During this analysis two challenges were merged and analysed, the first is the “Company bike challenge” which is also known as “Bike to work” has taken place within the European research project BITS  between the end of May and the beginning of July 2021 and as we can conclude from the name the focus was on companies and the way to work. The second challenge is the “Oldenburg Bicycle Challenge”, in which the data were collected from volunteer participants from Oldenburg over a one-year period (September 2020 – September 2021) . In this analysis, data from 24.05.2021 to 04.07.2021 is used.
The app users were required to download a spanish app called Ciclogreen in order to encourage cycling more frequently to work, thereby helping to reduce greenhouse gas emissions . In addition to logging distance and kilometers cycled, the app also recorded average speed, and average distance cycled anonymously. Through the Ciclogreen app, users could track their statistics, earn points, or “Ciclos,” and win prizes for commuting to work on their bikes and preventing climate change .
The challenge data were analyzed as part of a master’s thesis and the results are published on this website.
Global analysis of the data
Since the Company bike challenge was running from 24. May till 4. July 2021, but the Oldenburg bike challenge was running for a year, we used in this thesis just the Oldenburg data that was in the same timeframe as the company bike challenge. The initial dataset containing 6792 different trips, and after cleaning the data and removing the not interesting rows like the short trips, trips with speeds greater than 35 km/h or smaller than 5 km/h, and after we focused on the cities with an amount bigger than 50 trips, we got a final dataset containing 4070 trips all located in Germany.
The heatmap shows the distribution of the trips after the preprocessing. The more heat is mapped, the higher the number of trips in that city. For example, more trips have been ridden in Oldenburg city than in Ulm.
When do the app users cycle?
Figure 2 shows that almost one-quarter of the observed trips took place during peak hours, between 6-8:00 or 15- 17:00 on a weekdays, and in total the weekday trips took up 82% of the observed bicycle trips in the company bike challenge and that’s expected since the majority of the participants are app users.
Figure 3 shows the distribution of the trips as well as the number of cycled kilometers per day, and we can pretty clear see that even if the number of trips on Sundays is less than on Saturdays, but the participants have cycled more kilometers on Sundays.
How far do the app users cycle?
For the Company bike challenge, we have an average distance of 8.3 km, and the figure 4 shows that most of the participants have cycled for a distance between 5 and 10 km and about 2% have cycled for more than 30km, for the weekend the majority cycled between 5 and 20km.
How long do the app users cycle?
Regarding the cycled time, 50% of the app users have cycled for a maximum of 30 minutes and about 2% have cycled for more than 2 hours and that’s expected since on Weekdays the app users will cycle to their job which may not be so far from their houses.
How fast do the app users cycle?
Figure 6 shows the distribution of the trips per speed category and we can clearly see that the majority of participants have cycled on Weekday with an average speed between 10 and 20 km/h which is considered as medium speed category.
Under which weather condition cycle the app users more?
Figure 7 shows the distribution of the trips by weather condition. The change of the weather causes a high number of trips on some weather conditions on the same day, meaning that the same journey can be considered as to be made on a clear day and made on a rainy day if this weather change has happened.
How long/far/fast does the app users cycle in different weather conditions?
Figure 8 shows that the average duration and average distance on partially cloudy days or rainy days are shorter than the average duration on rainy partially cloudy days. The average speed on rainy, overcast days is the highest in comparison to the other days.
The 4070 trips we described in the first section (global analysis) are distributed in 21 german cities as follows:
Figure 9 shows how the company bike challenge trips are distributed in 21 german cities and as the Baron Mobility Service is based in Oldenburg, that explain the high number of trips in Oldenburg city compared to the big cities such as Berlin and Frankfurt.
Due to the fact that the data is compiled from customers of Baron and there are more customers located in one city than another, we cannot conclude a city is more bicycle-friendly than another based on the number of trips and kilometers cycled.
How are the trips distributed per hours in each city?
In the early morning and in the early evening at rush hour, we can see that there are the highest amounts of trips seen. As late as possible in the evenings and at night the number of bicycle trips was extremely low.
How are the trips distributed per days in each city?
An analysis of the distribution of trips per day is also performed. The file bellow illustrates the results, showing a higher number of trips during the week than during the weekend and that Sundays are the days with the fewest trips in almost all cities.
What is the differences and simalirities between different cities?
We can notice that, Boxdorf is the city with the largest average duration, followed by Hude and Frankfurt. Frankfurt, Datteln, and Rastede are the top three cities when it comes to average distances. It is worth mentioning that the average distance was greater than the total duration, which was 8.6 kilometers. According to the average speed, Oldenburg and Berlin are one of the cities with the lowest average speed compared to the total average speed of approximately 15 km/h.
The average duration and average distance are for almost all cities higher on the weekend but the average speed is almost the same in all days.
We can notice that the duration and distance of a rainy day in Berlin are shorter than they would be on a clear day. However, they are considered the largest for this city on a rainy, overcast day. No matter what the weather conditions are, Frankfurt and Oldenburg have almost the same average duration and average distance in almost every situation. It is interesting to note that the average speed in Berlin on a rainy, partially cloudy day is the highest compared to all 21 other analyzed cities irrespective of the weather conditions. It is also interesting to note that, on a rainy, overcast day in the same city, the average speed is the lowest.
