PREDICTING THE MOVEMENT OF PEOPLE IN LISBON’S NIGHTLIFE - Leveraging Machine Learning and Terminal Roaming Data to Support Urban Planning

Aug 5, 2026

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This master’s thesis investigates the prediction of mobility patterns across Lisbon’s nightlife areas, emphasizing temporal dynamics. Historical data is used alongside machine learning regression models to forecast nightlife activity levels, with the Multi-Layer Perceptron Regression achieving the highest accuracy. The study shows temporal signals—especially previous-hour observations—are the strongest predictors, while year and month variables also contribute meaningfully. Results demonstrate how terminal roaming data and predictive modeling can inform urban planning decisions and improve nightlife service scheduling and overcrowding management.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages55

Reading Time2 h 19 min

AccessFree to download

Introduction

Motivation

Research gap

Research Questions and Objectives

Work Project Structure

Literature review

Smart Cities

Mobility Patterns

Mobile Phone as Data Source

Analysing and Predicting Mobility Patterns

Nightlife Mobility

Methodology

Empirical Study

Business Understanding

Data Understanding

Data Collection

Data Preparation

Data Reduction

Exploratory Data Analysis

Data Transformation

Modelling

Feature Selection

Models and Parameters

Master Degree Program in MGI Information Management PREDICTING THE MOVEMENT OF PEOPLE IN LISBON’S NIGHTLIFE Leveraging Machine Learning and Terminal Roaming Data to Support Urban Planning Maria Carolina Bentes Pimenta Paisana Santos Project Work presented as partial requirement for obtaining the Master Degree in Information Management NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa PREDICTING THE MOVEMENT OF PEOPLE IN LISBON’S NIGHTLIFE Leveraging Machine Learning and Terminal Roaming Data to Support Urban Planning by Maria Carolina Bentes Pimenta Paisana Santos Project Work presented as partial requirement for obtaining the Master’s degree in Information Management, with a specialization in Business Intelligence and Knowledge Management Supervisor: Miguel de Castro Neto, PhD, NOVA Information Management School Co‐supervisor: Bruno Jardim, PhD, NOVA Information Management School STATEMENT OF INTEGRITY I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School. Maria Carolina Santos Lisbon, 12th July 2024 ACKNOWLEDGEMENTS I want to express my gratitude to all those who have supported and guided me throughout this journey. First and foremost, I would like to thank my family. To my parents and my brother, your belief in me and your continuous support have been fundamental. To Luís and Margarida thank you for your patience and support. Your encouragement kept me focused and motivated, and your support has been essential to the completion of this thesis. ABSTRACT This master’s thesis investigates the prediction of mobility patterns within Lisbon’s nightlife areas, focusing on temporal elements. The primary objective is to predict these movements using historical data and machine learning models. Various regression models were employed and evaluated based on their accuracy in forecasting nightlife activities, with the Multi-Layer Perceptron Regression model emerging as the most accurate. Findings revealed that temporal features, particularly previous hour data, were the most significant predictors with year and month features also playing important roles. This research supports the potential of leveraging machine learning and terminal data in urban planning, providing insights that can enhance decision-making and improve the nightlife experience through strategic service scheduling and overcrowding management. Ultimately, the understanding of mobility patterns and key determinants of nightlife activity in Lisbon enhances the city's advance towards the development of a smarter and more sustainable urban environment. KEYWORDS Smart Cities; Lisbon; Urban Planning; Mobility Patterns; Nightlife Mobility; Modelling Sustainable Development Goals (SDG): TABLE OF CONTENTS 1. Introduction .................................................................................................................. 1 1.1. Motivation ............................................................................................................. 1 1.2. Research gap ......................................................................................................... 1 1.3. Research Questions and Objectives ......................................................................1 1.4. Work Project Structure..........................................................................................2 2. Literature review ......................................................................................

FAQs

What is the main goal of the thesis?

To predict mobility patterns in Lisbon’s nightlife areas, with a strong focus on temporal elements, using historical data and machine learning models.

Which modeling approach performed best?

The Multi-Layer Perceptron Regression model produced the most accurate forecasts compared with other regression models evaluated.

Which features were most influential for prediction?

Temporal features were most significant, particularly data from the previous hour; year and month features also played important roles.

Aug 5, 2026

Upload by Levi