Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Enhancement of safety and comfort of cyclists at intersections

© The Institution of Engineering and Technology
Received 22/08/2017, Accepted 07/02/2018, Revised 28/01/2018, Published 08/02/2018

Cyclist safety is increasingly becoming a societal problem in Europe, as shown by road safety statistics. Frequent stops for red traffic lights at intersections are experienced by cyclists as a major inconvenience. This study introduces a green wave concept for cyclists, with focus on the traffic management and control aspects under cooperative intelligent transport systems applications. It especially addresses increasing stability of the adaptive control system, to overcome the drawbacks of both actuated and traditional adaptive control (which are too unpredictable for a green wave speed advice). In addition, solutions for avoiding increased delays for other traffic are investigated, as generally result from a classic green wave approach (with only fixed-time control) and traditional adaptive control. This study introduces an adaptive control algorithm for a real-time model-predictive controller and implements a plan-deviation cost function to address stabilisation. Simulation results show that the developed method increases stability of the adaptive control system, limits extra delays for other traffic and yields a high success rate for the green wave concept.

Inspec keywords: road traffic control; predictive control; intelligent transportation systems; cooperative systems; road safety; adaptive control; stability

Other keywords: control aspects; traffic management; real-time model-predictive controller; comfort enhancement; cooperative intelligent transport systems; road safety statistics; fixed-time control; cyclist safety; classic green wave approach; red traffic lights; plan-deviation cost function; stabilisation; safety enhancement; adaptive control system stability

Subjects: Road-traffic system control; Stability in control theory; Optimal control; Self-adjusting control systems

References

    1. 1)
      • 6. Hu, J., Shao, Y., Sun, M., et al: ‘Integrated optimal eco-driving on rolling terrain for hybrid electric vehicle with vehicle-infrastructure communication’, Transp. Res. C, Emerg. Technol., 2016, 68, pp. 228–244.
    2. 2)
      • 1. EROS.: ‘Traffic safety basic facts 2016 – cyclists’. EROS (European Road Safety Observatory), 2016.
    3. 3)
      • 12. Blokpoel, R.J., Lu, M: ‘Signal plan stabilization to enable eco-driving’. Proc. IEEE Intelligent Transportation Systems Conf., Yokohama, October 2017.
    4. 4)
      • 2. Fietsersbond.: ‘Discussions on C-ITS service of traffic light prioritisation for designated VRUs’, The C-MobILE workshop, Helmond, 27 June 2017.
    5. 5)
      • 15. M.A.V.E.N. Consortium: ‘MAVEN (managing automated vehicles enhances network) description of work’. MAVEN Consortium, Brussels, 2016 (restricted). Available at www.maven-its.eu: accessed January 2018.
    6. 6)
      • 9. Stebbins, S., Hickman, M., Kim, J., et al: ‘Characterising green light optimal speed advisory trajectories for platoon-based optimisation’. Proc. Transportation Research Board (TRB) Annual Meeting, Washington, DC, USA, 2017.
    7. 7)
      • 11. Blokpoel, R.J., Joueiai, M.: ‘Bicycle modelling in SUMO for accurate traffic simulation’. Proc. SUMO Conf., Berlin, Germany, 2016, pp. 1–10.
    8. 8)
      • 8. Blokpoel, R.J., Niebel, W.: ‘Advantage of cooperative traffic light control algorithms’. Proc. European Congress on Intelligent Transport Systems, Glasgow, UK, June 2016.
    9. 9)
      • 10. Stebbins, S., Kim, J., Hickman, M., et al: ‘Combining model predictive intersection control with green light optimal speed advisory in a connected vehicle environment’. Proc. Transportation Research Board (TRB) Annual Meeting, Washington, DC, USA, 2017.
    10. 10)
      • 7. Wang, M., Daamen, W., Hoogendoorn, S.P., et al: ‘Potential impacts of an optimization-based eco-acc system on traffic and environment’, IET Intell. Transp. Syst., 2014, 8, pp. 77–86.
    11. 11)
      • 14. Van Vliet, K., Turksma, S.: ‘ImFlow: policy-based adaptive urban traffic control first field experience’. Proc. European Congress on Intelligent Transport Systems, Dublin, Ireland, 2013.
    12. 12)
      • 3. European Commission.: ‘Platform for the deployment of cooperative intelligent transport systems in the EU (E03188) (C-ITS platform) final report’. DG MOVE – DG Mobility and Transport, Brussels, January 2016.
    13. 13)
      • 5. Jiang, H., Hu, J., An, S., et al: ‘Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment’, Transp. Res. C, Emerg. Technol., 2017, 79, pp. 290–307.
    14. 14)
      • 4. C-MobILE Consortium.: ‘C-MobILE (accelerating C-ITS Mobility Innovation and depLoyment in Europe) description of work’. C-MobILE Consortium, Brussels, 2017 (restricted). Available at http://c-mobile-project.eu/: accessed January 2018.
    15. 15)
      • 13. XCycle Consortium.: ‘XCycle (advanced measures to reduce cyclists’ fatalities and increase comfort in the interaction with motorised vehicles) description of work’. XCycle Consortium, Brussels, 2015 (restricted). Available at www.xcycle-h2020.eu/: accessed January 2018.
    16. 16)
      • 16. Lu, M., Blokpoel, R.J.: ‘A sophisticated intelligent urban road-transport network and cooperative systems infrastructure for highly automated vehicles’. Proc. World Congress on Intelligent Transport Systems, October, Montreal, 2016.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2017.0250
Loading

Related content

content/journals/10.1049/iet-its.2017.0250
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address