Research on the propagation mechanism of traffic congestion warning information with random interference

Huining Yan; Meihui Song; Hua Li; Qiubai Sun; Huining Yan; Meihui Song; Hua Li; Qiubai Sun

doi:10.3934/math.2025968

AIMS Mathematics

2025, Volume 10, Issue 9: 21774-21793. doi: 10.3934/math.2025968

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Research article

Research on the propagation mechanism of traffic congestion warning information with random interference

1.
School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, Liaoning, China
2.
School of Economics and Management, University of Science and Technology Beijing, Beijing, China
3.
School of Business Administration, University of Science and Technology Liaoning, Anshan, Liaoning, China
4.
Asset Company, University of Science and Technology Liaoning, Anshan, Liaoning, China

Received: 24 July 2025 Revised: 02 September 2025 Accepted: 15 September 2025 Published: 18 September 2025
MSC : 60H10, 92D30, 93E20

In social systems, information dissemination is affected by environmental factors. Moreover, positive information can promote social development. Therefore, a stochastic SEIR model was proposed to study the mechanism of traffic congestion warning information dissemination. In this article, we proved the existence of global positive solutions and the extinction of information, and proposed sufficient conditions for stationary distributions. Based on the Hamiltonian function, an optimal stochastic control strategy for the random propagation model was proposed. The numerical simulation results indicated that the theoretical results could be validated and compared with deterministic models. Adding random interference could promote the propagation of congestion information, it could promote the propagation of information, and better control traffic congestion. The volatility of congestion warning information propagation became more apparent with the increase of random disturbance intensity. By controlling random parameters, the propagation of congestion warning information could be effectively controlled, thereby controlling congestion. Moreover, the propagation effect of the proposed optimal stochastic control strategy was better than that of the random model, which verified the effectiveness of the proposed optimization control strategy.
- congestion warning information,
- stochastic SEIR model,
- random parametric perturbations,
- optimal stochastic control strategy,
- traffic congestion
Citation: Huining Yan, Meihui Song, Hua Li, Qiubai Sun. Research on the propagation mechanism of traffic congestion warning information with random interference[J]. AIMS Mathematics, 2025, 10(9): 21774-21793. doi: 10.3934/math.2025968

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Abstract

In social systems, information dissemination is affected by environmental factors. Moreover, positive information can promote social development. Therefore, a stochastic SEIR model was proposed to study the mechanism of traffic congestion warning information dissemination. In this article, we proved the existence of global positive solutions and the extinction of information, and proposed sufficient conditions for stationary distributions. Based on the Hamiltonian function, an optimal stochastic control strategy for the random propagation model was proposed. The numerical simulation results indicated that the theoretical results could be validated and compared with deterministic models. Adding random interference could promote the propagation of congestion information, it could promote the propagation of information, and better control traffic congestion. The volatility of congestion warning information propagation became more apparent with the increase of random disturbance intensity. By controlling random parameters, the propagation of congestion warning information could be effectively controlled, thereby controlling congestion. Moreover, the propagation effect of the proposed optimal stochastic control strategy was better than that of the random model, which verified the effectiveness of the proposed optimization control strategy.

