5, 2024, doi: 10.1080/19427867.2023.2204249

    arXiv 2024

  7. [7]

    Physiological and driving behaviour changes associated to different road intersections,

    N. Distefano et al., “Physiological and driving behaviour changes associated to different road intersections,” European Transport, vol. 77, 2020

    work page 2020

  8. [8]

    NCHRP 20-07/Task 414: Benefits of adaptive traffic control deployments—A review of evaluation studies,

    A. Stevanovic, N. Dobrota, and N. Mitrovic, “NCHRP 20-07/Task 414: Benefits of adaptive traffic control deployments—A review of evaluation studies,” NCHRP: Washington, DC, USA, 2019

    work page 2019

Show all 52 references
  1. [9]

    Large language models as traffic signal control agents: Capacity and opportunity,

    S. Lai, Z. Xu, W. Zhang, H. Liu, and H. Xiong, “Large language models as traffic signal control agents: Capacity and opportunity,” arXiv preprint arXiv:2312.16044, 2023

    2023 arXiv

  2. [10]

    Driving behavior classification at signalized intersections using vehicle kinematics: Application of unsupervised machine learning,

    N. O. Khanfar, M. Elhenawy, H. I. Ashqar, Q. Hussain, and W. K. M. Alhajyaseen, “Driving behavior classification at signalized intersections using vehicle kinematics: Application of unsupervised machine learning,” Int J Inj Contr Saf Promot, pp. 1–11, 2022

    2022

  3. [11]

    Heterogeneity in the driver behavior: an exploratory study using real-time driving data,

    J. Yarlagadda and D. S. Pawar, “Heterogeneity in the driver behavior: an exploratory study using real-time driving data,” J Adv Transp, vol. 2022, 2022

    2022

  4. [12]

    Interaction driver –bicyclist on rural roads: Effects of cross - sections and road geometric elements,

    F. Bella and M. Silvestri, “Interaction driver –bicyclist on rural roads: Effects of cross - sections and road geometric elements,” Accid Anal Prev, vol. 102, pp. 191–201, 2017

    2017

  5. [13]

    Driver Behavior at Roundabouts in Mixed Traffic: A Case Study Using Machine Learning,

    A. H. Farah, H. Rama, S. Deema, and A. H. I, “Driver Behavior at Roundabouts in Mixed Traffic: A Case Study Using Machine Learning,” J Transp Eng A Syst, vol. 150, no. 12, p. 05024004, Dec. 2024, doi: 10.1061/JTEPBS.TEENG-8325

    2024 doi

  6. [14]

    A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage,

    M. U. Hadi et al. , “A Survey on Large Language Models: Applications, Challenges, Limitations, and Practical Usage,” Jul. 2023, doi: 10.36227/techrxiv.23589741.v1

    2023 doi

  7. [15]

    Incorporating driving volatility measures in safety performance functions: Improving safety at signalized intersections,

    A. Mohammadnazar, A. L. Patwary, N. Moradloo, R. Arvin, and A. J. Khattak, “Incorporating driving volatility measures in safety performance functions: Improving safety at signalized intersections,” Accid Anal Prev, vol. 178, p. 106872, 2022

    2022

  8. [16]

    Incorporating driving behavior into vehicle fuel consumption prediction: methodology development and testing,

    H. I. Ashqar, M. Obaid, A. Jaber, R. Ashqar, N. O. Khanfar, and M. Elhenawy, “Incorporating driving behavior into vehicle fuel consumption prediction: methodology development and testing,” Discover Sustainability , vol. 5, no. 1, p. 344, 2024, doi: 10.1007/s43621-024-00511-z

    2024 doi

  9. [17]

    Languagempc: Large language models as decision makers for autonomous driving,

    H. Sha et al., “Languagempc: Large language models as decision makers for autonomous driving,” arXiv preprint arXiv:2310.03026, 2023

    2023 arXiv

  10. [18]

    ConnectGPT: Connect Large Language Models with Connected and Automated Vehicles,

    K. Tong and S. Solmaz, “ConnectGPT: Connect Large Language Models with Connected and Automated Vehicles,” in 2024 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2024, pp. 581–588

    2024

  11. [19]

    Large Language Models for Mobility in Transportation Systems: A Survey on Forecasting Tasks,

    Z. Zhang et al., “Large Language Models for Mobility in Transportation Systems: A Survey on Forecasting Tasks,” arXiv preprint arXiv:2405.02357, 2024

    2024 arXiv

  12. [20]

    Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects,

    M. U. Hadi et al., “Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects,” Authorea Preprints, 2023

    2023

  13. [21]

    Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing,

    H. I. Ashqar, A. Jaber, T. I. Alhadidi, and M. Elhenawy, “Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing,” arXiv preprint arXiv:2409.18286, 2024

    2024 arXiv

  14. [22]

    Chatgpt is on the horizon: Could a large language model be all we need for intelligent transportation?,

    O. Zheng, M. Abdel -Aty, D. Wang, Z. Wang, and S. Ding, “Chatgpt is on the horizon: Could a large language model be all we need for intelligent transportation?,” arXiv preprint arXiv:2303.05382, 2023

