## 5 Experimental Analysis
### Case Studies  
Three real-life case studies are considered for the experimental analysis, which differ in both the city considered (New York and Beijing) and the type of vehicle considered (bicycle and taxi). This choice allow the models to be assessed on usage patterns that are expected to be significantly distinct. Follows a brief description of the considered case studies:  
**BikeNYC.** In this first case study the behavior of bicycles in New York city is analyzed. The data has been collected by the _NYC Bike system_ in 2014, from 1 April to 30 September. Records from the last 10 days form the testing data set, while the rest is used for training. The length of each time period is of 1 h.  
**TaxiBJ.** In the second case study, a fleet of cabs and the city of Beijing are considered. Data have been collected in 4 different time periods: 1 July 2013-30 October 2013, 1 March 2014-30 June 2014, 1 March 2015-30 June 2015, 1 November 2015-15 April 2016. The last four weeks are test data and the others are used for training purposes. The length of each time period is set to 30 min.  
**TaxiNYC.** Finally, a data set containing data from a fleet of taxicabs in New York is considered. Data have been collected from 1 January 2009 to 31 December 2014. The last four weeks are test data and the others are used for training purposes. The length of each time period is set to one hour. This case study has been specifically created to perform a more thorough and sound experimental assessments than those presented in the literature.  
The city of New York has been tessellated into 16 \(\times\) 8 regions, while the city of Beijing has been divided into 32 \(\times\) 32 areas; the discrepancy in the number of regions considered is due to the large difference in extension between the two cities. The Beijing area (16,800 km\({}^{2}\)) is 22 times bigger than the New York area (781 km\({}^{2}\)).  
The Beijing taxi data set (TaxiBJ) and New York Bike data set (BikeNYC) are available via [4]; they are already structured to carry out the experiments reported in this work. As for the TaxiNYC dataset, available for experiments on GitHub ([[https://github.com/UNIMIBInside/Smart-Mobility-Prediction/tree/master/data/TaxiNYC](https://github.com/UNIMIBInside/Smart-Mobility-Prediction/tree/master/data/TaxiNYC)]([https://github.com/UNIMIBInside/Smart-Mobility-Prediction/tree/master/data/TaxiNYC](https://github.com/UNIMIBInside/Smart-Mobility-Prediction/tree/master/data/TaxiNYC)), accessed 5 October 2022.), it has been expressly built for this work by processing and structuring data available from the _NYC government website_ ([[https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page)]([https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page)), accessed 20 September 2021.).  
A Min-Max normalization has been applied to all data sets to convert traffic values based on the scale [\(-\)1, 1]. Note, however, that in the experiments a denormalization is applied to the expected values to be used in the evaluation.  
In the three experiments, public holidays, metadata (i.e., DayOfWeek, Weekday/Weekend) and weather have been considered as external factors. Specifically, the meteorological information reports the temperature, the wind speed, and the specific atmospheric situation (viz., sun, rain and snow).