Pith. sign in

REVIEW 4 major objections 5 minor 37 references

GSMT: Graph Fusion and Spatiotemporal TaskCorrection for Multi-Bus Trajectory Prediction

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read The paper claims a GPS-only hybrid model can predict all five buses' trajectories on a route at once, reaching 88.12% short-term and 66.12% long-term task accuracy.

desk verdict The construction is a sensible recombination, but the headline accuracy numbers rest on an underspecified metric and an internally inconsistent evaluation protocol. read the letter →

arxiv 2508.09227 v1 pith:DY3QCFZL submitted 2025-08-12 cs.LG cs.AIcs.CE

Fan Ding , Hwa Hui Tew , Junn Yong Loo , Susilawati , LiTong Liu , Fang Yu Leong , Xuewen Luo , Kar Keong Chin
show 1 more author
Jia Jun Gan
This is my paper · ORCID
classification cs.LGcs.AIcs.CE
keywords bustrajectorypredictiongraphattentionnetworksequence-to-sequenceLSTMGPS-onlymulti-agenttaskcorrectorintelligenttransportationfusion
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 sets out to show that the trajectories of an entire bus fleet on a fixed urban route can be predicted from GPS data alone, without cameras, extra sensors, or timetables. The proposed GSMT model first fuses a sequence of dynamic bus-position and speed graphs into a single graph, then runs that graph through a three-layer Graph Attention Network and a sequence-to-sequence LSTM, and finally refines the output with a task corrector that classifies driving into low-, medium-, and high-speed modes. On a real GPS dataset covering five buses on a busy Kuala Lumpur route, the paper reports 88.12% task accuracy for 15-minute forecasts and 66.12% for 25-minute forecasts, with MAE 0.0515 and 0.1510, and says these numbers beat four GAT-based comparison models. If accurate, this would give transit operators a lightweight way to watch the whole fleet, spot bus bunching, and feed arrival-time or passenger-flow systems.

What carries the argument

The load-bearing object is the fused dynamic graph plus the two-stage correction loop. Graph fusion computes $A_{\text{fused}} = \sum_{t=1}^{T} A^{(t)}$, adding the adjacency matrices of the per-timestep bus-interaction graphs into one matrix; this is what lets all five buses share information without learning separate graph models. The three-layer GAT then computes attention weights $\alpha^{(k)}_{ij}$ over neighboring buses and updates each node embedding with a residual MLP, after which a sequence-to-sequence LSTM encodes 50 minutes of history and decodes the future coordinate sequence. The task corrector closes the loop: it classifies the current driving state as low-, medium-, or high-speed and shifts the predicted trajectory along the velocity direction, providing the secondary refinement that the paper credits for the accuracy gain.

What would settle it

Recompute task accuracy on the same test data with the 5% margin applied pointwise at each minute, and with thresholds swept from 1% to 10%; if GSMT's reported 88.12% and 66.12% fall below the GAT+GRU baseline under the pointwise definition, or if the numbers cannot be reproduced from the paper's description of the metric, the outperformance claim collapses.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that multi-bus trajectory prediction along a fixed route can be treated as one graph-level sequence task rather than five independent prediction problems. GSMT sums the adjacency matrices of the dynamic graphs over time into a fused graph, applies a three-layer GAT to model interactions among the five buses, produces future coordinates with a sequence-to-sequence LSTM, and adjusts those coordinates with a task corrector built from historical speed modes. The reported result is that this pipeline achieves 88.12% task accuracy at the 15-minute horizon and 66.12% at the 25-minute horizon, with MAE 0.0515 and 0.1510, outperforming GAT+HA, GAT+LSTM, GAT+GRU, and GAT+transformer on the same GPS dataset. The paper also claims this is the first treatment of the same-route buses as a single fleet whose interactions are explicitly modeled.

Load-bearing premise

The load-bearing premise is that the task-accuracy metric—counting a forecast as correct when it falls inside a 5% error margin based on the dataset's average travel distance—is a valid and well-defined measure; the paper never specifies whether the margin is applied per time point, at the endpoint, or per whole sequence, or why 5% is the right threshold.

