1. Categorization of bike sharing systems
A case study from Budapest
Authors:
Tamás MÁTRAI, PhD Student
János TÓTH (PhD), Head of Department

2. Motivations
People lives in urban area will grow from 3,3 to 6,4 billion until 2050.
In several of her presentation Robin Chase quoted Stanford professor Banny Banerjee : "You can’t solve exponential problems with linear solutions.„
Sharing economy can be this non-linear solution.
Unfortunately, there is no comprehensive, worldwide comparative evaluation of the PBS systems. The data about the systems are not available in every cases.

3. Understanding PBS

4. Generations of Public Bike Sharing
First generation- Free systems (1965)
e.g.: White bikes - Amsterdam
Regular bikes with diferentiating colour
Free access (Anonim)
Free usage
No fix stations
Second generation- Coin based systems (1995)
e.g.: Bycyklen - Coppenhagen
Custom bikes
Access with coin (Anonim)
Fix stations
Third generation- ICT based systems (1998)
e.g.: Vélos á la Carte, Rennes
Custom bikes
Access with user card (User identification - registration required)
Free (usually in the first 30 minutes)
Fix station
Fourth generation systems - Complex, integrated systems (2005)
e.g.: Mol-Bubi, Budapest
Access with mobile device (User identification - registration required)
Real time information provision
Large scale integration with different systems

5. Components of the Framework
Database
PBS systems
~100 parameters
Categorization
Clustering
SWOT
Benchmark
Multi-Criteria Assessment
Absolute scale
Impact assessment
Transport modelling
Economic Analysis
System dynamics

6. Database Business model Socio-demographic background Fare system
type of the owner
type of the operator
main source of incomes
Business model
annual subscription fee
daily subscription fee
usage fee
integration with other services
Fare system
number of annual users
number of trips, revenue
Impact and usage statistics
main goal
other goals
Goals
city size
population
climate
topography
Socio-demographic background
length of PT network
modal share of different modes
length of dedicated cycling infrastructure
number of daily trips
Transport system details
service area size
number of docks
number of bikes
capital costs
annual operating expenditure
Base data of the PBS system

7. Categorization Main categorization criteria
City based
PBS system size based
Business model based
Accessibility based
There is no need for all of the parameters in clustering.
Our assumption is that 90% of the systems could be classified into one of the 5 clusters.
Based on the database
Determining grouping criteria
Cluster analysis
SWOT for all categories
Development of a self-assessment tool

8. Data about the Budapest system
Budapest in general
Mol BUBI
Population: inhabitants
Budapest downtown area 15 km2
Land area: 525 km2
98 docking station (2116 stands)
Road network length: km
1200 bicycles
Car trips per day: ~
~ € total cost
PT system length: km
Support in operation: MOL Hungary
PT passenger trips per day:
2000 trips / day
DCI length: 288 km
Cycling trips per day: ~

9. Mol Bubi in pictures

10. SWOT for Mol Bubi favorable geographical location
Strengths
favorable geographical location
dense dock stations network
intelligent bicycle distribution
On-board unit on the bikes
Weaknesses
Robust and heavy bikes
Long-term access with personel registration only
Opprtunities
growing share of cycling
growing tourism
climate change
incomplete bicycle network
dangerous cycling on some roads
effects of politics
Threats

11. Benchmark tool Based on the database
Assessment all systems in the database
Development of a self-assessment tool
Determination of the scale for each criterion
Transfers the qualitative criteria (objective)
Description of the qualitative criteria (subjective)
Assignment weights for the criteria
Based on the user surveys
Based on the operator surveys
Selection of the benchmark criteria
Important for the users
Important for the operators
Based on the database

12. Impact assessment Evaluation of social impacts
System dynamics
Impact on cycling
Impact on dedicated cycling infrastructure
Impact on transport culture
Calculating Economic Benefits
Travel Time reliability
Travel time reduction
Health benefit
Transport modelling
Impact on congestion
Mode choice
Route choice

13. Summary This paper illustrates the interim status of a PhD research.
The collection of data is the main task. Parallel with this, an elaboration of guidelines is in progress.
This tool could be use on a different level.
The methodology with a detailed list of parameters could be a planning aid and a shortlist of the properties could give a simply comparison of different systems for non-professional users.
The principle now is clear but the detailed elaboration is still under progress, it will be finished in this year.

14. Thank you for your Attention!
Tamás MÁTRAI
PhD Student, BME