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Impact of Connected and Automated Vehicles on Passenger Comfort of Traffic Flow with Vehicle-to-vehicle Communications

  • Transportation Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

Extended transit time and increased consumer expectations arouse an interest in passenger comfort research. Few studies have been conducted on passenger comfort of Connected and Automated Vehicles (CAV) traffic flow, thereby leaving a research gap. This paper focuses on filling this research gap and evaluating CAV impact on passenger comfort from the traffic flow perspective. Specifically, optimal stability of traffic flow mixed with Manual Driven Vehicles (MDV) and CAV is desired to improve passenger comfort. For describing stability condition of the mixed traffic flow, in which multiple connected feedbacks of CAV exist with Vehicle-to-Vehicle (V2V) communications, local vehicular platoons with uniform structure are considered to be the optimization objective. Its stability charts with respect to equilibrium speeds and CAV feedback gains are calculated based on transfer function theory, thereby controlling CAV feedback gains for optimal stability. The CAV impact on the passenger comfort is evaluated under optimal control results of CAV feedback gains, by using numerical simulations under car-following models. It is indicated that stability optimization benefits passenger comfort of the mixed CAV traffic flow.

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References

  • Bando, M., Hasebe, K., Nakayama, A., Shibata, A., and Sugiyama, Y. (1995). “Dynamical model of traffic congestion and numerical simulation.” Physical Review E, Vol. 51, No. 2, pp. 1035–1042, DOI: 10.1103/PhysRevE.51.1035.

    Article  Google Scholar 

  • Bellem, H., Schönenberg, T., Krems, J. F., and Schrauf, M. (2016). “Objective metrics of comfort: developing a driving style for highly automated vehicles.” Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 41, pp. 45–54, DOI: 10.1016/j.trf.2016.05.005.

    Article  Google Scholar 

  • Brackstone, M. and McDonald, M. (1999). “Car-following: a historical review.” Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 2, No. 4, pp. 181–196, DOI: 10.1016/S1369-8478(00)00005-X.

    Article  Google Scholar 

  • Chen, D., Laval, J., Zheng, Z., and Ahn, S. (2012). “A behavioral carfollowing model that captures traffic oscillations.” Transportation Research Part B: Methodological, Vol. 46, No. 6, pp. 744–761, DOI: 10.1016/j.trb.2012.01.009.

    Article  Google Scholar 

  • Dang, R., Wang, J., Li, S. E., and Li, K. (2015). “Coordinated adaptive cruise control system with lane-change assistance.” IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 5, pp. 2373–2383, DOI: 10.1109/TITS.2015.2389527.

    Article  Google Scholar 

  • Elbanhawi, M., Simic, M., and Jazar, R. (2015). “In the passenger seat: investigating ride comfort measures in autonomous cars.” IEEE Intelligent Transportation Systems Magazine, Vol. 7, No. 3, pp. 4–17, DOI: 10.1109/MITS.2015.2405571.

    Article  Google Scholar 

  • Fleiter, J. J., Lennon, A., and Watson, B. (2010). “How do other people influence your driving speed? Exploring the ‘who’and the ‘how’of social influences on speeding from a qualitative perspective.” Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 13, No. 1, pp. 49–62, DOI: 10.1016/j.trf.2009.10.002.

    Article  Google Scholar 

  • Ge, J. I. and Orosz, G. (2014). “Dynamics of connected vehicle systems with delayed acceleration feedback.” Transportation Research Part C: Emerging Technologies, Vol. 46, pp. 46–64, DOI: 10.1016/j.trc.2014.04.014.

    Article  Google Scholar 

  • Glaser, S., Vanholme, B., Mammar, S., Gruyer, D., and Nouveliere, L. (2010). “Maneuver-based trajectory planning for highly autonomous vehicles on real road with traffic and driver interaction.” IEEE Transactions on Intelligent Transportation Systems, Vol. 11, No. 3, pp. 589–606, DOI: 10.1109/TITS.2010.2046037.

    Article  Google Scholar 

  • Hoogendoorn, R., van Arem, B., and Hoogendoorn, S. (2014). “Automated driving, traffic flow efficiency, and human factors: Literature review.” Transportation Research Record: Journal of the Transportation Research Board, Vol. 2422, pp. 113–120, DOI: 10.3141/2422-13.

