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REVIEW 4 major objections 5 minor 41 references

NeuralMOVES: A lightweight and microscopic vehicle emission estimation model based on reverse engineering and surrogate learning

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

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Pith's one-line read By reverse engineering the MOVES simulator into a 109-million-point dataset and compressing it into a 2.4 MB neural network, NeuralMOVES reproduces MOVES CO2 estimates with a 6.013% mean absolute percentage error while staying…

desk verdict A useful practical surrogate for MOVES, but the reverse-engineering step has a bookkeeping flaw that calls the microscopic-label claim into question. read the letter →

arxiv 2502.04417 v1 pith:I7VZB5RP submitted 2025-02-06 cs.LG cs.AI

Edgar Ramirez-Sanchez , Catherine Tang , Yaosheng Xu , Nrithya Renganathan , Vindula Jayawardana , Zhengbing He , Cathy Wu This is my paper · ORCID
classification cs.LGcs.AI
keywords MOVESvehicleemissionssurrogatemodelreverseengineeringneuralnetworkCO2estimationeco-drivingmicroscopicmodeling
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

NeuralMOVES sets out to make MOVES, a certified vehicle emission simulator, usable in microscopic, real-time applications. The authors reverse engineer MOVES by running it on paired driving cycles that differ by a single appended acceleration step, subtracting the baseline cycle's total emissions to isolate the instantaneous emission of that step. This produces a 9.89 GB dataset of over 109 million speed-acceleration-environment-vehicle combinations, which is then compressed into a 2.4 MB neural network with two hidden layers of five neurons. Across over two million test scenarios built from five driving-behavior generators, the surrogate matches MOVES total emissions to a 6.013% mean absolute percentage error, and its differentiability and millisecond evaluation make it suitable for model predictive control, as shown in a signalized-intersection eco-driving case.

What carries the argument

The central object is the reverse-engineering identity $e(v, a, X) = E(\tau', X) - E(\tau, X)$, which treats MOVES as a black box and isolates the instantaneous emission of an acceleration step by subtracting the total emissions of a constant-speed baseline cycle from those of the same cycle with one appended step. MOVES total emissions are modeled as a mode-fraction-weighted sum of per-mode emission rates over a one-hour operating window, scaled linearly to cycle length, so the subtraction is only valid if emissions are additive over time and operating modes. The surrogate itself is a tanh-activated neural network with two hidden layers of five units, trained to minimize mean absolute percentage error; its differentiability is what allows it to be embedded in gradient-based optimization.

What would settle it

Construct a short driving cycle whose total emissions are compared against the sum of NeuralMOVES's second-by-second predictions over the same trace; if the summed prediction differs from MOVES's cycle total by more than the reported 6% for cycles that toggle rapidly between operating modes, the additive decomposition underlying the reverse engineering is inadequate. A stronger test is to run the same speed-acceleration trace in both MOVES and NeuralMOVES and compare against a chassis dynamometer measurement of a real vehicle, which would reveal whether the surrogate inherits MOVES's biases without its safeguards.

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Extended reading notes

Core claim

The paper claims that an industrial-grade macroscopic emission simulator can be reverse-engineered into a compact, differentiable, microscopic surrogate that stays faithful to the original. Concretely, NeuralMOVES maps speed, acceleration, road grade, temperature, humidity, vehicle type, age, and fuel type to an instantaneous CO2 emission rate, with outputs truncated below the idling emission, and the resulting model reproduces MOVES's total cycle emissions within roughly 6% mean absolute percentage error across diverse trajectories. This is achieved with a 2.4 MB architecture, a 4800x reduction over the extracted dataset, enabling emissions to be computed on the fly inside a control loop.

Load-bearing premise

The reverse-engineering step assumes that MOVES total emissions decompose additively over time steps and operating modes, so that the difference between two driving cycles equals the emission of the single newly added step; the paper's stated mode fractions for the appended cycle contain an off-by-one inconsistency that would bias the extracted instantaneous emissions if MOVES treats mode transitions nonlinearly.

