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REVIEW 3 major objections 5 minor 1 cited by

Artificial Intelligence in Traffic Systems

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This review claims AI can transform urban traffic management from signal timing to self-driving cars.

desk verdict A useful, broad narrative review of AI in traffic management, not a research advance; its optimistic 'quantum shift' conclusion is under-supported by an unstated convenience sample of sources, but the paper itself acknowledges real counter-evidence and deserves a serious referee as a review article. read the letter →

arxiv 2412.12046 v1 pith:YHYTHI6W submitted 2024-12-16 cs.AI

Ritwik Raj Saxena This is my paper · ORCID
classification cs.AI
keywords trafficmanagementartificialintelligenceintelligenttransportationsystemsautonomousvehiclesadaptivesignalcontrolmachinelearningpredictionsmartcities
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 paper is a review of the intersection of artificial intelligence and traffic management. The author tries to establish that AI-based systems—using fuzzy logic, reinforcement learning, deep neural networks, and evolutionary algorithms—can substantially improve how cities manage traffic. The claimed benefits include smoother signal timing, faster incident detection and emergency response, better parking and route guidance, safer autonomous vehicles, and lower emissions. The paper also catalogs the obstacles, such as data quality, privacy, cybersecurity, bias, and the cost of upgrading infrastructure. For a sympathetic reader, the value is a consolidated map of where AI is being applied and what must be solved before the promised transformation becomes routine.

What carries the argument

The central object of the review is the family of AI techniques applied to traffic: machine learning and deep learning for prediction, reinforcement learning for adaptive signal control, computer vision and sensor fusion for autonomous vehicles, fuzzy logic for handling uncertain traffic conditions, and V2X communication for connecting vehicles and infrastructure. The paper treats these techniques as the mechanism that converts raw traffic data from sensors, cameras, and GPS devices into dynamic decisions—signal timing changes, rerouting, incident alerts, and driving maneuvers—that rule-based systems cannot make at the same speed or scale.

What would settle it

Conduct a systematic literature search that explicitly counts published negative or null results from AI traffic-management deployments—pilots where travel times, emissions, or safety did not improve—and compare their frequency with positive reports; a substantial body of negative findings would undercut the review's conclusion that AI can reliably transform traffic management.

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Extended reading notes

Core claim

The paper's central claim is that existing research demonstrates AI's potential to 'transform the traffic landscape' and 'engender a quantum shift in traffic management.' It argues that data-driven AI systems are fundamentally better suited than traditional rule-based systems to handle dynamic, unpredictable urban traffic, because they learn from real-time and historical data, adapt signal timings, forecast congestion, detect incidents, and coordinate connected and autonomous vehicles. The author presents AI traffic management as a convergence of techniques—machine learning, reinforcement learning, computer vision, fuzzy logic, and V2X communications—that together promise safer roads, shorter travel times, better emergency response, and reduced environmental impact, provided that data privacy, cybersecurity, bias, interpretability, and infrastructure costs are addressed.

Load-bearing premise

The paper assumes that the examples and studies it cites are a fair, representative sample of AI traffic-management research, rather than a selection of successes; if failed deployments and negative results are systematically missing, the claimed potential would be overstated.

Editorial extensions

If this is right

  • Adaptive signal control systems, such as the Pittsburgh SURTRAC case, can smooth traffic flow in real time and reduce congestion.
  • AI-driven incident detection, as deployed in Singapore, can shorten emergency response times by automatically alerting services.
  • Connected vehicle technology (V2V, V2I, V2X) plus AI can predict hazards and coordinate vehicles before a human would notice.
  • Autonomous vehicles, from Level 2 driver assistance to Level 5 full automation, promise fewer collisions and better accessibility, but only if mixed-traffic behavior with human drivers is understood.
  • Realizing these benefits requires solving data privacy, cybersecurity, algorithmic bias, and the high cost of infrastructure upgrades.

Reading between the lines

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

  • Editorial inference: If the review's optimistic sample is representative, cities with limited budgets might get the most immediate return from adaptive signal control and incident detection, before investing in full autonomy.
  • Editorial inference: The Waymo data cited in the paper suggests that human drivers drive more aggressively behind autonomous vehicles; this implies that the safety benefits of AVs can be partly offset by human adaptation, a dynamic the paper notes but does not quantify.
  • Editorial inference: A testable extension would be a meta-analysis that separates reports of AI traffic pilots into successful, neutral, and failed outcomes, to see whether the publication record is as one-sided as the review implies.
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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. This paper is a narrative review of AI applications in traffic management, covering AI-powered traffic signal control, autonomous vehicles, smart parking, intelligent traffic management systems, ADAS, connected vehicle technologies, smart mobility, and related areas. It contrasts traditional rule-based traffic management with AI-driven approaches, then discusses challenges (data privacy, cybersecurity, bias, infrastructure costs, scalability, ethical issues, and human-AV interaction), environmental sustainability, and future directions. The paper concludes that AI 'has the potential to engender a quantum shift in traffic management.' It makes no empirical or methodological contribution; its contribution is a broad, non-systematic literature survey.

Significance. If the survey's selection of sources were representative, the paper would offer a useful broad map of AI-traffic research for a general computer-science audience. Its strengths include coverage of many application areas, explicit acknowledgment of challenges such as adversarial attacks and biased enforcement, and inclusion of some counter-evidence, notably the Waymo-based finding that human drivers can behave more aggressively behind autonomous vehicles. Many of the citations are credible peer-reviewed sources. However, because the review is narrative and non-systematic, with no stated search strategy or inclusion criteria, the general conclusion about AI's transformative potential is suggestive rather than established. The paper would be substantially strengthened by transparent methodology, quantification of the highlighted success cases, and removal or sourcing of unsupported empirical claims.

