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Iowa State University--Becoming the Best

Improving Estimations of Real-Time Traffic Speeds During Weather for Winter Performance Measurement

Researcher(s)

Principal investigator:

Co-principal investigator:

Project status

In progress

Start date: 11/15/13
End date: 12/31/16

Sponsor(s)/partner(s)

Sponsor(s):

Partner(s): possibly also use CWIMS branding on this one!

About the research

Abstract:

Winter highway maintenance is an annual multi-billion dollar operation aimed at improving the safety and mobility of the highway system. To help the winter highway maintenance agencies optimize the usage of resources, it is important to develop a performance measurement system that can evaluate how well maintenance activities have been performed. In Iowa Highway Research Board Project TR-491, researchers developed a performance measure based on average vehicle speed, which takes into account severity of the storm. The model uses 6 categorical variables to define a storm and compute the acceptable traffic speed drop. A previous Iowa Department of Transportation (DOT) agreement developed a sequential Bayesian dynamic model based on the model in TR-491, which is capable of predicting the acceptable drops during the storm, and allow uncertainty in input variables (sensor measurements) to propagate into uncertainty in speed reduction. One limitation of the sequential Bayesian model is that its uncertainty measure does not account for model uncertainty and the uncertainty in human-weather interaction. The model in TR-491 is based on survey of expert opinion, and its uncertainty is not considered in the original development and our follow up work. The uncertainty in human behavior under different weather conditions is also not considered due to lack of time. The Iowa DOT is interested in refining this sequential Bayesian model to produce more accurate real time prediction of traffic speed drops with better uncertainty measures so that it can be used to evaluate the performance of snow/ice removal efforts and the effectiveness of different snow removal methods. Ultimately, the DOT is interested in using this model to help managers reallocate resources to optimize objective functions such as minimizing the total costs or the speed drops. The DOT is interested in developing a dynamic model capable of predicting in real time acceptable drops in traffic speed at major highway during major weather events with realistic uncertainty measures. The primary usage of such model is to evaluate the performance of highway winter maintenance operations and optimize resource allocation. This proposed project would develop point level performance measurements based on an improved model, which can produce real time prediction of traffic speed drops with uncertainty measures. This model will be tested and improved based on traffic, weather, and maintenance activity data from several different states/regions.