Editorial extensions

If this is right

  • Researchers can perform MOVES-compatible emissions analysis without installing MOVES, since the 2.4 MB surrogate is open-sourced and runs on continuous input ranges.
  • Gradient-based eco-driving controllers can directly minimize emission predictions, because NeuralMOVES is differentiable and evaluates in milliseconds.
  • The reverse-engineering framework offers a template to distill other industrial-grade simulation software into fast surrogates, subject to legal permission.
  • Because the model decouples emissions from a fixed U.S. location, users can supply their own temperature, humidity, and road grade to estimate emissions for non-U.S. or customized scenarios.

Reading between the lines

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

  • If the slight positive skew in the error distribution is systematic, control policies optimized with NeuralMOVES could systematically under-value aggressive acceleration; a sensitivity analysis replacing the surrogate with the 10 GB lookup table inside the same MPC loop would reveal whether optimal trajectories change.
  • The apparent off-by-one in the mode fractions suggests the extracted instantaneous emissions may be least accurate at high acceleration values where the appended step occupies a large share of the cycle; varying the baseline length n and checking whether NeuralMOVES accuracy degrades would test this directly.
  • Reverse engineering running emissions for CO2 likely extends to other pollutants with separated running emission rates, so the same pipeline could yield a multi-pollutant microscopic surrogate.
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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

4 major / 5 minor

Summary. The paper introduces NeuralMOVES, a lightweight neural network surrogate for the EPA MOVES CO2 emission simulator. The authors first reverse-engineer MOVES by constructing pairs of driving cycles and differencing their total emissions to obtain second-by-second emission labels e(v,a,X), yielding a 9.89 GB dataset MOVESRE with over 109 million entries. They then train a two-layer, five-neuron neural network on this dataset, obtaining a reported 6.013% MAPE against MOVES over 2.3 million validation scenarios that combine 100 synthetic driving cycles with various environmental and vehicle settings. The model is 2.4 MB, differentiable, and runs in milliseconds. The paper also demonstrates an MPC-based eco-driving use case and publicly releases the code and dataset.

Significance. If the reverse-engineering method is correct, this is a practically valuable contribution: it would make MOVES-compatible microscopic CO2 emission estimates available in a lightweight, differentiable, real-time form, with open-source code and a large public dataset. The paper explicitly names its assumptions and limitations, including the unexplained -5% grade surge, which is commendable. However, the central label-construction step (Eq. (3)-(4)) contains an apparent off-by-one error and an unresolved ambiguity about which MOVES operating-mode second is isolated by the differencing procedure. Since all training labels and all validation claims rest on this step, the current manuscript does not yet establish the claimed microscopic fidelity.