major comments (3)
  1. [Introduction; Conclusion and Future Work] The paper's central claim—that AI 'has the potential to engender a quantum shift in traffic management'—is a claim about the balance of field evidence, but the paper provides no methodology to support representativeness. No search strategy, database list, inclusion/exclusion criteria, or quality assessment is stated. The three success cases used as evidence (Pittsburgh SURTRAC [41], Singapore incident detection [43], Waymo data [92]) are presented descriptively without effect sizes, control conditions, or explicit comparison to non-AI baselines. This is a load-bearing issue because the optimistic conclusion depends on the implicit assumption that the cited corpus is representative. The authors should either add a methods section describing the review protocol, substantially moderate the conclusion, or both.
  2. [AI Technologies and Their Applications in Traffic Management; Challenges Associated with Integrating Autonomous…] Several empirical claims that support the optimistic conclusion are presented without citations or with only vague support. For example, the paper states that 'the data-driven approach... has been found to be more reliable' than model-based approaches, but gives no source for this comparative finding. Similarly, in the AV integration section, the claim that AVs 'have also evinced considerable potential in bringing down the frequency of road accidents, including rear-ended, T-bone and frontal collisions' appears with no citation at all. These assertions are not auxiliary; they directly support the 'quantum shift' conclusion. They need to be either removed, supported with specific peer-reviewed evidence, or explicitly framed as the authors' opinion.
  3. [References; Figure 4] The reference base is uneven and includes non-scholarly sources that do not meet the evidentiary standard for a survey in a serious venue. Examples include a compliance blog [73], vendor web pages [79] and [81], a YouTube video [28], and a coursehero-hosted report [84]. The four self-citations [121]–[124] concern medical education, COVID-19 sentiment analysis, pharmacology, and an electromyographic vehicle concept; they do not support claims about traffic management and appear to be unrelated to the paper's topic. In addition, Figure 4 is described as 'Borrowed/Adapted directly from the Internet' without a source or permission note. The authors should replace or remove non-scholarly references, add sources for all empirical claims, and provide proper attribution for all figures.
minor comments (5)
  1. [Abstract and Conclusion] The phrase 'quantum shift' is a vague superlative; consider a more precise formulation, such as 'substantial and measurable improvements in specific traffic management functions.' The abstract and conclusion also repeat the same material nearly verbatim.
  2. [Various] Terminology is inconsistent and sometimes nonstandard: the paper uses 'AI-fueled,' 'AI-triggered,' 'AI-boosted,' 'AI-grounded,' and 'AI-oriented' interchangeably. Standardizing on 'AI-based' or 'AI-driven' would improve readability.
  3. [Advanced Driver Assistance Systems] Some sentences are grammatically incomplete or contain redundant phrasing. For example, 'Traffic Sign Recognition identifies road signs and displays and simplifies them for the drivers, especially the drivers who face some ophthalmic or optical trouble or other problems like dyslexia' is awkward and should be rewritten.
  4. [References] The reference list is inconsistent in formatting: some entries include DOIs, others only URLs, and one entry [46] has an unusual citation format ('Elsevier EBooks, 44–26'). The authors should normalize all references to a single style.
  5. [Discussion, Challenges, and Limitations] The discussion of counter-evidence is qualitatively acknowledged but not integrated. For instance, the Waymo finding that human drivers drive more aggressively behind autonomous vehicles is described, but the paper does not weigh how this dilutes the overall claimed benefits. A brief synthesis would strengthen the paper's balance.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: narrative review with no fitted parameters, derivations, or load-bearing self-citations.

full rationale

This paper is a narrative survey of AI applications in traffic management, not a derivation or modeling exercise. There are no equations, fitted parameters, or predictive quantities that could reduce to input definitions. The central claim — that existing research demonstrates AI's potential to transform traffic management — is an interpretive synthesis of cited external literature, and the cited success cases (SURTRAC, Singapore incident detection, Waymo data) are presented as independent empirical examples rather than as outputs of this paper's own assumptions. The four self-citations [121]-[124] appear only in the closing discussion on AI optimism generally and are unrelated to traffic systems; they do not supply any load-bearing premise for the traffic-management conclusions. The acknowledged limitation that the review has no stated search or inclusion criteria raises a question of representative sampling, but that is a completeness and correctness concern, not circularity. Because no step in the paper is defined in terms of its own conclusion, and no cited result is invoked to forbid alternatives or to force a choice, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

This is a narrative review, so there are no free parameters fitted to data and no invented entities. The only substantive premise is that the cited references faithfully represent the state of the art; because the paper does not describe a literature search protocol, this premise is unverified.

assumptions (1)
  • domain assumption The cited literature is an accurate and representative sample of research on AI in traffic management.
    The review's conclusion that AI can transform traffic management rests entirely on the credibility and coverage of its 124 references. The paper provides no systematic search methodology, so the selection could be biased toward positive results.

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Cite this review

Pith. "Pith review of Artificial Intelligence in Traffic Systems." pith.science (2026). https://pith.science/paper/YHYTHI6W

@misc{pith2026241212046,
  author       = {Pith},
  title        = {Pith review of: Artificial Intelligence in Traffic Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YHYTHI6W}},
  note         = {Machine review of arXiv:2412.12046}
}
read the original abstract

Existing research on AI-based traffic management systems, utilizing techniques such as fuzzy logic, reinforcement learning, deep neural networks, and evolutionary algorithms, demonstrates the potential of AI to transform the traffic landscape. This article endeavors to review the topics where AI and traffic management intersect. It comprises areas like AI-powered traffic signal control systems, automatic distance and velocity recognition (for instance, in autonomous vehicles, hereafter AVs), smart parking systems, and Intelligent Traffic Management Systems (ITMS), which use data captured in real-time to keep track of traffic conditions, and traffic-related law enforcement and surveillance using AI. AI applications in traffic management cover a wide range of spheres. The spheres comprise, inter alia, streamlining traffic signal timings, predicting traffic bottlenecks in specific areas, detecting potential accidents and road hazards, managing incidents accurately, advancing public transportation systems, development of innovative driver assistance systems, and minimizing environmental impact through simplified routes and reduced emissions. The benefits of AI in traffic management are also diverse. They comprise improved management of traffic data, sounder route decision automation, easier and speedier identification and resolution of vehicular issues through monitoring the condition of individual vehicles, decreased traffic snarls and mishaps, superior resource utilization, alleviated stress of traffic management manpower, greater on-road safety, and better emergency response time.

Figures

Figures reproduced from arXiv: 2412.12046 by the authors.

Figure 1
Figure 1. A Digest of the Challenges Associated with Traditional Traffic Management [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Diagrammatic Representation of Traffic Flow Theory. Here, Q stands for Traffic Flow, K stands for Traffic [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. A summary of applications of AI in traffic management [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Basic Description of Fuzzy Logic (Borrowed/Adapted directly from the Internet) [PITH_FULL_IMAGE:figures/full_fig_p018_4.png]
Figure 5
Figure 5. Figure 5: An example fuzzy system for traffic prediction based on weather conditions (given that there exists a definitive hailstorm). It consists of two inputs, three rules, and one output. The inputs are crisp (non-fuzzy) 10-point Likert scale quantities; the rules are fuzzy a…

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    A position paper advocating the use of predictive analytics to reduce workplace risks in India, with policy recommendations but no empirical evidence.

