理解骑行出行：博洛尼亚案例研究
Understanding Cycling Mobility: Bologna Case Study
AI总结：
本文基于意大利博洛尼亚六个月的32万余条自行车出行数据，分析骑行时空模式及其与天气、污染等因素的关系，并利用机器学习和深度学习方法预测未来30和60分钟的短时出行，最佳模型R平方达0.91。
摘要：
理解城市环境中的人类出行对于管理交通以及部署新的资源和服务至关重要。近年来，由于快速城市化和气候变化，这一问题变得更加严峻。在城市背景下，人类出行具有多个方面，而骑行是在旅游城市和历史城市中最环保且高效/有效的出行方式之一。本研究的主要目标是研究意大利博洛尼亚市内的骑行出行。我们使用了六个月的数据集，其中包含320,118条自报的自行车出行记录。具体而言，我们进行了多项描述性分析，以理解骑行用户的时空模式，从而了解热门道路和城市内最受欢迎的地点。该分析还结合了多个其他公共数据集，以探索可能影响骑行活动的变量，例如天气、污染和事件。本研究的主要结果表明，自行车使用与温度和降水更相关，而与风速和污染没有相关性。此外，我们还利用多种机器学习和深度学习方法预测近期（即未来30分钟和60分钟）的短时出行，这可以帮助地方政府机构进行城市规划。我们的最佳模型在30分钟时间间隔上取得了0.91的R平方、5.38的平均绝对误差和8.12的均方根误差。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
