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Smoothing Speed Variability in Age-Friendly Urban Traffic Management

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12742))

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Abstract

Traffic congestion has a negative impact on vehicular mobility, especially for senior drivers. Current approaches to urban traffic management focus on adaptive routing for the reduction of fuel consumption and travel time. Most of these approaches do not consider age-friendliness, in particular that speed variability is difficult for senior drivers. Frequent stop and go situations around congested areas are tiresome for senior drivers and make them prone to accidents. Moreover, senior drivers’ mobility is affected by factors such as travel time, surrounding vehicles’ speed, and hectic traffic. Age-friendly traffic management needs to consider speed variability in addition to drivers’ waiting time (which impacts fuel consumption and travel time). This paper introduces a multi-agent pheromone-based vehicle routing algorithm that smooths speed variability while also considering senior drivers during traffic light control. Simulation results demonstrate 17.6% improvement in speed variability as well as reducing travel time and fuel consumption by 11.6% and 19.8% respectively compared to the state of the art.

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Notes

  1. 1.

    Kelvin details - https://www.tchpc.tcd.ie/resources/clusters/kelvin.

References

  1. Peace, dignity and equality on a healthy planet. https://www.un.org/en/sections/issues-depth/ageing/. Accessed 19 Sept 2019

  2. Bailey, J.M., Golpayegani, F., Clarke, S.: CoMASig: a collaborative multi-agent signal control to support senior drivers. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 1239–1244 (2019)

    Google Scholar 

  3. Cao, Z., Guo, H., Zhang, J.: A multiagent-based approach for vehicle routing by considering both arriving on time and total travel time. ACM Trans. Intell. Syst. Technol. 9(3), 25:1–25:21 (2017)

    Google Scholar 

  4. Cao, Z., Jiang, S., Zhang, J., Guo, H.: A unified framework for vehicle rerouting and traffic light control to reduce traffic congestion. IEEE Trans. Intell. Transp. Syst. 18(7), 1958–1973 (2017)

    Article  Google Scholar 

  5. Doroudgar, S., Chuang, H.M., Perry, P.J., Thomas, K., Bohnert, K., Canedo, J.: Driving performance comparing older versus younger drivers. Traffic Inj. Prev. 18(1), 41–46 (2017)

    Article  Google Scholar 

  6. Ebnali, M., Ahmadnezhad, P., Shateri, A., Mazloumi, A., Heidari, M.E., Nazeri, A.R.: The effects of cognitively demanding dual-task driving condition on elderly people’s driving performance; real driving monitoring. Accid. Anal. Prev. 94, 198–206 (2016)

    Article  Google Scholar 

  7. Hamidi, H., Kamankesh, A.: An approach to intelligent traffic management system using a multi-agent system. Int. J. Intell. Transp. Syst. Res. 16(2), 112–124 (2017). https://doi.org/10.1007/s13177-017-0142-6

    Article  Google Scholar 

  8. He, Q., Head, K.L., Ding, J.: Multi-modal traffic signal control with priority, signal actuation and coordination. Transp. Res. Part C Emerg. Technol. 46, 65–82 (2014)

    Article  Google Scholar 

  9. Ho, M.C., Lim, J.M.Y., Soon, K.L., Chong, C.Y.: An improved pheromone-based vehicle rerouting system to reduce traffic congestion. Appl. Soft Comput. 84, 105702 (2019)

    Article  Google Scholar 

  10. Hultsch, D.F., MacDonald, S.W.S., Dixon, R.A.: Variability in reaction time performance of younger and older adults. J. Gerontol. Ser. B 57(2), 101–115 (2002)

    Article  Google Scholar 

  11. Jin, J., Ma, X.: Hierarchical multi-agent control of traffic lights based on collective learning. Eng. Appl. Artif. Intell. 68, 236–248 (2018). https://www.sciencedirect.com/science/article/pii/S0952197617302658

  12. Koppel, S., et al.: The driver behaviour questionnaire for older drivers: do errors, violations and lapses change over time? Accid. Anal. Prev. 113, 171–178 (2018)

    Article  Google Scholar 

  13. Krajzewicz, D., Hertkorn, G., Feld, C., Wagner, P.: Sumo (simulation of urban mobility); an open-source traffic simulation, pp. 183–187 (2002)

    Google Scholar 

  14. Ng, K., Lee, C., Zhang, S., Wu, K., Ho, W.: A multiple colonies artificial bee colony algorithm for a capacitated vehicle routing problem and re-routing strategies under time-dependent traffic congestion. Comput. Ind. Eng. 109, 151–168 (2017)

    Article  Google Scholar 

  15. Pan, J., Popa, I.S., Zeitouni, K., Borcea, C.: Proactive vehicular traffic rerouting for lower travel time. IEEE Trans. Veh. Technol. 62(8), 3551–3568 (2013)

    Article  Google Scholar 

  16. Raitanen, T., Törmäkangas, T., Mollenkopf, H., Marcellini, F.: Why do older drivers reduce driving? Findings from three European countries. Transport. Res. F Traffic Psychol. Behav. 6(2), 81–95 (2003)

    Article  Google Scholar 

  17. Richerzhagen, B., Stingl, D., Rückert, J., Steinmetz, R.: Simonstrator: simulation and prototyping platform for distributed mobile applications. In: Proceedings of the 8th International Conference on Simulation Tools and Techniques, SIMUTools 2015, pp. 99–108. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Brussels, BEL (2015)

    Google Scholar 

  18. Soon, K.L., Lim, J.M.Y., Parthiban, R., Ho, M.C.: Proactive eco-friendly pheromone-based green vehicle routing for multi-agent systems. Exp. Syst. Appl. 121, 324–337 (2019)

    Article  Google Scholar 

  19. Stipancic, J., Miranda-Moreno, L., Saunier, N., Labbe, A.: Surrogate safety and network screening: modelling crash frequency using GPS travel data and latent Gaussian spatial models. Accid. Anal. Prev. 120, 174–187 (2018)

    Article  Google Scholar 

  20. Stipancic, J., Miranda-Moreno, L., Saunier, N., Labbe, A.: Network screening for large urban road networks: using GPS data and surrogate measures to model crash frequency and severity. Accid. Anal. Prev. 125, 290–301 (2019)

    Article  Google Scholar 

  21. Sullivan, K.A., Smith, S.S., Horswill, M.S., Lurie-Beck, J.K.: Older adults’ safety perceptions of driving situations: towards a new driving self-regulation scale. Accid. Anal. Prev. 43(3), 1003–1009 (2011)

    Article  Google Scholar 

  22. Zhou, P., Braud, T., Alhilal, A., Hui, P., Kangasharju, J.: ERL: edge based reinforcement learning for optimized urban traffic light control. In: 2019 IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 849–854 (2019)

    Google Scholar 

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Authors and Affiliations

  1. University of Dublin Trinity College, Dublin, Ireland

    José Monreal Bailey, Hadi Tabatabaee Malazi & Siobhán Clarke

Authors
  1. José Monreal Bailey
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  2. Hadi Tabatabaee Malazi
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  3. Siobhán Clarke
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Corresponding author

Correspondence to José Monreal Bailey .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Bailey, J.M., Tabatabaee Malazi, H., Clarke, S. (2021). Smoothing Speed Variability in Age-Friendly Urban Traffic Management. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12742. Springer, Cham. https://doi.org/10.1007/978-3-030-77961-0_1

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  • DOI: https://doi.org/10.1007/978-3-030-77961-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77960-3

  • Online ISBN: 978-3-030-77961-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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