http://iet.metastore.ingenta.com
1887

Real-time traffic signal control for intersections based on dynamic O–D estimation and multi-objective optimisation: combined model and algorithm

Real-time traffic signal control for intersections based on dynamic O–D estimation and multi-objective optimisation: combined model and algorithm

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Detectors are challenged in providing stable and accurate information of the dynamic origin–destination (O–D) flows for real-time adaptive traffic signal timing operation. However, the dynamic O–D estimation technique is capable of providing a short-term turn-flow data for the signal-timing adjustment. Meanwhile, the real-time signal timing variations will affect the dynamic O–D flows in an actual network. Therefore, the dynamic O–D estimation and the real-time signal control closely interact with each. A combined model is proposed to dynamically calculate the signal adjustment answering the variation of the real-time O–D flows by minimising the accumulative queues in entering approaches. A case study was conducted to validate the proposed model and algorithms, and the result showed the traffic efficiency of the case study intersection was improved.

References

    1. 1)
      • 1. Nihan, N.L., Davis, G.A.: ‘Recursive estimation of origin-destination matrices from input/output counts’, Transp. Res. B Methodol., 1987, 21, (2), pp. 149163.
    2. 2)
      • 2. Nihan, N.L., Davis, G.A.: ‘Application of prediction-error minimization and maximum likelihood to estimate intersection O-D matrices from traffic counts’, Transp. Sci., 1989, 23, (2), pp. 7790.
    3. 3)
      • 3. Sherali, H.D., Arora, N., Hobeika, A.G.: ‘Parameter optimization methods for estimating dynamic origin-destination trip-tables’, Transp. Res. B Methodol., 1997, 31, (2), pp. 141157.
    4. 4)
      • 4. Li, B., Moor, B.D.: ‘Recursive estimation based on the equality-constrained optimization for intersection origin-destination matrices’, Transp. Res. B Methodol., 1999, 33, (3), pp. 203214.
    5. 5)
      • 5. Jiao, P.P., Lu, H.P., Liu, Y., et al: ‘A study of models and algorithms of dynamic OD matrix estimation for intersection’, Proc. East. Asia Soc. Transp. Stud., 2003, 4, pp. 885897.
    6. 6)
      • 6. Jiao, P.P.: ‘Study on estimation of dynamic origin-destination flows for intersections’, China Civil Eng. J., 2004, 37, (9), pp. 100103.
    7. 7)
      • 7. Li, R.M., Lu, H.P., Shi, Q.X.: ‘ANN-based prediction of turning rate of traffic flows at intersection’, China Civil Eng. J., 2007, 42, (6), pp. 743747.
    8. 8)
      • 8. Du, C.H., Huang, X.Y., Yang, Z.Y., et al: ‘Application of improved particle swarm algorithm in dynamic OD matrix estimation’, Comput. Eng. Appl., 2008, 44, (34), pp. 234238.
    9. 9)
      • 9. Tan, G.Z., Liu, L.D., Fan, W., et al: ‘Dynamic OD estimation using automatic vehicle location information’. 2011 6th IEEE Joint Int. Information Technology and Artificial Intelligence Conf. (ITAIC), Chongqing, China, August 2011, pp. 352355.
    10. 10)
      • 10. Frederix, R., Viti, F., Tampere, C.M.J.: ‘Dynamic origin–destination estimation in congested networks: theoretical findings and implications in practice’, Transportmetrica A Transp. Sci., 2013, 9, (6), pp. 494513.
    11. 11)
      • 11. Barcel, J., Montero, L., Bullejos, M., et al: ‘Dynamic OD matrix estimation exploiting bluetooth data in urban networks’. 14th Int. Conf. Automatic Control, Modelling & Simulation, and 11th Int. Conf. Microelectronics, Nanoelectronics, Optoelectronics, World Scientific and Engineering Academy and Society (WSEAS), Saint Malo, Mont Saint-Michel, France, April 2012, pp. 116121.
    12. 12)
      • 12. Lou, Y.Y., Yin, Y.F.: ‘A decomposition scheme for estimating dynamic origin-destination flows on actuation-controlled signalized arterials’, Transp. Res. C Emerg. Technol., 2010, 18, (5), pp. 643655.
    13. 13)
      • 13. Lu, Z.B, Rao, W.M., Wu, Y.J., et al: ‘A Kalman filter approach to dynamic OD flow estimation for urban road networks using multi-sensor data’, J. Adv. Transp., 2015, 49, pp. 210227.
    14. 14)
