Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Road Intersection Coordination Scheme for Mixed Traffic (Human Driven and Driver-Less Vehicles): A Systematic Review

  • Conference paper
  • First Online:
Intelligent Computing (SAI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 508))

Included in the following conference series:

  • 930 Accesses

Abstract

Autonomous vehicles (AV) are emerging with enormous potentials to solve many challenging road traffic problems. The AV emergence leads to a paradigm shift in the road traffic system, making the penetration of autonomous vehicles fast and its co-existence with human-driven cars inevitable. The migration from the traditional driving to the intelligent driving system with AV’s gradual deployment needs supporting technology to address mixed traffic systems problems; mixed driving behaviour in a car-following model, variation in vehicle type control means, the impact of a proportion of AV in traffic mixed traffic, and many more. The migration to fully AV will solve many traffic problems: desire to reclaim travel and commuting time, driving comfort, and accident reduction. Motivated by the above facts, this paper presents an extensive review of road intersection traffic management techniques with a classification matrix of different traffic management strategies and technologies that could effectively describe a mix of human and autonomous vehicles. It explores the existing traffic control strategies, analyse their compatibility in a mixed traffic environment. Then review their drawback and build on it for the proposed robust mix of traffic management schemes. Though many traffic control strategies have been in existence, the analysis presented in this paper gives new insights to the readers on the applications of the cell reservation strategy in a mixed traffic environment. The cell assignment and reservation method are the operations systems associated with the air traffic control systems used to coordinate aircraft landing. The proposed method identifies the cross collision point (CCP) in a 4-way road intersection and develops an optimisation strategy to assign vehicles to the CCP sequentially and efficiently. The traffic flow efficiency uses a hybrid Gipps car-following model to describe a 2-dimensional traffic behaviour involved in a mixed traffic system. Though many traffic control strategies have been in existence, the car-following model has shown to be very effective for optimal traffic flow performance. The main challenge with the car-following model is that it only controls traffic in the longitudinal pattern, which is not suitable in describing mixed traffic behaviour.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Abduljabbar, R., Dia, H., Liyanage, S., Bagloee, S.: Applications of artificial intelligence in transport: an overview. Sustainability 11(1), 189 (2019)

    Article  Google Scholar 

  2. Akcelik, R.: Gap-acceptance modelling by traffic signal analogy. Traff. Eng.+ Control 35(9), 498–501 (1994)

    Google Scholar 

  3. Akçelik, R.: A review of gap-acceptance capacity models. In: The 29th Conference of Australian Institutes of Transport Research (CAITR 2007), pp. 5–7. University of South Australia, Adelaide (2007)

    Google Scholar 

  4. Al-Jameel, H.A.Z., et al.: Examining and improving the limitations of Gazis-Herman-Rothery car following model (2009)

    Google Scholar 

  5. Anderson, J.M., Nidhi, K., Stanley, K.D., Sorensen, P., Samaras, C., Oluwatola, O.A.: Autonomous Vehicle Technology: A Guide for Policymakers. Rand Corporation, Santa Monica (2014)

    Google Scholar 

  6. Annell, S., Gratner, A., Svensson, L.: Probabilistic collision estimation system for autonomous vehicles. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp. 473–478. IEEE (2016)

    Google Scholar 

  7. Arnaout, G.M., Arnaout, J.-P.: Exploring the effects of cooperative adaptive cruise control on highway traffic flow using microscopic traffic simulation. Transp. Plan. Technol. 37(2), 186–199 (2014)

    Google Scholar 

  8. Au, T.-C., Zhang, S., Stone, P.: Autonomous intersection management for semi-autonomous vehicles. In: Handbook of Transportation, pp. 88–104 (2015)

    Google Scholar 

  9. Bazilinskyy, P., Kyriakidis, M., Dodou, D., de Winter, J.: When will most cars be able to drive fully automatically? Projections of 18,970 survey respondents. Transp. Res. F Traff. Psychol. Behav. 64, 184–195 (2019)

    Article  Google Scholar 

  10. Bento, L.C., Parafita, R., Nunes, U.: Intelligent traffic management at intersections supported by v2v and v2i communications. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 1495–1502. IEEE (2012)

    Google Scholar 

  11. Bingfeng, S.I., Zhong, M, Gao, Z.: Link resistance function of urban mixed traffic network. J. Transp. Syst. Eng. Inf. Technol. 8(1), 68–73 (2008)

    Google Scholar 

  12. Booth, L., Norman, R., Pettigrew, S.: The potential implications of autonomous vehicles for active transport. J. Transp. Health 15, 100623 (2019)

