Real-Time Traffic Prediction for Advanced Traffic Management Systems: Phase I

Principal Investigator:

Gary Davis, Professor, Civil, Environmental and Geo-Engineering

Project Summary:

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 area-wide 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 model 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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