Reference graph

Works this paper leans on

129 extracted references · 63 canonical work pages · cited by 1 Pith paper

  1. [41]

    https://doi.org/10.3141/2554-02

    work page doi:10.3141/2554-02

  2. [92]

    Hu, X., Zheng, Z., Chen, D., & Sun, J. (2023). Autonomous vehicle’s impact on traffic: Empirical evidence from Waymo Open Dataset and implications from modeling. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2023.3258145

    arXiv 2023

  3. [73]

    (2024, August 18)

    Fredrik Filipsson. (2024, August 18). Ethical Issues in AI for Autonomous Vehicles. Redress Compliance - Just Another WordPress Site. https://redresscompliance.com/ethical-issues-in-ai-for- autonomous-vehicles/

    2024

  4. [79]

    Sheng, Z., Huang, Z., & Chen, S. (2024). Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CA V trajectory control. arXiv.org. https://arxiv.org/abs/2408.17380

    arXiv 2024

  5. [81]

    (2024, January 15)

    Imagination Technologies. (2024, January 15). Production -Ready Autonomous Vehicles - Imagination. Imagination. https://www.imaginationtech.com/future-of-automotive/when-will- autonomous-cars-be-available/production-ready-autonomous-vehicles/

    2024

  6. [28]

    Priya Rai. (2021). Fundamental Traffic Flow Diagram II Traffic Flow Theory II Traffic Engg [Video]. YouTube. https://www.youtube.com/watch?v=M_WDTlaaiZ8

    2021

  7. [84]

    Imagination Technologies. (2023). What Are The Levels Of Autonomy In Self -Driving Cars? - Imagination. www.imaginationtech.com. https://www.imaginationtech.com/future-of- automotive/when-will-autonomous-cars-be-available/what-are-the-levels-of-autonomy-in-self- driving-cars/

    2023

  8. [121]

    Saxena, R. R. (2024). Beyond Flashcards: Designing an Intelligent Assistant for USMLE Mastery and Virtual Tutoring in Medical Education (A Study on Harnessing Chatbot Technology for Personalized Step 1 Prep). arXiv preprint arXiv:2409.10540. https://arxiv.org/abs/2409.10540

    arXiv 2024

  9. [124]

    Wolniak, R., & Stecuła, K. (2024). Artificial intelligence in smart cities—applications, barriers, and future directions: A review. Smart Cities 7, 1346-1389. https://doi.org/10.3390/smartcities7030057

    work page doi:10.3390/smartcities7030057 2024

Show all 129 references
  1. [1]

    Lu, J., Li, B., Li, H., & Al -Barakani, A. (2021). Expansion of city scale, traffic modes, traffic congestion, and air pollution. Cities, 108, 102974. https://doi.org/10.1016/j.cities.2020.102974

    2021

  2. [2]

    Çolak, S., Lima, A., & González, M. C. (2016). Understanding congested travel in urban areas. Nature Communications, 7(1). https://doi.org/10.1038/ncomms10793

    2016 doi

  3. [3]

    Iyer, L. S. (2021). AI -enabled applications towards intelligent transportation. Transportation Engineering, 5(100083), 100083. sciencedirect. https://doi.org/10.1016/j.treng.2021.100083

    2021

  4. [4]

    Revolutionizing Urban Traffic Management: An AI and Multi - Agent System Approach Leveraging IoT Technology

    Shabani, M., & Nazari, K. (2023). “Revolutionizing Urban Traffic Management: An AI and Multi - Agent System Approach Leveraging IoT Technology.” Indonesian Journal of Conservation, 12(2), 101–

    2023

  5. [5]

    S., Parjapati, G., Aswal, R., Raghav, A

    Bali, P . S., Parjapati, G., Aswal, R., Raghav, A. K., & Gupta, N. (2015). Intelligent Traffic Monitoring System (ITMS). Progress In Science and Engineering Research Journal, 18(3/02), 40 -44. https://www.researchgate.net/publication/306033057_Intelligent_Traffic_Monitoring_Sy...

    2015

  6. [6]

    Batura, O., Regeczi, D., Vassilev, A., Yagafarova, A., Bani, E., Bonneau, V ., Jacques, F., & Streel, A. D. (2021). Artificial intelligence in road transport: annex to cost of non -Europe report. Researchportal.unamur.be. https://researchportal.unamur.be/en/publications/artifi...

    2021

  7. [7]

    A., Safelnasr, Z., Yemane, N., Kedir, M., Shafiqurrahman, A., & Saeed, N

    Khalil, R. A., Safelnasr, Z., Yemane, N., Kedir, M., Shafiqurrahman, A., & Saeed, N. (2024, February 26). Advanced learning technologies for intelligent transportation systems: Prospects and challenges. IEEE Open Journal of Vehicular Technology. doi:10.1109/OJVT.2024.3369691

    2024

  8. [8]

    Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial Intelligence, Machine Learning and Deep Learning in Advanced Robotics, A Review. Cognitive Robotics, 3(1), 54 –70. sciencedirect. https://doi.org/10.1016/j.cogr.2023.04.001

    2023 doi

  9. [9]

    Sun, G., Qi, R., Liu, Y ., & Xu, F. (2024). A dynamic traffic signal scheduling system based on improved greedy algorithm. PloS One, 19(3), e0298417 –e0298417. https://doi.org/10.1371/journal.pone.0298417

    2024 doi

  10. [10]

    C., & Chin, D

    Spall, J. C., & Chin, D. C. (1997). Traffic-responsive signal timing for system-wide traffic control. Transportation Research Part C: Emerging Technologies, 5(3 -4), 153 –163. https://doi.org/10.1016/s0968-090x(97)00012-0

    1997 doi

  11. [11]

    I., Papadimitriou, E., & van Gelder, P

    Tselentis, D. I., Papadimitriou, E., & van Gelder, P. (2023). The usefulness of artificial intelligence for safety assessment of different transport modes. Accident Analysis & Prevention, 186, 107034. https://doi.org/10.1016/j.aap.2023.107034

