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Wireless Personal Communications

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28.02.2022

Mobility Models for Internet of Vehicles: A Survey

verfasst von: M. Kezia, K. V. Anusuya

Erschienen in: Wireless Personal Communications | Ausgabe 2/2022

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Abstract

The Internet of Vehicles framework largely relies on Vehicular Ad-hoc Network (VANET) to achieve the vision of connected, smart cars with mobility as a core component. Since deploying and testing a VANET in real-time is expensive, simulations are an essential tool as part of research on vehicular communication. Moreover, including an appropriate mobility model for a successful simulation of VANET is quite challenging because of its less realistic nature. This paper presents a survey on vehicular mobility models, emphasizing the realistic nature of vehicular movements and their corresponding challenges.

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Contreras-Castillo, J., Zeadally, S., & Guerrero-Ibanez, J. A. (2018). Internet of vehicles: Architecture, protocols, and security. IEEE Internet of Things Journal, 5(5), 3701–3709. https://doi.org/10.1109/jiot.2017.2690902CrossRef

Kaiwartya, O., Abdullah, A. H., Yue, C., Ayman, A., Mukesh, P., Chin-Teng, L., & Liu, X. (2016). Internet of vehicles: Motivation, layered architecture, network model, challenges, and future aspects. IEEE Access, 4, 5356–5373. https://doi.org/10.1109/access.2016.2603219CrossRef

Gasmi, R., & Aliouat, M. (2019). Vehicular Ad Hoc NETworks versus internet of vehicles - A comparative view. International Conference on Networking and Advanced Systems (ICNAS), 2019, 1–6.

Bai, F., & Helmy, A. (2004). A survey of mobility models. Wireless ad hoc networks (pp. 16–55). University of Southern California.

Harri, J., Filali, F., & Bonnet, C. (2009). Mobility models for vehicular ad hoc networks: a survey and taxonomy. IEEE Communications Surveys & Tutorials, 11(4), 19–41. https://doi.org/10.1109/SURV.2009.090403CrossRef

Jérôme, H. (2010). Vehicular mobility modelling for VANET. In Vehicular applications and inter-networking technologies (pp. 107–156).

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 Networks, 1(4), 383–403. https://doi.org/10.1016/s1570-8705(03)00040-4CrossRef

Josh, B., Maltz, D. A., Johnson D. B. (1998). A performance comparison of multi-hop wireless ad hoc network routing protocols. In ACM/IEEE international conference on Mobile computing and networking (pp. 1–13).

Gaikwad, D. S. & Zaveri, M. (2011). A Novel mobility model for realistic behaviour in Vehicular Ad Hoc Networks In 11th IEEE international conference on computer and information technology (pp. 597–602).

10.

Safaei, B., Mohammadsaleh, A., Khoosani, K. T., Zarbaf, S., Monazzah, A. M. H., Samie, F., Bauer, L., Henkel, J., & Ejlali, A. (2020). Impacts of mobility models on RPL-based mobile IoT infrastructures: An evaluative comparison and survey. IEEE Access, 8, 167779–167829. https://doi.org/10.1109/ACCESS.2020.3022793CrossRef

11.

Bai, F., Sadagopan, N., & Helmy, A. (2003). Important: a framework to systematically analyze the impact of mobility on performance of routing protocols for ad hoc networks. In Proceedings of the 22nd annual joint conference on the IEEE computer and communications societies (INFOCOM '03) (Vol. 2, pp. 825–835).

12.

Hoogendoorn, S. P., & Bovy, P. H. L. (2001). State-of-the-art of vehicular traffic flow modelling. Proceedings of the institution of mechanical engineers. Part I: Journal of Systems and Control Engineering, 215(4), 283–303. https://doi.org/10.1177/095965180121500402CrossRef

13.

Harri, J., Filali, F., & Bonnet, C. (2009). Fig. 10: General schema for car following models. Mobility models for vehicular ad hoc networks: A survey and taxonomy. IEEE Communications Surveys & Tutorials, 11(4), 19–41.CrossRef

14.

Gipps, P. G. (1981). A behavioural car-following model for computer simulation. Transportation Research Part B: Methodological, 15(2), 105–111. https://doi.org/10.1016/0191-2615(81)90037-0CrossRef

15.

