The world's first low-cost and open source bike sharing system. (new version in development, use working "breakthrough" release instead!)

Moyeu is the best way to enjoy Boston's bike sharing system on Android

Bike Now is the best way to enjoy Seattle's bike sharing system on Android

An end-to-end batch scoring machine learning system that produces hourly predictions of the number of arrivals and departures that will take place at various stations in Lyft's bike sharing system in Chicago.

Over the past decade, bicycle-sharing systems have been growing in number and popularity in cities across the world. Bicycle-sharing systems allow users to rent bicycles on a very short-term basis for a price. This allows people to borrow a bike from point A and return it at point B, though they can also return it to the same location if they'd like to just go for a ride. Regardless, each bike can serve several users per day. Thanks to the rise in information technologies, it is easy for a user of the system to access a dock within the system to unlock or return bicycles. These technologies also provide a wealth of data that can be used to explore how these bike-sharing systems are used. In this project, you will use data provided by Motivate, a bike share system provider for many major cities in the United States, to uncover bike share usage patterns. You will compare the system usage between three large cities: Chicago, New York City, and Washington, DC. Day:1 In this project, Students will make use of Python to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington. You will write code to import the data and answer interesting questions about it by computing descriptive statistics. They will also write a script that takes in raw input to create an interactive experience in the terminal to present these statistics. Technologies that will be covered are Numpy, Pandas, Matplotlib, Seaborn, Jupyter notebook. We will be giving the students a deep dive into the Data Analytical process Day:2 We will be giving the students an insight into one of the major fields of Machine Learning ie. Time Series forcasting we will be taking them through the relevant theory and make them understand of the importance and different techniques that are available to deal with it. After that we will be working hands on the bike share data set implementing different algorithms and understanding them to the core We aim to provide students an insight into what exactly is the job of a data analyst and get them familiarise to how does the entire data analysis process work. The session will be hosted by Shaurya Sinha a data analyst at Jio and Parag Mittal Software engineer at Microsoft.

Ford GoBike System Data Exploration and Findings Communication: This document explores a dataset with information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area in 2019

Bike sharing systems are a new generation of traditional bike rentals where the whole process from membership, rental and return back has become automatic. Through these systems, users are able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousand bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues. Apart from interesting real-world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. The goal here is to build an end-to-end regression task. Here the user will provide the data and the result will be given by the best performing hyper tuned Machine Learning model. The user will also get privileges to choose the deployment options.

A self-managed bike sharing system like alternative to socio-spatial inequalities.

Prediction of traffic patterns in bike sharing systems. Including dashboard for clustering analysis of stations in bike share networks based on traffic patterns.

Monitoring and data visualization dashboard for Zaragoza's Bizi bike-share system. 🚲📊

Prediction of demand in seoul bike sharing system

Predict bikes' availability for Barcelona public bicycle sharing system.

Route optimization for bike rebalancing within the BIXI bike-sharing system

A slim and low-network-use mobile web app for many common bike sharing systems.

Streaming ETL to join streams and visualize Bike Sharing System feed in Kibana Map at realtime

Interactive Flow-map of Aggregated Bike Movements between Bike Stations in Helsinki Region

Visualization made with processing in P5js showing hourly flow of bikes from one stations to another. Data comes from Buenos Aires City bike sharing system.

Find the closest bike, or the closest dock, in Philly's Indego bike sharing system. Idea: Ben Novack

Brief description of the GraphQL API used by Lisbon's Bike-Sharing System, GIRA

Web which agregates informations about bike sharing systems

🚴 A generic model and a distributed algorithm for optimising station-based bike-sharing systems

Flutter app for bike sharing system. Includes user management and map visualization

Graph convolutional neural network for forecasting traffic in the London bike-share system, where the graph convolutions pass spatial information between stations, and one-dimensional convolutions pass information from past traffic.

This repository explores a dataset about individual rides made in a bike-sharing system covering the greater San Francisco Bay area and created a slide deck to communicate my findings.