    Article  Google Scholar 

  13. Budhkar, A.K., Maurya, A.K.: Multiple-leader vehicle-following behavior in heterogeneous weak lane discipline traffic. Transp. Dev. Econ. 3(2), 20 (2017)

    Google Scholar 

  14. Chan, E., Gilhead, P., Jelinek, P., Krejci, P., Robinson, T.: Cooperative control of sartre automated platoon vehicles. In: 19th ITS World Congress ERTICO-ITS Europe European Commission ITS America ITS Asia-Pacific (2012)

    Google Scholar 

  15. Claes, R., Holvoet, T., Weyns, D.: A decentralized approach for anticipatory vehicle routing using delegate multiagent systems. IEEE Trans. Intell. Transp. Syst. 12(2), 364–373 (2011)

    Article  Google Scholar 

  16. Domeyer, J.E., Lee, J.D., Toyoda, H.: Vehicle automation–other road user communication and coordination: theory and mechanisms. IEEE Access 8, 19860–19872 (2020)

    Google Scholar 

  17. Dresner, K., Stone, P.: Traffic intersections of the future. In: Proceedings of the National Conference on Artificial Intelligence, vol. 21, p. 1593. AAAI Press; MIT Press, Menlo Park, Cambridge, London (1999, 2006)

    Google Scholar 

  18. Dresner, K., Stone, P.: A multiagent approach to autonomous intersection management. J. Artif. Intell. Res. 31, 591–656 (2008)

    Article  Google Scholar 

  19. Droździel, P., Tarkowski, S., Rybicka, I., Wrona, R.: Drivers’ reaction time research in the conditions in the real traffic. Open Eng. 10(1), 35–47 (2020)

    Article  Google Scholar 

  20. Eguchi, J., Koike, H.: Discrimination of an approaching vehicle at an intersection using a monocular camera. In: 2007 Intelligent Vehicles Symposium, pp. 618–623. IEEE (2007)

    Google Scholar 

  21. Emami, P., Pourmehrab, M., Martin-Gasulla, M., Ranka, S., Elefteriadou, L.: A comparison of intelligent signalized intersection controllers under mixed traffic. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 341–348. IEEE (2018)

    Google Scholar 

  22. Fajardo, D., Au, T.-C., Waller, S.T., Stone, P., Yang, D.: Automated intersection control: performance of future innovation versus current traffic signal control. Transp. Res. Rec. 2259, 223–232 (2011)

    Google Scholar 

  23. Feng, Y., Head, K.L., Khoshmagham, S., Zamanipour, M.: A real-time adaptive signal control in a connected vehicle environment. Transp. Res. Part C Emerg. Technol. 55, 460–473 (2015)

    Google Scholar 

  24. Ferreira, M.C.P., Tonguz, O., Fernandes, R.J., DaConceicao, H.M.F., Viriyasitavat, W.: Methods and systems for coordinating vehicular traffic using in-vehicle virtual traffic control signals enabled by vehicle-to-vehicle communications. US Patent 8,972,159, 3 March 2015

    Google Scholar 

  25. Friedrich, B.: The effect of autonomous vehicles on traffic. In: Maurer, M., Gerdes, J.C., Lenz, B., Winner, H. (eds.) Autonomous Driving, pp. 317–334. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-48847-8_16

    Chapter  Google Scholar 

  26. Fujii, H., Uchida, H., Yoshimura, S.: Agent-based simulation framework for mixed traffic of cars, pedestrians and trams. Transp. Res. Part C Emerg. Technol. 85, 234–248 (2017)

    Article  Google Scholar 

  27. Gong, S., Lili, D.: Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles. Transp. Res. Part B Methodol. 116, 25–61 (2018)

    Article  Google Scholar 

  28. Guo, J., Cheng, S., Liu, Y.: Merging and diverging impact on mixed traffic of regular and autonomous vehicles. IEEE Trans. Intell. Transp. Syst. 22(3), 1639–1649 (2020)

    Article  Google Scholar 

  29. Guo, L., Ge, P., Sun, D., Qiao, Y.: Adaptive cruise control based on model predictive control with constraints softening. Appl. Sci. 10(5), 1635 (2020)

    Article  Google Scholar 

  30. Haman, I.T., Kamla, V.C., Galland, S., Kamgang, J.C.: Towards an multilevel agent-based model for traffic simulation. Procedia Comput. Sci. 109, 887–892 (2017)

