Incorporating intelligent speed adaptation systems into microscopic traffic models

Access Full Text

Incorporating intelligent speed adaptation systems into microscopic traffic models

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

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

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.

© The Institution of Engineering and Technology
Published

Intelligent speed adaptation (ISA) systems are incorporated here into microscopic traffic models; Gipps' car-following model is discussed and the appropriate model parameters that need to be modified and additional ones that may need to be introduced are investigated. Driver behaviour under three different functionalities of ISA, namely informative, warning and intervening, is investigated through a driver simulator experiment. The impact of ISA systems on driver behaviour is a complex matter because it varies both among drivers and under different scenarios. The main parameters that capture the ‘reaction’ to the system are identified and are quantified through model parameters. These are driver speed, acceleration, deceleration, reaction time and effective size of the vehicle, and are estimated following the analysis of the simulator data. The resulting values confirm the necessity of parameter modification. The analysis performed for the incorporation of ISA into the traffic model indicated that a prerequisite of successful implementation is a deep understanding of the model parameters and dynamics.

Inspec keywords: behavioural sciences; driver information systems

Other keywords: intelligent speed adaptation systems; Gipps car-following model; microscopic traffic models; driver behaviour

Subjects: Social and behavioural sciences computing; Traffic engineering computing

References

    1. 1)
      • J.A. Michon , L. Evans , R. Schwing . (1985) A critical view of driver behaviour models. What do we know, what should we do, Human behaviour and traffic safety.
    2. 2)
      • M.R. Tayyaran , A.M. Khan . The effects of telecommuting and intelligent transportation systems on urban development. J. Urban Technol. , 2 , 87 - 100
    3. 3)
      • K. Brookhuis , D. de Waard . Limiting speed, towards an intelligent speed adapter (ISA). Transp. Res. F , 2 , 81 - 90
    4. 4)
    5. 5)
      • R. Liu , J. Tate . (2004) Network effects of intelligent speed adaptation systems’, Transportation.
    6. 6)
      • H.W. Warner , L. Åberg . The long-term effects of an ISA speed-warning device on drivers’ speeding behaviour. Transp. Res. F , 2 , 96 - 107
    7. 7)
      • Leonard, D.R., Though, J.B., Bagueley, P.C.: `CONTRAM: a traffic assignment model for predicting flows and queues during peak periods', TRRL Laboratory Report, R 841, Crowthorne, Transport and Road Research Laboratory, 1978.
    8. 8)
    9. 9)
      • Duncan, B.: `Calibration trials of TRL driving simulator', Unpublished project Report PA30079/95, 1995, Transport Research Laboratory, Crowthorne.
    10. 10)
      • Mauro, V.: `Evaluation of dynamic network control: simulation results using NEMIS urban microsimulator', Proc. 70th Annual Meeting of the Transportation Research Board, 1991.
    11. 11)
      • PTV Vision, VISUM online: ‘The intelligent traffic platform’, http://www.english.ptv.de/download/traffic/software/Visum-Online_Einleger_2005_e.pdf, Accessed December 2007.
    12. 12)
      • Sexton, B.F.: `Validation trial for testing impairment of driving due to alcohol', Transport Research Laboratory Report 226, 1997, Transport Research Laboratory, Crowthorne.
    13. 13)
      • D. van Vliet . SATURN – a modern assignment model. Traffic Eng. Control , 578 - 581
    14. 14)
      • L.S. Comte . New systems: new behaviour?. Transp. Res. F , 2 , 95 - 111
    15. 15)