    2023

  12. [12]

    Abdelrahman Osman Elfaki, Wassim Messoudi, Anas Bushnag, Shakour Abuzneid, & Tareq Alhmiedat. (2023). A Smart Real -Time Parking Control and Monitoring System. Sensors, 23(24), 9741–9741. https://doi.org/10.3390/s23249741

    2023 doi

  13. [13]

    Z., & Jang, Y

    Hasanujjaman, M., Chowdhury, M. Z., & Jang, Y . M. (2023). Sensor Fusion in Autonomous Vehicle with Traffic Surveillance Camera System: Detection, Localization, and AI Networking. Sensors, 23(6),

    2023

  14. [14]

    Almukhalfi , H., Noor, A., & Noor, T. H. (2024). Traffic management approaches using machine learning and deep learning techniques: A survey. Https://Www.sciencedirect.com/. https://doi.org/10.1016/j.engappai.2024.108147

    2024

  15. [15]

    Nazemi Absardi, Z., & Javidan, R. (2024). A predictive SD‐WAN traffic management method for IoT networks in multi‐datacenters using deep RNN. IET Communications. https://doi.org/10.1049/cmu2.12810

    2024 doi

  16. [16]

    R., Jin, P., & Adu-Gyamfi, Y

    Mandal, V ., Mussah, A. R., Jin, P., & Adu-Gyamfi, Y . (2020). Artificial Intelligence-Enabled Traffic Monitoring System. Sustainability, 12(21), 9177. https://doi.org/10.3390/su12219177

    2020 doi

  17. [17]

    Hindarto, D. (2023). Enhancing Road Safety with Convolutional Neural Network Traffic Sign Classification. Sinkron, 8(4), 2810–2818. https://doi.org/10.33395/sinkron.v8i4.13124

    2023 doi

  18. [18]

    Zhao, Z., Chen, W., Wu, X., Chen, P. C. Y ., & Liu, J. (2017). LSTM network: a deep learning approach for short‐term traffic forecast. IET Intelligent Transport Systems, 11(2), 68 –75. https://doi.org/10.1049/iet-its.2016.0208

    2017

  19. [19]

    L., Khoshmagham, S., & Zamanipour, M

    Feng, Y ., Head, K. L., Khoshmagham, S., & Zamanipour, M. (2015). A real-time adaptive signal control in a connected vehicle environment. Transportation Research Part C: Emerging Technologies, 55, 460–473. https://doi.org/10.1016/j.trc.2015.01.007

    2015 doi

  20. [20]

    Ilgin Guler, S., Menendez, M., & Meier, L. (2014). Using connected vehicle technology to improve the efficiency of intersections. Transportation Research Part C: Emerging Technologies, 46, 121–131. https://doi.org/10.1016/j.trc.2014.05.008

    2014 doi

  21. [21]

    A., Gundogan, K., Ge, L., & Liang, F

    Oladimeji, D., Gupta, K., Kose, N. A., Gundogan, K., Ge, L., & Liang, F. (2023). Smart transportation: an Overview of Technologies and Applications. Sensors, 23(8), 3880 –3880. https://doi.org/10.3390/s23083880

    2023 doi

  22. [22]

    Thorpe, C., Herbert, M., Kanade, T., & Shafer, S. (1991). Toward autonomous driving: the CMU. I. Perception. IEEE Expert, 6(4), 31–42. https://doi.org/10.1109/64.85919

    1991 doi

  23. [23]

    The Carnegie Mellon University Autonomous Land Vehicle Project (NA VLAB). (n.d.). www.cs.cmu.edu. https://www.cs.cmu.edu/afs/cs/project/alv/www/

  24. [24]

    Parisien, C., & Thagard, P. (2008). Robosemantics: How Stanley the V olkswagen Represents the World. Minds and Machines, 18(2), 169–178. https://doi.org/10.1007/s11023-008-9098-2

    2008 doi

  25. [25]

    P., Kazemi, F

    Samadi, S., Rad, A. P., Kazemi, F. M., & Jafarian, H. (2012). Performance Evaluation of Intelligent Adaptive Traffic Control Systems: A Case Study. Journal of Transportation Technologies, 02(03), 248–

    2012

  26. [26]

    Paul, A., Chauhan, R., Srivastava, R., & Baruah, M. (2016). Advanced Driver Assistance Systems. SAE Technical Paper Series. https://doi.org/10.4271/2016-28-0223

    2016 doi

  27. [27]

    75 Years of the Fundamental Diagram for Traffic Flow Theory. (2011). Transportation Research Board. https://onlinepubs.trb.org/onlinepubs/circulars/ec149.pdf

    2011

  28. [29]

    Yu, C., Ma, W., & Yang, X. (2020). A time-slot based signal scheme model for fixed-time control at isolated intersections. Transportation Research Part B: Methodological, 140, 176 –192. https://doi.org/10.1016/j.trb.2020.08.004

    2020 doi

  29. [30]

    Ahmed, T., Liu, H., & Gayah, V . V . (2024). Identification of optimal locations of adaptive traffic signal control using heuristic methods. International Journal of Transportation Science and Technology, 13, 122–136. https://doi.org/10.1016/j.ijtst.2023.12.003

    2024 doi

  30. [31]

    Guo, Q., Li, L., & Jeff) Ban, X. (2019). Urban traffic signal control with connected and automated vehicles: A survey. Transportation Research Part C: Emerging Technologies, 101, 313 –334. https://doi.org/10.1016/j.trc.2019.01.026

    2019 doi

  31. [32]

    Genser, A., & Kouvelas, A. (2022). Dynamic optimal congestion pricing in multi -region urban networks by application of a Multi-Layer-Neural network. Transportation Research Part C: Emerging Technologies, 134, 103485. https://doi.org/10.1016/j.trc.2021.103485

    2022

  32. [33]

    Aung, N., Zhang, W., Sultan, K., Dhelim, S., & Ai, Y . (2021). Dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in smart cities. Digital Communications and Networks, 7(4), 492–504. https://doi.org/10.1016/j.dcan.20...