    Google Scholar 

  31. Hara, T., Kiyohara, R.: Vehicle approaching model for t-junction during transition to autonomous vehicles. In: 2018 International Conference on Information Networking (ICOIN), pp. 304–309. IEEE(2018)

    Google Scholar 

  32. Heilig, M., Hilgert, T., Mallig, N., Kagerbauer, M., Vortisch, P.: Potentials of autonomous vehicles in a changing private transportation system-a case study in the Stuttgart region. Transp. Res. Procedia 26, 13–21 (2017)

    Article  Google Scholar 

  33. Horowitz, R., Varaiya, P.: Control design of an automated highway system. Proc. IEEE 88(7), 913–925 (2000)

    Article  Google Scholar 

  34. Huang, S., Ren, W.: Autonomous intelligent vehicle and its performance in automated traffic systems. Int. J. Control 72(18), 1665–1688 (1999)

    Article  Google Scholar 

  35. Jeong, E., Cheol, O., Lee, S.: Is vehicle automation enough to prevent crashes? role of traffic operations in automated driving environments for traffic safety. Acc. Anal. Prevent. 104, 115–124 (2017)

    Article  Google Scholar 

  36. Kamal, M.A.S., Imura, J., Ohata, A., Hayakawa, T., Aihara, K.: Control of traffic signals in a model predictive control framework. In: IFAC Proceedings Volumes, vol. 45, no. 24, pp. 221–226 (2012)

    Google Scholar 

  37. Kamal, M.A.S., Imura, J., Hayakawa, T., Ohata, A., Aihara, K.: A vehicle-intersection coordination scheme for smooth flows of traffic without using traffic lights. IEEE Trans. Intell. Transp. Syst. 16(3), 1136–1147 (2015)

    Article  Google Scholar 

  38. Kamal, M.A.S., Imura, J., Ohata, A., Hayakawa, T., Aihara, K.: Coordination of automated vehicles at a traffic-lightless intersection. In: 2013 16th International IEEE Conference on Intelligent Transportation Systems-(ITSC), pp. 922–927. IEEE (2013)

    Google Scholar 

  39. Kanthack, C.A.: Autonomous vehicles and driving under the influence: examining the ambiguity surrounding modern laws applied to future technology. Creighton L. Rev. 53, 397 (2019)

    Google Scholar 

  40. Kesting, A., Treiber, M., Helbing, D.: Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity. Philos. Trans. Roy. Soc. A Math. Phys. Eng. Sci. 368(1928), 4585–4605 (2010)

    Article  MATH  Google Scholar 

  41. Khondaker, B., Kattan, L.: Variable speed limit: a microscopic analysis in a connected vehicle environment. Transp. Res. Part C Emerg. Technol. 58, 146–159 (2015)

    Article  Google Scholar 

  42. Knorn, S., Donaire, A., Agüero, J.C., Middleton, R.H.: Passivity-based control for multi-vehicle systems subject to string constraints. Automatica 50(12), 3224–3230 (2014)

    Google Scholar 

  43. Lari, A., Douma, F., Onyiah, I.: Self-driving vehicles and policy implications: Current status of autonomous vehicle development and Minnesota policy implications. Minn. JL Sci. Technol. 16, 735 (2015)

    Google Scholar 

  44. Le Vine, S., Zolfaghari, A., Polak, J.: Autonomous cars: the tension between occupant experience and intersection capacity. Transp. Res. Part C Emerg. Technol. 52, 1–14 (2015)

    Article  Google Scholar 

  45. Li, N.: Large-scale realistic macro-simulation of vehicle movement on road networks (2013)

    Google Scholar 

  46. Liao, R.: Smart mobility: challenges and trends. In: Toward Sustainable and Economic Smart Mobility: Shaping the Future of Smart Cities, p. 1 (2020)

    Google Scholar 

  47. Lin, S.-H., Ho, T.-Y.: Autonomous vehicle routing in multiple intersections. In: Proceedings of the 24th Asia and South Pacific Design Automation Conference, pp. 585–590. ACM (2019)

    Google Scholar 

  48. Litman, T.: Autonomous Vehicle Implementation Predictions. Victoria Transport Policy Institute, Victoria, Canada (2017)

    Google Scholar 

  49. Liu, Y.-C.: Comparative study of the effects of auditory, visual and multimodality displays on drivers’ performance in advanced traveller information systems. Ergonomics 44(4), 425–442 (2001)

    Article  Google Scholar 

  50. Lu, X.-Y., Tan, H.-S., Shladover, S.E., Hedrick, J.K.: Automated vehicle merging maneuver implementation for ahs. Veh. Syst. Dyn. 41(2), 85–107 (2004)

