The field of cycling analytics has only recently started to develop due to limited access to open data sources. Accordingly, research and data sources are very divergent, with large differences in information used across studies. To improve this, and facilitate further research in the field, we propose the publication of a data set which links thousands of professional race results from the period 2017-2023 to detailed geographic information about the courses ridden, an essential aspect in road cycling analytics. Detailed Data Descriptor currenlty undergoing review process.When using the data set, please refer to: Janssens, B., Pappalardo, L., De Bock, J., Bogaert, M., & Verstockt, S. (2024). Geospatial Road Cycling Race Results Data Set. arXiv preprint arXiv:2410.09055.

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

According to an October 2022 survey, men in the United States were more likely to participate in cycling activities than women. 40 percent of men indicated that they had cycled at least once in the previous 12 months, while only 27 percent of women indicated that they had cycled in the previous year.

In 2022, India had the largest share of regular cyclists, who used their bikes at least once a week. It was closely followed by China and the Netherlands, where around ********** of the population are weekly bicycle riders. At the other end of the spectrum, only ** percent of Canadians cycle at least once a week.

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.

These datasets include data from the Sydney Cycling Survey (SCS). The SCS surveys over 11,000 individuals from 4,000 households. It collects information about cyclists, cycling trips, cycling participation and cycling mode share.

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 spatial data set contains the statistical evaluation of the city cycling competition in the city of Freiburg from the year 2024. 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". In the representation of the speed, all journeys on the road are averaged in terms of their speed. Please compare this data with the amount of traffic. Even with only a few recorded journeys on a traffic route, the average speed is calculated. However, the significance of a few trips at a certain point is very limited and possibly misleading.

Description:

The Bike Sensor Data Set for Vehicle Encounters is a comprehensive collection of open data aimed at studying and analyzing encounter between bicycles and vehicles in urban environments. This dataset combines data captured by a sensor platform integrated with a smartphone mounted on a bike. By including various smartphone sensors and timestamps for overtaking events, this dataset offers a rich source of information for investigating and understanding the dynamics of vehicle encounters from the perspective of cyclists.

https://data.uni-hannover.de/dataset/98d83b12-493d-40af-8c4e-58e8064795c5/resource/3d19a3a2-73fc-408b-9d1c-87800b7d4b79/download/mounted_platform.png" alt="Photo of the measurement setup of the prototype sensor platform on a bicycle. The logging unit is located on the luggage rack and the side sensor below it at the height of the rear wheel.">

The dataset contains sensor streams recorded during vehicle encounters, including:

• inertial measurement unit
• magnetic field sesnor
• GNSS
• illuminance sensor
• barometric pressure sensor
• side viewing range sensor

These sensors provide a multidimensional view of the cyclist's environment, capturing physical movements, orientation, environmental conditions, and the proximity of vehicles alongside the cyclist. This data enables researchers to analyze overtaking positions, distance statistics, and potential collision scenarios, enhancing our understanding of vehicle encounters and supporting interventions for cyclist safety.

Key Features of the Bike Sensor Data Set:

• Sensor Streams: The data set provides synchronized streams of data from the smartphone sensors, offering valuable insights into the cyclist's experiences during vehicle encounters.
• Timestamps: Each overtaking event is annotated with a timestamp, allowing for temporal analysis and correlation with the other provided data sources.
• Comprehensive Smartphone Sensor Data: The data set encompasses data from a wide array of privacy preserving smartphone sensors, enabling researchers to explore different dimensions of the vehicle encounter experience, such as speed, acceleration, heading, ambient conditions, and sound levels.
• Trajectory Metadata: Each trajectory is accompanied by a JSON file containing metadata such as date, time, location, duration, as well as metadata for each sensor stream. The inclusion of weather information from the Bright Sky API adds an environmental dimension to the dataset.

Potential Applications:

The Bike Sensor Data Set for Vehicle Encounters holds significant potential for a variety of applications, including but not limited to:

• Transportation and urban planning research
• Machine learning and data mining algorithms for cyclist safety prediction
• Human factors research in transportation and mobility
• Development and testing of cyclist-oriented mobile applications

By utilizing this data set, researchers and practitioners can gain valuable insights into the dynamics of vehicle encounters from a cyclist's perspective. This, in turn, can contribute to the development of safer and more cyclist-friendly urban environments, promoting sustainable and active transportation alternatives.

