Bicycle counts conducted around New York City at key locations. For the counter locations, please refer to the Bicycle Counters dataset.

The data may have lapses due to transmission issues cause by weather, connection interruptions, equipment malfunctions, vandalism, etc. The data will update as soon as it is feasible. The City makes no presentation as to the accuracy of the content and assumes no liability for omissions or errors in information contains on the website. Time is captured in GMT/UTC timezone.

Bicycle Training Data

## Overview

Bicycle Training Data is a dataset for object detection tasks - it contains Bicycles annotations for 127 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

In 2024, more than ** million people in the United States participated in outdoor cycling. From the total, road cycling was the most popular type of cycling, with over ** million participants.

The District Department of Transportation (DDOT) maintains a system of automated counters to measure the number of people walking and biking. DDOT began installing these counters in 2014, and now has 18 in operation. Counters have been installed in both bicycle lanes and trails. One location counts only pedestrians; 10 locations count only bikes; and 7 locations count people biking and walking. DDOT monitors the continuous data stream to analyze trends in walking and biking, assess the value of its facility investments, and apply this data to plan for new bike lanes and trails. Data will sometimes contain errors or contain gaps because the dashboard presents "raw" data direct from the system server and the devices in the field.

The annual bike and pedestrian count is a volunteer data collection effort each fall that helps the City understand where and how many people are biking and walking in Somerville, and how those numbers are changing over time. This program has been taking place each year since 2010. Counts are collected Tuesday, Wednesday, or Thursday for one hour in the morning and evening using a “screen line” method, whereby cyclists and pedestrians are counted as they pass by an imaginary line across the street and sidewalks. Morning count sessions begin between 7:15 and 7:45 am, and evening count sessions begin between 4:45 and 5:15 pm. Bike counts capture the number of people riding bicycles, so an adult and child riding on the same bike would be counted as two counts even though it is only one bike. Pedestrian counts capture people walking or jogging, people using a wheelchair or assistive device, children in strollers, and people using other micro-mobility devices, such as skateboards, scooters, or roller skates. While the City and its amazing volunteers do their best to collect accurate and complete data each year and the City does quality control to catch clear errors, it is not possible to ensure 100% accuracy of the data and not all locations have been counted every year of the program. There are also several external factors impacting counts that are not consistent year-to-year, such as nearby construction and weather. For these reasons, the counts are intended to be used to observe high-level trends across the city and at count locations, and not to extrapolate that biking and walking in Somerville has changed by a specific percentage or number. Data in this dataset are available at the location count level. To request data at the movement level, please contact transportation@somervillema.gov.

The dataset shows number of unique cyclists, count of bike trips (regardless of unique riders) both from different directions, total number of bike trips regardless of the direction they are taking, time taken to complete bike trips from different directions and other cycling statistics. These ranges were extractetd using peak riding seasons with AM hours counts between 7am and 10am and PM Hours count between 4pm and 8pm. These time frames were built from Strava's internal analysis that shows the typical patterns and travel areas. The street network used is within Glasgow boundary although it might stretch to streets in neighbouring local authorities. The figures were normalized so that the values fall between 0 and 1. A Data Dictionary file is attached which contains all the field names and their respective meanings. The data points used to heat the map were between 2013-01-01 to 2013-12-31 Data licensed from Strava, Inc Licence: None strava-opendata.zip - https://dataservices.open.glasgow.gov.uk/Download/Organisation/4cd61d96-69cc-4114-a7da-54a218863c0a/Dataset/03f86c25-f7d2-457f-ae08-b356ac41d60f/File/4e1925c5-73b5-443c-99c7-b0e5ae0ace50/Version/ea9809d4-29d7-4472-8f57-351b17416582

The share of children aged 6 to 12 who participate in bicycling on a regular basis in the United States decreased slightly between 2020 and 2021. According to the source, 18 percent of children participated in cycling on a regular basis in 2021, down from 18.2 percent in 2020.

Introduction

Bicycle Industry Statistics: The demand in the bicycle industry has witnessed eminent growth over the past few years due to the growing awareness about being health-sensitive among people across the world. People generally use bicycles for activities like sports, hiking, exercise, commuting, and others. Thus, the product manufacturers give a variety of designs for these applications to cover a larger customer base.