Chandler, R. E., Herman, R., & Montroll, E. W. (1958). Traffic dynamics: Studies in car following. Operations Research, 6(2), 165–184. https://doi.org/10.1287/opre.6.2.165MathSciNetCrossRef

16.

Ding, S., Chen, X., Fu, Z., & Peng, F. (2021). An extended car-following model in connected and autonomous vehicle environment: Perspective from the cooperation between drivers. Journal of Advanced Transportation. https://doi.org/10.1155/2021/2739129CrossRef

17.

Ding, S., Chen, X., Fu, Z., & Peng, F. (2021). Figure 12: Speed variation during the following process. (a) Speed variation of CAV follower. (b) Speed variation of HDV follower. An extended car-following model in connected and autonomous vehicle environment: Perspective from the cooperation between Drivers. Journal of Advanced Transportation. https://doi.org/10.1155/2021/2739129CrossRef

18.

Ding, S., Chen, X., Fu, Z., & Peng, F. (2021). Figure 13: Variation of acceleration and distance headway during the following process. (a) CAV follower. (b) HDV follower. An extended Car-following model in connected and autonomous vehicle environment: Perspective from the cooperation between drivers. Journal of Advanced Transportation. https://doi.org/10.1155/2021/2739129CrossRef

19.

VanetMobiSim Project official website. http://vanet.eurecom.fr

20.

Treiber, M., Hennecke, A., & Helbing, D. (2000). Congested traffic states in empirical observations and microscopic simulations. Physical Review E, 62(2), 1805–1824. https://doi.org/10.1103/physreve.62.1805CrossRefMATH

21.

Liu, H., Sun, D., & Zhao, M. (2016). A model prediction control based framework for optimization of signaled intersection: A cyber-physical perspective. Optik, 127(20), 10068–10075. https://doi.org/10.1016/j.ijleo.2016.07.094CrossRef

22.

Tian, J., & Meng, F. (2020). Comparison survey of mobility models in vehicular ad-hoc network (VANET). In IEEE 3rd international conference on automation, electronics and electrical engineering (AUTEEE) (pp. 337–342). doi:https://doi.org/10.1109/AUTEEE50969.2020.9315583

23.

Sharath, M. N., & Velaga, N. R. (2020). Enhanced intelligent driver model for two-dimensional motion planning in mixed traffic. Transportation Research Part C: Emerging Technologies, 120, 102780. https://doi.org/10.1016/j.trc.2020.102780CrossRef

24.

Krauss, S., Wagner, P., & Gawron, C. (1997). Metastable states in a microscopic model of traffic flow. Physical Review E, 55(5), 5597–5602. https://doi.org/10.1103/physreve.55.5597CrossRef

25.

SUMO Official Website. https://www.eclipse.org/sumo/

26.

Wiedemann, R.. (1974). Simulation des Straenverkehrsflusses, PhD thesis, Schriftenreihe des Instituts fur Verkehrswesen der Universitat, Karlruhe 8, Germany, 1974.

27.

VISSIM official website. https://www.ptvgroup.com/en/solutions/products/ptv-vissim/

28.

Nagel, K., & Schreckenberg, M. (1992). A cellular automaton model for freeway traffic. Journal de Physique I, 2(12), 2221–2229. https://doi.org/10.1051/jp1:1992277CrossRef

29.

Smith, L., Beckman, R., & Baggerly, K. (1995). https://code.google.com/p/transims/

30.

Lighthill, M. J., & Whitham, G. B. (1995). On kinematic waves: II. A theory of traffic flow on long crowded roads. Proceedings of the Royal Society of London A, 229, 317–345.MathSciNetMATH

31.

Jin, W. L. (2013). A multi-commodity Lighthill–Whitham–Richards model of lane-changing traffic flow. Transportation Research Part B: Methodological, 57, 361–377. https://doi.org/10.1016/j.trb.2013.06.002CrossRef

32.

Munjal, P., & J.pahl. (1961). An analysis of the Boltzmann-type statistical models for multi-lane traffic flow. Transportation Research, 3(1), 90112–90121.

33.