References

[1]	J. He, Z. He, B. Fan, Y. Chen, Optimal location of lane-changing warning point in a two-lane road considering different traffic flows, Physica A, 540 (2020), 123000. https://doi.org/10.1016/j.physa.2019.123000 doi: 10.1016/j.physa.2019.123000
[2]	M. Ahmad, Q. C. Chen, Z. Khan, M. Ahmad, F. Khurshid, Infrastructure-based vehicular congestion detection scheme for V2I, Int. J. Commun. Syst., 32 (2019), e3877. https://doi.org/10.1002/dac.3877 doi: 10.1002/dac.3877
[3]	Ö. Yilmaz, L. Görgu, M. J. O'Grady, Gregory M. P. O'Hare, Cloud-assisted mobile crowd sensing for route and congestion monitoring, IEEE Access, 9 (2021), 157984–157996. https://doi.org/10.1109/access.2021.3129932 doi: 10.1109/access.2021.3129932
[4]	Z. Sha, C. Li, W. Yue, J. Wu, Integrated Sensing, Communication and computing for targeted dissemination: A service-aware strategy for internet of vehicles, IEEE T. Veh. Technol., 74 (2025), 4273–4288. https://doi.org/10.1109/tvt.2024.3488176 doi: 10.1109/tvt.2024.3488176
[5]	A. Alobeidyeen, H. Y. Yang, L. L. Du, Information dissemination dynamics through Vehicle-to-Vehicle communication built upon traffic flow dynamics over roadway networks, Veh. Commun., 41 (2023), 100598. https://doi.org/10.1016/j.vehcom.2023.100598 doi: 10.1016/j.vehcom.2023.100598
[6]	H. J. Ning, Y. S. An, Y. X. Wei, N. Q. Wu, C. Mu, H. H. Cheng, et al., Modeling and analysis of traffic warning message dissemination system in VANETs, Veh. Commun., 39 (2023), 100566. https://doi.org/10.1016/j.vehcom.2022.100566 doi: 10.1016/j.vehcom.2022.100566
[7]	X. L. Cai, H. H. Zhang, W. W. Yue, Y. Chen, C. L. Li, Targeted dissemination of incident information with combinatorial traffic-communication optimization, IEEE Internet Things, 10 (2023), 7705–7718. https://doi.org/10.1109/jiot.2022.3191646 doi: 10.1109/jiot.2022.3191646
[8]	J. H. Xie, Z. Y. Yang, X. F. Lai, Y. Liu, X. B. Yang, T. H. Teng, et al., Deep reinforcement learning for dynamic incident-responsive traffic information dissemination, Transport. Res. E-Log., 166 (2022), 102871. https://doi.org/10.1016/j.tre.2022.102871 doi: 10.1016/j.tre.2022.102871
[9]	M. Saberi, H. Hamedmoghadam, M. Ashfaq, S. A. Hosseini, Z. Gu, S. Shafiei, et al., A simple contagion process describes spreading of traffic jams in urban networks, Nat. Commun., 11 (2020). https://doi.org/10.1038/s41467-020-15353-2
[10]	X. C. Zhang, Y. H. Hu, S. C. Lu, H. M. Guo, X. Y. Wei, J. Li, Dynamic analysis and control of online information dissemination model considering information beneficiaries, AIMS Math., 10 (2025), 4992–5020. https://doi.org/10.3934/math.2025229 doi: 10.3934/math.2025229
[11]	J. X. Qin, S. P. Li, Analysis of SEIR network epidemic model considering self-protection awareness, Int. J. Biomath., 2025. https://doi.org/10.1142/s1793524524501572
[12]	B. K. She, J. Liu, S. Sundaram, P. E. Paré, On a networked SIS epidemic model with cooperative and antagonistic opinion dynamics, IEEE T. Control Netw., 9 (2022), 1154–1165. https://doi.org/10.1109/tcns.2022.3145748 doi: 10.1109/tcns.2022.3145748
[13]	F. Z. Nian, X. Guo, J. Z. Li, A new spreading model in the environment of epidemic-related online rumors, Mod. Phys. Lett. B, 36 (2022). https://doi.org/10.1142/s0217984921505692
[14]	L. Ma, B. W. Li, J. Y. Wang, Dynamic evolutionary analysis of opinion leaders' and netizens' uncertain information dissemination behavior considering random interference, Front. Physics, 12 (2024). https://doi.org/10.3389/fphy.2024.1387312