major comments (4)
  1. [§2.1.1, Eq. (4)] The mode fractions for the custom cycle τ' are off by one. The cycle τ' is defined in Eq. (2) as an (n+1)-step cycle: n steps at speed v plus one appended step at v' = v + a∆t. The fractions of time spent in the two relevant speed/acceleration states should therefore be n/(n+1) and 1/(n+1), not (n-1)/n and 1/n as written. The authors must correct this, or explicitly justify why (n-1)/n is the correct fraction; as written, Eq. (4) is inconsistent with Eq. (2) and undermines the quantitative basis of the reversed-engineered emissions.
  2. [§2.1.1, Eq. (3)] The central extraction equation e(v,a,X) = E(τ',X) - E(τ,X) does not necessarily isolate the instantaneous emission of a single second at state (v,a). In MOVES's operating-mode framework, the appended step at speed v+a also changes the acceleration of the immediately preceding second (if acceleration is computed from the following speed), or the appended second itself is in state (v+a,a), not (v,a). The paper does not specify MOVES's exact speed/acceleration binning convention, so the extracted label e(v,a,X) may correspond to a two-second mixture or to a different (v,a) point. Because every training example and every subsequent validation depends on this label definition, the authors must provide an explicit mapping from cycle second-states to MOVES operating modes and demonstrate which seconds are included in the difference, with a concrete example for a small n (e.g., n=2).
  3. [§2.1.3] The acknowledged emission surge at a road grade of -5%, whose cause remains unclear, is a red flag for the quality of the reverse-engineered dataset. If the surge is an artifact of the differencing procedure (for instance, a symptom of the off-by-one or mode-mixing issue in Eq. (3)), the surrogate will encode this artifact. The authors should either identify the physical or software cause of the surge or demonstrate through targeted experiments that the validation MAPE of 6.013% is insensitive to this anomaly.
  4. [§3] The validation set varies driving dynamics broadly but uses environmental and vehicle factors on (or very near) the same discrete grid used to generate the training data (Table 1). The reported 6.013% MAPE therefore primarily measures interpolation fidelity over the static factors, not generalization to unseen environmental or vehicle conditions. To substantiate the claim of accuracy across 'diverse scenarios', the authors should report results on held-out static-factor values (e.g., continuous temperatures/humidities or grades not used in training) or explicitly split the MOVESRE grid into training and validation sets.
minor comments (5)
  1. [§3] There is a numerical inconsistency: the text states '22,869×100=2,296,900' and then refers to 'all 2,286,900 evaluations'. The correct product is 2,286,900; please fix the typo.
  2. [§2.2, Eq. (9)] The MAPE definition divides by e_i, which is undefined if e_i = 0. The paper should specify how zero or near-zero emission values are handled, especially since the model is later clipped at the idling emission value.
  3. [Abstract] The abstract uses 'Mean Average Percentage Error', which should be 'Mean Absolute Percentage Error (MAPE)' to match the body of the paper.
  4. [Table 2] The table reports an '11 epochs' neural network with MAPE 149.73%, which is far worse than the 300-epoch models; it would be helpful to state whether this row is an ablation for early stopping and why it is included in the main architecture comparison.
  5. [§2.2, Eq. (10)] The notation 'max{eNN, eidling} 7→ e' is ambiguous; it should be written as a definition, e.g., e = max(eNN, eidling), to clarify that the output is the truncation, not an additional mapping.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: NeuralMOVES is a surrogate trained on MOVES-derived labels and validated against held-out MOVES cycle outputs; the finite-difference extraction is a correctness risk, not a circular step.

full rationale

The paper's derivation chain is a standard surrogate-model pipeline: (i) define an instantaneous emission e(v,a,X) by finite differences of MOVES driving-cycle totals (Eq. 3); (ii) populate MOVESRE by querying MOVES over a discretized input grid (Eq. 7); (iii) train a neural network f_theta to minimize MAPE against e on MOVESRE (Eq. 9); and (iv) validate by comparing summed f_theta emissions with MOVES cycle totals on 2.3 million held-out scenarios (Section 3). The validation target is the same external simulator that generated the training labels, so the reported 6.013% MAPE measures surrogate fidelity to MOVES rather than physical ground truth, but this is the explicit surrogate-model contract and not a hidden circular reduction: the network is not fitted to the validation cycle totals, and the finite-difference labels are a derived quantity rather than the validation outputs themselves. The reverse-engineering assumption that MOVES emissions decompose additively over seconds/modes (Eq. 3 with Eq. 4) is a substantive modeling assumption, and the paper's own p1=(n-1)/n in Eq. (4) for an (n+1)-step custom cycle is an off-by-one inconsistency; these are correctness and label-validity risks, not circularity. Self-citations (Jayawardana et al. 2024 for city temperature-humidity pairs; Jayawardana and Wu 2022 for eco-driving test cycles) are minor and non-load-bearing; the central accuracy claim is benchmarked against MOVES, an independently maintained EPA simulator. No circular step meeting the required evidentiary standard is present.

