Browsing by Subject "Advanced traffic management systems"

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    Evaluation of the Effectiveness of ATM Messages Used During Incidents
    (Minnesota Department of Transportation, 2016-01) Rindels, Max; Zitzow, Stephen; Hourdos, John
    This project investigated the use of Intelligent Lane Control Signs based Active Traffic Management for Incident Management on a heavily traveled urban freeway. The subject of the research was the ILCS system on I-94 westbound in downtown Minneapolis. This location was selected because of the frequency of capacity reducing incidents occurring in this freeway segment. This research aimed to evaluate and quantify the effect the system has on drivers, specifically on inducing/directing a desirable lane selection behavior. The strength of various uses of the tool in managing traffic during incidents is explored instead of a general level of success in improving traffic. To achieve this goal, the centerpiece of this research was the comparison and modeling of the lane change rates under different strategies. This was a difficult task because all lane changes in the target freeway section had to be detected and geolocated. The research followed two main thrusts. The first was a detailed analysis of 28 incident events selected among approximately 481 events on record between 2012 and 2013. The second thrust was a statistical analysis testing a number of hypotheses prompted by questions proposed by the project Technical Advisory Panel. In general, it can be concluded that the use of ILCS for incident management has a significant effect on driver behavior and specifically in prompting proper lane selection under capacity reducing incidents.
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    Real-Time Traffic Prediction for Advanced Traffic Management Systems: Phase I
    (Intelligent Transportation Systems Institute, University of Minnesota, 1995-10) Davis, Gary A.; Stephanedes, Yorgos J.; Kang, Jeong-Gyu
    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.