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Open Access

Network Evolution Analysis of Vehicle Road-Driving Behavior Strategies and Design of Information Guidance Algorithm

College of Economics and Management, China Three Gorges University, Yichang 443002, China
National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing 401331, China
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Abstract

By analyzing the influence of time and safety factors on the behavior strategies of vehicles on the road, a network game evolution model between drivers that considers the behavior strategies of the driving vehicle itself and its neighbors is constructed, and the competition relationship between different types of cars is studied. The influence of the proportion of driving vehicle types on the potential risk of the road is also discussed. This paper presents a guidance algorithm for vehicle dynamic behavior preference information. The correctness of the algorithm is verified by an example. Research shows: The choice of behavior strategies, such as speeding and lane changing, is related to the expected benefits of time, safety, and neighboring vehicle strategies, and the critical value of payable benefits is obtained. The higher the proportion of aggressive vehicles on the road, the greater the potential risk on the road. Whether there is a vehicle in the adjacent lane of the driving vehicle will affect the type of driving vehicle. Information guidance helps to stabilize the state of vehicles on the road, and the policy transition probability also helps stabilize the form of vehicles cars on the road. Still, information guidance has a more significant impact on the transition of vehicle types. Finally, the guidance strategy of managers is given when the road is smooth and congested.

Keywords

user portraitinformation guidancenetwork gamerisk appetite

References

[1]

F. Y. Wang, Y. Lin, P. A. Ioannou, L. Vlacic, X. Liu, A. Eskandarian, Y. Lv, X. Na, D. Cebon, J. Ma, et al., Transportation 5.0: The DAO to safe, secure, and sustainable intelligent transportation systems, IEEE Trans. Intell. Transp. Syst., vol. 24, no. 10, pp. 10262–10278, 2023.
DOI Google Scholar
[2]

B. Zhou, A. Liu, V. Lau, J. Wen, S. Mumtaz, A. K. Bashir, and S. H. Ahmed, Performance limits of visible light-based positioning for internet-of-vehicles: Time-domain localization cooperation gain, IEEE Trans. Intell. Transp. Syst., vol. 22, no. 8, pp. 5374–5388, 2021.
DOI Google Scholar
[3]

S. Rasool, A. Saleem, M. Iqbal, T. Dagiuklas, S. Mumtaz, and Z. U. Qayyum, Docschain: Blockchain-based IoT solution for verification of degree documents, IEEE Trans. Comput. Soc. Syst., vol. 7, no. 3, pp. 827–837, 2020.
DOI Google Scholar
[4]

I. I. Sirmatel and N. Geroliminis, Economic model predictive control of large-scale urban road networks via perimeter control and regional route guidance, IEEE Trans. Intell. Transp. Syst., vol. 19, no. 4, pp. 1112–1121, 2018.
DOI Google Scholar
[5]

M. Rezaei, H. Noori, M. Mohammadkhani Razlighi, and M. Nickray, ReFOCUS: Multi-layers real-time intelligent route guidance system with congestion detection and avoidance, IEEE Trans. Intell. Transp. Syst., vol. 22, no. 1, pp. 50–63, 2021.
DOI Google Scholar
[6]

D. Calabuig, D. Martín-Sacristán, J. F. Monserrat, M. Botsov, and D. Gozálvez, Distribution of road hazard warning messages to distant vehicles in intelligent transport systems, IEEE Trans. Intell. Transp. Syst., vol. 19, no. 4, pp. 1152–1165, 2018.
DOI Google Scholar
[7]

I. Rasheed, F. Hu, Y. K. Hong, and B. Balasubramanian, Intelligent vehicle network routing with adaptive 3D beam alignment for mmWave 5G-based V2X communications, IEEE Trans. Intell. Transp. Syst., vol. 22, no. 5, pp. 2706–2718, 2021.
DOI Google Scholar
[8]

G. Xie, L. T. Yang, W. Wu, K. Zeng, X. Xiao, and R. Li, Security enhancement for real-time parallel in-vehicle applications by CAN FD message authentication, IEEE Trans. Intell. Transp. Syst., vol. 22, no. 8, pp. 5038–5049, 2021.
DOI Google Scholar
[9]

M. Yang, B. Ai, R. He, C. Shen, M. Wen, C. Huang, J. Li, Z. Ma, L. Chen, X. Li, et al., Machine-learning-based scenario identification using channel characteristics in intelligent vehicular communications, IEEE Trans. Intell. Transp. Syst., vol. 22, no. 7, pp. 3961–3974, 2021.
DOI Google Scholar
[10]

C. Tang, W. Hu, S. Hu, and M. E. J. Stettler, Urban traffic route guidance method with high adaptive learning ability under diverse traffic scenarios, IEEE Trans. Intell. Transp. Syst., vol. 22, no. 5, pp. 2956–2968, 2021.
DOI Google Scholar
[11]