    2021 doi

  33. [34]

    Selmoune, A., Liu, Z., & Lee, J. (2022). To pay or not to pay? Understanding public acceptance of congestion pricing: A case study of Nanjing. Electronic Research Archive, 30(11), 4136 –4156. https://doi.org/10.3934/era.2022209

    2022 doi

  34. [35]

    M., & Sing, T

    Agarwal, S., Koo, K. M., & Sing, T. F. (2015). Impact of electronic road pricing on real estate prices in Singapore. Journal of Urban Economics, 90, 50–59. https://doi.org/10.1016/j.jue.2015.09.004

    2015 doi

  35. [36]

    Ozbay, K., Yanmaz-Tuzel, O., & Holguín -Veras, J. (2006). Theoretical Derivation of Value of Travel Time and Demand Elasticity. Transportation Research Record: Journal of the Transportation Research Board, 1985(1), 248–256. https://doi.org/10.1177/0361198106198500127

    2006 doi

  36. [37]

    A., Davis, B., & Cui, X

    Shbaklo, S. A., Davis, B., & Cui, X. (2016). Study to Evaluate Express Toll Lanes on Florida’s Turnpike. Transportation Research Record: Journal of the Transportation Research Board, 2554(1), 10–

    2016

  37. [38]

    Nohekhan, A., Zahedian, S., & Sadabadi, K. F. (2021). 66 Inner -Investigating the impacts of I Beltway dynamic tolling system . Https://Www.sciencedirect.com/. https://doi.org/10.1016/j.treng.2021.100059

    2021

  38. [39]

    Kim, Y ., Tak, H., Kim, S., & Yeo, H. (2024). A hybrid approach of traffic simulation and machine learning techniques for enhancing real -time traffic prediction. Transportation Research Part C: Emerging Technologies, 160, 104490–104490. https://doi.org/10.1016/j.trc.2024.104490

    2024

  39. [40]

    -Y ., & Askary, S

    Guo, M., Wang, P., Chan, C. -Y ., & Askary, S. (2019). A Reinforcement Learning Approach for Intelligent Traffic Signal Control at Urban Intersections. IEEE Xplore. https://doi.org/10.1109/ITSC.2019.8917268

    2019

  40. [42]

    N., O’Hara, A., Burnier, C., & Ozbay, K

    Vasudevan, M., Townsend, H., Dang, T. N., O’Hara, A., Burnier, C., & Ozbay, K. (2020, July 1). Identifying Real-World Transportation Applications Using Artificial Intelligence (AI): Summary of Potential Application of AI in Transportation (Inc. Noblis, Ed. ). ROSA P. https://r...

    2020

  41. [44]

    Haas, R.E., Bhattacharjee, S., Möller, D.P.F. (2020). Advanced Driver Assistance Systems. In: Akhilesh, K., Möller, D. (eds) Smart Technologies. Springer, Singapore. https://doi.org/10.1007/978 - 981-13-7139-4_27

    2020 doi

  42. [45]

    Smith, S., Barlow, G., Xie, X.-F., & Rubinstein, Z. (2013). Smart Urban Signal Networks: Initial Application of the SURTRAC Adaptive Traffic Signal Control System. Proceedings of the International Conference on Automated Planning and Scheduling, 23, 434 –442. https://doi.org/1...

    2013 doi

  43. [46]

    S., Howard, M

    Davenport, J. S., Howard, M. G., Busby, R. H., Preston, S., & Chandler, M. J. H. (2003). Road Transport. Elsevier EBooks, 44–26. https://doi.org/10.1016/b978-075064637-6/50044-7

    2003 doi

  44. [47]

    R., Jamlos, M

    Khan, A. R., Jamlos, M. F., Osman, N., Ishak, M. I., Dzaharudin, F., Yeow, Y . K., & Khairi, K. A. (2021). DSRC Technology in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) IoT System for Intelligent Transportation System (ITS): A Review. Lect ure Notes in Electr...

    2021

  45. [48]

    F., Qaiser, A., Abrar, N., & Kulsoom, U

    Nasim, S. F., Qaiser, A., Abrar, N., & Kulsoom, U. (2023). Implementation of AI in Traffic Management: Need, Current Techniques and Challenges. Pakistan Journal of Scientific Research, 3(1), 20–25. https://doi.org/10.57041/pjosr.v3i1.942

    2023 doi

  46. [49]

    Doecke, S., Grant, A., & Anderson, R. W. G. (2015). The Real-World Safety Potential of Connected Vehicle Technology. Traffic Injury Prevention, 16(sup1), S31 –S35. https://doi.org/10.1080/15389588.2015.1014551

    2015

  47. [50]

    mogi.bme.hu. (2024). Article 10. Vehicle to Environment interactions (V2E). mogi.bme.hu. https://www.mogi.bme.hu/TAMOP/jarmurendszerek_iranyitasa_angol/ch10.html

    2024

  48. [51]

    Office of the Assistant Secretary for Research and Technology. (2020). Intelligent Transportation Systems - Vehicle-to-Pedestrian (V2P) Communications for Safety. dot.gov. https://www.its.dot.gov/research_archives/safety/v2p_comm_safety.htm

    2020

  49. [52]

    P., & Choudhary, J

    Nigam, N., Singh, D. P., & Choudhary, J. (2023). A Review of Different Components of the Intelligent Traffic Management System (ITMS). Symmetry, 15(3), 583. https://doi.org/10.3390/sym15030583

    2023 doi

  50. [53]

    Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11(1), 189. https://doi.org/10.3390/su11010189

    2019 doi

  51. [54]

    Bhatti, G., Mohan, H., & Raja Singh, R. (2021). Towards the Future of Smart Electric vehicles: Digital Twin Technology. Renewable and Sustainable Energy Reviews, 141(110801), 110801. https://doi.org/10.1016/j.rser.2021.110801

    2021

  52. [55]

    M., & Szabó, G

    Rahman, Md. M., & Szabó, G. (2021). Multi -objective urban land use optimization using spatial data: A systematic review. Sustainable Cities and Society, 74, 103214. https://doi.org/10.1016/j.scs.2021.103214

    2021

  53. [56]

    Heijnen, P., Chappin, É. J. L., & Nikolic, I. (2014). Infrastructure Network Design with a Multi - Model Approach: Comparing Geometric Graph Theory with an Agent-Based Implementation of an Ant Colony Optimization. Journal of Artificial Societies and Social S imulation, 17(4). ...

