Understanding Cycling Mobility: Bologna Case Study
Taron Davtian, Flavio Bertini, Rajesh Sharma
TL;DR
The study tackles understanding cycling mobility in Bologna and its drivers by analyzing the Bella Mossa 2017 dataset (six months, 320,118 trips) alongside supplementary weather, pollution, and events data. It employs descriptive analyses to reveal temporal and spatial patterns, hub locations, and the impact of external variables, and develops short-term forecasts using multiple models, with LSTM delivering the best performance for 30- and 60-minute horizons (e.g., $R^2 \approx 0.91$, $MAE \approx 5.38$, $RMSE \approx 8.12$ for 30-minute forecasts). Key findings indicate a strong temperature- and precipitation-driven dynamic, limited influence from wind and pollution, and a three-hub structure guiding trip flows; the predictive work demonstrates practical utility for urban planning and bike-sharing operations. The work provides actionable insights for infrastructure planning, traffic management, and demand-aware redistribution, and sets the stage for cross-city comparisons using similar multi-source datasets.
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.