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

The central claim rests on the differencing assumption in Eq. (3) and the linear scaling in Eq. (5), plus the surrogate's interpolation ability. The only tuned hyperparameters are the baseline cycle length, initialization scaling, and discretization grid; no new physical entities are introduced.

free parameters (4)
  • Baseline cycle length n = 5
    Chosen to extract running emissions; changing n would alter the extracted instantaneous emission values.
  • Initialization scaling factor = 0.97
    Tuned hyperparameter that reduces MAPE from 7.85% to 6.01% in Table 2.
  • Temperature-humidity combinations = 21 points
    10 points from U.S. cities plus 11 added via optimization; these 21 points define the environmental support of the dataset and surrogate.
  • Discretization resolutions = speed 0.5, accel 0.1, grade 5, temp 5, humidity 5
    Coarse discretization choices affect the extraction and the generalization gap of the surrogate.
assumptions (4)
  • domain assumption MOVES emissions are linearly proportional to operating time (Eq. 5).
    The scaling from 1-hour to n-second cycles assumes linearity in time, which may not hold exactly for cold starts or mode transitions.
  • domain assumption The difference of two driving cycle emissions isolates the instantaneous emission of the added step (Eq. 3).
    This is the core reverse-engineering assumption; it requires that emissions from different operating modes add linearly and that the baseline cycle's contribution is unchanged when one step is appended.
  • domain assumption Surrogate trained on a discrete grid generalizes to continuous inputs.
    The paper asserts the NN interpolates smoothly between discrete factor values; no rigorous guarantee is provided.
  • domain assumption MOVES is an authoritative ground truth.
    All accuracy claims are relative to MOVES, not measured real-world emissions.

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Pith. "Pith review of NeuralMOVES: A lightweight and microscopic vehicle emission estimation model based on reverse engineering and surrogate learning." pith.science (2026). https://pith.science/paper/I7VZB5RP

@misc{pith2026250204417,
  author       = {Pith},
  title        = {Pith review of: NeuralMOVES: A lightweight and microscopic vehicle emission estimation model based on reverse engineering and surrogate learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/I7VZB5RP}},
  note         = {Machine review of arXiv:2502.04417}
}
read the original abstract

The transportation sector significantly contributes to greenhouse gas emissions, necessitating accurate emission models to guide mitigation strategies. Despite its field validation and certification, the industry-standard Motor Vehicle Emission Simulator (MOVES) faces challenges related to complexity in usage, high computational demands, and its unsuitability for microscopic real-time applications. To address these limitations, we present NeuralMOVES, a comprehensive suite of high-performance, lightweight surrogate models for vehicle CO2 emissions. Developed based on reverse engineering and Neural Networks, NeuralMOVES achieves a remarkable 6.013% Mean Average Percentage Error relative to MOVES across extensive tests spanning over two million scenarios with diverse trajectories and the factors regarding environments and vehicles. NeuralMOVES is only 2.4 MB, largely condensing the original MOVES and the reverse engineered MOVES into a compact representation, while maintaining high accuracy. Therefore, NeuralMOVES significantly enhances accessibility while maintaining the accuracy of MOVES, simplifying CO2 evaluation for transportation analyses and enabling real-time, microscopic applications across diverse scenarios without reliance on complex software or extensive computational resources. Moreover, this paper provides, for the first time, a framework for reverse engineering industrial-grade software tailored specifically to transportation scenarios, going beyond MOVES. The surrogate models are available at https://github.com/edgar-rs/neuralMOVES.

Figures

Figures reproduced from arXiv: 2502.04417 by the authors.

Figure 1
Figure 1. Contribution of the paper. parameters correctly. Additionally, the setup also requires high-performance computing infrastructure and massive storage (exceeding 100 GB per region), making it impractical for many real-time applications. In contrast, MOVEStar uses a bottom-up approach to replicate the MOVES framework in a simplified manner. It provides a lightweight, microscopic alternative to MOVES while lacks accurac… view at source ↗
Figure 2
Figure 2. Impact of environment and vehicle factors on the instantaneous CO [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Impact of vehicle dynamics on the instantaneous CO [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Synthetic driving cycles for validating NeuralMOVES. (a) Random speed; (b) Sinusoidal speed; [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]
Figure 5
Figure 5. Figure 5: Emission estimation error of NeuralMOVES against MOVES under various scenarios. (a) [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: Optimized trajectories for 2019 passenger car model (a)-(c) and 2019 bus model (d)-(f) given by [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]

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Reviewed August 8, 2026 · model on record in the stance chip above.