# Tourist Behaviour Analysis Based on Digital Pattern of Life--An Approach and Case Study
## 4 Tourist Behaviour Analysis Case Studies
### Classification  
This case study describes a model that is able to classify road trips made in a selected region based on traveler ratings. In this case study tourists evaluate their trips on a five-point scale, where 0--they did not like the trip at all, 5--they liked the trip completely. The result of the model can be used to improve the performance of recommendation services or travel itinerary services. To train the classifying model a sample of tourist trips were prepared with the selection of input parameters (Table 2), which take into account the data of the route itself, information about the tourist, as well as contextual information about the tourist region. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Feature** & \(Route_{1}\) & \(Route_{2}\) & \(Route_{3}\) \\ \hline distance (km) & 80 & 10 & 3 \\ duration (s) & 3010 & 685 & 400 \\ attractions count & 4 & 3 & 1 \\ average speed (km/h) & 95 & 60 & 27 \\ maximum speed (km/h) & 154 & 80 & 63 \\ average acceleration (m/s\({}^{2}\)) & 6 & 3 & 1 \\ maximum acceleration (m/s\({}^{2}\)) & 10 & 4 & 2.1 \\ driver distraction event count & 2 & 0 & 0 \\ driver drowsiness event count & 1 & 0 & 1 \\ driver eating event count & 0 & 1 & 0 \\ driver smoking event count & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset route example. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**\#** & **Input Feature** & **Unit Measurement** & **Description** \\ \hline
1 & distance & meters & Total amount of meters of a tourist trip \\
2 & duration & seconds & Tourist trip total duration \\
3 & attractions count & integer number & Total amount of visited by tourist attractions \\
4 & attractions rating & float number & Total rating of all visited attractions, based on experts ratings \\
5 & average speed & m/s & Average car speed during the tourist trip \\
6 & maximum speed & m/s & Maximum amount of car speed during the trip \\
7 & average acceleration & m/s\({}^{2}\) & Average car acceleration during the tourist trip \\
8 & maximum acceleration & m/s\({}^{2}\) & Maximum car acceleration during the trip \\
9 & road traffic & integer number & Traffic jams rating during the tourist trip \\
10 & weather & string & String representation of weather situation during the tourist trip \\
11...15 & critical events & number & Potentially dangerous events during the tourist trip \\
16...29 & attraction preferences & number & List of tourist attraction preferences \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of the classification neural network input layer features. The input parameters presented in Table 2 were broken down into several categories: route characteristics, driving style characteristics, contextual parameters and parameters related to critical events and tourist attractions preferences:  
1. The characteristics of the route are described by parameters 1, 2, 3 and 4 and contain basic information about the tourist route--distance, duration, total number of visited attractions and their overall rating;
2. The driving style of the car is determined by parameters 5-8 and reflects the behaviour of the car when driving along the route;
3. Contextual parameters describe the situation in the tourist region and are determined by 9-10 features. Traffic is estimated in the range \([0;10]\), where score 0 represents situation when o traffic jams were occurred during the route and score 10 describes complete route obstruction. The weather situation is described from a categorized bucket with the following entries: \"sunny\", \"cloudy\", \"windy\", \"rainy\", \"snowy\". The selected contextual parameters can directly affect the perception of a tourist trip, for example, in rainy weather, visiting open spaces will be difficult; in traffic jams, it can be difficult to visit the planned attraction on time, etc;
4. The section of critical events (\(11\ldots 15\)) describes the number of dangerous events that happened to the driver during a tourist trip that can negatively affect the tourist.