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.

In 2024, the number of people participating in road cycling in the United States amounted to approximately **** million. This marked an increase over the previous year's figure of **** million.

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.

The system used is the Phasespace Impulse X2E motion capture system, featuring active LEDs. This system uses 24 cameras designed for capturing 3D motion through modulated LEDs. These cameras incorporate pairs of linear scanner arrays operating at high frequencies, enabling the capture of the position of bright spots of light generated by the LEDs.

Methodology for Mocap Data CollectionParticipants received detailed information about the mocap data collection procedure and purpose. Informed consent was obtained, ensuring understanding and agreement and a pre-session questionnaire collected demographic and health information. Next, the participant’s road or time trial (TT) bike was placed on a turbotrainer and the meticulous marker placement on the cyclist’s body.

Cyclists followed a specific workout plan based on their bike type. The plan included warm-up, cycling positions, and recovery intervals.

Sensors were strategically put mostly on the back side of the torso due to the body position while cycling.

Workout:

• Warm-Up: 1 minute in any position.- 30 seconds at 60 RPM- 15 seconds at 75 RPM- 15 seconds at 90 RPM- 10 seconds at 100 RPM- 10 seconds at 110 RPM

Cycling Positions and Cadences

For Road Bikes:

The workout was performed for each of the following positions with 1 minute between each position to allow for recuperation:- Straight Arms- Comfortable- Aggressive- Aero Position- Standing (not above 90 RPM)

For TT Bikes:

The workout was performed for each of the following positions with 1 minute between each position to allow for recuperation:- Comfortable- Aero Position- Standing (not above 90 RPM)

Participants were monitored throughout the session to ensure well-being and comfort. They had the option to terminate the procedure if they feel unwell or wish to stop.

The motion capture dataset is organized as follows.There is a dedicated folder for each participant, labeled according to the following naming convention:

"1_RB_M42_20230719_PK"

1 = Index Number

RB = Road Bike (RB) or Time Trial bike (TT)

M42 = Gender (M/F) and age

20230719 = Capture Date (YYYYMMDD)

PK = Unique Identifier of participant

In each folder there are three files:

C3D = Motion Capture Raw Data

BVH = BioVision Hierarchy (BVH), data mapped to skeletal data ready for animation (errors may still remain)

GPX = Data from related zwift workout

The Project (Smart Cyclo) is funded by the European Union Recovery and Resilience Facility of the NextGenerationEU instrument, through the Research and Innovation Foundation.

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

The Principal Bicycle Network (PBN) is a network of proposed and existing cycle routes that help people cycle for transport, and provide access to major destinations in Victoria The Principal Bicycle Network (PBN) is a network of proposed and existing cycle routes that help people cycle for transport, and provide access to major destinations in Victoria. Cycling for transport includes riding bicycles to work, to school, shopping, visiting friends etc. The PBN is also a 'bicycle infastructure planning tool' to guide State investment in the development of transport bicycle network. The PBN is one of a number of network planning tools. (other examples include individual Council networks) Together these networks make up the developing cycle infrastructure of Victoria. The PBN makes use of many local roads and off-road paths, as well as State arterial roads. New bicycle facilities on the PBN are designed with the principle of increasing separation between cyclists and motorists, and giving priority to cyclists at key intersections.

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.

Local authority level data on walking a cycling among adults

Phase 1: ASK

Key Objectives:

1. Business Task * Cyclist is looking to increase their earnings, and wants to know if creating a social media campaign can influence "Casual" users to become "Annual" members.

2. Key Stakeholders: * The main stakeholder from Cyclist is Lily Moreno, whom is the Director of Marketing and responsible for the development of campaigns and initiatives to promote their bike-share program. The other teams involved with this project will be Marketing & Analytics, and the Executive Team.

