# Transferring knowledge from monitored to unmonitored areas for forecasting parking spaces

## TL;DR

This paper explores transferring parking occupancy data from sensor-equipped areas to unmonitored regions in smart cities by leveraging geographic similarity, aiming to reduce sensor deployment costs.

## Contribution

It introduces a method to estimate parking occupancy in unmonitored areas using similarity measures from geographic data, extending sensor-based parking forecasts.

## Key findings

- Effective transfer of occupancy rates between areas
- Reduced need for extensive sensor deployment
- Potential for cost-effective parking management

## Abstract

Smart cities around the world have begun monitoring parking areas in order to estimate available parking spots and help drivers looking for parking. The current results are promising, indeed. However, existing approaches are limited by the high cost of sensors that need to be installed throughout the city in order to achieve an accurate estimation. This work investigates the extension of estimating parking information from areas equipped with sensors to areas where they are missing. To this end, the similarity between city neighborhoods is determined based on background data, i.e., from geographic information systems. Using the derived similarity values, we analyze the adaptation of occupancy rates from monitored- to unmonitored parking areas.