Title: Estimating Historical Hourly Traffic Volumes via Machine Learning and Vehicle Probe Data: A Maryland Case Study,
Abstract: This paper focuses on the problem of estimating historical traffic volumes
between sparsely-located traffic sensors, which transportation agencies need to
accurately compute statewide performance measures. To this end, the paper
examines applications of vehicle probe data, automatic traffic recorder counts,
and neural network models to estimate hourly volumes in the Maryland highway
network, and proposes a novel approach that combines neural networks with an
existing profiling method. On average, the proposed approach yields 24% more
accurate estimates than volume profiles, which are currently used by
transportation agencies across the US to compute statewide performance
measures. The paper also quantifies the value of using vehicle probe data in
estimating hourly traffic volumes, which provides important managerial insights
to transportation agencies interested in acquiring this type of data. For
example, results show that volumes can be estimated with a mean absolute
percent error of about 21% at locations where average number of observed probes
is between 30 and 47 vehicles/hr, which provides a useful guideline for
assessing the value of probe vehicle data from different vendors.