Improving Estimates of Real-Time Traffic Speeds During Weather for Winter Maintenance Performance Measurements, April 2017

(2017) Improving Estimates of Real-Time Traffic Speeds During Weather for Winter Maintenance Performance Measurements, April 2017. Transportation, Department of

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Abstract

This report describes two related projects, the second of which builds on the first. In Part I, a model was developed to relate weather variables to traffic flow changes at a local level. Weather station data and maintenance crew reports were used to develop an empirical adaptive stochastic model using a Bayesian formulation. Data from early time segments provide a prior quantification of the expected effects of weather variables on traffic speed over subsequent time segments. Data in the next time segment are then used to adjust these quantification's to reflect observed traffic speeds during that period. Thus, rather than explicitly determining numerous temporally dependent interactions, the main effects associated with key factors are allowed to undergo small shifts over time to fit the data. The model incorporates an auto-regressive error structure to reflect temporal dependencies in observations that occur at reasonably high frequencies. In Part II, INRIX and Wavetronix traffic data and limited weather information were used to develop models for detecting abnormal traffic patterns and predicting traffic speed and volume at any location on a network. Multivariate quantiles were estimated for the INRIX observations, and the INRIX data were compared with the estimated quantiles to identify abnormal traffic patterns in both space and time. An online interactive app was developed to visualize the results and inform decisions about winter maintenance. A dynamic Bayesian model was implemented at two Wavetronix sensor locations where weather information was available, with the corresponding median curve as the baseline. The fitting results were satisfactory. The INRIX data's spatial structure was explored, and curve Kriging was used to predict traffic speed and volume at any location. The prediction method worked well.