    2023 arXiv

  15. [23]

    TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models,

    Y. Ren, Y. Chen, S. Liu, B. Wang, H. Yu, and Z. Cui, “TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models,” Mar. 2024

    2024

  16. [24]

    TrafficGPT: Viewing, processing and interacting with traffic foundation models,

    S. Zhang et al., “TrafficGPT: Viewing, processing and interacting with traffic foundation models,” Transp Policy (Oxf) , vol. 150, pp. 95 –105, May 2024, doi: 10.1016/j.tranpol.2024.03.006

    2024 doi

  17. [26]

    Receive, reason, and react: Drive as you say, with large language models in autonomous vehicles,

    C. Cui, Y. Ma, X. Cao, W. Ye, and Z. Wang, “Receive, reason, and react: Drive as you say, with large language models in autonomous vehicles,” IEEE Intelligent Transportation Systems Magazine, 2024

    2024

  18. [27]

    Toward driving scene understanding: A dataset for learning driver behavior and causal reasoning,

    V. Ramanishka, Y.-T. Chen, T. Misu, and K. Saenko, “Toward driving scene understanding: A dataset for learning driver behavior and causal reasoning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7699–7707

    2018

  19. [28]

    Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challenges,

    M. M. Elhenawy et al., “Visual Reasoning and Multi-Agent Approach in Multimodal Large Language Models (MLLMs): Solving TSP and mTSP Combinatorial Challenges,” Mach Learn Knowl Extr, vol. 6, no. 3, 2024, doi: 10.3390/make6030093

    2024 doi

  20. [29]

    Multitask Learning for Crash Analysis: A Fine-Tuned LLM Framework Using Twitter Data,

    S. Jaradat, R. Nayak, A. A. Paz, H. I. Ashqar, and M. Elhenawy, “Multitask Learning for Crash Analysis: A Fine-Tuned LLM Framework Using Twitter Data,” Smart Cities, vol. 7, no. 5, 2024, doi: 10.3390/smartcities7050095

    2024 doi

  21. [30]

    Large Language Model -Driven Urban Traffic Signal Control,

    Y. Tang, X. Dai, C. Zhao, Q. Cheng, and Y. Lv, “Large Language Model -Driven Urban Traffic Signal Control,” in 2024 Australian & New Zealand Control Conference (ANZCC), 2024, pp. 67–71. doi: 10.1109/ANZCC59813.2024.10432823

    2024

  22. [31]

    Advancing ITS Applications with LLMs: A Survey on Traffic Management, Transportation Safety, and Autonomous Driving,

    D. Zhang, H. Zheng, W. Yue, and X. Wang, “Advancing ITS Applications with LLMs: A Survey on Traffic Management, Transportation Safety, and Autonomous Driving,” in International Joint Conference on Rough Sets, Springer, 2024, pp. 295–309

    2024

  23. [32]

    iLLM -TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement,

    A. Pang, M. Wang, M. -O. Pun, C. S. Chen, and X. Xiong, “iLLM -TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement,” arXiv preprint arXiv:2407.06025, 2024

    2024 arXiv

  24. [33]

    LLM -assisted light: Leveraging large language model capabilities for human -mimetic traffic signal control in complex urban environments,

    M. Wang, A. Pang, Y. Kan, M. -O. Pun, C. S. Chen, and B. Huang, “LLM -assisted light: Leveraging large language model capabilities for human -mimetic traffic signal control in complex urban environments,” arXiv preprint arXiv:2403.08337, 2024

    2024 arXiv

  25. [34]

    TPLLM: A traffic prediction framework based on pretrained large language models,

    Y. Ren, Y. Chen, S. Liu, B. Wang, H. Yu, and Z. Cui, “TPLLM: A traffic prediction framework based on pretrained large language models,” arXiv preprint arXiv:2403.02221, 2024

    2024 arXiv

  26. [35]

    Drivegpt4: Interpretable end -to-end autonomous driving via large language model,

    Z. Xu et al., “Drivegpt4: Interpretable end -to-end autonomous driving via large language model,” arXiv preprint arXiv:2310.01412, 2023

    2023 arXiv

  27. [36]

    Traffic control systems handbook,

    R. L. Gordon et al., “Traffic control systems handbook,” United States. Federal Highway Administration. Office of Technology Applications, 1996

    1996

  28. [37]

    Traffic signal systems research: Past, present, and future trends,

    C. M. Day et al. , “Traffic signal systems research: Past, present, and future trends,” Centennial Papers, 2019

    2019

  29. [38]

    Using Multimodal Large Language Models (MLLMs) for Automated Detection of Traffic Safety- Critical Events,

    M. Abu Tami, H. I. Ashqar, M. M. Elhenawy, S. Glaser, and A. Rakotonirainy, “Using Multimodal Large Language Models (MLLMs) for Automated Detection of Traffic Safety- Critical Events,” Vehicles, vol. 6, no. 3, 2024, doi: 10.3390/vehicles6030074

    2024 doi

  30. [39]

    Vision language models in autonomous driving and intelligent transportation systems,

