  Traffic volume information is critical for intelligent transportation
systems. It serves as a key input to transportation planning, roadway design,
and traffic signal control. However, the traffic volume data collected by
fixed-location sensors, such as loop detectors, often suffer from the missing
data problem and low coverage problem. The missing data problem could be caused
by hardware malfunction. The low coverage problem is due to the limited
coverage of fixed-location sensors in the transportation network, which
restrains our understanding of the traffic at the network level. To tackle
these problems, we propose a probabilistic model for traffic volume
reconstruction by fusing fixed-location sensor data and probe vehicle data. We
apply the probabilistic principal component analysis (PPCA) to capture the
correlations in traffic volume data. An innovative contribution of this work is
that we also integrate probe vehicle data into the framework, which allows the
model to solve both of the above-mentioned two problems. Using a real-world
traffic volume dataset, we show that the proposed method outperforms
state-of-the-art methods for the extensively studied missing data problem.
Moreover, for the low coverage problem, which cannot be handled by most
existing methods, the proposed model can also achieve high accuracy. The
experiments also show that even when the missing ratio reaches 80%, the
proposed method can still give an accurate estimate of the unknown traffic
volumes with only a 10% probe vehicle penetration rate. The results validate
the effectiveness and robustness of the proposed model and demonstrate its
potential for practical applications.
