Real-Time Traffic Prediction for Advanced Traffic Management Systems: Phase I
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Real-Time Traffic Prediction for Advanced Traffic Management Systems: Phase I
Published Date
1995-10
Publisher
Intelligent Transportation Systems Institute, University of Minnesota
Type
Report
Abstract
It has been recommended that Advanced Traffic Management Systems (ATMS) must work in real-time, must respond to
and predict changes in traffic conditions, and must included areawide detection surveillance. To support such ATMS,
this project developed a tractable, stochastic model of freeway traffic flow and travel demand which satisfies three
primary objectives. First, the model should generate real-time estimates of traffic state variables from loop detector data,
which can in turn be used as time-varying initial conditions for more comprehensive simulation models, such as
KRONOS or FREESIM. Second, the model should generate its own predictions of mainline and off-ramp traffic
volumes, as well as calculate the expected error associated with these predictions, thus supporting the use of both
deterministic and stochastic optimization for determining traffic management actions. Third, the model should be
capable of full on-line implementation, in that it should be capable of estimating required parameters from traffic
detector data.
The basic model was developed by combining ideas from the theory of Markov population processes with a new for the
relationship between traffic flow and density, producing a stochastic version of a simple-continuum model. Kalman
filtering was then applied to the basic model to develop algorithms for (1) estimating from loop detector counts the
traffic density in freeway sections broken down by destination off-ramp, (2) predicting main-line and off-ramp traffic
volumes from given on-ramp volumes and, (3) computing adaptive estimates of the freeway's origin destination matrix
from loop detector counts. Monte Carlo simulation tests were used to evaluate three different methods for off-line
estimation of model parameters, as well as to assess the accuracy of the density estimates and volume predictions. The
results indicated that the estimation and prediction model tends to be robust with respect to the parameter estimation
scheme, and that the model generates a reasonable characterization of estimation and prediction uncertainty. Limited
tests with field data tended to confirm the simulation results, and to emphasize the importance of real-time estimation of
freeway origin-destination matrices in generating accurate predictions.
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Intelligent Transportation Systems Institute
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Davis, Gary A.; Stephanedes, Yorgos J.; Kang, Jeong-Gyu. (1995). Real-Time Traffic Prediction for Advanced Traffic Management Systems: Phase I. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/155370.
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