Design, Analysis and Detection of Urban Mobility Patterns in Guayaquil through Supervised Learning Algorithms

Abstract

This work explores the use of GPS trajectory data (Git Hub) and supervised learning algorithms to analyze mobility patterns in the city of Guayaquil-Ecuador. The analysis reveals that identifying mobility patterns is crucial to improve urban planning and optimize public transport in terms of its innovations from 2024. The methodology has included data collection from various open access sources, data preprocessing and the use of a Random Forest Classifier to detect mobility patterns. The results indicate a model accuracy of 50%, opening the possibilities for future specialized research to optimize the model and collect more data to improve its performance and propose new StartUPS for the city. The data compiled supports the implementation of the Single Card for Public Transport (TUT), which modernizes and unifies the payment system, facilitating the collection of essential data for analysis. The research concludes that the integration of data from applications such as Waze and Moovit is crucial for efficient and sustainable urban planning and that supervised learning algorithms are effective tools for urban mobility analysis and leaving open the possibility of exploring additional data and other algorithms to improve the accuracy of models and the generation of new mobility routes.