# Deep Reinforcement Learning-Based Dynamic Pricing for Parking Solutions
## 3 Proposed Method
### Deep Reinforcement Learning-Based Dynamic Pricing (DRP-DP)
#### 3.2.4 Decision Maker  
In this study, the driver acts as the decision maker, which affects the overall rewards returned in each time step of the episodes. The driver module makes a decision on the parking area and whether to visit the area or to shift to other time steps due to dynamic pricing adjustments. Table 1 shows the notation used in the driver decision-making process.  
Every driver has his/her own preferred distance between the parking area and the destination \(D\)), parking lot price (\(P\)) as well as parking occupancy (\(O\)). The values for distance, price and occupancy preferences are normalised between 0 and 1. The distance and price preference pair uses preference value in common. For example, a price-preferred driver opts for a preference value of (0.7, 0.3) as they think highly of price. On the other hand, a distance-preferred driver prefers (0.3, 0.7), while a balanced-preferred driver uses (0.5, 0.5). Occupancy preference is a standalone option because it is common that drivers tend to avoid visiting a full parking area. The locations of player (\(L_{P}\)), opponent (\(O\)), driver (\(L_{D}\)) and driver's destination (\(L_{d}\)) are represented using latitude and longitude. \(D_{P}\) and \(D_{O}\) are the measured distances between the driver's destination and the location of the player and opponent, these two values will be used in the equation of the driver to make a decision. \(P_{P}\) and \(P_{O}\) are the calculated prices if the driver parks at the parking area of the player and opponent. Equation (2) shows the decision-making process of the driver.  
\[((D*D_{P})+(P*\frac{P_{P}}{S_{P}})+(O*O_{P}))<((D*D_{O})+(P*\frac{P_{O}}{S_{P} })+(O*O_{O})) \tag{2}\]  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Notation** & **Unit** & **Description** \\ \hline D & \([0,\dots,1]\) & Distance preference. \\ P & \([0,\dots,1]\) & Price preference. \\ O & \([0,\dots,1]\) & Occupancy preference. \\ L\({}_{P}\) & \([\text{lat},\text{lng}]\) & Location of the player. \\ L\({}_{O}\) & \([\text{lat},\text{lng}]\) & Location of the opponent. \\ L\({}_{D}\) & \([\text{lat},\text{lng}]\) & Location of the driver. \\ L\({}_{d}\) & \([\text{lat},\text{lng}]\) & Location of driver’s destination. \\ D\({}_{P},\text{DO}\) & \([\text{meter}]\) & Distance from driver’s destination to the location of player and opponent. \\ P\({}_{P},\text{PO}\) & \([\text{float}]\) & Price of player and opponent. \\ S\({}_{P}\) & \([\text{float}]\) & Sum of price of player and opponent. \\ O\({}_{P},\text{OO}\) & \([0,\dots,1]\) & Parking occupancy of player and opponent. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation used in driver decision making process.  
Figure 8: Sample of the vehicle flow after the SMA.  
Equation (2) is divided into two parts. The first part on the left represents the player preference score, while the second part represents the opponent. The preference score is the total from the distance score, price score and occupancy score, and each will be normalised to 0 and 1 and multiplied by its preference value ratio.