    Google Scholar 

  51. Luettel, T., Himmelsbach, M., Wuensche, H.-J.: Autonomous ground vehicles-concepts and a path to the future. Proc. IEEE 100(Special Centennial Issue), 1831–1839 (2012)

    Google Scholar 

  52. Le Maitre, M., Prorok, A.: Effects of controller heterogeneity on autonomous vehicle traffic. arXiv preprint arXiv:2005.04995 (2020)

  53. Marisamynathan, S., Vedagiri, P.: Modeling pedestrian delay at signalized intersection crosswalks under mixed traffic condition. Procedia Soc. Behav. Sci. 104, 708–717 (2013)

    Article  Google Scholar 

  54. Matcha, B.N., Namasivayam, S.N., Fouladi, M.H., Ng, K.C., Sivanesan, S., Noum, S.Y.E.: Simulation strategies for mixed traffic conditions: areview of car-following models and simulation frameworks. J. Eng. 2020 (2020)

    Google Scholar 

  55. Mathew, T.V., Munigety, C.R., Bajpai, A.: Strip-based approach for the simulation of mixed traffic conditions. J. Comput. Civil Eng. 29(5), 04014069 (2015)

    Google Scholar 

  56. Milanés, V., Pérez, J., Onieva, E., González, C.: Controller for urban intersections based on wireless communications and fuzzy logic. IEEE Trans. Intell. Transp. Syst. 11(1), 243–248 (2010)

    Article  Google Scholar 

  57. Montanaro, U., Dixit, S., Fallah, S., Dianati, M., Stevens, A., Oxtoby, D., Mouzakitis, Al.: Towards connected autonomous driving: review of use-cases. Veh. Syst. Dyn. 1–36 (2018)

    Google Scholar 

  58. Mueller, E.A.: Aspects of the history of traffic signals. IEEE Trans. Veh. Technol. 19(1), 6–17 (1970)

    Google Scholar 

  59. Naiem, A., Reda, M., El-Beltagy, M., El-Khodary, I.: An agent based approach for modeling traffic flow. In: 2010 The 7th International Conference on Informatics and Systems (INFOS), pp. 1–6. IEEE (2010)

    Google Scholar 

  60. Omae, M., Ogitsu, T., Honma, N., Usami, K.: Automatic driving control for passing through intersection without stopping. Int. J. Intell. Transp. Syst. Res. 8(3), 201–210 (2010)

    Google Scholar 

  61. Pakusch, C., Stevens, G., Bossauer, P.: Shared autonomous vehicles: potentials for a sustainable mobility and risks of unintended effects. In: ICT4S, pp. 258–269 (2018)

    Google Scholar 

  62. Papageorgiou, M., Diakaki, C., Dinopoulou, V., Kotsialos, A., Wang, Y.: Review of road traffic control strategies. Proc. IEEE 91(12), 2043–2067 (2003)

    Article  Google Scholar 

  63. Payre, W., Cestac, J., Delhomme, P.: Intention to use a fully automated car: Attitudes and a priori acceptability. Transp. Res. F Traff. Psychol. Behav. 27, 252–263 (2014)

    Article  Google Scholar 

  64. Pfoser, D., Theodoridis, Y.: Generating semantics-based trajectories of moving objects. Comput. Environ. Urban Syst. 27(3), 243–263 (2003)

    Article  Google Scholar 

  65. Ploeg, J., Serrarens, A.F.A., Heijenk, G.J.: Connect drive: design and evaluation of cooperative adaptive cruise control for congestion reduction. J. Mod. Transp. 19(3), 207–213 (2011)

    Google Scholar 

  66. Raju, N., Arkatkar, S., Easa, S., Joshi, G.: Customizing the following behavior models to mimic the weak lane based mixed traffic conditions. Transportmetrica B Transp. Dyn. 1–28 (2021)

    Google Scholar 

  67. Rios-Torres, J., Malikopoulos, A.A.: A survey on the coordination of connected and automated vehicles at intersections and merging at highway on-ramps. IEEE Trans. Intell. Transp. Syst. 18(5), 1066–1077 (2017)

    Google Scholar 

  68. Schrank, D., Eisele, B., Lomax, T.: Tti’s 2012 Urban Mobility Report. Texas A&M Transportation Institute. The Texas A&M University System, vol. 4 (2012)

    Google Scholar 

  69. Schrank, D., Lomax, T., Eisele, B.: 2012 urban mobility report. Texas Transportation Institute (2012). http://mobility.tamu.edu/ums/report