Editorial extensions

If this is right

  • Because the model needs only GPS coordinates and speed, it can be deployed in cities that lack the multimodal sensor networks assumed by most trajectory predictors.
  • Predicting all five buses at once lets operators detect emerging bus bunching directly from the forecast, rather than inferring it from separate arrival-time predictions.
  • The graph-fusion design is scalable to additional nodes, so the same architecture could be extended from one route to multiple routes by adding buses as graph nodes.
  • The reported task-accuracy numbers, if replicated, would make GSMT a lightweight baseline for GPS-only fleet prediction that downstream systems for arrival time and passenger flow could build on.

Reading between the lines

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

  • The 5% task-accuracy margin is never specified pointwise versus sequence-level; a natural editorial extension is to report both, and the ranking of models could change under the stricter definition.
  • Because the evaluation uses only five buses on one route, the 88.12% and 66.12% figures are route-specific; scaling to larger fleets would test whether graph fusion degrades as node count grows.
  • The task corrector's three fixed speed modes are a coarse discretization; extending it to more modes or a continuous speed-conditioned adjustment is a testable variation that might improve long-term accuracy.
  • A standardized task-accuracy definition would let this GPS-only pipeline serve as a benchmark for fleet-level trajectory prediction in other developing-region transit systems.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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 proposes GSMT, a two-stage model for multi-bus trajectory prediction from GPS data only. The architecture combines a graph fusion module (summing adjacency matrices of dynamic graphs), a three-layer Graph Attention Network, a sequence-to-sequence LSTM, and a task corrector that clusters historical trajectories into speed modes and refines the initial forecasts. The model is evaluated on a real-world GPS dataset from Kuala Lumpur Route 710 with five buses. The authors report that GSMT achieves 88.12% short-term (15-minute) and 66.12% long-term (25-minute) 'task accuracy' with MAE 0.0515 and 0.1510, outperforming four baselines (GAT+HA, GAT+LSTM, GAT+GRU, GAT+transformer) on both horizons. The central claim is that GSMT is the first method to predict the trajectories of an entire bus fleet on a route by explicitly modeling bus-bus interactions using only GPS data.

Significance. If the reported results hold, the paper addresses a practically relevant problem: trajectory prediction for a bus fleet using only low-cost GPS data, with a model that explicitly accounts for interactions among buses. The real-world dataset and the comparison against four baselines are useful elements, and the idea of a task corrector that refines raw predictions using historical motion modes is interesting. The paper also ships a concrete architecture that is conceptually scalable to larger fleets. However, the current evaluation has several load-bearing ambiguities: the 'task accuracy' metric in Section III-C is not operationally defined, the time-step and input-length descriptions in Section V are internally inconsistent, and the task corrector in Section IV-E is described only qualitatively. As a result, the headline accuracy numbers and the claim of significant outperformance are not checkable as written. The paper's contribution is potentially valuable, but the evaluation protocol must be clarified and strengthened before the claims can be assessed.

