Abstract
Traffic congestion is one of the biggest challenges around the world, resulting in multiple harmful consequences such as air pollution, road fatalities, and traffic jams. Thus, it is a vital need to develop preventive mechanisms that allow better traffic management and alleviate the burden put on the transport network. Vehicle-to-anything (V2X) communication is gaining massive research interest as a promising solution to mobility challenges. This technology will enable vehicles and the infrastructure to form a distributed network constantly exchanging traffic information in real-time. The availability of timely information can help road users make optimal choices and enable effective autonomous traffic control. Therefore, to avoid traffic congestion, mobility models need to be established to study vehicular dynamics and to forecast future traffic conditions. The main goal of this study is to develop a traffic model based on Markov chain to tackle the congestion issue in a highway environment. Based on traffic data collected from vehicles through V2X technology, the model studies the evolution of traffic flow along a multiple lane divided highway and locally calculates estimates of the expected number of vehicles traveling on a highway segment. Performance measures are then inferred to detect possible congestion and then prevent it from happening. The numerical results presented in this study validate the model accuracy and show its ability to reproduce the fundamental mobility aspects in a highway environment.
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References
Bui KHN, Camacho D, Jung JE (2017) Real-time traffic flow management based on inter-object communication: a case study at intersection. Mob Netw Appl 22(4):613–624
Posner J, Tseng L, Aloqaily M, Jararweh Y (2021) Federated learning in vehicular networks: opportunities and solutions. IEEE Netw 35(2):152–159
Alam F, Mehmood R, Katib I, Altowaijri SM, Albeshri A (2019) TAAWUN: A decision fusion and feature specific road detection approach for connected autonomous vehicles. Mobile Networks and Applications, 1–17
Al Ridhawi I, Aloqaily M, Boukerche A, Jararweh Y (2021) Enabling Intelligent IoCV Services at the Edge for 5G Networks and Beyond. IEEE Transactions on Intelligent Transportation Systems
Tran L, Mun MY, Lim M, Yamato J, Huh N, Shahabi C (2020) DeepTRANS: a deep learning system for public bus travel time estimation using traffic forecasting. Proceedings of the VLDB Endowment 13(12):2957–2960
Lana I, Del Ser J, Velez M, Vlahogianni EI (2018) Road traffic forecasting: Recent advances and new challenges. IEEE Intell Transp Syst Mag 10(2):93–109
Vlahogianni EI, Karlaftis MG, Golias JC (2014) Short-term traffic forecasting: Where we are and where we’re going. Transport Res Part C Emerg Technol 43:3–19
Ros FJ, Martinez JA, Ruiz PM (2014) A survey on modeling and simulation of vehicular networks: communications, mobility, and tools. Comput Commun 43:1–15
Cheng PC, Lee KC, Gerla M, Härri J (2010) GeoDTN+ Nav: geographic DTN routing with navigator prediction for urban vehicular environments. Mob Netw Appl 15(1):61–82
Khamer L, Labraoui N, Gueroui AM, Zaidi S, Ari AAA (2021) Road network layout based multi-hop broadcast protocols for Urban Vehicular Ad-hoc Networks. Wireless Networks, 1-20
Naja A, Boulmalf M, Essaaidi M (2019) A distributed priority-based rebroadcasting protocol for VANETs: mitigating the storm problem. Mob Netw Appl 24(5):1555–1568
Yoshihiro T, Araki D, Sakaguchi H, Shibata N (2018) Providing Reliable Communications over Static-node-assisted Vehicular Networks Using Distance-vector Routing. Mob Netw Appl 23(5):1376–1393
Xia F, Rahim A, Kong X, Wang M, Cai Y, Wang J (2017) Modeling and analysis of large-scale urban mobility for green transportation. IEEE Trans Indust Inform 14(4):1469–1481
Afdhal A, Ahmadiar A, Nasaruddin N (2018) V2V Mobility Modeling and Simulation Using PBC Messages for Traffic Congestion Mitigation. In: 2018 IEEE International Conference on Communication, Networks and Satellite (Comnetsat). IEEE, pp 28-33
Akhtar N, Ergen SC, Ozkasap O (2014) Vehicle mobility and communication channel models for realistic and efficient highway VANET simulation. IEEE Trans Veh Technol 64(1):248– 262
Kamakshi S, Sriram VS (2020) Modularity based mobility aware community detection algorithm for broadcast storm mitigation in VANETs. Ad Hoc Netw 104:102161
Kafi MA, Ben-Othman J, Mokdad L, Badache N (2017) Performance analysis and evaluation of REFIACC using queuing networks. Simul Model Pract Theory 71:15–26
Mokdad L, Ben-Othman J, Nguyen AT (2015) DJAVAN: Detecting Jamming attacks in Vehicle Ad hoc Networks. Perform Eval 87:47–59
Mokdad L, Ben-Othman J, Yahya B, Niagne S (2014) Performance evaluation tools for QoS MAC protocol for wireless sensor networks. Ad Hoc Netw 12:86–99
