A Planned-route-aware Two-Stage Neural Model for Urban Traffic Congestion Prediction

Okazaki, Shota; Takuya Yoshihiro

doi:10.51094/jxiv.4612

##article.authors##

Okazaki, Shota 和歌山大学
Takuya Yoshihiro Wakayama University, Faculty of Systems Engineering https://researchmap.jp/tac

DOI:

https://doi.org/10.51094/jxiv.4612

キーワード:

Congestion Prediction、 Deep Learning、 ITS、 Urban Traffic、 Vehicle's Planned Route

抄録

Traffic congestion prediction on urban roads is fundamentally challenging because congestion is often triggered by localized and highly nonlinear interactions around signalized intersections, including turning queues, lane interference, and temporary vehicle concentration. Most existing methods rely on statistical variables or aggregated traffic states and therefore do not explicitly represent how individual vehicles and their planned movements lead to future congestion. This paper proposes a planned-route-aware two-stage deep learning framework for predicting urban traffic congestion 10-20 min ahead using only microscopic vehicle-level information, namely, each individual vehicle ’s current position and planned route. In the first stage, a Vehicle Movement Prediction (VMP) model estimates the future position of each individual vehicle from route-aware local traffic information, including direction-specific traffic volumes and turning-direction labels on surrounding roads. In the second stage, a Congestion Prediction (CP) model predicts future congestion from historical traffic volumes and direction-specific near-future traffic volumes obtained by aggregating the predicted vehicle positions. By explicitly introducing future vehicle positions as an intermediate representation, the proposed method bridges microscopic traffic behavior and urban congestion prediction. Simulationbased experiments on a signalized road network show that the proposed framework detects congestion earlier than comparison methods without planned routes or without the VMP model. These results indicate that both planned-route information and explicit vehicle-position prediction are essential for early urban congestion prediction.

利益相反に関する開示

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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引用文献

B. Pishue, “2023 INRIX global traffic scorecard with Q1 2024 update,” INRIX Research Report, 2024.

S. Sánchez González, F. Bedoya-Maya, and A. Calatayud, “Understanding the effect of raffic congestion on accidents using big data,” Sustainability, vol. 13, no. 13, Art. no. 7500, 2021.

H. W. Cho, E. M. Robartes, and M. D. Fontaine, Operational and Safety Effects of the I-95 Variable Speed Limit System in Fredericksburg, Virginia. Virginia Transportation Research Council, 2025.

R. Liu and S. Y. Shin, “A review of traffic flow prediction methods in intelligent transportation system construction,” Appl. Sci., vol. 15, Art. no. 3866, 2025.

Y. Zhang, Q. Zhou, J. Wang, A. Kouvelas, and M. A. Makridis, “CASAformer: Congestion-aware sparse attention transformer for traffic speed prediction,” Commun. Transp. Res., vol. 5, Art. no. 100174, 2025.

C. Zheng, X. Fan, C. Wang, and J. Qi, “GMAN: A graph multi-attention network for traffic prediction,” in Proc. AAAI Conf. Artif. Intell., vol. 34, 2020, pp. 1234–1241.

W. Zhang, L. Zhang, J. Han, H. Liu, Y. Fu, J. Zhou, Y. Mei, and H. Xiong, “Irregular traffic time series forecasting based on asynchronous spatiotemporal graph convolutional networks,” in Proc. 30th ACM SIGKDD Conf. Knowl. Discovery Data Mining, 2024, pp. 4302–4313.

J. Yao, C. Zhu, Y. Wang, Y. Liao, and Y. Peng, “Capacity matching study of different functional lanes at signalized intersections,” Systems, vol. 13, no. 10, Art. no. 901, 2025.

K. L. Soon, R. K. C. Chan, J. M.-Y. Lim, and R. Parthiban, “Short-term traffic forecasting model - prevailing trends and guidelines,” Transp. Saf. Environ., Art. no. tdac058, 2022.

R. Shirakami, T. Kitahara, K. Takeuchi, and H. Kashima, “QTNet: Theory-based queue length prediction for urban traffic,” in Proc. 29th ACM SIGKDD Conf. Knowl. Discovery Data Mining, 2023.

G. Jin, L. Liu, F. Li, and J. Huang, “Spatiotemporal graph neural point process for traffic congestion event prediction,” in Proc. AAAI Conf. Artif. Intell., vol. 37, 2023.

S. Sun, J. Chen, and J. Sun, “Traffic congestion prediction based on GPS trajectory data,” Int. J. Distrib. Sensor Netw., vol. 15, no. 5, 2019.

