Resumen de Design smart its services through innovative data analsys modeling
Nowadays, one of the most important problems in urban areas concerns traffic congestion. This, in turn, has an impact on the economy, nature, human health, city architecture, and many other facets of life. Part of the vehicular traffic in cities is caused by parking space availability. The drivers of private vehicles usually want to leave their vehicles as close as possible to their destination. However, the parking slots are limited and may not be enough to sustain the demand, especially when the destination pertains to an attractive area. Thus, individuals looking for a place to park their vehicles contribute to increasing traffic flow density on roads where the parking demand cannot be satisfied.
An Internet of Things (IoT) approach allows us to know the state of the parking system (availability of the parking slots) in real time through wireless networks of sensor devices. An intelligent treatment of this data could generate forecasted information that may be useful in improving management of on-street parking, thus having a notable effect on urban traffic. Smart parking systems first appeared in 2015, with IoT platforms in Santander, San Francisco and Melbourne. That is the year when those cities began to provide on-street real-time parking data in order to offer new services to their citizens. One of the most interesting services that these kinds of platforms can offer is parking availability forecasting, for which the first works in this field studied the temporal and spatial correlations of parking occupancy to support short-term forecasts (no more than 30 minutes). Those short-term forecasts are not useful at all to the end user of this service; thus, the necessary prediction intervals should be at the order of magnitude of hours.
In this context, this thesis focuses on using parking and other sources of data to characterize and model different parking systems. The methodology used employs novel techniques for providing real-time forecasts of parking availability based on data from sensors with certain inaccuracies due to their mechanical nature. The models are developed from four different methodologies: ARIMA, multilayer perceptron (MLP), long-short term memory (LSTM) and gated recurrent unit (GRU). The first has been the standard approach to forecasting in the ITS literature, while the latter ones have proven to be the best neural network (NN) architectures for solving a wide set of sequential data problems, such as those presented in this work. As far as we know, LSTM and GRU methods (recurrent neural network approaches) have been used recently with good results in traffic forecasting, but not for parking. In addition, we propose using exogenous data such as weather conditions and calendar effects, thereby converting the problem from univariate to multivariate. It is shown here how NN methods naturally handle the increased complexity in the problem. The reason for using exogenous variables is that they can offer relevant information that cannot be inferred from the sensor measurements.
The proposed methods have been intensively compared by creating parking models for parking sectors in five cities around the world. The results have been analysed in order to identify and provide exhaustive guidelines and insights into the inner mechanisms of parking systems while also ascertaining how the idiosyncrasies of each method are reflected in the model forecasts.
When comparing the results according to their disciplines of origin (ARIMA from statistics and NN methods from machine learning), neither of the proposed methodologies is clearly better than the other, as both can provide forecasts with low error but by different means. ARIMA has shown lower error rates in small-sized sectors where the more recent status of the parking system is more relevant; while the NN methods are more capable of providing forecasts for large-sized sectors where patterns are dependent on long time horizons.