major comments (4)
  1. [Section III-C, Table I] The 'task accuracy' metric is not operationally defined. The text states that a trajectory is considered correct if it falls within a 5% error margin based on the average travel distance in the dataset, but it does not specify whether the average is computed per timestep, per forecast horizon, or per full trip, nor whether the tolerance is applied to every predicted point, to the final point only, or to the trajectory as a whole. These different readings change the tolerance by orders of magnitude (tens of meters versus hundreds of meters or more), so the reported 88.12% and 66.12% accuracy values and the resulting model ranking in Table I are not uniquely determined. Please provide an explicit formula for the metric, including the exact base distance and the point-wise or sequence-level criterion for correctness.
  2. [Section V.A, Section V.B] The evaluation protocol is internally inconsistent. Section V.A first states 'we set the time step to one minute' and then says 'a 5-minute average step was used to replace the uneven sampling intervals,' while Section V.B states that 'a 5-minute time step was used' for both tasks. With a 50-minute history, a 5-minute step gives 10 input graphs, not the stated 13 or 23; a 1-minute step gives 50 graphs, also not 13 or 23. Similarly, the output of 3 or 5 graphs for 15- and 25-minute forecasts is consistent with a 5-minute step but not with the one-minute statement. The input-output sequence lengths and the time step must be reconciled before the experiments can be reproduced or the results interpreted.
  3. [Section IV.E] The task corrector, which is a central component of GSMT, is described only in qualitative terms. There are no equations, algorithms, or implementation details specifying how historical trajectories are clustered into the three speed modes, how the classifier maps input features to modes, or how the predicted trajectory is adjusted by 'modifying the trajectory computation' and 'analyzing the direction aligned with the velocity vector.' This omission prevents replication and makes it impossible to determine whether the reported gains come from the task corrector, the Seq2Seq LSTM, or the GAT module. The corrector must be specified precisely for the paper to be reproducible.
  4. [Section VI, Table I] The claim that GSMT 'significantly outperforms' the baselines is not supported by the reported statistics. Table I contains single-point estimates of MAE and mission accuracy without standard deviations, number of runs, or significance tests. For the 15-minute short-term MAE, the gap between GSMT (0.0515) and GAT+GRU (0.0605) may be within run-to-run variability, especially given the small number of nodes (five buses). Please report multiple-seed results with confidence intervals and a statistical significance test, or explicitly state that 'significant' is used in a practical rather than statistical sense.
minor comments (5)
  1. [Section IV.B, Eq. (1)] The fused adjacency matrix is obtained by summing adjacency matrices over time. Please justify why this summation does not discard information about the temporal ordering of bus interactions, which is a key signal for forecasting future positions.
  2. [Section VI] The statement that 'STGCN and T-GCN perform poorly' is not substantiated because these methods are absent from Table I and no results for them are reported anywhere in the paper. Please either add the corresponding results or remove the claim.
  3. [Section V.A] The sentence 'Each time step is averaged and missing values are interpolated' should specify the averaging window and the interpolation method (e.g., linear, spline), since these choices affect the resulting trajectory data and the reported accuracy.
  4. [Section IV.C] In the softmax normalization, the denominator uses index k in N_i while the numerator uses i,j; renaming the summation index (e.g., l in N_i) would improve clarity. Also, 'RELU' should be 'ReLU'.
  5. [Figure 3] The figure caption describes red/blue colors in the main plot and green/black/blue-cross colors in the subplots, but the panels are not explicitly labeled. Adding panel labels (a), (b), etc., would make the visualization easier to interpret.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation found; the prediction pipeline is a standard supervised GAT plus seq2seq LSTM with a data-driven corrector, and the reported accuracies are empirical rather than definitionally forced.

full rationale

The manuscript's central claim is that a GAT plus sequence-to-sequence LSTM, refined by a speed-mode task corrector, predicts future GPS coordinates of five buses. Nothing in the derivation defines the predicted output in terms of the evaluation metric or of the fitted corrector. The task corrector (Section IV-E) clusters historical trajectories into three speed modes and adjusts the initial prediction; this is a learned post-processor trained on historical data, not a fitted parameter renamed as a prediction, and no equation shows the final output equal to a cluster centroid or to the 5% margin. The task-accuracy metric (Section III-C) uses a 5% error margin based on average travel distance in the dataset; this is a tolerance choice and is underspecified (the base distance and point of application are unclear), which is a correctness and checkability concern rather than a circular reduction. The only self-citations ([22], [25]) appear in the related-work survey as examples of GNN and GAT applications and carry none of the paper's load-bearing assumptions. There is no uniqueness theorem, no ansatz imported from prior work, and no renaming of a known result as a derivation. Therefore, under the stated circularity rules, the paper is self-contained and no step reduces by construction to its own inputs.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical or mathematical entities. The task corrector is a software component built from existing concepts (clustering, classification, vector adjustment), not an invented entity. The central claims rest on the free parameters and domain assumptions listed above, several of which are arbitrary or unjustified.

