Understanding Cycling Mobility: Bologna Case Study

TL;DR

This study analyzes cycling mobility in Bologna using six months of trip data, revealing key factors influencing bike usage and developing predictive models for short-term trip forecasting to aid urban planning.

Contribution

The paper provides a comprehensive analysis of cycling patterns in Bologna and introduces machine learning models for accurate short-term trip prediction.

Findings

Bike usage correlates with temperature and precipitation.

No significant correlation between bike trips and pollution or wind speed.

Predictive models achieved high accuracy with R^2 of 0.91 for 30-minute forecasts.

Abstract

Understanding human mobility in urban environments is of the utmost importance to manage traffic and for deploying new resources and services. In recent years, the problem is exacerbated due to rapid urbanization and climate changes. In an urban context, human mobility has many facets, and cycling represents one of the most eco-friendly and efficient/effective ways to move in touristic and historical cities. The main objective of this work is to study the cycling mobility within the city of Bologna, Italy. We used six months dataset that consists of 320,118 self-reported bike trips. In particular, we performed several descriptive analysis to understand spatial and temporal patterns of bike users for understanding popular roads, and most favorite points within the city. This analysis involved several other public datasets in order to explore variables that can possibly affect the cycling activity, such as weather, pollution, and events. The main results of this study indicate that bike usage is more correlated to temperature, and precipitation and has no correlation to wind speed and pollution. In addition, we also exploited various machine learning and deep learning approaches for predicting short-term trips in the near future (that is for the following 30, and 60 minutes), that could help local governmental agencies for urban planning. Our best model achieved an R square of 0.91, a Mean Absolute Error of 5.38 and a Root Mean Squared Error of 8.12 for the 30-minutes time interval.