Abstract
Vehicular Ad Hoc Networks (VANETs) represent a significant leap forward in the deployment of intelligent transport systems. These networks enable vehicles to instantly exchange traffic information with the aim of smoothing traffic flows and intensifying drivers comfort. In this context, this study addresses the issue of traffic congestion description and detection in multi-lane highways. By making use of collected information, a Markov chain based mobility model is proposed to predict the future road traffic states. Based on the obtained stationary distribution probabilities, performance criteria in steady-state are inferred and computed for different road configurations. The numerical results validate the model demonstrated in the paper.
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References
Kafi, M.A., Ben-Othman, J., Mokdad, L., Badache, N.: Performance analysis and evaluation of REFIACC using queuing networks. Simul. Model. Pract. Theory 71, 15–26 (2017)
Mokdad, L., Ben-Othman, J., Nguyen, A.T.: DJAVAN: detecting jamming attacks in vehicle ad hoc networks. Perform. Eval. 87, 47–59 (2015)
Mokdad, L., Ben-Othman, J., Yahya, B., Niagne, S.: Performance evaluation tools for QoS MAC protocol for wireless sensor networks. Ad Hoc Netw. 12, 86–99 (2014)
Davies, V.A.: Evaluating mobility models within an ad hoc network. Master’s thesis, advisor: Tracy Camp. Department of Mathematical and Computer Sciences, Colorado School of Mines (2000)
Vetriselvi, V., Parthasarathi, R.: Trace based mobility model for ad hoc networks. In: Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2007), p. 81. IEEE, October 2007
Förster, A., Bin Muslim, A., Udugama, A.: TRAILS-A trace-based probabilistic mobility model. In: Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, pp. 295–302, October 2018
Beiró, M.G., Panisson, A., Tizzoni, M., Cattuto, C.: Predicting human mobility through the assimilation of social media traces into mobility models. EPJ Data Sci. 5(1), 1–15 (2016). https://doi.org/10.1140/epjds/s13688-016-0092-2
Haklay, M., Weber, P.: OpenStreetMap: “user-generated street maps.”. IEEE Pervasive Comput. 7(4), 12–18 (2008)
Kotz, D., Henderson, T.: CRAWDAD: a community resource for archiving wireless data at Dartmouth. IEEE Pervasive Comput. 4(4), 12–14 (2005)
Bai, F., Sadagopan, N., Helmy, A.: The IMPORTANT framework for analyzing the Impact of Mobility on Performance Of RouTing protocols for Adhoc NeTworks. Ad Hoc Netw. 1(4), 383–403 (2003)
Nagel, K., Schreckenberg, M.: A cellular automaton model for freeway traffic. J. Phys. I 2(12), 2221–2229 (1992)
Zhang, G., Xu, W.: Cellular automaton traffic model considering driver’s reaction to velocity and headway distance with variable possibility of randomization. In: IOP Conference Series: Materials Science and Engineering, vol. 392, no. 6, p. 062024. IOP Publishing, July 2018
Lighthill, M.J., Whitham, G.B.: On kinematic waves II. A theory of traffic flow on long crowded roads. Proc. Roy. Soc. Lond. Ser. A Math. Phys. Sci. 229(1178), 317–345 (1955)
Richards, P.I.: Shock waves on the highway. Oper. Res. 4(1), 42–51 (1956)
Liu, H.X., Wu, X., Ma, W., Hu, H.: Real-time queue length estimation for congested signalized intersections. Transp. Res. Part C Emerg. Technol. 17(4), 412–427 (2009)
Yang, H., Rakha, H., Ala, M.V.: Eco-cooperative adaptive cruise control at signalized intersections considering queue effects. IEEE Trans. Intell. Transp. Syst. 18(6), 1575–1585 (2016)
Briesemeister, L.: Group membership and communication in highly mobile ad hoc networks (2001)
Krauß, S.: Microscopic modeling of traffic flow: investigation of collision free vehicle dynamics. Doctoral dissertation (1998)
Pop, M.D., Proştean, O., Proştean, G.: Multiple lane road car-following model using Bayesian reasoning for lane change behavior estimation: a smart approach for smart mobility. In: Proceedings of the 3rd International Conference on Future Networks and Distributed Systems, pp. 1–8, July 2019
Legendre, F., Borrel, V., de Amorim, M.D., Fdida, S.: Modeling mobility with behavioral rules: the case of incident and emergency situations. In: Cho, K., Jacquet, P. (eds.) AINTEC 2006. LNCS, vol. 4311, pp. 186–205. Springer, Heidelberg (2006). https://doi.org/10.1007/11930181_14
Gipps, P.G.: Behavioral car-following model for computer simulation. Transport. Res. 15(2), 105–111 (1981)
Kharrazi, S., Almén, M., Frisk, E., Nielsen, L.: Extending behavioral models to generate mission-based driving cycles for data-driven vehicle development. IEEE Trans. Veh. Technol. 68(2), 1222–1230 (2018)
Gawron, C.: An iterative algorithm to determine the dynamic user equilibrium in a traffic simulation model. Int. J. Mod. Phys. C 9(03), 393–407 (1998)
Mirchandani, P.B., Zou, N.: Queuing models for analysis of traffic adaptive signal control. IEEE Trans. Intell. Transp. Syst. 8(1), 50–59 (2007)
Cremer, M., Landenfeld, M.: A mesoscopic model for saturated urban road networks. In: Traffic and Granular Flow, vol. 97, pp. 169–180 (1998)
Mohimani, G.H., Ashtiani, F., Javanmard, A., Hamdi, M.: Mobility modeling, spatial traffic distribution, and probability of connectivity for sparse and dense vehicular ad hoc networks. IEEE Trans. Veh. Technol. 58(4), 1998–2007 (2008)
Grassmann, W.K., Taksar, M.I., Heyman, D.P.: Regenerative analysis and steady state distributions for Markov chains. Oper. Res. 33(5), 1107–1116 (1985)
Krajewski, R., Bock, J., Kloeker, L., Eckstein, L.: The highD dataset: a drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, pp. 2118–2125 (2018). https://doi.org/10.1109/ITSC.2018.8569552
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Oumaima, E.J., Jalel, B.O., Véronique, V. (2021). A Stochastic Traffic Model for Congestion Detection in Multi-lane Highways. In: Foschini, L., El Kamili, M. (eds) Ad Hoc Networks. ADHOCNETS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-030-67369-7_7
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