Browsing by Subject "Markov chains"

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    Field Study of Driver Behavior at Permitted Left-Turn Indications
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2013-03) Davis, Gary A.; Mudgal, Abhisek
    A digital video camera was used to record left-turning vehicles and through vehicles at an urban intersection. A total of 39 left-turn events, with a total of 195 gap decisions, were identified and vehicle trajectories corresponding to those were extracted from the video and transformed into real coordinates using photogrammetry. Bayes estimates of each opposing vehicle’s distance, speed, and time-to-arrival were then computed from the trajectories and used as predictors in logit models of acceptance/rejection decisions. It was found, when models are penalized for the numbers of their parameters, that arrival time, the ratio of initial distance to initial speed, was best predictor. This contrasts with an earlier study that found distance clearly superior to arrival time. This may be due to the fact that in the current study, speeds and initial distances were substantially higher than in the earlier study.
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    Remaining Service Life Asset Measure, Phase 2
    (Minnesota Department of Transportation, 2022-02) Matias de Oliveira, Jhenyffer; Khani, Alireza; Davis, Gary; Marasteanu, Mihai
    The main objectives of phase 2 of this project were to obtain relevant data to calculate the percent remaining service life interval (PRSI) and two additional metrics and to perform Markov chain analysis and dynamic programming to determine how much time and funding is required to bring the system to a stable configuration, which allows for more consistent planning. First, relevant pavement management data was obtained from MnDOT and preliminary data analyses were performed. The prediction models and optimization process currently used by MnDOT were investigated and summarized. Next, two additional metrics, Asset Sustainability Ratio and Deferred Preservation Liability, were calculated for MnDOT’s network. Then details of the estimation process of state-to-state transition probabilities to be used in the Markov chain model were presented. To allow for site-specific variation, ordinal logistic regression models were incorporated in the Markov chain model. The results were used in a dynamic programming optimization methodology to obtain baseline and optimal policies for different scenarios and a user-friendly excel spreadsheet tool was developed. Finally, a summary of the work performed followed by conclusions and recommendations was presented.