    2014 doi

  54. [57]

    Vlahogianni, E. I. (2015). Optimization of traffic forecasting: Intelligent surrogate modeling. Transportation Research Part C: Emerging Technologies, 55, 14 –23. https://doi.org/10.1016/j.trc.2015.03.016

    2015 doi

  55. [58]

    Shaar, N., Alshraideh, M., & AlDajani, I. M. (2024). A Comparative Study for the Traffic Predictions in Smart Cities Using Artificial Intelligence Techniques: Survey. Reconciliation, Conflict Transformation, and Peace Studies, 191–211. https://doi.org/10.1007/978-3-031-47839-0_10

    2024 doi

  56. [59]

    Delahaye, D., Chaimatanan, S., & Mongeau, M. (2018). Simulated Annealing: From Basics to Applications. Handbook of Metaheuristics, 1–35. https://doi.org/10.1007/978-3-319-91086-4_1

    2018 doi

  57. [60]

    Chen, C., Fan, X., Zheng, C., Xiao, L., Cheng, M., & Wang, C. (2018). SDCAE: Stack Denoising Convolutional Autoencoder Model for Accident Risk Prediction Via Traffic Big Data. Conference: 2018 Sixth International Conference on Advanced Cloud and Big Data ( CBD). https://doi.or...

    2018

  58. [61]

    Y ., & Lee, S

    Sohn, S. Y ., & Lee, S. H. (2003). Data fusion, ensemble and clustering to improve the classification accuracy for the severity of road traffic accidents in Korea. Safety Science, 41(1), 1 –14. https://doi.org/10.1016/s0925-7535(01)00032-7

    2003 doi

  59. [62]

    Sawhney, M., Shah, B., Y u, R., Rubtsov, E., & Goodman, P. (2020). Uber: Applying Machine Learning to Improve the Customer Pickup Experience. Sage. https://doi.org/10.4135/9781529741728

    2020 doi

  60. [63]

    The US Department of Transportation. (2022). Artificial Intelligence (AI) for Intelligent Transportation Systems (ITS) Challenges and Potential Solutions, Insights, and Lessons Learned. The US Department of Transportation. https://rosap.ntl.bts.gov/view/dot/66971/dot_66971_DS1.pdf

    2022

  61. [64]

    Parvez Farazi, N., Zou, B., Ahamed, T., & Barua, L. (2021). Deep reinforcement learning in transportation research: A review. Transportation Research Interdisciplinary Perspectives, 11, 100425. https://doi.org/10.1016/j.trip.2021.100425

    2021

  62. [65]

    S., Azarbayjani, M., & Tabkhi, H

    Rashvand, N., Hosseini, S. S., Azarbayjani, M., & Tabkhi, H. (2023). A Novel Neural Network Approach for Predicting the Arrival Time of Buses for Smart On -Demand Public Transit. ArXiv.org. https://doi.org/10.48550/arXiv.2303.15495

    2023 doi arXiv

  63. [66]

    Hafssa, R., Fouad, J., & Moufad, I. (2024). Towards Sustainable and Smart Urban Logistics: literature review and emerging trends. https://hal.science/hal-04652837/document

    2024

  64. [67]

    Alshriem, M. (2020). The Use of Artificial Intelligence in Traffic Violation Data Analysis. International Journal of Engineering Research and Technology, 13(4), 644. https://doi.org/10.37624/ijert/13.4.2020.644-652

    2020 doi

  65. [68]

    Giannaros, A., Karras, A., Theodorakopoulos, L., Karras, C., Kranias, P., Schizas, N., Kalogeratos, G., & Tsolis, D. (2023). Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions. Journal of C ybersecurity and Pri...

    2023 doi

  66. [69]

    Zhou, X., Zhang, Y ., & Guo, H. (2024). Scheduling Method of Demand-Responsive Transit Based on Reservation Considering Vehicle Size and Mileage. Applied Sciences, 14(19), 8836 –8836. https://doi.org/10.3390/app14198836

    2024 doi

  67. [70]

    M., Rutkowski, A.-F., & Saunders, C

    de Nobrega, K. M., Rutkowski, A.-F., & Saunders, C. (2024). The whole of cyber defense: Syncing practice and theory. The Journal of Strategic Information Systems, 33(4), 101861 –101861. https://doi.org/10.1016/j.jsis.2024.101861

    2024

  68. [71]

    Trimarchi, A., & Scott, B. I. (2017). The Digital Aviation Industry: A Balancing Act Between Cybersecurity and European Consumer Protection. Air and Space Law, 42(Issue 4/5), 443 –462. https://doi.org/10.54648/aila2017030

    2017 doi

  69. [72]

    L., Mukhtiar, N., Chowdhry, B

    Dhirani, L. L., Mukhtiar, N., Chowdhry, B. S., & Newe, T. (2023). Ethical Dilemmas and Privacy Issues in Emerging Technologies: A Review. Sensors, 23(3), 1151. https://www.mdpi.com/1424- 8220/23/3/1151

    2023

  70. [74]

    Othman, K. (2022). Exploring the implications of autonomous vehicles: a comprehensive review. Innovative Infrastructure Solutions, 7(2). https://doi.org/10.1007/s41062-022-00763-6

    2022 doi

  71. [75]

    Ruddick, J., Ceusters, G., Kriekinge, V ., Genov, E., Coosemans, T., & Messagie, M. (2024). Real- world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems. arXiv.org. https://arxiv.org/abs/2408.07435

    2024 arXiv

  72. [76]

    Akhtar, M., & Moridpour, S. (2021). A Review of Traffic Congestion Prediction Using Artificial Intelligence. Journal of Advanced Transportation, 2021, e8878011. https://doi.org/10.1155/2021/8878011

    2021 doi

  73. [77]

    Wani, A., Kumari, D., & Singh, J. (2024). Ethics in the Driver’s Seat: Unravelling the Ethical Dilemmas of AI in Autonomous Driving. SAE Technical Papers on CD -ROM/SAE Technical Paper Series. https://doi.org/10.4271/2024-01-2023

    2024 doi

  74. [78]

    A., Malami, S

    Musa, A. A., Malami, S. I., Alanazi, F., Ounaies, W., Alshammari, M., & Haruna, S. I. (2023). Sustainable Traffic Management for Smart Cities Using Internet -of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations. Sustainable Traffic Manage...