free parameters (3)
  • Task accuracy error margin = 5% of average travel distance
    Headline accuracy is defined by this ad hoc threshold in Section III-C; the paper does not justify 5% or specify how the margin is applied to the trajectory.
  • Speed mode count and cluster boundaries = three modes (low/medium/high)
    Task corrector clusters historical trajectories into three speed classes in Section IV-E; the number of modes and boundaries are data-derived and not independently validated.
  • Averaging window = 5 minutes
    Section V-A replaces uneven GPS samples with 5-minute averages; this preprocessing choice sets the temporal resolution and is not derived from the data characteristics.
assumptions (5)
  • domain assumption Cleaned GPS data (after outlier removal and imputation) accurately reflects true bus positions.
    Section V-A states extreme outliers are removed and missing values interpolated, but no validation of the cleaning is provided; errors here propagate to the evaluation.
  • ad hoc to paper Every pair of buses on the same route is connected in the graph, and attention can learn the interaction weights.
    Section IV-C states all buses on the route are connected with bidirectional edges; the paper does not test alternative graph structures.
  • ad hoc to paper Summing adjacency matrices across time steps (Eq. 1) preserves the temporal information needed for forecasting.
    The graph fusion step collapses the temporal dimension in the graph before the GAT; no justification is given for why temporal ordering in the graph is unnecessary.
  • domain assumption Three speed modes capture the motion patterns relevant for correction.
    Section IV-E assumes low, medium, and high speed modes are sufficient for the task corrector, without presenting clustering results or sensitivity analysis.
  • domain assumption 5-minute averaging of non-uniform samples yields a valid trajectory representation.
    Section V-A picks a 5-minute step for the model; the choice affects the horizon and resolution and is not justified against the raw sampling frequency.

how reviews work

0 comments
Cite this review

Pith. "Pith review of GSMT: Graph Fusion and Spatiotemporal TaskCorrection for Multi-Bus Trajectory Prediction." pith.science (2026). https://pith.science/paper/DY3QCFZL

@misc{pith2026250809227,
  author       = {Pith},
  title        = {Pith review of: GSMT: Graph Fusion and Spatiotemporal TaskCorrection for Multi-Bus Trajectory Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DY3QCFZL}},
  note         = {Machine review of arXiv:2508.09227}
}
read the original abstract

Accurate trajectory prediction for buses is crucial in intelligent transportation systems, particularly within urban environments. In developing regions where access to multimodal data is limited, relying solely on onboard GPS data remains indispensable despite inherent challenges. To address this problem, we propose GSMT, a hybrid model that integrates a Graph Attention Network (GAT) with a sequence-to-sequence Recurrent Neural Network (RNN), and incorporates a task corrector capable of extracting complex behavioral patterns from large-scale trajectory data. The task corrector clusters historical trajectories to identify distinct motion patterns and fine-tunes the predictions generated by the GAT and RNN. Specifically, GSMT fuses dynamic bus information and static station information through embedded hybrid networks to perform trajectory prediction, and applies the task corrector for secondary refinement after the initial predictions are generated. This two-stage approach enables multi-node trajectory prediction among buses operating in dense urban traffic environments under complex conditions. Experiments conducted on a real-world dataset from Kuala Lumpur, Malaysia, demonstrate that our method significantly outperforms existing approaches, achieving superior performance in both short-term and long-term trajectory prediction tasks.

Figures

Figures reproduced from arXiv: 2508.09227 by the authors.

