Pith. sign in

REVIEW 4 major objections 6 minor 51 references

Explore the Use of Time Series Foundation Model for Car-Following Behavior Analysis

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A pretrained time series foundation model, fine-tuned on the Open ACC dataset, forecasts car-following acceleration with an RMSE of 0.53, a 33.75% improvement over the Intelligent Driver Model.

desk verdict First benchmark of Chronos on car-following data, but the headline 33.75% improvement over IDM is not yet supported because the IDM baseline is uncalibrated and the evaluation tasks may not be aligned. read the letter →

arxiv 2501.07034 v1 pith:ISVXGEEX submitted 2025-01-13 cs.LG

Luwei Zeng , Runze Yan This is my paper · ORCID
classification cs.LG
keywords car-followingtimeseriesfoundationmodelsChronosOpenACCdatasetaccelerationpredictionIntelligentDriverModelfine-tuningtrafficsimulation
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 show that a general-purpose pretrained time series model, Chronos, can be used for car-following behavior prediction without being trained from scratch on traffic data. On the Open ACC dataset's Casale experiment, zero-shot Chronos forecasts follower acceleration with a mean RMSE of 0.60, on par with deep learning baselines like DeepAR, WaveNet, and TFT. Fine-tuning Chronos on 80% of the trajectories and adding covariate corrections drops the RMSE to 0.53, a 33.75% improvement over an IDM baseline and a 12-37% reduction relative to the other baselines. The authors read this as evidence that foundation models offer a scalable, adaptable alternative for transportation trajectory forecasting, and they present it as the first application of time series foundation models to car-following behavior.

What carries the argument

The engine of the paper is Chronos, a pretrained probabilistic time series model that scales and quantizes time series values into a fixed token vocabulary and trains a text-to-text transformer-style language model with cross-entropy loss; forecasts are sampled autoregressively. Around this, the paper builds a residual-correction mechanism: Chronos produces a base forecast of follower acceleration, then a gradient-boosted tree model (LightGBM) fits the forecast residuals using dynamic covariates (space gap, speed difference, follower speed). Multi-window backtesting, rolling a six-second history forward in three-second steps, supplies the RMSE numbers that grade every model.

What would settle it

Calibrate IDM's parameters on the training split (instead of taking values from prior literature) and measure RMSE on the same test trajectories; if the fitted IDM reaches or beats 0.53, the headline improvement over IDM collapses.

Watch

Extended reading notes

Core claim

On the Casale experiment of the Open ACC dataset, Chronos without any fine-tuning predicts following-vehicle acceleration with mean RMSE around 0.60, matching the accuracy of task-specific deep learning models (DeepAR, WaveNet, TFT) and beating the traditional IDM and ETS baselines. With fine-tuning on the training split, Chronos reaches a mean RMSE of 0.53, which the paper reports as a 33.75% improvement over IDM (0.80) and a 12-37% reduction over ETS and the deep learning baselines. The best configuration adds dynamic covariates (space gap, speed difference, follower speed) through a residual-correction step, and the smallest 46-million-parameter Chronos performs as well as the 200-million and 710-million versions. The paper claims this demonstrates that pretrained time series foundation models can be adapted to car-following analysis with minimal data and without the parameter-calibration burden of traditional car-following models.

Load-bearing premise

The claimed improvement over IDM assumes the IDM baseline is strong, but the paper does not report calibrating IDM parameters on the training split, so IDM might underperform a properly fitted version and inflate the reported gain.

Editorial extensions

If this is right

  • Fine-tuning a pretrained time series foundation model on a modest trajectory dataset can replace training a deep learning car-following model from scratch while improving accuracy.
  • The 46-million-parameter Chronos variant is sufficient for this task, so deployment on modest hardware is plausible.
  • Because the forecast is produced from historical acceleration plus covariate corrections, it no longer requires knowing the leader's current speed at inference, which traditional car-following formulas require.
  • The same recipe (pretrained forecaster plus residual correction on covariates) is a template for other transportation time series tasks like traffic flow or speed prediction.

