Network-Wide Traffic Volume Estimation Based on Probe Vehicle Data

Abstract

We propose a method for estimating traffic volumes on all roads of the network, which is relevant to various traffic use cases. By combining large-scale probe-vehicle data with stationary detector data, this method builds a model to estimate the probe-vehicle penetration rate at road level, which then allows for traffic volume estimation. Trained by penetration rates from only 60% of the stationary detectors in the Netherlands, our model is able to predict the probe-vehicle penetration rate with a mean absolute percentage error (MAPE) of 8.3%, which decreases to 6% on motorways. Increasing the proportion of detectors used for training did not significantly improve model performance. This suggests that our method can be used in countries with less-developed detector infrastructure as well, provided a minimum coverage of detectors is achieved for all regions and road classes of interest. Traffic volume estimation quality ( $\mathrm{MAP}{\mathrm{E}}_q$ ) is constrained by the quality of the penetration rate estimation ( $\mathrm{MAP}{\mathrm{E}}_p$ ) and the sampling error, denoted as $\mathrm{MAP}{\mathrm{E}}_n$ . Using the estimated penetration rates, traffic volumes on motorways can be estimated with a $\mathrm{MAP}{\mathrm{E}}_q$ of 25% within about 2 min of collecting probe-vehicle observations and 7% within a day. Thus, our traffic volume estimations can support real-time operations on motorways. For roads other than motorways, the maximum accuracy is limited to a $\mathrm{MAP}{\mathrm{E}}_q$ of about 12%, and significantly longer observation periods are needed. Still, most offline use cases can be covered (e.g., annual average daily traffic).