Cetin, N., Nagel, B. A., & K (2003). A large-scale agent based traffic microsimulation based on queue model. In Swiss transport research conference (STRC). Monte Vertia.

34.

Jérôme, H. (2010). Figure 5.1: The multilayer modeling concept of flow, path, and trip modeling as addressed in this chapter Vehicular mobility modelling for VANET. Vehicular Applications and Inter-Networking Technologies, 107–156.

35.

Jérôme, H. (2010). Figure 5.15: Agent-centric versus flow-centric path planning. Vehicular mobility modeling for VANET. Vehicular Applications and Inter-Networking Technologies, 107–156.

36.

MATSim official website. (2009). http://www.matsim.org

37.

AIMSUN official website. (2009). http://www.aimsun.com

38.

CORSIM: Microscopic traffic simulation model. https://ops.fhwa.dot.gov/trafficanalysistools/corsim.htm

39.

Batabyal, S., & Bhaumik, P. (2015). Mobility models, traces and impact of mobility on opportunistic routing algorithms: A survey. IEEE Communications Surveys & Tutorials, 17(3), 1679–1707. https://doi.org/10.1109/COMST.2015.2419819CrossRef

40.

The Cab spotting Project. (2006). https://stamen.com/work/cabspotting/

41.

CRAWDAD Data Set Dieselnet/UMass. https://crawdad.org/umass/diesel/20080914/

42.

Feng, H., & Youji, X. (2016). An empirical study on evolution of the connectivity for VANETs based on taxi GPS traces. International Journal of Distributed Sensor Networks. https://doi.org/10.1155/2016/2580465CrossRef

43.

Ibadah, N., Minaoui, K., Rziza, M., Oumsis, M., & Benavente-Peces, C. (2018). Smart collection of real-time vehicular mobility traces. Future Internet, 10, 78.CrossRef

44.

Michael, D., Tobias, P., Wolf-Bastian, P., Lars, W. (2010). A new mobility trace for realistic large-scale simulation of bus-based DTNs. In Proceedings of the 5th ACM workshop on challenged networks (CHANTS '10) (pp. 71–74). Association for Computing Machinery. doi:https://doi.org/10.1145/1859934.1859950

45.

Fujimoto, R., Guensler, R., Hunter, M., et al. (2006). CRAWDAD data set GAtech/vehicular (v. 2006–03–15). http://crawdad.cs.dartmouth.edu/gatech/vehicular

46.

UDel Models for Simulation of Urban Mobile Wireless Networks. (2007). http://udelmodels.eecis.udel.edu/

47.

Zheng, Q., Hong, X., & Liu, J. (2006). An agenda-based mobility model. In 39th IEEE annual simulation symposium (pp. 188–195). Huntsville, AL, USA. doi:https://doi.org/10.1109/ANSS.2006.11

48.

Realistic Vehicular Traces from the Multi-agent Microscopic Traffic Simulator (MMTS). (2006). https://www.lst.inf.ethz.ch/research/ad-hoc/realistic-vehicular-traces.html

49.

Najah, A. A., & Hatem, A. Z. (2016). Driver behavior modeling: Developments and future directions. International Journal of Vehicular Technology. https://doi.org/10.1155/2016/6952791CrossRef

50.

Ali, N. A., & Abou-zeid, H. (2016). Figure 1: Driver behavior modeling (DBM): Sensing, applications, and future systems. Driver behavior modeling: Developments and future directions. International Journal of Vehicular Technology. https://doi.org/10.1155/2016/6952791CrossRef

51.

Legendre, F., Borrel, V., De Amorim, M. D., et al. (2006). Reconsidering microscopic mobility modelling for self-organizing networks. IEEE Network Magazine, 20(6), 4–12. https://doi.org/10.1109/MNET.2006.273114CrossRef

52.

Balmer M. (2007). Travel demand modeling for multi-agent traffic simulations: Algorithms and Systems. Ph.D. thesis. ETH Zurich, Switzerland.

53.

Doniec, A., Espie, S., Mandiau, R., & Piechowiak, S. (2006). Non-normative behavior in multi-agent system: Some experiments. In IEEE/WIC/ACM international conference on intelligent agent technology (pp. 30–36). doi:https://doi.org/10.1109/IAT.2006.96

54.