Reading between the lines

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

  • A natural test the paper leaves for future work is cross-dataset evaluation: fine-tune on one ACC dataset and evaluate on a held-out one; a persistent gain would support general driving dynamics rather than dataset-specific fitting.
  • The residual-correction wrapper is model-agnostic; applying it to other time series foundation models would clarify whether the gain comes from Chronos or from the covariate adapter.
  • Since the smallest model already saturates performance, larger-scale versions may not help this short-horizon task; the limit may be the information content of the inputs rather than model capacity.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The manuscript proposes using Amazon's Chronos time-series foundation model to forecast the acceleration of the following vehicle in the Open ACC Casale car-following dataset. It compares zero-shot and fine-tuned Chronos (with a LightGBM residual model to incorporate covariates) against IDM, ETS, DeepAR, WaveNet, and TFT, reporting RMSE. The headline claim is that fine-tuned Chronos achieves RMSE 0.53, a 33.75% improvement over IDM and a 12–37% reduction over the other baselines.

Significance. If the comparison is made rigorous, this would be a useful first demonstration that pretrained time-series foundation models can be applied to car-following behavior without training from scratch, with zero-shot performance competitive with task-specific deep learning models and fine-tuning on a small dataset providing further gains. The study is clearly exploratory: it uses one dataset and one foundation model. The core quantitative claim is not yet fully supported, but the direction is worth publishing after substantial revision.

major comments (4)
  1. [Evaluation Metric / Intelligent Drive Model] The IDM and time-series models are not shown to be evaluated on the same forecasting task. The Evaluation Metric section describes a multi-window backtest for the time-series models (6 seconds of context, 3-second forecast horizon, repeated over the test set; Eqs. 11-12). The Intelligent Drive Model section, in contrast, only states that the first 80% of trajectories are used for training and the remaining 20% for testing, with RMSE of the predicted acceleration computed. No backtesting protocol is described for IDM. If IDM predicts the current-step acceleration from the observed gap and speeds rather than the 3-second-ahead forecast, the RMSE values are not comparable and the headline 33.75% improvement would be an artifact of task mismatch. Please specify and apply a single shared evaluation protocol to all models.
  2. [Intelligent Drive Model] The IDM baseline parameters are taken from the literature, not calibrated to the Open ACC training split. The text states that parameter values and ranges come from Treiber, Milanés, Kim, and Souza, and no calibration procedure is described. Because the paper's central claim is an improvement over IDM, the comparison is only meaningful if IDM is fitted to the training data or the authors justify why the literature values are a strong baseline. Please calibrate IDM parameters on the training split (or report both zero-shot and calibrated IDM RMSE) and document the calibrated values.
  3. [Results / Figure 2] The reported performance of the deep learning baselines is not backed by per-model results. Figure 2 is described in the text, but the text only gives RMSE values for IDM (0.80), ETS (0.84), zero-shot Chronos (0.60), and fine-tuned Chronos (0.53). The abstract's claim of a 12–37% reduction over DeepAR, WaveNet, and TFT requires a table listing each model's mean RMSE and standard deviation. In addition, no statistical significance test accompanies the 0.60 vs 0.53 difference, so it is unclear whether the fine-tuning gain is meaningful. Provide a results table with per-model RMSE across backtest windows and a significance test or confidence intervals.
  4. [Methods (Eqs. 1-12)] The manuscript does not actually display the equations it references: Equations 1–12 appear as blank placeholders in the text. Without the IDM formula, the covariate integration scheme (Eqs. 4-10), and the backtesting aggregation (Eqs. 11-12), the methods are not reproducible. The experimental configuration is also incomplete: the context length and forecast horizon are stated as 6 and 3 seconds in the description of Figure 1, but the Chronos model size used for the headline result, the fine-tuning procedure (epochs, learning rate, data split by trajectory), and LightGBM hyperparameters are not given. Please include all equations and a complete experimental configuration.
minor comments (6)
  1. [Results] The text refers to the statistical baseline as 'EST' in the Results section; this should be 'ETS'.
  2. [Figure 1 caption] The caption says 'greed boxes' where it should say 'green boxes'.
  3. [Introduction] The phrase 'out-of-the-pocket capability' should be 'out-of-the-box capability'.
  4. [Related Work / References] Reference numbering is inconsistent: 'Hong et al. (33)' and 'Darlow et al. (35)' point to references 33 and 35, but those entries are Kim and Heaslip and Moor et al., respectively; the works by Hong et al. and Darlow et al. are not in the reference list.
  5. [Table 2] Table 2 has blank placeholders for variable names and units, making the table unreadable; please fill in the missing entries.
  6. [Data Overview and Process] The data-cleaning description states that acceleration and Frenet position are calculated, but the exact cleaning steps and formulas from Zhou et al. are not described; please specify the processing procedure.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: Chronos is an externally pretrained model, fine-tuning and LightGBM residual fitting are standard supervised learning, and no claim reduces to its inputs by construction.