[15]	Y. Z. Zhou, J. W. Zhang, C. Y. Zhu, H. Wang, Modelling and analysis of rumour propagation based on stochastic optimal control, Alex. Eng. J., 61 (2022), 12869–12880. https://doi.org/10.1016/j.aej.2022.06.057 doi: 10.1016/j.aej.2022.06.057
[16]	T. Li, Y. Luo, X. Wan, Q. Li, Q. Liu, R. Wang, et al., A malware detection model based on imbalanced heterogeneous graph embeddings, Expert Syst. Appl., 246 (2024), 123109. https://doi.org/10.1016/j.eswa.2023.123109 doi: 10.1016/j.eswa.2023.123109
[17]	A. Di Crescenzo, P. Paraggio, S. Spina, Stochastic growth models for the spreading of fake news, Mathematics, 11 (2023), 3597. https://doi.org/10.3390/math11163597 doi: 10.3390/math11163597
[18]	K. Myilsamy, M. S. Kumar, A. S. Kumar, Optimal control of a stochastic rumour propagation in online social networks, Int. J. Mod. Phys. C, 34 (2023), 2350162. https://doi.org/10.1142/s0129183123501620 doi: 10.1142/s0129183123501620
[19]	F. Nian, Z. Zhang, The influence of opinion leaders on public opinion spread and control strategies in online social networks, IEEE T. Comput. Soc. Sy., 2025. https://doi.org/10.1109/TCSS.2025.3537701
[20]	F. Nian, L. Luo, X. Liu, Information dissemination evolution driven by hierarchical relationship, IEEE T. Comput. Soc. Sy., 11 (2023), 1967–1978. https://doi.org/10.1109/TCSS.2023.3293058 doi: 10.1109/TCSS.2023.3293058
[21]	S. Nyawa, D. Tchuente, S. F. Wamba, COVID-19 vaccine hesitancy: A social media analysis using deep learning, Ann. Oper. Res., 339 (2024), 477–515. https://doi.org/10.1007/s10479-022-04792-3 doi: 10.1007/s10479-022-04792-3
[22]	A. M. A. Oraiqat, O. S. Ulichev, Y. V. Meleshko, H. S. AlRawashdeh, O. O. Smirnov, L. I. Polishchuk, Modeling strategies for information influence dissemination in social networks, J. Amb. Intel. Hum. Comp., 13 (2022), 2463–2477. https://doi.org/10.1007/s12652-021-03364-w doi: 10.1007/s12652-021-03364-w
[23]	B. Ravi, M. Kumar, Y. C. Hu, S. Hassan, B. Kumar, Stochastic modeling and performance analysis in balancing load and traffic for vehicular ad hoc networks: A review, Int. J. Netw. Manag., 33 (2023), e2224. https://doi.org/10.1002/nem.2224 doi: 10.1002/nem.2224
[24]	S. Lu, Modeling dynamics of traffic flow, information creation and spread through vehicle-to-vehicle communications: A kinetic approach, Int. J. Nonlin. Mech., 175 (2025), 105096. https://doi.org/10.1016/j.ijnonlinmec.2025.105096 doi: 10.1016/j.ijnonlinmec.2025.105096
[25]	C. Xie, Z. Bao, A. Chen, Disrupted transportation networks under different information availability and stochasticity situations, Transport. Res. C-Emer., 150 (2023), 104097. https://doi.org/10.1016/j.trc.2023.104097 doi: 10.1016/j.trc.2023.104097
[26]	L. Zheng, X. Li, Simulation‐based optimization method for arterial signal control considering traffic safety and efficiency under uncertainties, Comput. -Aided Civ. Inf., 38 (2023), 640–659. https://doi.org/10.1111/mice.12876 doi: 10.1111/mice.12876
[27]	X. Mao, Stationary distribution of stochastic population systems, Syst. Control Lett., 60 (2011), 398–405. https://doi.org/10.1016/j.sysconle.2011.02.013 doi: 10.1016/j.sysconle.2011.02.013
[28]	S. D. Kang, X. L. Hou, Y. H. Hu, H. Y. Liu, Dynamic analysis and optimal control of a stochastic information spreading model considering super-spreader and implicit exposer with random parametric perturbations, Front. Phys., 11 (2023). https://doi.org/10.3389/fphy.2023.1194804

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