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Automatic measurement of traffic variables for intelligent transportation systems applications

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  • Nam, Do H.
  • Drew, Donald R.

Abstract

This paper presents a methodology for automatic measurement of major traffic variables for Intelligent Transportation Systems applications (ITS) in real-time. It is an illustration of the integration of theory, measurement, and application. Such effort has been overlooked since the advent of ITS due to the number of different disciplines involved. The inductive methodology adopted in this study includes the development of a new dynamic traffic flow model which is based on the characteristics of the stochastic vehicle counting process and the principle of conservation of vehicles. The model estimates spatial traffic variables, such as link travel times, as a function of time directly from flow measurements. It satisfies the traffic dynamics through a new form of the equation of conservation of vehicles. Analysis results show that the estimates are in qualitative and quantitative agreement with empirical data aggregated at 2-min intervals. The advantages of this inductive model include real-time applicability, computational efficiency, and transportability over traditional deductive models such as continuum flow models. It appears to be promising in applying to real world problems.

Suggested Citation

  • Nam, Do H. & Drew, Donald R., 1999. "Automatic measurement of traffic variables for intelligent transportation systems applications," Transportation Research Part B: Methodological, Elsevier, vol. 33(6), pages 437-457, August.
    • Handle: RePEc:eee:transb:v:33:y:1999:i:6:p:437-457
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    1. Papageorgiou, Markos & Blosseville, Jean-Marc & Hadj-Salem, Habib, 1989. "Macroscopic modelling of traffic flow on the Boulevard Périphérique in Paris," Transportation Research Part B: Methodological, Elsevier, vol. 23(1), pages 29-47, February.
    2. Michalopoulos, Panos G. & Yi, Ping & Lyrintzis, Anastasios S., 1993. "Continuum modelling of traffic dynamics for congested freeways," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 315-332, August.
    3. Dailey, D. J., 1993. "Travel-time estimation using cross-correlation techniques," Transportation Research Part B: Methodological, Elsevier, vol. 27(2), pages 97-107, April.
    4. Ross, Paul, 1988. "Traffic dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 22(6), pages 421-435, December.
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