full rationale

The paper's central claim is an empirical benchmark: an externally pretrained Chronos model, optionally fine-tuned on the training split and augmented with a LightGBM residual model, is compared against IDM, ETS, DeepAR, WaveNet, and TFT. There is no derivation chain in which a predicted quantity is defined in terms of the quantity it is claimed to predict. Chronos parameters come from Ansari et al.'s public pretrained model, not from this paper's test data. Fine-tuning is stated to use the training dataset, and the LightGBM residual model is fit to residuals, which is standard ensemble residual learning rather than a fitted value being renamed a prediction. IDM is an external physics-based model with parameters taken from the literature, so any weakness there is a baseline-calibration or fairness concern, not circularity. The ambiguity about whether IDM and the deep learning baselines share the same multi-window backtesting protocol is a benchmarking and verifiability issue, but it does not make the argument self-referential. There are no load-bearing self-citations, no imported uniqueness theorems, and no ansatz smuggled in through the authors' prior work. The comparison may be difficult to reproduce or unfair as designed, but every step that supports the headline improvement is logically independent of the conclusion.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the assumption that the IDM baseline is fair and that the train/test split and covariate set capture the car-following process. The only hand-set numbers are the IDM parameter set and the context/forecast windows. No new entities are introduced. Fine-tuning and LightGBM residual correction are fitted on training data, which is ordinary supervised learning rather than circular derivation.

free parameters (4)
  • IDM parameter set (a, v0, delta, s0, T, b) = values/ranges from Treiber et al., Milanés et al., Kim et al., and Souza et al.; no calibration reported
    The IDM baseline RMSE of 0.80 depends on this hand-chosen set. An uncalibrated IDM makes the 33.75% improvement claim untrustworthy.
  • Context length and forecast horizon = 6 s context and 3 s forecast horizon
    These task-defining choices set the difficulty of the forecasting problem; no sensitivity analysis is provided.
  • Chronos model size for the headline result = small (46M parameters), with base and large also tested
    The paper reports similar RMSE across sizes and the small model has the lowest value; without significance testing, selecting the small model may capitalize on noise.
  • LightGBM hyperparameters = not reported
    The residual correction model that incorporates covariates is fit with LightGBM, but no hyperparameters are given, so the covariate benefit cannot be reproduced or assessed.
assumptions (3)
  • domain assumption The 80/20 trajectory split yields independent training and test sets.
    Assumed in the Setup; if trajectories share route or traffic conditions, test errors may be optimistic.
  • domain assumption Acceleration of the follower can be forecast from its own history plus space gap, speed difference, and follower speed, without ACC controller state.
    Defines the task in the Covariates section; missing controller state or driver intent could explain the 0.53 RMSE residual.
  • ad hoc to paper IDM parameters from cited literature are a fair baseline without re-calibration on Open ACC data.
    The IDM section takes parameter values and ranges from literature, making the comparison favorable to Chronos if IDM is not fitted.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Explore the Use of Time Series Foundation Model for Car-Following Behavior Analysis." pith.science (2026). https://pith.science/paper/ISVXGEEX

@misc{pith2026250107034,
  author       = {Pith},
  title        = {Pith review of: Explore the Use of Time Series Foundation Model for Car-Following Behavior Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ISVXGEEX}},
  note         = {Machine review of arXiv:2501.07034}
}
read the original abstract