S. Guo, Y. Lin, N. Feng, C. Song, and H. Wan, “Attention based spatial-temporal graph convolutional networks for traffic flow forecasting,” in Proc. AAAI Conf. Artif. Intell., vol. 33, 2019, pp. 922–929.

N. Tsalikidis, A. Mystakidis, P. Koukaras, M. Ivaškevičius, L. Mork¯unait˙e, D. Ioannidis, P. A. Fokaides, C. Tjortjis, and D. Tzovaras, “Urban traffic congestion prediction: A multi-step approach utilizing sensor data and weather information,” Smart Cities, vol. 7, no. 1, pp. 233–253, 2024.

M. Tong and H. Xue, “Highway traffic volume forecasting based on seasonal ARIMA model,” J. Highway Transp. Res. Develop., vol. 3, no. 2, pp. 109–112, 2008.

M.-J. Hao and B.-Y. Hsieh, “Greenshields modelbased fuzzy system for predicting traffic congestion on highways,” IEEE Access, vol. 12, Art. no. 115868, 2024.

B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,” arXiv preprint arXiv:1709.04875, 2018.

Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Datadriven traffic forecasting,” in Proc. Int. Conf. Learn. Represent. (ICLR), 2018.

Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph WaveNet for deep spatial-temporal graph modeling,” in Proc. 28th Int. Joint Conf. Artif. Intell. (IJCAI), 2019, pp. 1907–1913.

L. Bai, L. Yao, C. Li, X. Wang, and C. Wang, “Adaptive graph convolutional recurrent network for traffic forecasting,” in Adv. Neural Inf. Process. Syst., vol. 33, 2020.

C.-S. Li and M.-C. Chen, “A data mining based approach for travel time prediction in freeway with non-recurrent congestion,” Neurocomputing, vol. 133, pp. 74–83, 2014.

F. Aljuaydi, B. Wiwatanapataphee, and Y. H. Wu, “Multivariate machine learning-based prediction models of freeway traffic flow under non-recurrent events,” Alexandria Eng. J., vol. 65, pp. 151–162, 2023.

F.-H. Tseng, J.-H. Hsueh, C.-W. Tseng, Y.-T. Yang, H.-C. Chao, and L.-D. Chou, “Congestion prediction with big data for real-time highway traffic,” IEEE Access, vol. 6, pp. 57311–57323, 2018.

Y. Zhang, J. Chen, F. Chen, and J. Gao, “Improved heterogeneous spatiotemporal graph network model for traffic flow prediction at highway toll stations,” Sustainability, vol. 17, Art. no. 7905, 2025.

S. Hu, J. Gu, and S. Li, “Research on urban road traffic flow prediction based on Sa-dynamic graph convolutional neural network,” Mathematics, vol. 13, no. 3, Art. no. 416, 2025.

M. Attioui and M. Lahby, “A systematic literature review of traffic congestion forecasting: From machine learning techniques to large language models,” Vehicles, vol. 7, no. 4, Art. no. 142, 2025.

Y. Chen, J. Huang, H. Xu, J. Guo, and L. Su, “Road traffic flow prediction based on dynamic spatiotemporal graph attention network,” Sci. Rep., vol. 13, Art. no. 14729, 2023.

A. Nigam and S. Srivastava, “Hybrid deep learning models for traffic stream variables prediction during rainfall,” Multimodal Transp., vol. 2, Art. no. 100052, 2023.

X. Yang, Y. Yuan, and Z. Liu, “Short-term traffic speed prediction of urban road with multi-source data,” IEEE Access, vol. 8, pp. 87541–87555, 2020.

C. Liu, S. Yang, Q. Xu, Z. Li, C. Long, Z. Li, and R. Zhao, “Spatial-temporal large language model for traffic prediction,” arXiv preprint arXiv:2401.10134, 2024.

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

R. Kumar, J. Mendes-Moreira, and J. Chandra, “Spatio-temporal parallel transformer based model for traffic prediction,” ACM Trans. Knowl. Discovery Data, vol. 18, no. 9, Art. no. 213, 2024.

J. Wang, X. Duan, P. Wang, A.-G. Qiu, and Z. Chen, “Predicting urban signal-controlled intersection congestion events using spatio-temporal neural point process,” Int. J. Digit. Earth, vol. 17, Art. no. 2376270, 2024.

R. Dai, S. Xu, Q. Gu, C. Ji, and K. Liu, “Hybrid spatio-temporal graph convolutional network: Improving traffic prediction with navigation data,” in Proc. 26th ACM SIGKDD Conf. Knowl. Discovery Data Mining, 2020.

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