L. Tang, H. Zhou, and X. Zhang, An ordered selection model of travel information demand based on quantitative phenotypic selection set, (in Chinese), Journal of Transportation Engineering, vol. 19, no. 4, pp. 151–160, 2019.
Google Scholar
[12]
G. Manogaran, R. Alsabet, A. Uprety, and D. B. Rawat, GTSM—Graph-transient security model for intelligent transportation system information exchange, IEEE Trans. Intell. Transp. Syst., vol. 24, no. 8, pp. 8421–8430, 2023.
DOI
[13]

Z. Zhang and H. Huang, The influence of social network information on travel time choice behavior, (in Chinese), Transportation System Engineering and Information, vol. 17, no. 5, pp. 22–28, 2017.
Google Scholar
[14]

Q. Lv, M. Xu, G. Xu, Y. Liu, and X. Zhou, Vehicle multi station target selection and impact analysis under different information modes, (in Chinese), Transportation System Engineering and Information, vol. 19, no. 1, pp. 62–68, 2019.
Google Scholar
[15]

M. Tang and J. Wang, Research on evaluation content of traffic guidance electronic information screen, (in Chinese), Highway Engineering, vol. 44, no. 3, pp. 258–263, 2019.
Google Scholar
[16]

F. Romero, J. Gomez, T. Rangel, R. Jurado-Piña, and J. M. Vassallo, The influence of variable message signs on en-route diversion between a toll highway and a free competing alternative, Transportation, vol. 47, no. 4, pp. 1665–1687, 2020.
DOI Google Scholar
[17]

H. Gan, To switch travel mode or not? Impact of Smartphone delivered high-quality multimodal information, IET Intell. Transp. Syst., vol. 9, no. 4, pp. 382–390, 2015.
DOI Google Scholar
[18]

H. Gan and X. Ye, Will commute drivers switch to park-and-ride under the influence of multimodal traveler information? A stated preference investigation, Transp. Res. Part F Traffic Psychol. Behav., vol. 56, pp. 354–361, 2018.
DOI Google Scholar
[19]

X. M. Lou, L. Cheng, and Z. M. Chu, Modelling travellers’ en-route path switching in a day-to-day dynamical system, Transp. B Transp. Dyn., vol. 5, no. 1, pp. 15–37, 2017.
DOI Google Scholar
[20]

A. Rapoport, E. J. Gisches, T. Daniel, and R. Lindsey, Pre-trip information and route-choice decisions with stochastic travel conditions: Experiment, Transp. Res. Part B Methodol., vol. 68, pp. 154–172, 2014.
DOI Google Scholar
[21]

E. Ben-Elia and E. Avineri, Response to travel information: A behavioural review, Transp. Rev., vol. 35, no. 3, pp. 352–377, 2015.
DOI Google Scholar
[22]

C. Pronello, A. Duboz, and V. Rappazzo, Towards smarter urban mobility: Willingness to pay for an advanced traveller information system in Lyon, Sustainability, vol. 9, no. 10, p. 1690, 2017.
DOI Google Scholar
[23]

Z. Lv, L. Qiao, K. Cai, and Q. Wang, Big data analysis technology for electric vehicle networks in smart cities, IEEE Trans. Intell. Transp. Syst., vol. 22, no. 3, pp. 1807–1816, 2021.
DOI Google Scholar
[24]

S. Gargoum and K. El-Basyouny, Effects of LiDAR point density on extraction of traffic signs: A sensitivity study, Transp. Res. Rec. J. Transp. Res. Board, vol. 2673, no. 1, pp. 41–51, 2019.
DOI Google Scholar
[25]

C. Zhao, F. Liao, X. Li, and Y. Du, Macroscopic modeling and dynamic control of on-street cruising-for-parking of autonomous vehicles in a multi-region urban road network, Transp. Res. Part C Emerg. Technol., vol. 128, p. 103176, 2021.
DOI Google Scholar
[26]

M. Li, J. Lu, and J. Sun, Network traffic flow evolution model under multi type information, (in Chinese), Transportation System Engineering and Information, vol. 20, no. 4, pp. 97–105, 2020.
Google Scholar
[27]

J. Dong, S. Chen, Y. Li, R. Du, A. Steinfeld, and S. Labi, Space-weighted information fusion using deep reinforcement learning: The context of tactical control of lane-changing autonomous vehicles and connectivity range assessment, Transp. Res. Part C Emerg. Technol., vol. 128, p. 103192, 2021.
DOI Google Scholar
[28]

K. Tang, H. Zhang, and B. Y. Xie, Efficiency evaluation of traffic guidance system based on multi-agent simulation, (in Chinese), Journal of System Simulation, vol. 30, no. 7, pp. 2630–2639, 2018.
Google Scholar
[29]