Modeling car-following behavior is essential for traffic simulation, analyzing driving patterns, and understanding complex traffic flows with varying levels of autonomous vehicles. Traditional models like the Safe Distance Model and Intelligent Driver Model (IDM) require precise parameter calibration and often lack generality due to simplified assumptions about driver behavior. While machine learning and deep learning methods capture complex patterns, they require large labeled datasets. Foundation models provide a more efficient alternative. Pre-trained on vast, diverse time series datasets, they can be applied directly to various tasks without the need for extensive re-training. These models generalize well across domains, and with minimal fine-tuning, they can be adapted to specific tasks like car-following behavior prediction. In this paper, we apply Chronos, a state-of-the-art public time series foundation model, to analyze car-following behavior using the Open ACC dataset. Without fine-tuning, Chronos outperforms traditional models like IDM and Exponential smoothing with trend and seasonality (ETS), and achieves similar results to deep learning models such as DeepAR and TFT, with an RMSE of 0.60. After fine-tuning, Chronos reduces the error to an RMSE of 0.53, representing a 33.75% improvement over IDM and a 12-37% reduction compared to machine learning models like ETS and deep learning models including DeepAR, WaveNet, and TFT. This demonstrates the potential of foundation models to significantly advance transportation research, offering a scalable, adaptable, and highly accurate approach to predicting and simulating car-following behaviors.

Figures

Figures reproduced from arXiv: 2501.07034 by the authors.

Figure 4
Figure 4. Visualization of the forecast versus actual acceleration of the following vehicle within one time window [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

51 extracted references · 30 canonical work pages

  1. [1]

    Explore the Use of Time Series Foundation Model for Car-Following Behavior Analysis Luwei Zeng Post-Doctoral Researcher Department of Civil, Environmental & Architectural Engineering University of Kansas, Lawrence, KS 66045 luweizeng@ku.edu Runze Yan Post-Doctoral Researcher Center for Data Science Nell Hodgson Woodruff School of Nursing Emory University,...

    work page 2015

  2. [2]

    Wang, Z., Y. Shi, W. Tong, Z. Gu, and Q. Cheng. Car-Following Models for Human-Driven Vehicles and Autonomous Vehicles: A Systematic Review. Journal of Transportation Engineering, Part A: Systems, Vol. 149, No. 8, 2023, p. 04023075. https://doi.org/10.1061/JTEPBS.TEENG-7836

    doi:10.1061/jtepbs.teeng-7836 2023

  3. [3]

    Newell, G. F. Nonlinear Effects in the Dynamics of Car Following. Operations Research, Vol. 9, No. 2, 1961, pp. 209–229

    work page 1961

  4. [4]

    and climate studies (Bodnar et al., 2024). These foundation models consistently outperform traditional statistical and deep learning models in these applications, highlighting their necessity and benefits for transportation research. In this study, we select Amazon’s Chronos as our example because of its exceptional performance as a foundational model for...

    work page 2024

  5. [5]

    Yang, X., H. U. Ahemd, Y. Huang, and P. Lu. Cumulatively Anticipative Car-Following Model with Enhanced Safety for Autonomous Vehicles in Mixed Driver Environments. Smart Cities, Vol. 6, No. 5, 2023, pp. 2260–2281. https://doi.org/10.3390/smartcities6050104

    work page doi:10.3390/smartcities6050104 2023

  6. [6]

    Van Lint, K

    Van Wageningen-Kessels, F., H. Van Lint, K. Vuik, and S. Hoogendoorn. Genealogy of Traffic Flow Models. EURO Journal on Transportation and Logistics, Vol. 4, No. 4, 2015, pp. 445–473. https://doi.org/10.1007/s13676-014-0045-5

    work page doi:10.1007/s13676-014-0045-5 2015

  7. [7]

    Milanés, V., and S. E. Shladover. Modeling Cooperative and Autonomous Adaptive Cruise Control Dynamic Responses Using Experimental Data. Transportation Research Part C: Emerging Technologies, Vol. 48, 2014, pp. 285–300. https://doi.org/10.1016/j.trc.2014.09.001

    work page doi:10.1016/j.trc.2014.09.001 2014

  8. [8]

    (Micheal) Chen, and L

    Li, L., X. (Micheal) Chen, and L. Zhang. A Global Optimization Algorithm for Trajectory Data Based Car-Following Model Calibration. Transportation Research Part C: Emerging Technologies, Vol. 68, 2016, pp. 311–332. https://doi.org/10.1016/j.trc.2016.04.011

    doi:10.1016/j.trc.2016.04.011 2016

Show all 51 references
  1. [9]

    Hennecke, and D

    Treiber, M., A. Hennecke, and D. Helbing. Congested Traffic States in Empirical Observations and Microscopic Simulations. Physical Review E, Vol. 62, No. 2, 2000, pp. 1805–1824. https://doi.org/10.1103/PhysRevE.62.1805

    doi

  2. [10]