Amer, A., Rakha, H., El-Shawarby, I. (2011). Agent-based behavioral modeling framework of driver behavior at the onset of yellow indication at signalized intersections. In 14th international IEEE conference on intelligent transportation systems (ITSC) (pp. 1809–1814). doi:https://doi.org/10.1109/ITSC.2011.6082887

55.

Dia H., & Panwai, S. (2014). Intelligent mobility for smart cities: Driver behaviour models for assessment of sustainable transport. In: IEEE fourth international conference on big data and cloud computing (pp. 625–632). doi:https://doi.org/10.1109/BDCloud.2014.50

56.

He, B., Zhang, D., Liu, S., Liu, H., Han, D., & Ni, L. M. (2018). Profiling driver behavior for personalized insurance pricing and maximal profit. In IEEE international conference on big data (Big Data) (pp. 1387–1396). doi:https://doi.org/10.1109/BigData.2018.8622491

57.

Hernafi, Y., Ben Ahmed, M., & Bouhorma, M. (2016). An approaches’ based on intelligent transportation systems to dissect driver behavior and smart mobility in smart city. In 4th IEEE international colloquium on information science and technology (CiSt) (pp. 886–895). doi:https://doi.org/10.1109/CIST.2016.7805013

58.

Lindorfer, M., Mecklenbräuker, C. F., & Ostermayer, G. (2018). Modeling the imperfect driver: Incorporating human factors in a microscopic traffic model. IEEE Transactions on Intelligent Transportation Systems, 19(9), 2856–2870. https://doi.org/10.1109/TITS.2017.2765694CrossRef

59.

Rossi, A., Barlacchi, G., Bianchini, M., & Lepri, B. (2020). Modeling taxi drivers’ behaviour for the next destination prediction. IEEE Transactions on Intelligent Transportation Systems, 21(7), 2980–2989. https://doi.org/10.1109/TITS.2019.2922002CrossRef

60.

Wang, X., Guo, Y., Bai, C., Yuan, Q., Liu, S., & Han, J. (2020). Driver’s intention identification with the involvement of emotional factors in two-lane roads. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2020.2995837CrossRef

61.

Amirat, H., Lagraa, N., Fournier-Viger, P., & Ouinten, Y. (2020). NextRoute: A lossless model for accurate mobility prediction. Journal of Ambient Intelligence and Humanized Computing, 11(7), 2661–2681. https://doi.org/10.1007/s12652-019-01327-wCrossRef

62.

Xuan, S., Hiroshi, K., & Ryosuke, S. (2016). Deeptransport: prediction and simulation of human mobility and transportation mode at a citywide level. In Proceedings of the twenty-fifth international joint conference on artificial intelligence (IJCAI'16) (pp. 2618–2624). AAAI Press.

63.

Lv, Z., J. Xu, Kai Zheng, Hongzhi Yin, Pengpeng Zhao and X. Zhou. (2018). LC-RNN: A deep learning model for traffic speed prediction, IJCAI, 3470–3476.

64.

Jian, Z. (2018). deep learning-based vehicular mobility models for intelligent transportation systems. Ph.D Thesis, Automatic Control Engineering. Ecole Centrale de Lille.

65.

Jian, Z. (2018). Figure 3.9: Differences of the flow chart between enhanced method and the original one. Deep learning-based vehicular mobility models for intelligent transportation systems. Ph.D Thesis, Automatic Control Engineering. Ecole Centrale de Lille.

66.

Naskath, J., Paramasivan, B., & Aldabbas, H. (2021). A study on modeling vehicles mobility with MLC for enhancing vehicle-to-vehicle connectivity in VANET. Journal of Ambient Intelligence and Humanized Computing, 12(8), 8255–8264. https://doi.org/10.1007/s12652-020-02559-xCrossRef

Titel: Mobility Models for Internet of Vehicles: A Survey
verfasst von: M. Kezia
K. V. Anusuya
Publikationsdatum: 28.02.2022
Verlag: Springer US
Erschienen in: Wireless Personal Communications / Ausgabe 2/2022
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-022-09637-7

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