Some of the sections given by the manufacturers also involve road, hybrid, and mountain bikes. Factors like growing traffic congestion, growing surrounding problems, and urbanization are driving industrial growth. This article will shed more light on “Bicycle Industry Statistics.â€

The Fremont Bridge Bicycle Counter began operation in October 2012 and records the number of bikes that cross the bridge using the pedestrian/bicycle pathways. Inductive loops on the east and west pathways count the passing of bicycles regardless of travel direction. The data consists of a date/time field: Date, east pathway count field: Fremont Bridge NB, and west pathway count field: Fremont Bridge SB. The count fields represent the total bicycles detected during the specified one hour period. Direction of travel is not specified, but in general most traffic in the Fremont Bridge NB field is travelling northbound and most traffic in the Fremont Bridge SB field is travelling southbound.

This table contains data on the percent of population aged 16 years or older whose commute to work is 10 or more minutes/day by walking or biking for California, its regions, counties, and cities/towns. Data is from the U.S. Census Bureau, American Community Survey, and from the U.S. Department of Transportation, Federal Highway Administration, and National Household Travel Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Active modes of transport, bicycling and walking alone and in combination with public transit, offer opportunities to incorporate physical activity into the daily routine. Physical activity is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Automobile commuting is associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Consequently the transition from automobile-focused transport to public and active transport offers environmental health benefits, including reductions in air pollution, greenhouse gases and noise pollution, and may lead to greater overall safety in transportation. More information about the data table and a data dictionary can be found in the About/Attachments section.

In 2024, the number of traffic accidents caused by bicycles amounted to approximately 16.78 thousand cases. This was a decrease from around 17.61 thousand cases in the previous year.Is riding a bicycle safe in Japan? Although traffic in Japan may be considered relatively safe in international comparison, the high share of pedal cyclists among casualties is striking. Moreover, the case fatality rate of bicycle riders may be comparably low to other road users, but in absolute terms, fatalities exceeded that of other two-wheelers.Surveys indicate cyclists themselves are aware of the risks. According to one survey, one in two pedal-assist electric bicycle (pedelec) riders admitted having had an accident or close call.Who is likely to get involved in bicycle accidents? Bicycles are a popular mode of transportation in Japan, especially among young people on school runs and the elderly. For the elderly, bikes are a cheap and flexible mode of transportation. If they have returned their driver’s license, it might even be the only option left to maintain a certain degree of mobility and independence. Accordingly, the likelihood among elderly and underage cyclists to get involved in traffic accidents is relatively higher.The nature of their risks might differ, however. Although pedelecs have become increasingly popular in recent years among both groups, elderly cyclists with limited stability tend to struggle more with e-bikes, which built-in electric motor makes them heavier and faster than the average bicycle. Since people are aware of this issue, there have been e-bike training courses for the elderly organized by vendors, the police, and local governments. One can hardly measure the direct effects of such initiatives. At least, the casualty rate of elderly cyclists decreased steadily over the past decade. It remains to be seen if this trend continues.

Various datasets with statistics of Research, Information and Statistics.

Theme: Traffic, Detail level: Amsterdam

Bicycle counts conducted around New York City at key locations. For the counts data, please refer to the Bicycle Counts dataset. Bicycle Counts: https://data.cityofnewyork.us/Transportation/Bicycle-Counts/uczf-rk3c

Blue Bikes (formerly Hubway) is jointly owned and managed by the municipalities of Boston, Arlington, Brookline, Cambridge, Chelsea, Everett, Malden, Medford, Newton, Revere, Salem, Somerville, and Watertown. This external website provides datasets on Blue Bikes usage.

It includes:

• Comprehensive set of trip histories which is updated monthly
• Real time system data, published in open General Bikeshare Feed Specification (GBFS) format - a format recommended by the North American Bike Share Association (NABSA).

This data is provided according to the Blue Bikes Data License Agreement.

The spatial data set contains the statistical evaluation of the city cycling competition in the city of Freiburg from 2021. Available is the average speed of cyclists, the number of trips per direction, the number of trips per section and a heat map of cycling. The data is "open data". The amount of traffic represents the number of recorded journeys per route. The original GPS tracks are drawn to the underlying traffic network by means of a routing algorithm. Due to the ever-present inaccuracy of the GPS, this is not always error-free. If two lanes are laid out as separate lanes in the traffic network, it can happen that different GPS tracks are drawn on different lanes and thus parallel path representations occur.