    Article  Google Scholar 

  • Jayachandran, R. and Krishnapillai, S. (2013). “Modeling and optimization of passive and semi-active suspension systems for passenger cars to improve ride comfort and isolate engine vibration.” Journal of Vibration and Control, Vol. 19, No. 10, pp. 1471–1479, DOI: 10.1177/1077546312445199.

    Article  Google Scholar 

  • Jia, D. and Ngoduy, D. (2016). “Enhanced cooperative car-following traffic model with the combination of V2V and V2I communication.” Transportation Research Part B: Methodological, Vol. 90, pp. 172–191, DOI: 10.1016/j.trb.2016.03.008.

    Article  Google Scholar 

  • Jiang, R., Wu, Q., and Zhu, Z. (2001). “Full velocity difference model for a car-following theory.” Physical Review E, Vol. 64, No. 1, pp. 017101, DOI: 10.1103/PhysRevE.64.017101.

    Article  Google Scholar 

  • Kesting, A. and Treiber, M. (2013). Traffic flow dynamics: Data, models and simulation, Springer-Verlag, New York, USA.

    MATH  Google Scholar 

  • Kesting, A., Treiber, M., and Helbing, D. (2007). “General lane-changing model MOBIL for car-following models.” Transportation Research Record, Vol. 1999, No. 1, pp. 86–94, DOI: 10.3141/1999-10.

    Article  Google Scholar 

  • Kesting, A., Treiber, M., and Helbing, D. (2010). “Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity.” Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, Vol. 368, No. 1928, pp. 4585–4605, DOI: 10.1098/rsta.2010.0084.

    Article  MATH  Google Scholar 

  • Lefèvre, S., Carvalho, A., and Borrelli, F. (2016). “A learning-based framework for velocity control in autonomous driving.” IEEE Transactions on Automation Science and Engineering, Vol. 13, No. 1, pp. 32–42, DOI: 10.1109/TASE.2015.2498192.

    Article  Google Scholar 

  • Li, K. and P. Ioannou. (2004). “Modeling of traffic flow of automated vehicles.” IEEE Transactions on Intelligent Transportation Systems, Vol. 5, No. 2, pp. 99–113, DOI: 10.1109/TITS.2004.828170.

    Article  MathSciNet  Google Scholar 

  • Li, S., Li, K., Rajamani, R., and Wang, J. (2011). “Model predictive multi-objective vehicular adaptive cruise control.” IEEE Transactions on Control Systems Technology, Vol. 19, No. 3, pp. 556–566, DOI: 10.1109/TCST.2010.2049203.

    Article  Google Scholar 

  • Li, Y., Zhang, L., Peeta, S., He, X., Zheng, T., and Li, Y. (2016). “A carfollowing model considering the effect of electronic throttle opening angle under connected environment.” Nonlinear Dynamics, Vol. 85, No, 4, pp. 2115–2125, DOI: 10.1007/s11071-016-2817-y.

    Article  Google Scholar 

  • Lin, C. F., Juang, J. C., and Li, K. R. (2014). “Active collision avoidance system for steering control of autonomous vehicles.” IET Intelligent Transport Systems, Vol. 8, No. 6, pp. 550–557, DOI: 10.1049/ietits.2013.0056.

    Article  Google Scholar 

  • Luo, Y., Chen, T., Zhang, S., and Li, K. (2015). “Intelligent hybrid electric vehicle ACC with coordinated control of tracking ability, fuel economy, and ride comfort.” IEEE Transactions on Intelligent Transportation Systems, Vol. 16, No. 4, pp. 2303–2308, DOI: 10.1109/TITS.2014.2387356.

    Article  Google Scholar 

  • Mahmassani, H. S. (2016). “50th anniversary invited article—autonomous vehicles and connected vehicle systems: Flow and operations considerations.” Transportation Science, Vol. 50, No. 4, pp. 1140–1162, DOI: 10.1287/trsc.2016.0712.

    Article  Google Scholar 

  • Meng, Q. and Weng, J. (2011). “Evaluation of rear-end crash risk at work zone using work zone traffic data.” Accident Analysis & Prevention, Vol. 43, No. 4, pp. 1291–1300, DOI: 10.1016/j.aap.2011.01.011.