3. Business Task: * Comparing the two kinds of users and defining how they use the platform, what variables they have in common, what variables are different, and how can they get Casual users to become Annual members

Phase 2: PREPARE:

Key Objectives:

1. Determine Data Credibility * Cyclist provided data from years 2013-2021 (through March 2021), all of which is first-hand data collected by the company.

2. Sort & Filter Data: * The stakeholders want to know how the current users are using their service, so I am focusing on using the data from 2020-2021 since this is the most relevant period of time to answer the business task.

#Installing packages
install.packages("tidyverse", repos = "http://cran.us.r-project.org")
install.packages("readr", repos = "http://cran.us.r-project.org")
install.packages("janitor", repos = "http://cran.us.r-project.org")
install.packages("geosphere", repos = "http://cran.us.r-project.org")
install.packages("gridExtra", repos = "http://cran.us.r-project.org")

library(tidyverse)
library(readr)
library(janitor)
library(geosphere)
library(gridExtra)

#Importing data & verifying the information within the dataset
all_tripdata_clean <- read.csv("/Data Projects/cyclist/cyclist_data_cleaned.csv")

glimpse(all_tripdata_clean)

summary(all_tripdata_clean)

Phase 3: PROCESS

Key Objectives:

1. Cleaning Data & Preparing for Analysis: * Once the data has been placed into one dataset, and checked for errors, we began cleaning the data. * Eliminating data that correlates to the company servicing the bikes, and any ride with a traveled distance of zero. * New columns will be added to assist in the analysis, and to provide accurate assessments of whom is using the bikes.

#Eliminating any data that represents the company performing maintenance, and trips without any measureable distance
all_tripdata_clean <- all_tripdata_clean[!(all_tripdata_clean$start_station_name == "HQ QR" | all_tripdata_clean$ride_length<0),] 

#Creating columns for the individual date components (days_of_week should be run last)
all_tripdata_clean$day_of_week <- format(as.Date(all_tripdata_clean$date), "%A")
all_tripdata_clean$date <- as.Date(all_tripdata_clean$started_at)
all_tripdata_clean$day <- format(as.Date(all_tripdata_clean$date), "%d")
all_tripdata_clean$month <- format(as.Date(all_tripdata_clean$date), "%m")
all_tripdata_clean$year <- format(as.Date(all_tripdata_clean$date), "%Y")

** Now I will begin calculating the length of rides being taken, distance traveled, and the mean amount of time & distance.**

#Calculating the ride length in miles & minutes
all_tripdata_clean$ride_length <- difftime(all_tripdata_clean$ended_at,all_tripdata_clean$started_at,units = "mins")

all_tripdata_clean$ride_distance <- distGeo(matrix(c(all_tripdata_clean$start_lng, all_tripdata_clean$start_lat), ncol = 2), matrix(c(all_tripdata_clean$end_lng, all_tripdata_clean$end_lat), ncol = 2))
all_tripdata_clean$ride_distance = all_tripdata_clean$ride_distance/1609.34 #converting to miles

#Calculating the mean time and distance based on the user groups
userType_means <- all_tripdata_clean %>% group_by(member_casual) %>% summarise(mean_time = mean(ride_length))


userType_means <- all_tripdata_clean %>% 
 group_by(member_casual) %>% 
 summarise(mean_time = mean(ride_length),mean_distance = mean(ride_distance))

Adding in calculations that will differentiate between bike types and which type of user is using each specific bike type.

#Calculations

with_bike_type <- all_tripdata_clean %>% filter(rideable_type=="classic_bike" | rideable_type=="electric_bike")

with_bike_type %>%
 mutate(weekday = wday(started_at, label = TRUE)) %>% 
 group_by(member_casual,rideable_type,weekday) %>%
 summarise(totals=n(), .groups="drop") %>%
 
with_bike_type %>%
 group_by(member_casual,rideable_type) %>%
 summarise(totals=n(), .groups="drop") %>%

 #Calculating the ride differential
 
 all_tripdata_clean %>% 
 mutate(weekday = wkday(started_at, label = TRUE)) %>% 
 group_by(member_casual, weekday) %>% 
 summarise(number_of_rides = n()
      ,average_duration = mean(ride_length),.groups = 'drop') %>% 
 arrange(me...