G. N. Bifulco, G. E. Cantarella, F. Simonelli, and P. Velonà, Advanced traveller information systems under recurrent traffic conditions: Network equilibrium and stability, Transp. Res. Part B Methodol., vol. 92, pp. 73–87, 2016.
DOI Google Scholar
[30]
L. Cheng, X. M. Lou, J. Zhou, and J. Ma, A mixed stochastic user equilibrium model considering influence of advanced traveller information systems in degradable transport network, J. Cent. South Univ. , vol. 25, no. 5, pp. 1182–1194, 2018.
DOI
[31]

E. Ben-Elia, R. Di Pace, G. N. Bifulco, and Y. Shiftan, The impact of travel information’s accuracy on route-choice, Transp. Res. Part C Emerg. Technol., vol. 26, pp. 146–159, 2013.
DOI Google Scholar
[32]

K. Liu and J. Zhou, Research on mixed user equilibrium model under traffic information guidance, (in Chinese), System Engineering Theory and Practice, vol. 40, no. 2, pp. 415–425, 2020.
Google Scholar
[33]

N. C. Iraganaboina, T. Bhowmik, S. Yasmin, N. Eluru, and M. A. Abdel-Aty, Evaluating the influence of information provision (when and how) on route choice preferences of road users in Greater Orlando: Application of a regret minimization approach, Transp. Res. Part C Emerg. Technol., vol. 122, p. 102923, 2021.
DOI Google Scholar
[34]

Y. Zhang, Y. Li, R. Wang, M. S. Hossain, and H. Lu, Multi-aspect aware session-based recommendation for intelligent transportation services, IEEE Trans. Intell. Transp. Syst., vol. 22, no. 7, pp. 4696–4705, 2021.
DOI Google Scholar
[35]

L. Zhu, F. R. Yu, Y. Wang, B. Ning, and T. Tang, Big data analytics in intelligent transportation systems: A survey, IEEE Trans. Intell. Transp. Syst., vol. 20, no. 1, pp. 383–398, 2019.
DOI Google Scholar
[36]

Y. Gong, Evolutionary game analysis of travel path selection behavior under information guidance, (in Chinese), Highway, vol. 60, no. 1, pp. 108–113, 2015.
Google Scholar
[37]

Y. Wang, J. Yu, W. Qiu, H. Shen, X. Cheng, and C. Lin, Evolutionary game model and analysis method of network group behavior, (in Chinese), Journal of Computer Science, vol. 38, no. 2, pp. 282–300, 2015.
Google Scholar
[38]

G. Comert, Z. Khan, M. Rahman, and M. Chowdhury, Grey models for short-term queue length predictions for adaptive traffic signal control, Expert Syst. Appl., vol. 185, p. 115618, 2021.
DOI Google Scholar
[39]

J. Lee, S. Kwak, S. Park, and S. Park, Cluster-based stable BSM dissemination system for safe autonomous platooning, Comput. Mater. Continua, vol. 71, no. 1, pp. 321–338, 2022.
DOI Google Scholar
[40]

P. Li, J. Shi, and X. Liu, Modeling of competitive driving behavior based on theory of planned behavior, (in Chinese), Transportation System Engineering and Information, vol. 16, no. 1, pp. 92–98, 2016.
Google Scholar
[41]

P. Baran, P. Zieliński, and Ł. Dziuda, Personality and temperament traits as predictors of conscious risky car driving, Saf. Sci., vol. 142, p. 105361, 2021.
DOI Google Scholar
[42]

H. Hu, X. Jia, K. Liu, and B. Sun, Self-adaptive traffic control model with behavior trees and reinforcement learning for AGV in industry 4.0, IEEE Trans. Ind. Inform., vol. 17, no. 12, pp. 7968–7979, 2021.
DOI Google Scholar
[43]
D. J. Sun and L. Elefteriadou, A driver behavior-based lane-changing model for urban arterial streets, Transp. Sci., vol. 48, no. 2, pp. 184–205, 2014.
DOI
[44]

B. Yu, H. Zhou, L. Wang, Z. Wang, and S. Cui, An extended two-lane car-following model considering the influence of heterogeneous speed information on drivers with different characteristics under honk environment, Phys. A Stat. Mech. Appl., vol. 578, p. 126022, 2021.
DOI Google Scholar
[45]

Y. Guo, X. Wang, X. Lan, and T. Su, Traffic target location estimation based on tensor decomposition in intelligent transportation system, IEEE Trans. Intell. Transp. Syst., vol. 25, no. 1, pp. 816–828, 2024.
DOI Google Scholar
[46]

X. Chen, J. Sun, Z. Ma, J. Sun, and Z. Zheng, Investigating the long- and short-term driving characteristics and incorporating them into car-following models, Transp. Res. Part C Emerg. Technol., vol. 117, p. 102698, 2020.
DOI Google Scholar
[47]