    2023 doi

  75. [80]

    Du, X., & Tan, K. K. (2015). Autonomous Reverse Parking System Based on Robust Path Generation and Improved Sliding Mode Control. IEEE Transactions on Intelligent Transportation Systems, 16(3), 1225–1237. https://doi.org/10.1109/tits.2014.2354423

    2015

  76. [82]

    Tesla. (2023). Autopilot Features | Model S Owner’s Manual | Tesla. Tesla. https://www.tesla.com/ownersmanual/models/en_us/GUID-20F2262F-CDF6-408E-A752- 2AD9B0CC2FD6.html

    2023

  77. [83]

    Abdelkader, G., Elgazzar, K., & Khamis, A. (2021). Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities. Sensors, 21(22), 7712. https://doi.org/10.3390/s21227712

    2021 doi

  78. [85]

    Yu, H., Jiang, R., He, Z., Zheng, Z., Li, L., Liu, R., & Chen, X. (2021). Automated vehicle-involved traffic flow studies: A survey of assumptions, models, speculations, and perspectives. Transportation Research Part C: Emerging Technologies, 127, 103101. https://doi.org/10.10...

    2021

  79. [86]

    Jafari, S., Shahbazi, Z., & Byun, Y .-C. (2021). Traffic Control Prediction Design Based on Fuzzy Logic and Lyapunov Approaches to Improve the Performance of Road Intersection. Processes, 9(12),

    2021

  80. [87]

    Tafidis, P ., Farah, H., Brijs, T., & Pirdavani, A. (2021). Safety implications of higher levels of automated vehicles: a scoping review. Transport Reviews, 42(2), 245 –267. https://doi.org/10.1080/01441647.2021.1971794

    2021

  81. [88]

    Albdairi, M., Almusawi, A., & Qadri, S. S. S. M. (2024). Impact of autonomous vehicle driving behaviors on signalized intersection performance: A review. Usak University Journal of Engineering Sciences, 7. https://doi.org/10.47137/uujes.1477505

    2024 doi

  82. [89]

    Stanley, B., & Gyimesi, K. (2016). A New Relationship – People and Cars. IBM Institute for Business Value: Armonk, NY , USA, 21. https://www.coursehero.com/file/155919210/IBV-A-new- relationship-people-and/

    2016

  83. [90]

    Amirgholy, M., Shahabi, M., & Gao, H. O. (2020). Traffic automation and lane management for communicant, autonomous, and human -driven vehicles. Transportation Research Part C: Emerging Technologies, 111, 477–495. https://doi.org/10.1016/j.trc.2019.12.009

    2020 doi

  84. [91]

    de Zwart, R., Kamphuis, K., & Cleij, D. (2023). Driver behavioral adaptations to simulated automated vehicles: Potential implications for traffic microsimulation. Transportation Research Part F: Traffic Psychology and Behaviour, 92, 255–265. https://doi.org/10.1016/j.trf.2022.11.012

    2023 doi

  85. [93]

    C., Suarez-Bertoa, R., Vass, S., & Ciuffo, B

    Mattas, K., Albano, G., Donà, R., Galassi, M. C., Suarez-Bertoa, R., Vass, S., & Ciuffo, B. (2022). Driver models for the definition of safety requirements of automated vehicles in international regulations: Application to motorway driving conditions. Accident Analysis & Preve...

    2022

  86. [94]

    J., & Kockelman, K

    Fagnant, D. J., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: Opportunities, barriers, and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167–181. https://doi.org/10.1016/j.tra.2015.04.003

    2015 doi

  87. [95]

    Oppenheim, I., Parmet, Y ., & Oron-Gilad, T. (2022). Can driver behavior be traced to gender role, sex, and age? In K. Plant & G. Praetorius (Eds.), Human Factors in Transportation (AHFE 2022 International Conference, vol. 60). AHFE Open Access, AHFE Intern ational, USA. https...

    2022 doi

  88. [96]

    Al-Raeei, M. (2024). The smart future for sustainable development: Artificial intelligence solutions for sustainable urbanization. Sustainable Development, 1–10. https://doi.org/10.1002/sd.3131

    2024 doi

  89. [97]

    Elassy, M., Al-Hattab, M., Takruri, M., & Badawi, S. (2024). Intelligent transportation systems for sustainable smart cities. Transportation Engineering, 16, 100252. https://doi.org/10.1016/j.treng.2024.100252

    2024

  90. [98]

    R., Prelevic, M., Pantovic, V ., Stojanovic, M., Vidojevic, D., Vranjes, D., & Bodolo, I

    Vujadinovic, L., Damnjanovic, A., Cakic, A., Petkovic, D. R., Prelevic, M., Pantovic, V ., Stojanovic, M., Vidojevic, D., Vranjes, D., & Bodolo, I. (2024). AI -driven approach for enhancing sustainability in urban public transportation. Sustainability, 16(17 ), 7763. https://d...

    2024 doi

  91. [99]

    Ait Ouallane, A., Bahnasse, A., Bakali, A., & Talea, M. (2022). Overview of road traffic management solutions based on IoT and AI. Procedia Computer Science, 198, 518 –523. https://doi.org/10.1016/j.procs.2021.12.279

    2022 doi

  92. [100]

    J., Naeem, F., Khan, S., Khan, K

    Siddiqi, S. J., Naeem, F., Khan, S., Khan, K. S., & Tariq, M. (2022). Towards AI -enabled traffic management in multipath TCP: A survey. Computer Communications, 181, 412 –427. https://doi.org/10.1016/j.comcom.2021.09.030

    2022 doi

  93. [101]

    Unni, A., Trende, A., Pauley, C., Weber, L., Biebl, B., Kacianka, S., Lüdtke, A., Bengler, K., Pretschner, A., Fränzle, M., & Rieger, J. W. (2022). Investigating Differences in Behavior and Brain in Human-Human and Human-Autonomous Vehicle Interactions in Time-Critical Situati...