      • H. Alm , L. Nilsson . The effects of a mobile telephone task on driver behaviour in a car following situation. Accid. Anal. Prev. , 5 , 707 - 715
    16. 16)
      • Liu, R., van Vliet, D., Watling, D.P.: `DRACULA: dynamic route assignment combining user learning and microsimulAtion', Proc. PTRC European Transport Forum, September 1995, Coventry, p. 143–152.
    17. 17)
      • I. Spyropoulou , M. Penttinen , M. Karlaftis , T. Vaa , J. Golias . ITS solutions and accident risks: prospective and limitations. Transp. Rev. , 5 , 549 - 572
    18. 18)
      • A. Kemeny , F. Panerei . Evaluating perception in driving simulation experiments. Trends Cogn. Sci. , 1 , 31 - 37
    19. 19)
      • S.P. Washington , M.G. Karlaftis , F.L. Mannering . (2003) Statistical and econometric methods for transportation analysis.
    20. 20)
      • S. Jamson . Would those who need ISA, use it? Investigating the relationship between drivers’ speed choice and their use of a voluntary ISA system. Transp. Res. F , 3 , 195 - 206
    21. 21)
      • M.A. Regan , K.L. Young , T.J. Triggs . Impact on driving performance of intelligent speed adaptation, following distance warning and seatbelt reminder systems: key findings from the TAC SafeCar project. IEE Proc. Intell. Transp. Syst. , 1 , 51 - 62
    22. 22)
      • M. del Castillo , P. Pintado , F.G. Benitez . The reaction time of drivers and the stability of traffic flow. Transp. Res. B , 1 , 35 - 60
    23. 23)
      • P. Hidas . Modelling lane changing and merging in microscopic traffic simulation. Transp. Res. C. , 351 - 371
    24. 24)
      • S. Vlassenroot , S. Broekx , J. De Mol , I.L. Panis , T. Brijs , G. Wets . Driving with intelligent speed adaptation: final results of the Belgian ISA-trial. Transp. Res. A , 3 , 267 - 279
    25. 25)
      • Silcock, J.P.: `SIGSIM version 1.0 users guide', , Working Paper from the Centre for Transport Studies, UCL, 1993.
    26. 26)
      • R. Liu , R. Kitamura , M. Kuwahara . (2005) The DRACULA dynamic network microsimulation model, Simulation approaches in transportation analysis: recent advances and challenges.
    27. 27)
      • P. Hidas . A car following model for urban traffic simulation. Traffic Eng. Control , 5 , 300 - 309
    28. 28)
      • M.P. Reed , P.A. Green . Comparison of driving performance on-road and in a low-cost simulator using a concurrent telephone dialling task. Ergonomics , 8 , 1015 - 1037
    29. 29)
      • Law, M., Crosta, D.A.: `SIGSIM user guide part A: SIGSIM theory (version 1.1)', Working Paper from the Transport Operations Research Group, 1999, Newcastle University and Centre for Transport Studies, UCL.
    30. 30)
      • H.H. van der Molen , A.M.T. Botticher , J.A. Rothengatter , R.A. De Bruin . (1987) Risk models for traffic participants: a concerted effort for theorethical operationalizations, Road users and traffic safety.
    31. 31)
    32. 32)
      • Barcelo, J., Ferrer, J.L.: `Microscopic simulation of vehicle guidance systems with AIMSUN2', Proc. 13 Euro Conf., 1994, Glasgow.
    33. 33)
      • Gipps, P.G., Wilson, B.G.: `MULTISIM: a computer package for simulating multi-lane traffic flows', Proc. 4th Biennial Conf. Simulation Society of Australia, August 1980, Queensland.
    34. 34)
      • A. Varhelyi , T. Makinen . The effects of in-car speed limiters: field studies. Transp. Res. C , 3 , 191 - 211
    35. 35)
    36. 36)
      • I. Spyropoulou . Gipps car following model – an in-depth analysis. Transportmetrica , 3 , 231 - 245
    37. 37)
      • C. Ho , N. Reed , C. Spence . Assessing the effectiveness of ‘intuitive’ vibrotactile warning signals in preventing front-to-rear-end collisions in a driving simulator'. Accid. Anal. Prev. , 5 , 988 - 996
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its_20080034
Loading

Related content

content/journals/10.1049/iet-its_20080034
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading