Understanding Cycling Mobility: Bologna Case Study

• URL: http://arxiv.org/abs/2109.04243v1
• Date: Thu, 9 Sep 2021 13:11:35 GMT
• Title: Understanding Cycling Mobility: Bologna Case Study
• Authors: Taron Davtian, Flavio Bertini and Rajesh Sharma
• Abstract summary: 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. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Evaluating the effects of Data Sparsity on the Link-level Bicycling Volume Estimation: A Graph Convolutional Neural Network Approach [54.84957282120537]
  We present the first study to utilize a Graph Convolutional Network architecture to model link-level bicycling volumes. We estimate the Annual Average Daily Bicycle (AADB) counts across the City of Melbourne, Australia using Strava Metro bicycling count data. Our results show that the GCN model performs better than these traditional models in predicting AADB counts.
  arXiv  Detail & Related papers  (2024-10-11T04:53:18Z)
• Mining individual daily commuting patterns of dockless bike-sharing users: a two-layer framework integrating spatiotemporal flow clustering and rule-based decision trees [3.3988622291971247]
  This paper presents a framework to mine individual cyclists' daily home-work commuting patterns from dockless bike-sharing trip data with user IDs. The effectiveness and applicability of the framework is demonstrated by over 200 million bike-sharing trip records in Shenzhen. Lots of bike-sharing commuters live near urban villages and old communities with lower costs of living, especially in the central city.
  arXiv  Detail & Related papers  (2024-07-13T09:30:51Z)
• From Counting Stations to City-Wide Estimates: Data-Driven Bicycle Volume Extrapolation [0.0]
  Street-level bicycle volume information would aid cities in planning targeted infrastructure improvements to encourage cycling. The data currently available to cities and citizens often only comes from sparsely located counting stations. This paper extrapolates bicycle volume beyond these few locations to estimate bicycle volume for the entire city of Berlin.
  arXiv  Detail & Related papers  (2024-06-26T16:01:53Z)
• Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents [9.204800002382044]
  We find significant causal effects of urban form on trip emissions and inter-feature effects, which had been neglected in previous work. In more monocentric cities, we identify spatial corridors where subcenter-oriented development is more relevant than increased access to the main center. Our work demonstrates a novel application of machine learning that enables new research addressing the needs of causality, generalizability, and contextual specificity for scaling evidence-based urban climate solutions.
  arXiv  Detail & Related papers  (2023-08-31T09:57:52Z)
• Improving short-term bike sharing demand forecast through an irregular convolutional neural network [16.688608586485316]
  The study proposes an irregular convolutional Long-Short Term Memory model (IrConv+LSTM) to improve short-term bike sharing demand forecast. The proposed model is evaluated with a set of benchmark models in five study sites, which include one dockless bike sharing system in Singapore, and four station-based systems in Chicago, Washington, D.C., New York, and London. The model also achieves superior performance in areas with varying levels of bicycle usage and during peak periods.
  arXiv  Detail & Related papers  (2022-02-09T10:21:45Z)
• METEOR: A Massive Dense & Heterogeneous Behavior Dataset for Autonomous Driving [42.69638782267657]
  We present a new and complex traffic dataset, METEOR, which captures traffic patterns in unstructured scenarios in India. METEOR consists of more than 1000 one-minute video clips, over 2 million annotated frames with ego-vehicle trajectories, and more than 13 million bounding boxes for surrounding vehicles or traffic agents. We use our novel dataset to evaluate the performance of object detection and behavior prediction algorithms.
  arXiv  Detail & Related papers  (2021-09-16T01:01:55Z)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)
• Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
  We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
  arXiv  Detail & Related papers  (2021-06-22T15:40:21Z)
• Exploring the weather impact on bike sharing usage through a clustering analysis [7.541020519717183]
  This study aims to explore how and in what magnitude weather impacts bike usage in Washington D.C. Bike usage data and weather data are gathered for the city of Washington D.C. and are analyzed using k-means clustering algorithm.
  arXiv  Detail & Related papers  (2020-08-17T12:24:37Z)
• Directional Primitives for Uncertainty-Aware Motion Estimation in Urban Environments [46.080970595942645]
  We introduce the concept of directional primitives, which is a representation of prior information of road networks. Experiments conducted on highways, intersections, and roundabouts in the Carla simulator, as well as real-world urban driving datasets, indicate that primitives lead to better uncertainty-aware motion estimation.
  arXiv  Detail & Related papers  (2020-07-01T00:22:31Z)
• Measuring Spatial Subdivisions in Urban Mobility with Mobile Phone Data [58.720142291102135]
  By 2050 two thirds of the world population will reside in urban areas. This growth is faster and more complex than the ability of cities to measure and plan for their sustainability. To understand what makes a city inclusive for all, we define a methodology to identify and characterize spatial subdivisions.
  arXiv  Detail & Related papers  (2020-02-20T14:37:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.