About Dataset

This case study is a part of Google Data Analytics course. Cyclistic is a fictional bike-sharing company, however, the data is real. It encompasses information about bike-sharing stations in Chicago and total rides with rented bikes during more than 10 years, from 2013 until February 2023.

The business task is to help design the marketing strategy. The project owner aims at converting casual riders into annual members. To achieve that goal the marketing team needs to better understand how annual members and casual riders differ in using rented bikes.

My specific task was to analyze the available data of rides and provide 3 main recommendation for the marketing strategy, based on the data analysis.

The requirement was to analyze the data for the last 12 months. However, I decided to use the whole dataset, since it was openly available for the whole period of operations.

Data License Agreement

Lyft Bikes and Scooters, LLC (“Bikeshare”) operates the City of Chicago’s (“City”) Divvy bicycle sharing service. Bikeshare and the City are committed to supporting bicycling as an alternative transportation option. As part of that commitment, the City permits Bikeshare to make certain Divvy system data owned by the City (“Data”) available to the public, subject to the terms and conditions of this License Agreement (“Agreement”). By accessing or using any of the Data, you agree to all of the terms and conditions of this Agreement.

License. Bikeshare hereby grants to you a non-exclusive, royalty-free, limited, perpetual license to access, reproduce, analyze, copy, modify, distribute in your product or service and use the Data for any lawful purpose (“License”). Prohibited Conduct. The License does not authorize you to do, and you will not do or assist others in doing, any of the following

Use the Data in any unlawful manner or for any unlawful purpose; Host, stream, publish, distribute, sublicense, or sell the Data as a stand-alone dataset; provided, however, you may include the Data as source material, as applicable, in analyses, reports, or studies published or distributed for non-commercial purposes; Access the Data by means other than the interface Bikeshare provides or authorizes for that purpose; Circumvent any access restrictions relating to the Data; Use data mining or other extraction methods in connection with Bikeshare's website or the Data; Attempt to correlate the Data with names, addresses, or other information of customers or Members of Bikeshare; and State or imply that you are affiliated, approved, endorsed, or sponsored by Bikeshare. Use or authorize others to use, without the written permission of the applicable owners, the trademarks or trade names of Lyft Bikes and Scooters, LLC, the City of Chicago or any sponsor of the Divvy service. These marks include, but are not limited to DIVVY, and the DIVVY logo, which are owned by the City of Chicago. No Warranty. THE DATA IS PROVIDED “AS IS,” AS AVAILABLE (AT BIKESHARE’S SOLE DISCRETION) AND AT YOUR SOLE RISK. TO THE MAXIMUM EXTENT PROVIDED BY LAW BIKESHARE DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING THE IMPLIED WARRANTIES OF MERCHANTABILITY FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. BIKESHARE FURTHER DISCLAIMS ANY WARRANTY THAT THE DATA WILL MEET YOUR NEEDS OR WILL BE OR CONTINUE TO BE AVAILABLE, COMPLETE, ACCURATE, TIMELY, SECURE, OR ERROR FREE.

Limitation of Liability and Covenant Not to Sue. Bikeshare, its parent, affiliates and sponsors, and their respective directors, officers, employees, or agents will not be liable to you or anyone else for any loss or damage, including any direct, indirect, incidental, and consequential damages, whether foreseeable or not, based on any theory of liability, resulting in whole or in part from your access to or use of the Data. You will not bring any claim for damages against any of those persons or entities in any court or otherwise arising out of or relating to this Agreement, the Data, or your use of the Data. In any event, if you were to bring and prevail on such a claim, your maximum recovery is limited to $100 in the aggregate even if you or they had been advised of the possibility of liability exceeding that amount. Ownership and Provision of Data. The City of Chicago owns all right, title, and interest in the Data. Bikeshare may modify or cease providing any or all of the Data at any time, without notice, in its sole discretion. No Waiver. Nothing in this Agreement is or implies a waiver of any rights Bikeshare or the City of Chicago has in the Data or in any copyrights, patents, or trademarks owned or licensed by Bikeshare, its parent, affiliates or sponsors. The DIVVY trademarks are owned by the City of Chicago. Termination of Agreement. Bikeshare may terminate this Agreement at any time and for any reason in its sole discretion. Termination will be effective ...