    Article  Google Scholar 

  • Milakis, D., Van Arem, B., and Van Wee, B. (2017). “Policy and society related implications of automated driving: A review of literature and directions for future research.” Journal of Intelligent Transportation Systems, Vol. 21, No, 4, pp. 324–348, DOI: 10.1080/15472450.2017.1291351.

    Article  Google Scholar 

  • Milanés, V. and Shladover, S. E. (2014). “Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data.” Transportation Research Part C: Emerging Technologies, Vol. 48, pp. 285–300, DOI: 10.1016/j.trc.2014.09.001.

    Article  Google Scholar 

  • Moon, S., Moon, I., and Yi, K. (2009). “Design, tuning, and evaluation of a full-range adaptive cruise control system with collision avoidance.” Control Engineering Practice, Vol. 17, No. 4, pp. 442–455, DOI: 10.1016/j.conengprac.2008.09.006.

    Article  Google Scholar 

  • Newell, G. F. (1961). “Nonlinear effects in the dynamics of car following.” Operations Research, Vol. 9, No. 2, pp. 209–229, DOI: 10.1287/opre.9.2.209.

    Article  MathSciNet  MATH  Google Scholar 

  • Newell, G. F. (2002). “A simplified car-following theory: a lower order model.” Transportation Research Part B: Methodological, Vol. 36, No. 3, pp. 195–205, DOI: 10.1016/S0191-2615(00)00044-8.

    Article  Google Scholar 

  • Ngoduy, D. (2013). “Instability of cooperative adaptive cruise control traffic flow: A macroscopic approach.” Communications in Nonlinear Science and Numerical Simulation, Vol. 18, No. 10, pp. 2838–2851, DOI: 10.1016/j.cnsns.2013.02.007.

    Article  MathSciNet  MATH  Google Scholar 

  • Ni, D., Leonard, J. D., Jia, C., and Wang, J. (2015). “Vehicle longitudinal control and traffic stream modeling.” Transportation Science, Vol. 50, No. 3, pp. 1016–1031, DOI: 10.1287/trsc.2015.0614.

    Article  Google Scholar 

  • Paddan, G. S. and Griffin, M. J. (2002). “Evaluation of whole-body vibration in vehicles.” Journal of Sound and Vibration, Vol. 253, No. 1, pp. 195–213, DOI: 10.1006/jsvi.2001.4256.

    Article  Google Scholar 

  • Qin, Y. Y. and Wang H. (2018). “Analytical framework of string stability of connected and autonomous platoons with electronic throttle angle feedback.” Transportmetrica A: Transport Science, pp. 1–23, DOI: 10.1080/23249935.2018.1518964.

    Google Scholar 

  • Raimondi, F. M. and Melluso, M. (2008). “Fuzzy motion control strategy for cooperation of multiple automated vehicles with passengers comfort.” Automatica, Vol. 44, No. 11, pp. 2804–2816, DOI: 10.1016/j.automatica.2008.04.012.

    Article  MathSciNet  MATH  Google Scholar 

  • Sau, J., Monteil, J., Billot, R., and El Faouzi, N. E. (2014). “The root locus method: Application to linear stability analysis and design of cooperative car-following models.” Transportmetrica B: Transport Dynamics, Vol. 2, No. 1, pp. 60–82, DOI: 10.1080/21680566.2014.893416.

    Google Scholar 

  • Shladover, S. E. (2018). “Connected and automated vehicle systems: introduction and overview.” Journal of Intelligent Transportation Systems, Vol. 22, No. 3, pp. 190–200, DOI: 10.1080/15472450.2017.1336053.

    Article  Google Scholar 

  • Shladover, S., Su, D., and Lu, X. Y. (2012). “Impacts of cooperative adaptive cruise control on freeway traffic flow.” Transportation Research Record: Journal of the Transportation Research Board, Vol. 2324, pp. 63–70, DOI: 10.3141/2324-08.

    Article  Google Scholar 

  • Sun, J., Zheng, Z., and Sun, J. (2018). “Stability analysis methods and their applicability to car-following models in conventional and connected environments.” Transportation Research Part B: Methodological, Vol. 109, pp. 212–237, DOI: 10.1016/j.trb.2018.01.013.

