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Graphical route information panel for the urban freeway network in Shanghai, China

Graphical route information panel for the urban freeway network in Shanghai, China

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The concept of using graphical route information panel (GRIP) to improve the dynamic traffic management efficiency is becoming increasingly popular recently. Colour-coded level-of-service (LOS) map and route travel time are important information displayed by GRIP. This study addresses the graphical information provision issue and the travel time prediction issue, based on research and development of Shanghai urban freeway GRIPs. It first presents function and location of Shanghai GRIP, philosophy of GRIP panel layout and basic principle of LOS display method. Then it focuses on segmentation of LOS map and proposes three segmentation approaches: the detector-based approach, the ramp-based approach and the hybrid approach. Opinions and lessons learned regarding advantages and disadvantages of different segmentation approaches are given. Next, it proposes a macroscopic traffic flow model-based travel time prediction approach for GRIP. The traffic flow model incorporated in the prediction approach is a validated high-order continuum model. The preliminary performance test of the approach is conducted using real-traffic data of a three-lane urban freeway section of Yan-An elevated road in Shanghai. The results reveal that the travel time prediction approach performs well both under free-flowing conditions and under congested conditions. Last, it gives concluding remarks.

References

    1. 1)
      • Hirokazu, M., Mitsuru, S.: `Graphic information provision system on metropolitan expressway', Proc. Seventh World Congress on Intelligent Transportation Systems, 2000, Turin, Italy.
    2. 2)
      • Schönfeld, G., Reischl, A., Tsavachidis, M.: `Dynamic driver information goes graphical – a new quality in urban traffic information', Proc. Seventh World Congress on Intelligent Transportation Systems, 2000, Turin, Italy.
    3. 3)
      • Kazuyoshi, S., Tsuyoshi, S.: `Provision of metropolitan expressways information by graphic before entering expressways', Proc. Eighth World Congress on Intelligent Transportation Systems, 2001, Sydney, Australia.
    4. 4)
      • Alkim, T., Schenk, B.: `Graphic route information', Proc. Eighth World Congress on Intelligent Transportation Systems, 2001, Sydney, Australia.
    5. 5)
      • Kloot, G.: `Melbourne's arterial travel time system', Proc. Sixth World Congress on Intelligent Transport Systems, 1999, Toronto, Canada.
    6. 6)
      • H.C. Gan , L.J. Sun , J.Y. Chen , W.P. Yuan . Advanced traveler information system for metropolitan expressways in Shanghai, China. Transp. Res. Rec. , 35 - 40
    7. 7)
      • Gan, H.C., Sun, L.J.: `Advanced traveler information system for metropolitan expressways in Shanghai, China', Proc. 2008 Int. Conf. on Chinese Logistics and Transportation Professionals, 2008, Chengdu, China, 3, p. 1810–1818.
    8. 8)
      • H.C. Gan . (2006) Study on key technologies for the urban expressway network traffic guidance system’. PhD dissertation.
    9. 9)
      • A. Richards , M. McDonald , G. Fisher , M. Brackstone . Investigation of driver comprehension of traffic information on graphical congestion display panels using a driving simulator. Eur. J. Transp. Infrastruct. Res. , 4 , 417 - 435
    10. 10)
      • Dicke-Ogenia, M., Brookhuis, K.: `Improved access to cities through travel information on full color information panels', European Transport Conf., 2008, p. 16.
    11. 11)
      • Gan, H.C., Ye, X., Fan, B.Q.: `Drivers’ en-route diversion response to graphical variable message sign in Shanghai, China', Proc. 10th Int. Conf. on Applications of Advanced Technologies in Transportation, 2008, Greece.
    12. 12)
      • H.C. Gan , L.J. Sun , J.Y. Chen . Study of traveler behavior under influence of advanced traveler information system. J. Tongji Univ. , 11 , 1484 - 1488
    13. 13)
      • European Communities. TROPIC. Traffic optimisation by the integration of information and control. Transport Research Fourth Framework Programme. EC Contract RO-96-SC.303/2 TROPICII Trila Phase. Final Report. Office for Official Publications of the European Communities, Luxemburg, 1999.
