  Public road authorities and private mobility service providers need
information derived from the current and predicted traffic states to act upon
the daily urban system and its spatial and temporal dynamics. In this research,
a real-time parking area state (occupancy, in- and outflux) prediction model
(up to 60 minutes ahead) has been developed using publicly available historic
and real time data sources. Based on a case study in a real-life scenario in
the city of Arnhem, a Neural Network-based approach outperforms a Random
Forest-based one on all assessed performance measures, although the differences
are small. Both are outperforming a naive seasonal random walk model. Although
the performance degrades with increasing prediction horizon, the model shows a
performance gain of over 150% at a prediction horizon of 60 minutes compared
with the naive model. Furthermore, it is shown that predicting the in- and
outflux is a far more difficult task (i.e. performance gains of 30%) which
needs more training data, not based exclusively on occupancy rate. However, the
performance of predicting in- and outflux is less sensitive to the prediction
horizon. In addition, it is shown that real-time information of current
occupancy rate is the independent variable with the highest contribution to the
performance, although time, traffic flow and weather variables also deliver a
significant contribution. During real-time deployment, the model performs three
times better than the naive model on average. As a result, it can provide
valuable information for proactive traffic management as well as mobility
service providers.