Z. H. Khattak, M. D. Fontaine, and B. L. Smith, Exploratory investigation of disengagements and crashes in autonomous vehicles under mixed traffic: An endogenous switching regime framework, IEEE Trans. Intell. Transp. Syst., vol. 22, no. 12, pp. 7485–7495, 2021.
DOI Google Scholar
[48]

D. Xu, Z. Ding, X. He, H. Zhao, M. Moze, F. Aioun, and F. Guillemard, Learning from naturalistic driving data for human-like autonomous highway driving, IEEE Trans. Intell. Transp. Syst., vol. 22, no. 12, pp. 7341–7354, 2021.
DOI Google Scholar
[49]

T. Shang, P. Wu, B. Tang, J. Bai, and L. Zhou, Study on vehicle lane changing game behavior in small distance section from tunnel to interchange, (in Chinese), Chinese Journal of Safety Science, vol. 31, no. 10, pp. 68–75, 2021.
Google Scholar
[50]

Z. Liu and Z. Song, Strategic planning of dedicated autonomous vehicle lanes and autonomous vehicle/toll lanes in transportation networks, Transp. Res. Part C Emerg. Technol., vol. 106, pp. 381–403, 2019.
DOI Google Scholar
[51]
D. A. Lazar, S. Coogan, and R. Pedarsani, Capacity modeling and routing for traffic networks with mixed autonomy, in Proc. IEEE 56th Annual Conf. Decision and Control (CDC), Melbourne, Australia, 2017, pp. 5678–5683.
DOI
[52]

J. Zha, C. Li, and S. Fan, The effect of stability-based strategy updating on cooperation in evolutionary social dilemmas, Appl. Math. Comput., vol. 413, p. 126603, 2022.
DOI Google Scholar
[53]

H. Li, X. Li, X. Xu, J. Liu, and B. Ran, Modeling departure time choice of metro passengers with a smart corrected mixed logit model—A case study in Beijing, Transp. Policy, vol. 69, pp. 106–121, 2018.
DOI Google Scholar
[54]

Z. R. Moghaddam, M. Jeihani, S. Peeta, and S. Banerjee, Comprehending the roles of traveler perception of travel time reliability on route choice behavior, Travel. Behav. Soc., vol. 16, pp. 13–22, 2019.
DOI Google Scholar
[55]

M. Thorhauge, E. Cherchi, and J. Rich, How flexible is flexible? Accounting for the effect of rescheduling possibilities in choice of departure time for work trips, Transp. Res. Part A Policy Pract., vol. 86, pp. 177–193, 2016.
DOI Google Scholar
[56]

M. Thorhauge, J. Swait, and E. Cherchi, The habit-driven life: Accounting for inertia in departure time choices for commuting trips, Transp. Res. Part A Policy Pract., vol. 133, pp. 272–289, 2020.
DOI Google Scholar
[57]

J. Lizbetin and L. Bartuska, The influence of human factor on congestion formation on urban roads, Procedia Eng., vol. 187, pp. 206–211, 2017.
DOI Google Scholar
[58]

F. W. Siebert and F. L. Wallis, How speed and visibility influence preferred headway distances in highly automated driving, Transp. Res. Part F Traffic Psychol. Behav., vol. 64, pp. 485–494, 2019.
DOI Google Scholar
[59]

A. Talebpour and H. S. Mahmassani, Influence of connected and autonomous vehicles on traffic flow stability and throughput, Transp. Res. Part C Emerg. Technol., vol. 71, pp. 143–163, 2016.
DOI Google Scholar
[60]

M. R. Yanko and T. M. Spalek, Route familiarity breeds inattention: A driving simulator study, Accid. Anal. Prev., vol. 57, pp. 80–86, 2013.
DOI Google Scholar
[61]

S. Wu, C. Wu, and J. Zhu, Microblog user dynamic portrait generation based on interest transfer, (in Chinese), Information Science, vol. 39, no. 8, pp. 103–111, 2021.
Google Scholar
[62]

H. K. Lo, X. W. Luo, and B. W. Y. Siu, Degradable transport network: Travel time budget of travelers with heterogeneous risk aversion, Transp. Res. Part B Methodol., vol. 40, no. 9, pp. 792–806, 2006.
DOI Google Scholar
Journal of Social Computing
Volume 5 Issue 1,
March 2024
Pages 58-87
DOI: 10.23919/JSC.2024.0002
Cite this article:
Lyu T, Shi L, He W. Network Evolution Analysis of Vehicle Road-Driving Behavior Strategies and Design of Information Guidance Algorithm. Journal of Social Computing, 2024, 5(1): 58-87. https://doi.org/10.23919/JSC.2024.0002

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Received: 15 May 2023
Revised: 23 February 2024
Accepted: 27 February 2024
Published: 30 March 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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