    14. 14)
      • M. Abdel-Aty , P. Abdalla . Modeling drivers diversion from normal routes under ATIS using generalized estimating equations and binomial probit link function. Transportation , 327 - 348
    15. 15)
      • R.C. Jou , S.H. Lam , M.C. Weng , C.C. Chen . Real time traffic information and ITS impacts on route switching behavior of expressway drivers. J. Adv. Transp. , 187 - 223
    16. 16)
    17. 17)
      • P. Bonsall . The influence of route guidance advice on route choice in urban networks. Transportation , 1 - 23
    18. 18)
      • M. Wardman , P.W. Bonsall , J.D. Shires . Driver response to variable message signs: a stated preference investigation. Transp. Res. C , 389 - 405
    19. 19)
      • S. Peeta , J.L. Ramos , R. Pasupathy . Content of variable message signs and on-line driver behavior. Transp. Res. Rec. , 102 - 108
    20. 20)
      • P. Bonsall , P. Firmin , M. Anderson , I. Palmer , P. Balmforth . Validating the results of a route choice simulator. Transp. Res. C , 371 - 387
    21. 21)
      • K. Chatterjee , N.B. Hounsell , P.E. Firmin , P.W. Bonsall . Driver response to variable message sign information in London. Transp. Res. C , 149 - 169
    22. 22)
      • Gan, H.C., Sun, L.J., Chen, J.Y., Du, Y.C.: `Application of human factors engineering into the ATMS design', Proc. 85th Annual Meeting of Transportation Research Board, 2006, Washington DC, USA.
    23. 23)
      • K.E. Wunderlich , D.E. Kaufman , R.L. Smith . Link travel time prediction for decentralized route guidance architectures. IEEE Trans. Intell. Transp. Syst. , 1 , 4 - 14
    24. 24)
      • S.I-Jy. Chien , C.M. Kuchipudi . Dynamic travel time prediction with real-time and historic data. J. Transp. Eng. , 608 - 615
    25. 25)
      • S. Chien , M. Chen . Dynamic freeway travel time prediction using probe vehicle data: link-based vs. path-based.
    26. 26)
      • M. D'Angelo , H. Al-Deek , M. Wang . Travel time prediction for freeway corridors. Transp. Res. Rec. , 184 - 191
    27. 27)
      • D. Park , L. Rilett , G. Han . Spectral basis neural networks for real-time travel time forecasting. J. Transp. Eng. , 6 , 515 - 523
    28. 28)
      • W.H. Lee , S.S. Tseng , S.H. Tsai . A knowledge based real-time travel time prediction system for urban network. Expert Syst. Appl. , 3 , 4239 - 4247
    29. 29)
      • J.W.C. van Lint , S.P. Hoogendoorn , H.J. van Zuylen . Accurate freeway travel time prediction with state-space neural networks under missing data. Transp. Res. Pt. C , 347 - 369
    30. 30)
      • X.Y. Zhang , J.A. Rice . Short-term travel time prediction. Transp. Res. Pt. C , 187 - 210
    31. 31)
      • C.P.IJ. van Hinsbergen , J.W.C. van Lint , H.J. van Zuylen . Bayesian committee of neural networks to predict travel times with confidence intervals. Transp. Res. Pt. C , 5 , 498 - 509
    32. 32)
      • D. Paterson , G. Rose . A recursive, cell processing model for predicting freeway travel times. Transp. Res. Pt. C , 4 , 432 - 453
    33. 33)
      • L. Sun , J. Yang , H. Mahmassani . Travel time estimation based on piecewise truncated quadratic speed trajectory. Transp. Res. Pt. A , 1 , 173 - 186
    34. 34)
      • J. Yeon , L. Elefteriadou , S. Lawphongpanich . Travel time estimation on a freeway using discrete time Markov chains. Transp. Res. Pt. B , 4 , 325 - 338
    35. 35)
      • H. Lee , N.K. Chowdhury , J. Chang . A new travel time prediction method for intelligent transportation systems. Lect. Notes Comput. Sci. , 473 - 483
    36. 36)
      • M. Papageorgiou , J.M. Blosseville . Modeling and real-time control of traffic flow on the southern part of Boulevard Peripherique in Paris. Part I: Modeling. Transp. Res. Pt. A , 5 , 345 - 360
    37. 37)
      • H.C. Gan , L.J. Sun , Y. Hao , J.Y. Chen . Modeling urban expressway traffic flow using high-order continuum model. J. Tongji Univ. , 5 , 602 - 606
    38. 38)
      • Cremer, M.: `On the calculation of individual travel times by macroscopic models', Proc. Sixth Int. VNIS Conf., 1995, Seattle, WA, p. 187–193.
    39. 39)
      • M. Cremer , M. Papageorgiou . Parameter identification for a traffic flow model. Automation , 837 - 843
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