Davies VA (2000) Evaluating mobility models within an ad hoc network (Master’s thesis advisor. Tracy Camp, Dept. of Mathematical and Computer Sciences.Colorado School of Mines)
Vetriselvi V, Parthasarathi R (2007) Trace based mobility model for ad hoc networks. In: Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2007). IEEE, pp 81-81
Förster A, Bin Muslim A, Udugama A (2018) 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
Beiró MG, Panisson A, Tizzoni M, Cattuto C (2016) Predicting human mobility through the assimilation of social media traces into mobility models. EPJ Data Science 5(1):30
Lian J, Li Y, Gu W, Huang SL, Zhang L (2020) Mining regional mobility patterns for urban dynamic analytics. Mob Netw Appl 25(2):459–473
Zhang D, Huang H, Zhou J, Xia F, Chen Z (2013) Detecting hot road mobility of vehicular ad hoc networks. Mob Netw Appl 18(6):803–813
Balsa-Barreiro J, Valero-Mora PM, Menéndez M., Mehmood R (2020) Extraction of naturalistic driving patterns with geographic information systems. Mob Netw Appl, 1–17
Haklay M, Weber P (2008) Openstreetmap: User-generated street maps. IEEE Pervasive Computing 7(4):12–18
Kotz D, Henderson T (2005) Crawdad: a community resource for archiving wireless data at dartmouth. IEEE Pervasive Computing 4(4):12–14
Lopez PA, Behrisch M, Bieker-Walz L, Erdmann J, Flötteröd YP, Hilbrich R, ..., WieBner E (2018) Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, pp 2575– 2582
SUMO Homepage https://www.eclipse.org/sumo/
Khairnar VD, Pradhan SN (2011) Mobility models for vehicular ad-hoc network simulation. In: 2011 IEEE Symposium on Computers & Informatics. IEEE, pp 460–465
Fiore M, Harri J, Filali F, Bonnet C (2007) Vehicular mobility simulation for VANETs. In: 40th Annual Simulation Symposium (ANSS’07). IEEE, pp 301-309
VanetMobiSim 1.1. http://vanet.eurecom.fr/
Bai F, Sadagopan N, Helmy A (2003) The IMPORTANT framework for analyzing the Impact of Mobility on Performance Of RouTing protocols for Adhoc NeTworks. Ad Hoc Netw 1(4):383–403
Nagel K, Schreckenberg M (1992) A cellular automaton model for freeway traffic. Journal de Physique I 2(12):2221– 2229
Zhang G, Xu W (2018) 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. IOP Publishing. p 062024)
Lighthill MJ, Whitham GB (1955) On kinematic waves II. A theory of traffic flow on long crowded roads. Proceedings of the Royal Society of London. Series A Mathematical and Physical Sciences 229 (1178):317–345
Richards PI (1956) Shock waves on the highway. Operations Research 4(1):42–51
Liu HX, Wu X, Ma W, Hu H (2009) Real-time queue length estimation for congested signalized intersections. Transportation Research Part C: Emerging Technologies 17(4):412– 427
Yang H, Rakha H, Ala MV (2016) Eco-cooperative adaptive cruise control at signalized intersections considering queue effects. IEEE Trans Intell Transp Syst 18(6):1575–1585
Briesemeister L (2001) Group membership and communication in highly mobile ad hoc networks
Krauß S (1998) Microscopic modeling of traffic flow. Investigation of collision free vehicle dynamics. (Doctoral dissertation)
Pop MD, Proştean O, Proştean G (2019) 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
Legendre F, Borrel V, De Amorim MD, Fdida S (2006) Modeling mobility with behavioral rules: The case of incident and emergency situations. In: Asian Internet Engineering Conference. Springer, Berlin, pp 186–205
Gipps PG (1981) Behavioral car-following model for computer simulation. Transport Res 15 (2):105–111
Kharrazi S, Almén M, Frisk E, Nielsen L (2018) Extending behavioral models to generate mission-based driving cycles for data-driven vehicle development. IEEE Trans Veh Technol 68(2):1222–1230
Gawron C (1998) An iterative algorithm to determine the dynamic user equilibrium in a traffic simulation model. Int J Mod Phys C 9(03):393–407
Mirchandani PB, Zou N (2007) Queuing models for analysis of traffic adaptive signal control. IEEE Trans Intell Transp Syst 8(1):50–59
Cremer M, Landenfeld M (1998) A mesoscopic model for saturated urban road networks. In Traffic and Granular Flow 97:169–180
Mohimani GH, Ashtiani F, Javanmard A, Hamdi M (2008) 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
Grassmann WK, Taksar MI, Heyman DP (1985) Regenerative analysis and steady state distributions for Markov chains. Oper Res 33(5):1107–1116
Krajewski R, Bock J, Kloeker L, Eckstein L (2018) 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 . https://doi.org/10.1109/ITSC.2018.8569552, pp 2118–2125
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Joubari, O.E., Othman, J.B. & Vèque, V. Markov Chain Mobility Model for Multi-lane Highways. Mobile Netw Appl 27, 1286–1298 (2022). https://doi.org/10.1007/s11036-021-01893-4
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DOI: https://doi.org/10.1007/s11036-021-01893-4