Documentation of Bike Sharing APIs 🚴🛴🛵

Documentation for the General Bikeshare Feed Specification, a standardized data feed for shared mobility system availability. Maintained by MobilityData

SwiftUI, Jetpack Compose, Compose for Desktop and Compose for Web based Kotlin Multiplatform project (using CityBikes API http://api.citybik.es/v2/). Uses Room for local persistence

A data specification to enable right-of-way regulation, digital policy, geofencing, and two-way communication between mobility companies and public agencies worldwide for any regulated, shared vehicle.

bike sharing + python = pybikes

A demo of share bike using DDD, MicroService and Spring Cloud

🚲 一个基于19版青桔单车wxapp 的改良 Demo

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

基于深度学习的共享单车预测与调度解决方案，使用神经网络构建单车需求量与时间段和地理画像的关联，预测不同区域单车需求量；使用蚁群算法规划最优单车调度路径。

ReserveLogic is an online booking platform / software for Real estate marketplace (Realty marketplace), Rental booking, Room sharing, Hotel booking, Office/Parking Space sharing, Car sharing, Bike sharing, Boat sharing, and other Airbnb clones like finder, etc

Project bike sharing predictor

Shared bikes availability in your city

Divvy.Vision is an open-source visualization platform of Chicago's Divvy bike share program.

An example of a Broadway pipeline for a bike sharing app with RabbitMQ and PostgreSQL

Bike sharing at your terminal 🚲

Bike share data visualisation

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

Visualizing Bike Sharing Networks with rCharts and Shiny

⚽ Bike ⚾ Sharing 🥎 Demand 🏀Forecasting 🏐 Time 🎮 Series 🥌 Analysis is 🎳 a data ⛸ science ✈ focused on 🚁 predicting 🚀 bike 🛸 demand 🚟 time 🚠 series 🚞 techniques ⛴ analyzing 🚢 historical 🚒 bike 🛺 weather 🚋 data 🚂 seasonal 🚃 trends this 🚅 helps 🏩 optimize 🏦 planning 🕍 resource 🏠 allocation 🕌 and 🔐 operational 🪣 efficiency 💶

🚲 Git scraping for bike sharing APIs

An android app to display bike shared network status

Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

共享单车时空数据分析与管理系统基于大数据平台框架，对现有的共享单车数据进行流量的时间和空间统计。通过某一个时期的共享单车大数据的分析和管理，得以观察到某区域或者某时间段的单车具体情况，也实现了管理员对单车的远程操作，让当代城市中共享单车的摆放和调度更加具有合理性。

It is a web app tutorial project made with streamlit, a ML web app tool. It has some super cool features that can eliminate the need of any web framework. So, a data scientist can focus entirely on the analytics part rather than worrying about managing frontend and backend with any sort of framework. Do check out this project!

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.

Analysis of public dataset about bicycle trips under Bay Area Bike Share program

Analysis and model development for the Kaggle Bike Sharing Dataset.

Share stationary bike data with Strava, Garmin Connect and Golden Cheetah

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.