    X. Zhou, M. Liu, B. L. Zagar, E. Yurtsever, and A. C. Knoll, “Vision language models in autonomous driving and intelligent transportation systems,” arXiv preprint arXiv:2310.14414, 2023

    2023 arXiv

  31. [40]

    Leveraging Multimodal Large Language Models (MLLMs) for Enhanced Object Detection and Scene Understanding in Thermal Images for Autonomous Driving Systems,

    H. I. Ashqar, T. I. Alhadidi, M. Elhenawy, and N. O. Khanfar, “Leveraging Multimodal Large Language Models (MLLMs) for Enhanced Object Detection and Scene Understanding in Thermal Images for Autonomous Driving Systems,” Automation, vol. 5, no. 4, pp. 508–526, 2024

    2024

  32. [41]

    Hybrid LLM-DDQN based Joint Optimization of V2I Communication and Autonomous Driving,

    Z. Yan, H. Zhou, H. Tabassum, and X. Liu, “Hybrid LLM-DDQN based Joint Optimization of V2I Communication and Autonomous Driving,” arXiv preprint arXiv:2410.08854, 2024

    2024 arXiv

  33. [42]

    Secure and Scalable Multi-Modal Vehicle Systems: A Cloud-Based Framework for Real -Time LLM -Driven Interactions,

    B. Desai and K. Patil, “Secure and Scalable Multi-Modal Vehicle Systems: A Cloud-Based Framework for Real -Time LLM -Driven Interactions,” Innovative Computer Sciences Journal, vol. 9, no. 1, pp. 1–11, 2023

    2023

  34. [43]

    Traffic signal control enhancements under vehicle infrastructure integration systems.,

    H. Rakha, I. Zohdy, J. Du, B. B. Park, J. Lee, and M. El -Metwally, “Traffic signal control enhancements under vehicle infrastructure integration systems.,” Mid-Atlantic Universities Transportation Center, 2011

    2011

  35. [44]

    Agile Approaches for Cybersecurity Systems, IoT and Intelligent Transportation,

    Y. M. Tashtoush et al., “Agile Approaches for Cybersecurity Systems, IoT and Intelligent Transportation,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2021.3136861

    2022

  36. [45]

    Overview of Road Traffic Management Solutions based on IoT and AI,

    A. A. Ouallane, A. Bahnasse, A. Bakali, and M. Talea, “Overview of Road Traffic Management Solutions based on IoT and AI,” Procedia Comput Sci, vol. 198, pp. 518–523, 2022, doi: 10.1016/j.procs.2021.12.279

    2022 doi

  37. [46]

    Traffic management approaches using machine learning and deep learning techniques: A survey,

    H. Almukhalfi, A. Noor, and T. H. Noor, “Traffic management approaches using machine learning and deep learning techniques: A survey,” Eng Appl Artif Intell, vol. 133, p. 108147, Jul. 2024, doi: 10.1016/j.engappai.2024.108147

    2024

  38. [47]

    Advancing ITS Applications with LLMs: A Survey on Traffic Management, Transportation Safety, and Autonomous Driving,

    D. Zhang, H. Zheng, W. Yue, and X. Wang, “Advancing ITS Applications with LLMs: A Survey on Traffic Management, Transportation Safety, and Autonomous Driving,” 2024, pp. 295–309. doi: 10.1007/978-3-031-65668-2_20

    2024 doi

  39. [48]

    Enhancing Network Management Using Code Generated by Large Language Models,

    S. K. Mani et al. , “Enhancing Network Management Using Code Generated by Large Language Models,” in Proceedings of the 22nd ACM Workshop on Hot Topics in Networks, New York, NY, USA: ACM, Nov. 2023, pp. 196–204. doi: 10.1145/3626111.3628183

    2023

  40. [49]

    MTD -GPT: A Multi-Task Decision-Making GPT Model for Autonomous Driving at Unsignalized Intersections,

    J. Liu, P. Hang, X. Qi, J. Wang, and J. Sun, “MTD -GPT: A Multi-Task Decision-Making GPT Model for Autonomous Driving at Unsignalized Intersections,” in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) , IEEE, Sep. 2023, pp. 5154–5161. doi: ...

    2023

  41. [50]

    ConnectGPT: Connect Large Language Models with Connected and Automated Vehicles,

    K. Tong and S. Solmaz, “ConnectGPT: Connect Large Language Models with Connected and Automated Vehicles,” in 2024 IEEE Intelligent Vehicles Symposium (IV) , IEEE, Jun. 2024, pp. 581–588. doi: 10.1109/IV55156.2024.10588835

    2024

  42. [51]

    Prompting GPT –4 to support automatic safety case generation,

    M. Sivakumar, A. B. Belle, J. Shan, and K. Khakzad Shahandashti, “Prompting GPT –4 to support automatic safety case generation,” Expert Syst Appl, vol. 255, p. 124653, Dec. 2024, doi: 10.1016/j.eswa.2024.124653

    2024

  43. [52]

    DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction,

    R. Jiang et al., “DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management, New York, NY, USA: ACM, Oct. 2021, pp. 4515–

    2021

  44. [4525]

    doi: 10.1145/3459637.3482000

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.