    Article  Google Scholar 

  • Talebpour, A. and Mahmassani, H. S. (2016). “Influence of connected and autonomous vehicles on traffic flow stability and throughput.” Transportation Research Part C: Emerging Technologies, Vol. 71, pp. 143–163, DOI: 10.1016/j.trc.2016.07.007.

    Article  Google Scholar 

  • Tang, T. Q., Shi, W., Shang, H., and Wang, Y. (2014). “A new carfollowing model with consideration of inter-vehicle communication.” Nonlinear Dynamics, Vol. 76, No. 4, pp. 2017–2023, DOI: 10.1007/s11071-014-1265-9.

    Article  Google Scholar 

  • Tang, T. Q., Yi, Z. Y., Zhang, J., and Zheng, N. (2017). “Modelling the driving behaviour at a signalised intersection with the information of remaining green time.” IET Intelligent Transport Systems, Vol. 11, No. 9, pp. 596–603, DOI: 10.1049/iet-its.2017.0191.

    Article  Google Scholar 

  • Treiber, M., Hennecke, A., and Helbing, D. (2000). “Congested traffic states in empirical observations and microscopic simulations.” Physical Review E, Vol. 62, No. 2, pp. 1805–1824, DOI: 10.1103/PhysRevE.62.1805.

    Article  MATH  Google Scholar 

  • Vogel, K. (2003). “A comparison of headway and time to collision as safety indicators.” Accident Analysis & Prevention, Vol. 35, No. 3, pp. 427–433, DOI: 10.1016/S0001-4575(02)00022-2.

    Article  MathSciNet  Google Scholar 

  • Wang, Z., Chen, X. M., Ouyang, Y., and Li, M. (2015). “Emission mitigation via longitudinal control of intelligent vehicles in a congested platoon.” Computer-Aided Civil and Infrastructure Engineering, Vol. 30, No. 6, pp. 490–506, DOI: 10.1111/mice.12130.

    Article  Google Scholar 

  • Wang, H., Wang, W., Chen, J., Xu, C. C., and Li, Y. (2018). “Can we trust the speed-spacing relationship estimated by car-following model from non-stationary trajectory data?” Transportmetrica A: Transport Science, pp. 1–23, DOI: 10.1080/23249935.2018.1466211.

    Google Scholar 

  • Ward, J. A. (2009). Heterogeneity, lane-changing and instability in traffic: A mathematical approach, PhD Thesis, University Bristol, Bristol, UK.

    Google Scholar 

  • Weng, J., Xue, S., Yang, Y., Yan, X., and Qu, X. (2015). “In-depth analysis of drivers’ merging behavior and rear-end crash risks in work zone merging areas.” Accident Analysis & Prevention, Vol. 77, pp. 51–61, DOI: 10.1016/j.aap.2015.02.002.

    Article  Google Scholar 

  • Wu, Z., Liu, Y., and Pan, G. (2009). “A smart car control model for brake comfort based on car following.” IEEE Transactions on Intelligent Transportation Systems, Vol. 10, No. 1, pp. 42–46, DOI: 10.1109/TITS.2008.2006777.

    Article  Google Scholar 

  • Yang, C. D., Ozbay, K., and Ban, X. (2017). “Developments in connected and automated vehicles.” Journal of Intelligent Transportation Systems, Vol. 21, No. 4, pp. 251–254, DOI: 10.1080/15472450.2017.1337974.

    Article  Google Scholar 

  • Zhang, H. M. and Kim, T. (2005). “A car-following theory for multiphase vehicular traffic flow.” Transportation Research Part B: Methodological, Vol. 39, No. 5, pp. 385–399, DOI: 10.1016/j.trb.2004.06.005.

    Article  Google Scholar 

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Qin, Y., Wang, H. & Ran, B. Impact of Connected and Automated Vehicles on Passenger Comfort of Traffic Flow with Vehicle-to-vehicle Communications. KSCE J Civ Eng 23, 821–832 (2019). https://doi.org/10.1007/s12205-018-1990-6

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  • DOI: https://doi.org/10.1007/s12205-018-1990-6

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