1> Background information Bluebikes is Metro Boston’s public bike share program, with more than 1800 bikes at over 200 stations across Boston and nearby areas. The bikes sharing program launched in 2011. The program aimed for individuals to use it for short-term basis for a price. It allows individuals to borrow a bike from a dock station after using it, which makes it ideal for one-way trips. The City of Boston is committed to providing bike share as a part of the public transportation system. However, to build a transport system that encourages bicycling, it is important to build knowledge about the current bicycle flows, and what factors are involved in the decision-making of potential bicyclists when choosing whether to use the bicycle. It is logical to make hypotheses that age and gender, bicycle infrastructure, safety perception are possible determinants of bicycling. On the short-term perspective, it has been shown that weather plays an important role whether to choose the bicycle. 2> Data collection The Bluebikes collects and provides system data to the public. The datasets used in the project can be download through this link (https://www.bluebikes.com/system-data). Based on this time series dataset (start from 2017-01-01 00:00:00 to 2019-03-31 23:00:00), we could have the information includes: Trip duration, start time and data, stop time and data, start station name and id, end station name and id, bike id, user type (casual or subscribed), birth year, gender. Besides, any trips that were below 60 seconds in length is considered as potentially false starts, which is already removed in the datasets. The number of bicycles used during a particular time period, varies over time based on several factors, including the current weather conditions, time of the day, time of the year and the current interest of the biker to use the bicycle as a transport mode. The current interest is different between subscribed users and casual users, so we should analyze them separately. Factors such as season, day of a week, month, hour, and if a holiday can be extracted from the date and time column in the datasets. Since we would analyze the hourly bicycle rental flow, we need hourly weather conditions data from 2017-01-01 00:00:00 to 2019-03-31 23:00:00 to complete our regression model of prediction. The weather data used in the project is scrapped using python selenium from Logan airport station (42.38 °N, 71.04 °W) webpage (https://www.wunderground.com/history/daily/us/ma/boston/KBOS/date/2019-7-15) maintained by weather underground website. The hourly weather observations include time, temperature, dew point, humidity, wind, wind speed, wind gust, pressure, precipitation, precipitation accumulated, condition. 3> The problem The aims of the project are to gain insight of the factors that could give short-term perspective of bicycle flows in Boston. It also aimed to investigate the how busy each station is, the division of bicycle trip direction and duration of the usage of a busy station and the mean flows variation within a day or during that period. The addition to the factors included in the regression model, there also exist other factors than influence how the bicycle flows vary over longer periods time. For example, general tendency to use the bicycle. Therefore, there is potential to improve the regression model accuracy by incorporating a long-term trend estimate taken over the time series of bicycle usage. Then the result from the machine learning algorithm-based regression model should be compared with the time series forecasting-based models. 4> Possible solutions Data preprocessing/Exploration and variable selection: date approximation manipulation, correlation analysis among variables, merging data, scrubbing for duplicate data, verifying errors, interpolation for missing values, handling outliers and skewness, binning low frequent levels, encoding categorical variables. Data visualization: split number of bike usage by subscribed/casual to build time series; build heatmap to present how busy is each station and locate the busiest station in the busiest period of a busy day; using boxplot and histogram to check outliers and determine appropriate data transformation, using weather condition text to build word cloud. Time series trend curve estimates: two possible way we considered are fitting polynomials of various degrees to the data points in the time series or by using time series decomposition functions and forecast functions to extract and forecast. We would emphasize on the importance to generate trend curve estimates that do not follow the seasonal variations: the seasonal variations should be captured explicitly by the input weather related variables in the regression model. Prediction/regression/time series forecasting: It is possible to build up multilayer perceptron neural network regressor to build up models and give prediction based on all variables of data, time and weather. However, considering the interpretability of model, we prefer to build regression models based on machine learning algorithms (like random forest or SVM) respectively for subscribed/casual users. Then the regressor would be combined with trend curve extracted and forecasted by ARIMA, and then comparing with the result of time series forecasting by STL (Seasonal and Trend decomposition using Loess) with multiple seasonal periods and the result of TBATS (Trigonometric Seasonal, Box-Cox Transformation, ARMA residuals, Trend and Seasonality).

ReactJS Bike Share System

Mobike Bike-Sharing System Dispatch Optimization Using Reinforcement Learning

This project report presents an analysis of the NYC Bike Share system using Tableau visualization. The NYC Bike Share system provides an alternative mode of transportation, allowing users to rent bicycles for short trips within the city. This report aims to provide a comprehensive overview of the system's current status and usage patterns.

This My SQL, and Tableau project analyzing Bike-sharing systems, it is an an automated evolution of traditional bike rentals. There are 500+ bike share programs worldwide with over 500K bikes, potentially addressing traffic, environmental, and health issues. This case study analyzes the bike-sharing rental dataset from Kaggle.

The project covers data analysis of Bike Share System for 3 major cities of USA - NYC, Chicago and Washington ; using Python programming language and it's appropriate modules.