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This paper focuses on the problem of estimating historical traffic volumesbetween sparsely-located traffic sensors, which transportation agencies need toaccurately compute statewide performance measures. To this end, the paperexamines applications of vehicle probe data, automatic traffic recorder counts,and neural network models to estimate hourly volumes in the Maryland highwaynetwork, and proposes a novel approach that combines neural networks with anexisting profiling method. On average, the proposed approach yields 24% moreaccurate estimates than volume profiles, which are currently used bytransportation agencies across the US to compute statewide performancemeasures. The paper also quantifies the value of using vehicle probe data inestimating hourly traffic volumes, which provides important managerial insightsto transportation agencies interested in acquiring this type of data. Forexample, results show that volumes can be estimated with a mean absolutepercent error of about 21% at locations where average number of observed probesis between 30 and 47 vehicles/hr, which provides a useful guideline forassessing the value of probe vehicle data from different vendors.

Estimating Historical Hourly Traffic Volumes via Machine Learning and Vehicle Probe Data: A Maryland Case Study