@misc{10481/87215,
year = {2023},
month = {6},
url = {https://hdl.handle.net/10481/87215},
abstract = {Currently, due to climate change and the increase of tourists in all environments,
managers of organizations located in vulnerable places and politicians must apply
human resource management techniques, materials and environmental protection
policies, just as companies do in their routine management, complying with environmental laws and being proactive in the application of other non-mandatory techniques.
The first step to apply these policies is to know the current situation. In this sense,
this master’s thesis deals with the knowledge of the traffic that circulates in a vulnerable place, such as the villages of the Poqueira Valley, located at the foot of the
natural site of Sierra Nevada. Specifically, we have carried out a project in which we
have designed the deployment of traffic sensors, managed the implementation with
the sensor company and defined strategies for the extraction of knowledge from the
collected data. Subsequently, machine learning techniques are applied on the collected data to study traffic patterns.
We propose a pattern analysis pipeline that utilizes multiple data sources and simplifies the selection of clustering and normalization algorithms. In our research, we
highlight the importance of choosing the right normalization algorithm to scale the
input features of heterogeneous data efficiently. This is critical for understanding
and analyzing mobility patterns. To validate our approach, we use data from four
license plate recognition (LPR) cameras collected over nine months in a district of
the Alpujarra Granadina. We also incorporated additional databases with information on origin, income, and vacation data, giving us a dataset of more than 50,000
vehicles.
By applying our pipeline and analyzing this large dataset, we identified diverse traffic patterns between residents and visitors in a rural tourist area. The results of our
study provide valuable insights to data analysts on factors to consider when selecting suitable algorithms for analyzing heterogeneous datasets. Our findings are of
utmost importance for both managers of national parks, which are particularly vulnerable to climate change-related threats, and managers of organizations located in
these areas. Furthermore, they provide policymakers with a deeper understanding
of mobility patterns in environmentally sensitive areas. This, in turn, facilitates the
achievement of sustainability in these territories across three dimensions: economic,
social, and environmental.},
organization = {LifeWatch ERIC},
keywords = {IoT},
keywords = {sensors},
keywords = {clustering},
keywords = {smart villages},
keywords = {explainability},
title = {Exploring Vehicle Behavior in Smart Villages Environments: A Comprehensive Clustering Pipeline for Analysis and Insights},
doi = {10.30827/Digibug.87215},
author = {Bolaños Martinez, Daniel},
}