Figure 1
Figure 1. GPS trajectories of Route 710 in Kuala Lumpur from Prasarana [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The model integrates a 3-layer GAT, Seq2Seq LSTM, and a task correction module to achieve accurate trajectory prediction. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Visualization of the bus routes in the test set and model prediction. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

37 extracted references · 33 canonical work pages

  1. [1]

    Real-time bus arrival delays analysis using seemingly unrelated regression model,

    Q. Zhang, Z. Ma, P. Zhang, Y . Ling, and E. Jenelius, “Real-time bus arrival delays analysis using seemingly unrelated regression model,” Transportation, pp. 1–32, 2024

    work page 2024

  2. [2]

    Optimizing city transit: Iot and gradient boosting algorithms for ac- curate bus arrival predictions,

    S. Shanthi, B. M. Kannan, R. Babuji, S. Sivakumar, N. Malathiet al., “Optimizing city transit: Iot and gradient boosting algorithms for ac- curate bus arrival predictions,” in2024 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2024, pp. 1–5

    work page 2024

  3. [3]

    A sequence and network embedding method for bus arrival time prediction using gps trajectory data only,

    C. Li, S. Lin, H. Zhang, H. Zhao, L. Liu, and N. Jia, “A sequence and network embedding method for bus arrival time prediction using gps trajectory data only,”IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 5, pp. 5024–5038, 2023

    2023

  4. [4]

    Vehicle trajectory prediction based on lstm recurrent neural networks,

    A. Ip, L. Irio, and R. Oliveira, “Vehicle trajectory prediction based on lstm recurrent neural networks,” in2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring). IEEE, April 2021, pp. 1–5

    work page 2021

  5. [5]

    Graph and recurrent neural network- based vehicle trajectory prediction for highway driving,

    X. Mo, Y . Xing, and C. Lv, “Graph and recurrent neural network- based vehicle trajectory prediction for highway driving,” in2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE, 2021, pp. 1934–1939

    work page 2021

  6. [6]

    Spatio-temporal graph neural networks for predictive learning in urban computing: A survey,

    G. Jin, Y . Liang, Y . Fang, Z. Shao, J. Huang, J. Zhang, and Y . Zheng, “Spatio-temporal graph neural networks for predictive learning in urban computing: A survey,”IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 10, pp. 5388–5408, 2024

    work page 2024

  7. [7]

    Crowd flow forecasting with multi-graph neural networks,

    X. Zhang, R. Cao, Z. Zhang, and Y . Xia, “Crowd flow forecasting with multi-graph neural networks,” in2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020, pp. 1–7

    work page 2020

  8. [8]

    Spatial-temporal fusion graph neural networks for traffic flow forecasting,

    M. Li and Z. Zhu, “Spatial-temporal fusion graph neural networks for traffic flow forecasting,” inProceedings of the AAAI conference on artificial intelligence, vol. 35, no. 5, 2021, pp. 4189–4196

    work page 2021

Show all 37 references
  1. [9]

    A multi- head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks,

    S. Reza, M. C. Ferreira, J. J. Machado, and J. M. R. Tavares, “A multi- head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks,”Expert Systems with Applications, vol. 202, p. 117275, 2022

    2022

  2. [10]

    Long-term traffic flow forecasting using a hybrid cnn-bilstm model,

    M. M ´endez, M. G. Merayo, and M. N ´u˜nez, “Long-term traffic flow forecasting using a hybrid cnn-bilstm model,”Engineering Applications of Artificial Intelligence, vol. 121, p. 106041, 2023

    2023

  3. [11]

    Bus travel time prediction with real-time traffic information,

    J. Ma, J. Chan, G. Ristanoski, S. Rajasegarar, and C. Leckie, “Bus travel time prediction with real-time traffic information,”Transportation Research Part C: Emerging Technologies, vol. 105, pp. 536–549, 2019

    2019

  4. [12]

    Dynamic bus arrival time prediction with artificial neural networks,

    S. I.-J. Chien, Y . Ding, and C. Wei, “Dynamic bus arrival time prediction with artificial neural networks,”Journal of transportation engineering, vol. 128, no. 5, pp. 429–438, 2002

    2002

  5. [13]

    Traffic-ggnn: predicting traffic flow via attentional spatial-temporal gated graph neural networks,

    Y . Wang, J. Zheng, Y . Du, C. Huang, and P. Li, “Traffic-ggnn: predicting traffic flow via attentional spatial-temporal gated graph neural networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 18 423–18 432, 2022