    Chen, X., M. Zhu, K. Chen, P. Wang, H. Lu, H. Zhong, X. Han, X. Wang, and Y. Wang. FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling. Scientific Data, Vol. 10, No. 1, 2023, p

    2023

  3. [11]

    Schakel, W. J., B. Van Arem, and B. D. Netten. Effects of Cooperative Adaptive Cruise Control on Traffic Flow Stability. Presented at the 2010 13th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2010), Funchal, Madeira Island, Portugal,

    2010

  4. [12]

    Morton, J., T. A. Wheeler, and M. J. Kochenderfer. Analysis of Recurrent Neural Networks for Probabilistic Modeling of Driver Behavior. IEEE Transactions on Intelligent Transportation Systems, Vol. 18, No. 5, 2017, pp. 1289–1298. https://doi.org/10.1109/TITS.2016.2603007

    2017

  5. [13]

    Lin, Y., P. Wang, Y. Zhou, F. Ding, C. Wang, and H. Tan. Platoon Trajectories Generation: A Unidirectional Interconnected LSTM-Based Car-Following Model. IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 3, 2022, pp. 2071–2081. https://doi.org/10.1109/TITS....

    2022

  6. [14]

    Ma, L., and S. Qu. A Sequence to Sequence Learning Based Car-Following Model for Multi-Step Predictions Considering Reaction Delay. Transportation Research Part C: Emerging Technologies, Vol. 120, 2020, p. 102785. https://doi.org/10.1016/j.trc.2020.102785

    2020

  7. [15]

    Bengio, M

    Zhang, C., S. Bengio, M. Hardt, B. Recht, and O. Vinyals. Understanding Deep Learning (Still) Requires Rethinking Generalization. Commun. ACM, Vol. 64, No. 3, 2021, pp. 107–115. https://doi.org/10.1145/3446776

    2021 doi

  8. [16]

    Bommasani, R., D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill, E. Brynjolfsson, S. Buch, D. Card, R. Castellon, N. Chatterji, A. Chen, K. Creel, J. Q. Davis, D. Demszky, C. Donahue, M. Doumbouya, E. Durmus, S. Ermon...

    arXiv

  9. [17]

    Sun, and J

    Huang, X., J. Sun, and J. Sun. A Car-Following Model Considering Asymmetric Driving Behavior Based on Long Short-Term Memory Neural Networks. Transportation Research Part C: Emerging Technologies, Vol. 95, 2018, pp. 346–362. https://doi.org/10.1016/j.trc.2018.07.022

    2018 doi

  10. [18]

    Ansari, A. F., L. Stella, C. Turkmen, X. Zhang, P. Mercado, H. Shen, O. Shchur, S. S. Rangapuram, S. P. Arango, S. Kapoor, J. Zschiegner, D. C. Maddix, H. Wang, M. W. Mahoney, K. Torkkola, A. G. Wilson, M. Bohlke-Schneider, and Y. Wang. Chronos: Learning the Language of Time S...

    arXiv

  11. [19]

    Pettenuzzo, and S

    Carriero, A., D. Pettenuzzo, and S. Shekhar. Macroeconomic Forecasting with Large Language Models. http://arxiv.org/abs/2407.00890. Accessed Aug. 1,

  12. [20]

    Fan, P., J. Guo, H. Zhao, J. S. Wijnands, and Y. Wang. Car-Following Modeling Incorporating Driving Memory Based on Autoencoder and Long Short-Term Memory Neural Networks. Sustainability, Vol. 11, No. 23, 2019, p

    2019

  13. [21]

    Wang, and B

    Xiao, L., M. Wang, and B. van Arem. Realistic Car-Following Models for Microscopic Simulation of Adaptive and Cooperative Adaptive Cruise Control Vehicles. Transportation Research Record, Vol. 2623, No. 1, 2017, pp. 1–9. https://doi.org/10.3141/2623-01

    2017 doi

  14. [22]

    SHLADOVER Deputy Director, S. E. Review of the State of Development of Advanced Vehicle Control Systems (AVCS). Vehicle System Dynamics, Vol. 24, No. 6–7, 1995, pp. 551–595. https://doi.org/10.1080/00423119508969108

    1995 doi

  15. [23]