  70. Seshia, S.A., Sadigh, D., Sastry, S.S.: Towards verified artificial intelligence. arXiv preprint arXiv:1606.08514 (2016)

  71. Shahgholian, M., Gharavian, D.: Advanced traffic management systems: an overview and a development strategy. arXiv preprint arXiv:1810.02530 (2018)

  72. Sharon, G., Stone, P.: A protocol for mixed autonomous and human-operated vehicles at intersections. In: Sukthankar, G., Rodriguez-Aguilar, J.A. (eds.) AAMAS 2017. LNCS (LNAI), vol. 10642, pp. 151–167. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71682-4_10

    Chapter  Google Scholar 

  73. Swaroop, D.V.A.H.G., Hedrick, J.K., Chien, C.C., Ioannou, P.: A comparison of spacing and headway control laws for automatically controlled vehicles 1. Veh. Syst. Dyn. 23(1), 597–625 (1994)

    Google Scholar 

  74. Teply, S., Abou-Henaidy, M.I., Hunt, J.D.: Gap acceptance behaviour-aggregate and logit perspectives: part 1. Traff. Eng. Control (1997)

    Google Scholar 

  75. Theodoridis, Y., Silva, J.R.O., Nascimento, M.A.: On the generation of spatiotemporal datasets. In: Güting, R.H., Papadias, D., Lochovsky, F. (eds.) SSD 1999. LNCS, vol. 1651, pp. 147–164. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48482-5_11

    Chapter  Google Scholar 

  76. Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Phys. Rev. E 62(2), 1805 (2000)

    Article  MATH  Google Scholar 

  77. Tzouramanis, T., Vassilakopoulos, M., Manolopoulos, Y.: On the generation of time-evolving regional data. GeoInformatica 6(3), 207–231 (2002)

    Article  MATH  Google Scholar 

  78. Uno, A., Sakaguchi, T., Tsugawa, S.: A merging control algorithm based on inter-vehicle communication. In: 1999 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems, Proceedings, pp. 783–787. IEEE (1999)

    Google Scholar 

  79. Van Arem, B., Van Driel, C.J.G., Visser, R.: The impact of cooperative adaptive cruise control on traffic-flow characteristics. IEEE Trans. Intell. Transp. Syst. 7(4), 429–436 (2006)

    Google Scholar 

  80. Vial, J.J.B., Devanny, W.E., Eppstein, D., Goodrich, M.T.: Scheduling autonomous vehicle platoons through an unregulated intersection. arXiv preprint arXiv:1609.04512 (2016)

  81. Wakui, N., Takayama, R., Mimura, T., Kamiyama, N., Maruyama, K., Sumino, Y.: Drinking status of heavy drinkers detected by arrival time parametric imaging using sonazoid-enhanced ultrasonography: study of two cases. Case Rep. Gastroenterol. 5(1), 100–109 (2011)

    Article  Google Scholar 

  82. Wang, M., Daamen, W., Hoogendoorn, S.P., van Arem, B.: Rolling horizon control framework for driver assistance systems. part ii: cooperative sensing and cooperative control. Transp. Res. Part C Emerg. Technol. 40, 290–311 (2014)

    Google Scholar 

  83. Zeng, Q., Wu, C., Peng, L., Li, H.: Novel vehicle crash risk detection based on vehicular sensory system. In: Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 622–626. IEEE (2015)

    Google Scholar 

  84. Zhao, W., Ngoduy, D., Shepherd, S., Liu, R., Papageorgiou, M.: A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection. Transp. Res. Part C Emerg. Technol. 95, 802–821 (2018)

    Article  Google Scholar 

  85. Zhou, Y., Ahn, S., Chitturi, M., Noyce, D.A.: Rolling horizon stochastic optimal control strategy for ACC and CACC under uncertainty. Transp. Res. Part C Emerg. Technol. 83, 61–76 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science Department, University of Reading, Reading, UK

    Ekene F. Ozioko & Fredric Stahl

  2. Computer Science Department, University of Göttingen, Göttingen, Germany

    Julian Kunkel & Fredric Stahl

Authors
  1. Ekene F. Ozioko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julian Kunkel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fredric Stahl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ekene F. Ozioko .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ozioko, E.F., Kunkel, J., Stahl, F. (2022). Road Intersection Coordination Scheme for Mixed Traffic (Human Driven and Driver-Less Vehicles): A Systematic Review. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 508. Springer, Cham. https://doi.org/10.1007/978-3-031-10467-1_4

Download citation

Publish with us

Policies and ethics

Search

Navigation