Forecasting bike-sharing demand in Seoul

: a comprehensive analysis

Abstract

Bike sharing programs represent the future of mobility, contributing to the creation of a ”green” economy and a more sustainable future. Providing the city with a stable and accurate supply of bicycles is a major concern for the city of Seoul. The objective of this research is to provide useful insights on how effectively forecast bike sharing demand through automated processes. Side goals are related with the difference between models’ performances as well as with drawing causal effects. Examining the public rented bicycles in the city, time series forecasting is imple mented through different methods, exploring both parametric and non parametric models such as Seasonal ARIMAs with exogenous variables, Multiple Linear Re gression and Support Vector Regression. This study takes into consideration mostly weather-related features, consistently with previous literature. Rides are intuitively influenced by features like temperature as well as by time effects that occur in certain periods of the year. After inspecting the different relationship between response variable and features, models were fit and tested. Consistently with regression errors measured on test set, SVR can be considered the best model for the aim of this research.

Date of Award	4 Jul 2024
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Pedro Afonso Fernandes (Supervisor)