How are the trips classified per speed category per city?
Fig 10 shows and confirme what found in figure 6. In almost all analyzed cities the app users have cycled with a average speed between 10 and 20 km/h. Bellow you can find a map that shows the distribution of the trips per speed category, for each city.
Where and how often did users brake in each city?
To check the braking process, I calculated the “speed change” between each two following steps in a trip, and the ‘acceleration’, which is the change in speed (Km/h) divided by duration (seconds). If the value of acceleration is less than -1 (Km/h/s) then we have a braking process. Figure 11 shows us that in Frankfurt, Cottbus, and Boxdorf, the app users have not braked much compared to Oldenburg or Berlin.
In the following maps you can find where and how often did users brake in each city. The stronger the heat is mapped, the more braking processes could be identified.
How long and where did app users stand still during a trip in each city?
to analyse the waiting time, an analysis of the duration between successive tracked steps is needed. but based on the data, it is not immediately recognizable, if the bicycle came to a standstill for a specific reason like whether its stop was the result of a traffic light or something else. Moreover, due to slight jumps in position, a certain tolerance range must also be considered which includes very low speeds for which a running drive becomes unrealistic. The tolerance value set for this research was 4.36 km/h, which corresponds to the lower limit of a pedestrian’s average speed. Figure 12 shows the comparison of the average waiting time in seconds between different cities.
A detailed maps can be explored by clicking on the name of choosen city. Each map represents where the app users had waited. Heat maps where the colours are darker indicate longer waiting times, and we can see that the majority of the waiting spots are located at intersections.
In which areas for each city did slow speeds occur?
The following maps show the low-speed spots per city, which are the areas where the speeds were slow in comparison to the average speed. In general, the more heat is mapped, the more often and more severely the speed falls below the average.
The second part of this work is to check if the company Bike Challenge data represent the reality or not using the counting data then using the accident data.
The counting data is data from stationary sensors, and which contains the number of bicycles in different stations. We have it for Ulm, Berlin and Oldenburg city. Figure 13 shows the location of each counting station in each city.
Distribution of the bicycles per station
As noticed in figure 13, Ulm city contains only one counting station. In other hand Oldenburg contains 12 and Berlin 24 counting stations. Figure 14 represents the distribution of the bicycles per station and figure 15 shows the distribution of the tracked bicycles per station per day.
Ratio between the total number of all cyclists and the trips measured at a measuring station
Starting with Ulm which contains 1 counting station, the Company Bike Challenge data doesn’t represent the reality for this city except between 6-7 (am/pm). Figure 16 shows a comparison between the number of tracked bicycles and the number of observed trips.
For Berlin city, which contain 24 counting stations, there is no representativity and that can be seen in figure 17, that sows a comparison between the number of tracked bicycles by the counting stations and the number of observed trips in berlin.
Finally we have Oldenburg city, in which the Company Bike Challenge data represent somehow the reality specially in the mornings and that can be seen in figure 18.
The following maps represent the location of the companies (blue markers), the location of the counting stations (purple markers) and the company bicke challenge trips in different colors. Each color represent a unique trip, that can be activated or deactivated.
The accident data are received from the “Police Inspection” of Oldenburg, and we have it for three years (2019-2020-2021). They deliver all the information about the bicycle accidents in Oldenburg city, which are distributed as figure 19 shows. The majority of the accident are “EK” which is for Turning/Crossing Accidents. The location of the accidents can be found as html file via the download button.
Distribution of the accident per accident type per year/month/day
Over the years, the number of bicycle accidents have decreased in Oldenburg. This can be seen in figure 20. This also shows that September is the month with the highest number of bicycle accidents. The majority of the accidents have taken place during the week days and not in the weekends.
Distribution of the accidents per accident type per street
The accidents are usually taking place in the principle streets, and that’s the case for the bicycle accidents in Oldenburg. Alexanderstr. and Nadorsterstr. are the streets with the highest amount of EK and AB accident. Figure 21 represents the distribution of the accidents per accident type per street.
How the app users cycle near the accident spots?
The heatmap shows the speed in the EK accident area. The stronger the heat is mapped, the heavier the speed was on the mapped roads. For example, roads colored dark orange have much higher speed than roads colored lime green.
The whole map, that contain all the accident type can be downloaded as html file via the download button in two forms. First one is as a heatmap and the second one shows the speed in every step of the trips in accident area. The third file shows a heatmap of the speed in the accident area per level of accident (fatal, serious, light)
 BITS (2021), BITS – Bicycles and Intelligent Transport Systems, https://northsearegion.eu/bits/.
 Communications CIE Oldenburg Bike Challenge and Company Challenge proves: App with gamification and tracking functions can lead to an uptake of cycling [Online] // northsearegion. – 2021. – https://northsearegion.eu/bits/news/oldenburg-bike-challenge-and-company-challenge-proves-app-with-gamification-and-tracking-functions-can-lead-to-an-uptake-of-cycling/.
 Programme North Sea Region The Bike Challenge “cycle to work” starts in Germany [Online]. – 2021. – https://northsearegion.eu/bits/news/the-bike-challenge-cycle-to-work-starts-in-germany/ .