    Chowdhury, K

    Rahman, M., M. Chowdhury, K. Dey, M. R. Islam, and T. Khan. Evaluation of Driver Car-Following Behavior Models for Cooperative Adaptive Cruise Control Systems. Transportation Research Record, Vol. 2622, No. 1, 2017, pp. 84–95. https://doi.org/10.3141/2622-08

    2017 doi

  16. [24]

    Das, A., W. Kong, R. Sen, and Y. Zhou. A Decoder-Only Foundation Model for Time-Series Forecasting. http://arxiv.org/abs/2310.10688. Accessed Jul. 16,

    arXiv

  17. [25]

    Li, X., T. Yang, J. Liu, X. Qin, and S. Yu. Effects of Vehicle Gap Changes on Fuel Economy and Emission Performance of the Traffic Flow in the ACC Strategy. PLOS ONE, Vol. 13, No. 7, 2018, p. e0200110. https://doi.org/10.1371/journal.pone.0200110

    2018 doi

  18. [26]

    J., and C.-L

    Goodall, N. J., and C.-L. Lan. Car-Following Characteristics of Adaptive Cruise Control from Empirical Data. Journal of Transportation Engineering, Part A: Systems, Vol. 146, No. 9, 2020, p. 04020097. https://doi.org/10.1061/JTEPBS.0000427

    2020 doi

  19. [27]

    Bodnar, C., W. P. Bruinsma, A. Lucic, M. Stanley, J. Brandstetter, P. Garvan, M. Riechert, J. Weyn, H. Dong, A. Vaughan, J. K. Gupta, K. Tambiratnam, A. Archibald, E. Heider, M. Welling, R. E. Turner, and P. Perdikaris. Aurora: A Foundation Model of the Atmosphere. http://arxi...

    arXiv

  20. [29]

    Pipes, L. A. An Operational Analysis of Traffic Dynamics. Journal of Applied Physics, Vol. 24, No. 3, 1953, pp. 274–281. https://doi.org/10.1063/1.1721265

    1953 doi

  21. [30]

    McDonald

    Brackstone, M., and M. McDonald. Car-Following: A Historical Review. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 2, No. 4, 1999, pp. 181–196. https://doi.org/10.1016/S1369-8478(00)00005-X

    1999 doi

  22. [31]

    Shladover, N

    VanderWerf, J., S. Shladover, N. Kourjanskaia, M. Miller, and H. Krishnan. Modeling Effects of Driver Control Assistance Systems on Traffic. Transportation Research Record, Vol. 1748, No. 1, 2001, pp. 167–174. https://doi.org/10.3141/1748-21

    2001 doi

  23. [33]

    Kim, B., and K. P. Heaslip. Identifying Suitable Car-Following Models to Simulate Automated Vehicles on Highways. International Journal of Transportation Science and Technology, Vol. 12, No. 2, 2023, pp. 652–664. https://doi.org/10.1016/j.ijtst.2023.02.003

    2023 doi

  24. [34]

    Jurj, S. L., D. Grundt, T. Werner, P. Borchers, K. Rothemann, and E. Möhlmann. Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning. Energies, Vol. 14, No. 22, 2021, p

    2021

  25. [35]

    Banerjee, Z

    Moor, M., O. Banerjee, Z. S. H. Abad, H. M. Krumholz, J. Leskovec, E. J. Topol, and P. Rajpurkar. Foundation Models for Generalist Medical Artificial Intelligence. Nature, Vol. 616, No. 7956, 2023, pp. 259–265. https://doi.org/10.1038/s41586-023-05881-4. Zeng and Yan 14

    2023 doi

  26. [36]

    González, G. G., P. Casas, E. Martínez, and A. Fernández. Timeless Foundations: Exploring DC-VAEs as Foundation Models for Time Series Analysis. Presented at the 2024 8th Network Traffic Measurement and Analysis Conference (TMA),

    2024

  27. [37]

    Zhou, H., K. Ma, S. Liang, X. Li, and X. Qu. ULTra-AV: A Unified Longitudinal Trajectory Dataset for Automated Vehicle. http://arxiv.org/abs/2406.00009. Accessed Jul. 15,

    arXiv

  28. [38]

    Saifuzzaman, M., and Z. Zheng. Incorporating Human-Factors in Car-Following Models: A Review of Recent Developments and Research Needs. Transportation Research Part C: Emerging Technologies, Vol. 48, 2014, pp. 379–403. https://doi.org/10.1016/j.trc.2014.09.008

    2014 doi

  29. [39]

    Shazeer, A

    Raffel, C., N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research, Vol. 21, No. 140, 2020, pp. 1–67

    2020

  30. [40]

    Chronos + LightGBM

    family, ranging from 20M to 710M parameters. It utilizes a large collection of publicly available datasets, supplemented by a synthetic dataset generated via Gaussian processes to enhance generalization. Chronos is based on the T5 architecture, and has five different sub-model...