      • 14. Webster, F.V.: ‘Traffic signal settings’. Road Research Technical Paper No. 39, Her Majesty's Stationery Office, London, 1958, pp. 124.
    15. 15)
      • 15. Webster, F.V., Cobbe, B.M.: ‘Traffic signals’. Road Research Technical Paper No. 56, Her Majesty's Stationery Office, London, 1966, pp. 206207.
    16. 16)
      • 16. Akcelik, R.: ‘Traffic signals: capacity and timing analysis’ (Australian Road Research Board, Melbourne, 1981), pp. 1120.
    17. 17)
      • 17. Akcelik, R., Rouphail, N.M.: ‘Estimation of delays at traffic signals for variable demand conditions’, Transp. Res., 1993, 27B, (2), p. 109.
    18. 18)
      • 18. Akcelik, R., Rouphail, N.M.: ‘Overflow queues and delays with random and platooned arrivals at signalized intersections’, J. Adv. Transp., 1994, 28, (3), pp. 227251.
    19. 19)
      • 19. Transportation Research Board: ‘Highway capacity manual 2000’ (National Research Council, Washington, DC, 2000), pp. 11189.
    20. 20)
      • 20. Heydecker, B.: ‘Uncertainty and variability in traffic signal calculations’, Transp. Res. B Methodol., 1987, 21, (1), pp. 7985.
    21. 21)
      • 21. Ribeiro, P.C.M.: ‘Handling traffic fluctuation with fixed-time plans calculated by TRANSYT’, Traffic Eng. Control, 1994, 35, pp. 362366.
    22. 22)
      • 22. Chang, J., Wu, D.W.: ‘Multi-objective intersection signal control model’, J. Dalian Univ. Technol., 2000, 40, (6), pp. 653656.
    23. 23)
      • 23. Gao, Y.F., Xu, L.H., Hu, H., et al: ‘Multi-objective optimization method for fixed-time signal control at intersection’, China J. Highw. Transp., 2011, 24, (5), pp. 8288.
    24. 24)
      • 24. Li, R.M., Lu, H.P.: ‘Traffic signal control multi-object optimization based on genetic algorithm’, J. Changan Univ. (Natural Science Edition), 2009, 29, (3), pp. 8588.
    25. 25)
      • 25. Yin, Y.F.: ‘Robust optimal traffic signal timing’, Transp. Res. B. Methodol., 2008, 42, (10), pp. 911924.
    26. 26)
      • 26. Zhang, L.H., Yin, Y.F.: ‘Robust synchronization of actuated signals on arterials’, Transp. Res. Rec., 2009, 2080, pp. 111119.
    27. 27)
      • 27. Feng, Y., Head, K.L., Khoshmagham, S., et al: ‘A real-time adaptive signal control in a connected vehicle environment’, Transp. Res. C Emerg. Technol., 2015, 55, pp. 460473.
    28. 28)
      • 28. Abdoos, M., Mozayani, N., Bazzan, A.L.: ‘Traffic light control in non-stationary environments based on multi agent Q-learning’. 14th Int. IEEE Conf. Intelligent Transportation Systems (ITSC), Washington, DC, USA, October 2011, pp. 15801585.
    29. 29)
      • 29. Jin, J.C., Ma, X.L.: ‘Adaptive group-based signal control by reinforcement learning’. 18th Euro Working Group on Transportation (EWGT), Transportation Research Procedia, Delft, The Netherlands, July 2015, vol. 10, pp. 207216.
    30. 30)
      • 30. El-Tantawy, S., Abdulhai, B.: ‘Multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC)’. 15th Int. IEEE Conf. Intelligent Transportation Systems (ITSC), Anchorage, AK, USA, September 2012, pp. 11401150.
    31. 31)
      • 31. Yang, I., Jayakrishnan, R.: ‘Real-time network-wide traffic signal optimization considering long-term green ratios based on expected route flows’, Transp. Res. C Emerg. Technol., 2015, 60, pp. 241257.
    32. 32)
      • 32. Stevanovic, A., Stevanovic, J., Zhang, K., et al: ‘Optimizing traffic control to reduce fuel consumption and vehicular emissions: an integrated approach of VISSIM, CMEM, and VISGAOST’, Transp. Res. Rec. J. Transp. Res. Board, 2009, 2128, (2128), pp. 105113.
    33. 33)
      • 33. Park, B., Yun, I., Ahn, K.: ‘Stochastic optimization of sustainable traffic signal control’, Int. J. Sust. Transp., 2009, 3, (4), pp. 263284.
    34. 34)
      • 34. Ma, D., Nakamura, H.: ‘Cycle length optimization at isolated signalized intersections from the viewpoint of emission’. 7th Int. Conf. Traffic and Transportation Studies (ICTTS), Kunming, China, August 2010, vol. 1, pp. 275284.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2018.5308
Loading

Related content

content/journals/10.1049/iet-its.2018.5308
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address