    2022

  6. [14]

    Lstm network: a deep learning approach for short-term traffic forecast,

    Z. Zhao, W. Chen, X. Wu, P. C. Chen, and J. Liu, “Lstm network: a deep learning approach for short-term traffic forecast,”IET intelligent transport systems, vol. 11, no. 2, pp. 68–75, 2017

    2017

  7. [15]

    Stcnn: A spatio-temporal convo- lutional neural network for long-term traffic prediction,

    Z. He, C.-Y . Chow, and J.-D. Zhang, “Stcnn: A spatio-temporal convo- lutional neural network for long-term traffic prediction,” in2019 20th IEEE international conference on mobile data management (MDM). IEEE, 2019, pp. 226–233

    2019

  8. [16]

    Revisiting spatial- temporal similarity: A deep learning framework for traffic prediction,

    H. Yao, X. Tang, H. Wei, G. Zheng, and Z. Li, “Revisiting spatial- temporal similarity: A deep learning framework for traffic prediction,” inProceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, 2019, pp. 5668–5675

    2019

  9. [17]

    Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,

    B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,”arXiv preprint arXiv:1709.04875, 2017

    2017 arXiv

  10. [18]

    Time-series extreme event forecasting with neural networks at uber,

    N. Laptev, J. Yosinski, L. E. Li, and S. Smyl, “Time-series extreme event forecasting with neural networks at uber,” inInternational conference on machine learning, vol. 34. sn, 2017, pp. 1–5

    2017

  11. [19]

    Short-term traffic forecasting: Where we are and where we’re going,

    E. I. Vlahogianni, M. G. Karlaftis, and J. C. Golias, “Short-term traffic forecasting: Where we are and where we’re going,”Transportation Research Part C: Emerging Technologies, vol. 43, pp. 3–19, 2014

    2014

  12. [20]

    Diffusion convolutional re- current neural network: Data-driven traffic forecasting,

    Y . Li, R. Yu, C. Shahabi, and Y . Liu, “Diffusion convolutional re- current neural network: Data-driven traffic forecasting,”arXiv preprint arXiv:1707.01926, 2017

    2017 arXiv

  13. [21]

    Deep spatio-temporal residual networks for citywide crowd flows prediction,

    J. Zhang, Y . Zheng, and D. Qi, “Deep spatio-temporal residual networks for citywide crowd flows prediction,” inProceedings of the AAAI conference on artificial intelligence, vol. 31, no. 1, 2017

    2017

  14. [22]

    St-hcss: Deep spatio-temporal hypergraph convolutional neural network for soft sensing,

    H. H. Tew, F. Ding, G. Li, J. Y . Loo, C.-M. Ting, Z. Y . Ding, and C. P. Tan, “St-hcss: Deep spatio-temporal hypergraph convolutional neural network for soft sensing,” inICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, ...

    2025

  15. [23]

    Gbtte: Graph attention network based bus travel time estimation,

    Y . Rong, J. Yao, J. Liu, Y . Fang, W. Luo, H. Liu, J. Ma, Z. Dan, J. Lin, Z. Wuet al., “Gbtte: Graph attention network based bus travel time estimation,” inProceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023, pp. 4794–4800

    2023

  16. [24]

    Predicting traffic propagation flow in urban road network with multi-graph convolutional network,

    H. Yang, Z. Li, and Y . Qi, “Predicting traffic propagation flow in urban road network with multi-graph convolutional network,”Complex & Intelligent Systems, vol. 10, no. 1, pp. 23–35, 2024

    2024

  17. [25]

    Kans: Knowledge discovery graph attention network for soft sensing in multivariate industrial processes,

    H. H. Tew, G. Li, F. Ding, X. Luo, J. Y . Loo, C.-M. Ting, Z. Y . Ding, and C. P. Tan, “Kans: Knowledge discovery graph attention network for soft sensing in multivariate industrial processes,” in2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, ...