The data provides counts of bike trips (both directions summed unless otherwise noted) at the following locations:

1_ALEX: Ottawa approach to the NCC Alexandra Bridge Bikeway This counter was not operational for most of 2010 due to bridge construction

2_ORPY: NCC Ottawa River Pathway approximately 100m east of the Prince of Wales Bridge

3_COBY: NCC Eastern Canal Pathway approximately 100m north of the Corktown Bridge. WINTER counter

4_CRTZ: NCC Western Canal Pathway approximately 200m north of “The Ritz”

5_LMET Laurier Segregated Bike lane just west of Metcalfe WINTER counter

6_LLYN Laurier Segregated Bike lane just east of Lyon. WINTER counter

7_LBAY Laurier Segregated Bike lane just west of Bay. WINTER counter

8_SOMO Somerset bridge over O-Train west-bound direction only WINTER counter (best effort- see notes)

9_OYNG O-Train Pathway just north of Young Street

10_OGLD O-Train Pathway just north of Galdstone Avenue

11_OBVW O-Train Pathway just north of Bayview Station

12a_ADAWE Adàwe Crossing Bikes. WINTER counter

12b_ADAWE Adàwe Crossing Pedestrians. WINTER counter

The data is divided into quarterly files with options to download daily volumes for all locations.

Accuracy: The counters are considered accurate to within a range of +0%, -5% of the bikes that cross over the sensing section of the pathway or bike lane.

What is Counted:

Most large wheeled strollers, wheelchairs, mobility scooters will typically be counted as 'bikes' Some skateboards and roller-bladers will be counted as bikes. Carbon-fibre framed bikes will be counted, as long as their wheel rims are metallic (aluminium or steel) Cars, trucks and snow-ploughs will typically not be counted since they are massive enough to be detected as non-bikes and therefore be ignored by the sensor electronics.

ByPass:

On most routes where bike counters are installed, cyclists can in fact by-pass the counters. In this sense the total bike traffic along a route may be higher that the count. For example, at the Alexandra Bride count site, some cyclists use the pedestrian-only section of the board-walk instead of the bike-track where the sensors are located- these cyclists are not counted. Some cyclists use the roadway instead of the bike-track, these cyclists are also not counted.

On multi-use pathways, the 'bypass' is quite low, occasionally a cyclist may veer off the track onto the grass, this also happens for a short period in early spring while some ice remains on the pathways.

Winter Counts:

Only the counters designated as 'winter' counters have valid data after the first snow accumulation, until snow has been removed or melted away in spring. Winter counters imply some winter maintenance of the pathway or bike lane. The Somerset counter bike lane (westbound only) is cleared of snow on a best-effort basis only, and typically will not show all the cyclists along that route immediately following a heavy snow-fall.

Out of Service:

There may be gaps in counter data if the counters were being serviced, or if the pathway/road at the counter site was under construction. In these cases the counter data will be '', with the field highlighted in grey.

Update Frequency: The data set includes available counts from Jan 1st, 2010 for daily volumes and from July 2012 to June 2013 for the 15 min interval counts for the Laurier bike lane, and. These will be up-dated on an on-going basis. New data will be posted approximately one month after the end of each quarter. Some data from late in the fourth quarter may lag until the second quarter of the following year, for counter stations which are not accessible during winter. Data from the counter at Laurier and Metcalfe can also be found on a public website (follow Ottawa.ca/bikelane). This data will be updated on a daily or weekly basis (worst-case).

Contact: Tim Wei

BIKED is a dataset composed of 4500 individually designed bicycle models sourced from hundreds of designers. We expect BIKED to enable a variety of data-driven design applications for bicycles and support the development of data-driven design methods. The dataset is comprised of a variety of design information including assembly images, component images, numerical design parameters, and class labels. In this paper, we first discuss the processing of the dataset, and then highlight some prominent research questions that BIKED can help address. Of these questions, we further explore the following in detail: (1) How can we explore, understand, and visualize the current design space of bicycles and utilize this information? We apply unsupervised embedding methods to study the design space and identify key takeaways from this analysis. (2) When designing bikes using algorithms, under what conditions can machines understand the design of a given bike? We train a multitude of classifiers to understand designs, then examine the behavior of these classifiers through confusion matrices and permutation-based interpretability analysis. 3) Can machines learn to synthesize new bicycle designs by studying existing ones? We test Variational Autoencoders on random generation, interpolation, and extrapolation tasks after training on BIKED data. The dataset and code are available online.