    2016

  31. [41]

    Chen, Y.-L

    Yuan, L., D. Chen, Y.-L. Chen, N. Codella, X. Dai, J. Gao, H. Hu, X. Huang, B. Li, C. Li, C. Liu, M. Liu, Z. Liu, Y. Lu, Y. Shi, L. Wang, J. Wang, B. Xiao, Z. Xiao, J. Yang, M. Zeng, L. Zhou, and P. Zhang. Florence: A New Foundation Model for Computer Vision. http://arxiv.org/...

    arXiv

  32. [42]

    Flunkert, J

    Salinas, D., V. Flunkert, J. Gasthaus, and T. Januschowski. DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. International Journal of Forecasting, Vol. 36, No. 3, 2020, pp. 1181–1191. https://doi.org/10.1016/j.ijforecast.2019.07.001

    2020 doi

  33. [43]

    van den, S

    Oord, A. van den, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu. WaveNet: A Generative Model for Raw Audio. http://arxiv.org/abs/1609.03499. Accessed Jul. 31,

    arXiv

  34. [44]

    De Souza, F., and R. Stern. Calibrating Microscopic Car-Following Models for Adaptive Cruise Control Vehicles: Multiobjective Approach. Journal of Transportation Engineering, Part A: Systems, Vol. 147, No. 1, 2021, p. 04020150. https://doi.org/10.1061/JTEPBS.0000475

    2021 doi

  35. [45]

    Talavera-Llames, A

    Galicia, A., R. Talavera-Llames, A. Troncoso, I. Koprinska, and F. Martínez-Álvarez. Multi-Step Forecasting for Big Data Time Series Based on Ensemble Learning. Knowledge-Based Systems, Vol. 163, 2019, pp. 830–841. https://doi.org/10.1016/j.knosys.2018.10.009

    2019 doi

  36. [46]

    Chung, H. W., L. Hou, S. Longpre, B. Zoph, Y. Tay, W. Fedus, Y. Li, X. Wang, M. Dehghani, S. Brahma, A. Webson, S. S. Gu, Z. Dai, M. Suzgun, X. Chen, A. Chowdhery, A. Castro-Ros, M. Pellat, K. Robinson, D. Valter, S. Narang, G. Mishra, A. Yu, V. Zhao, Y. Huang, A. Dai, H. Yu, ...

    arXiv

  37. [47]

    Kourentzes, and J

    Svetunkov, I., N. Kourentzes, and J. K. Ord. Complex Exponential Smoothing. Naval Research Logistics (NRL), Vol. 69, No. 8, 2022, pp. 1108–1123. https://doi.org/10.1002/nav.22074

    2022 doi

  38. [50]

    Lim, B., S. Ö. Arık, N. Loeff, and T. Pfister. Temporal Fusion Transformers for Interpretable Multi-Horizon Time Series Forecasting. International Journal of Forecasting, Vol. 37, No. 4, 2021, pp. 1748–1764. https://doi.org/10.1016/j.ijforecast.2021.03.012

    2021 doi

  39. [828]

    https://doi.org/10.1038/s41597-023-02718-7

    doi

  40. [1961]

    and Intelligent Driver Model (IDM) (Treiber et al., 2000), and some new models are built upon them (Milanés & Shladover, 2014; Schakel et al., 2010; Yang et al., 2023). However, these car-following models depend on mathematical formulas, requiring meticulous parameter calibrat...

    2000

  41. [2024]

    and Amazon’s Chronos (Ansari et al., 2024), have recently been released and applied across various fields such as economics (Carriero et al.,

    2024

  42. [6755]

    https://doi.org/10.3390/su11236755

    doi

  43. [7572]

    https://doi.org/10.3390/en14227572

    doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.