    2024

  18. [26]

    Mtl-lstm: Multi-task learning-based lstm for urban traffic flow fore- casting,

    M. Karimzadeh, S. M. Schwegler, Z. Zhao, T. Braun, and S. Sargento, “Mtl-lstm: Multi-task learning-based lstm for urban traffic flow fore- casting,” in2021 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2021, pp. 564–569

    2021

  19. [27]

    Improv- ing multi-agent trajectory prediction using traffic states on interactive driving scenarios,

    C. Vishnu, V . Abhinav, D. Roy, C. K. Mohan, and C. S. Babu, “Improv- ing multi-agent trajectory prediction using traffic states on interactive driving scenarios,”IEEE Robotics and Automation Letters, vol. 8, no. 5, pp. 2708–2715, 2023

    2023

  20. [28]

    Transportation mode identification with gps trajectory data and gis information,

    J. Li, X. Pei, X. Wang, D. Yao, Y . Zhang, and Y . Yue, “Transportation mode identification with gps trajectory data and gis information,” Tsinghua Science and Technology, vol. 26, no. 4, pp. 403–416, 2021

    2021

  21. [29]

    Ais-based intelligent vessel trajectory prediction using bi-lstm,

    C.-H. Yang, C.-H. Wu, J.-C. Shao, Y .-C. Wang, and C.-M. Hsieh, “Ais-based intelligent vessel trajectory prediction using bi-lstm,”IEEE Access, vol. 10, pp. 24 302–24 315, 2022

    2022

  22. [30]

    A semisupervised end-to-end framework for transportation mode detection by using gps-enabled sensing devices,

    Z. Li, G. Xiong, Z. Wei, Y . Lv, N. Anwar, and F.-Y . Wang, “A semisupervised end-to-end framework for transportation mode detection by using gps-enabled sensing devices,”IEEE Internet of Things Journal, vol. 9, no. 10, pp. 7842–7852, 2021

    2021

  23. [31]

    Adapt: Efficient multi-agent trajectory prediction with adaptation,

    G. Aydemir, A. K. Akan, and F. G ¨uney, “Adapt: Efficient multi-agent trajectory prediction with adaptation,” inProceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 8295–8305

    2023

  24. [32]

    Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network,

    X. Mo, Z. Huang, Y . Xing, and C. Lv, “Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network,”IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 9554–9567, 2022

    2022

  25. [33]

    Spatial-temporal residential short- term load forecasting via graph neural networks,

    W. Lin, D. Wu, and B. Boulet, “Spatial-temporal residential short- term load forecasting via graph neural networks,”IEEE Transactions on Smart Grid, vol. 12, no. 6, pp. 5373–5384, 2021

    2021

  26. [34]

    Deep learning methods for vessel trajectory prediction based on recurrent neural networks,

    S. Capobianco, L. M. Millefiori, N. Forti, P. Braca, and P. Willett, “Deep learning methods for vessel trajectory prediction based on recurrent neural networks,”IEEE Transactions on Aerospace and Electronic Systems, vol. 57, no. 6, pp. 4329–4346, 2021

    2021

  27. [35]

    Dynamic travel time prediction models for buses using only gps data,

    W. Fan and Z. Gurmu, “Dynamic travel time prediction models for buses using only gps data,”International Journal of Transportation Science and Technology, vol. 4, no. 4, pp. 353–366, 2015

    2015

  28. [36]

    Predicting irregu- larities in arrival times for transit buses with recurrent neural networks using gps coordinates and weather data,

    O. Alam, A. Kush, A. Emami, and P. Pouladzadeh, “Predicting irregu- larities in arrival times for transit buses with recurrent neural networks using gps coordinates and weather data,”Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 7813–7826, 2021

    2021

  29. [37]

    Convolutional long-short term memory network with multi-head attention mechanism for traffic flow prediction,

    Y . Wei and H. Liu, “Convolutional long-short term memory network with multi-head attention mechanism for traffic flow prediction,”Sen- sors, vol. 22, no. 20, p. 7994, 2022

    2022

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.