    2022

  94. [102]

    Ferreira, M., & d'Orey, P. M. (2012). On the impact of virtual traffic lights on carbon emissions mitigation. IEEE Transactions on Intelligent Transportation Systems, 13(1), 284 –295. https://doi.org/10.1109/TITS.2011.2169791

    2012

  95. [103]

    Quattrone, A., Cirianni, F., & Comi, A. (2023). Mobility control centre and artificial intelligence for sustainable urban districts. Information, 14, 581. https://doi.org/10.3390/info14100581

    2023 doi

  96. [104]

    Shulajkovska, M., Smerkol, M., Noveski, G., Bohanec, M., & Gams, M. (2024). Artificial intelligence-based decision support system for sustainable urban mobility. Electronics, 13(18), 3655. https://doi.org/10.3390/electronics13183655

    2024 doi

  97. [105]

    Lee, C. K. H., & Leung, E. K. H. (2023). Spatiotemporal analysis of bike -share demand using DTW-based clustering and predictive analytics. Transportation Research Part E: Logistics and Transportation Review, 180, 103361. https://doi.org/10.1016/j.tre.2023.103361

    2023

  98. [106]

    https://doi.org/10.1007/978-981-33-4597-3_10

    doi

  99. [107]

    https://doi.org/10.15294/ijc.v12i2.47322

    doi

  100. [108]

    P., Ijemaru, G

    Ang, L.-M., Seng, K. P., Ijemaru, G. K., & Zungeru, A. M. (2019). Deployment of IoV for smart cities: Applications, architecture, and challenges. IEEE Access, 7, 6473 –6492. https://doi.org/10.1109/ACCESS.2018.2887076

    2019

  101. [109]

    Sharma, R., & Singhal, M. (2025). Sustainable transport using AI and IoT. In P. Whig, N. Silva, A. A. Elngar, N. Aneja, & P. Sharma (Eds.), Sustainable development through machine learning, AI and IoT (ICSD 2024, Communications in Computer and Information Science, vol. 2196). ...

    2025 doi

  102. [110]

    Ahmad, I., Osasona, F., Dawodu, S., Obi, O., Anyanwu, A., & Onwusinkwue, S. (2024). Emerging 5G technology: A review of its far-reaching implications for communication and security. World Journal of Advanced Research and Reviews, 21(1), 2474-2486. doi:10.30574/wjarr.2024.21.1.0346

    2024 doi

  103. [111]

    Küpper, C., Rösch, J., & Winkler, H. (2022). Empirical findings for the usage of 5G as a basis for real-time locating systems (RTLS) in the automotive industry. Procedia CIRP , 107, 1287 –1292. https://doi.org/10.1016/j.procir.2022.05.146

    2022 doi

  104. [112]

    Bıyık, C., Abareshi, A., Paz, A., Arce Ruiz, R., Battarra, R., Rogers, C. D. F., & Lizarraga, C. (2021). Smart mobility adoption: A review of the literature. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 146. https://doi.org/10.3390/joitmc7020146

    2021 doi

  105. [113]

    Vijayalakshmi, S., Savita, & Durgadevi, P. (2023). AI and IoT in improving resilience of smart energy infrastructure. In S. Vijayalakshmi, B. Balusamy, & R. K. Dhanaraj (Eds.), AI-powered IoT in the energy industry (Power Systems). Springer, Cham. https://doi.org/10.1007/978-3...

    2023 doi

  106. [114]

    Qian, Y ., Wu, D., Bao, W., & Lorenz, P. (2019). The Internet of Things for smart cities: Technologies and applications. IEEE Network, 33(2), 4 –5. https://doi.org/10.1109/MNET.2019.8675165

    2019

  107. [115]

    F., & Jhanjhi, N

    Humayun, M., Almufareh, M. F., & Jhanjhi, N. Z. (2022). Autonomous traffic system for emergency vehicles. Electronics 11(4), 510. https://doi.org/10.3390/electronics11040510

    2022 doi

  108. [116]

    Bharadiya, J. (2023). Artificial intelligence in transportation systems: A critical review. American Journal of Computing and Engineering 6, 34-45. https://doi.org/10.47672/ajce.1487

    2023 doi

  109. [117]

    E., Krogstie, J., Kaboli, A., & Alahi, A

    Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024). Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environmental Science and Ecotechnology 19, 100330. https://do...

    2024

  110. [118]

    Ullah, I., Adhikari, D., Su, X., Palmieri, F., Wu, C., & Choi, C. (2024). Integration of data science with the intelligent IoT (IIoT ): Current challenges and future perspectives. Digital Communications and Networks. https://doi.org/10.1016/j.dcan.2024.02.007

    2024 doi

  111. [119]

    Kakkavas, G., Diamanti, M., Stamou, A., Karyotis, V ., Bouali, F., Pinola, J., Apilo, O., Papavassiliou, S., & Moessner, K. (2022). Design, Development, and Evaluation of 5G -Enabled Vehicular Services: The 5G -HEART Perspective. Sensors (Basel, Switzerland), 22(2), 426. https...

    2022 doi

  112. [120]

    Dikshit, S., Atiq, A., Dwivedi, V ., & Thusu, A. (2023). The use of artificial intelligence to optimize the routing of vehicles and reduce traffic congestion in urban areas. EAI Endorsed Transactions on Energy Web, 10. https://doi.org/10.4108/ew.4613

    2023 doi

  113. [122]

    Saxena, R. R. (2024). Examining Reactions about COVID -19 Vaccines: A Systematic Review of Studies Utilizing Deep Learning for Sentiment Analysis. Authorea Preprints. https://doi.org/10.36227/techrxiv.171262869.98334885/v1

    2024

  114. [123]

    R., & Saxena, R

    Saxena, R. R., & Saxena, R. (2024). Applying Graph Neural Networks in Pharmacology. Authorea Preprints. https://doi.org/10.36227/techrxiv.170906927.71541956/v1

    2024

  115. [125]

    Mensah, G. B. (2023). Artificial intelligence and ethics: A comprehensive review of bias mitigation, transparency, and accountability in AI systems. https://doi.org/10.13140/RG.2.2.23381.19685/1

    2023

  116. [126]

    P., Tiedje, J

    Xu, Y ., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y ., Dong, F., Qiu, C.-W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y ., Fu, C., Yin, Z., Liu, M., Roepman, R., Dietmann, S., Virta, M., Kengara, F., Zhang, Z., Zhang, L., Zhao, T., D ai, J., Yang, J.,...

    2021

  117. [130]

    R., Nelavala, S

    Saxena, R. R., Nelavala, S. S., & Saxena, R. (2023). MuscleDrive: A Proof of Concept Describing the Electromyographic Navigation of a Vehicle. FMDB Transactions on Sustainable Health Science Letters, 1(2), 107 -117. https://www.fmdbpub.com/uploads/articles/170340553189494.%20F...

    2023

  118. [259]

    https://doi.org/10.4236/jtts.2012.23027

    2012

  119. [2205]

    https://doi.org/10.3390/pr9122205

    doi

  120. [3335]

    https://doi.org/10.3390/s23063335

    doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.