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

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.

Understanding PBS

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

Components of the Framework Database 50-100 PBS systems ~100 parameters Categorization Clustering SWOT Benchmark Multi-Criteria Assessment Absolute scale Impact assessment Transport modelling Economic Analysis System dynamics

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

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

Data about the Budapest system Budapest in general Mol BUBI Population: 1 750 000 inhabitants Budapest downtown area 15 km2 Land area: 525 km2 98 docking station (2116 stands) Road network length: 4 500 km 1200 bicycles Car trips per day: ~1 600 000 ~3 670 000 € total cost PT system length: 3 300 km Support in operation: MOL Hungary PT passenger trips per day: 2 220 000 2000 trips / day DCI length: 288 km Cycling trips per day: ~150 000

Mol Bubi in pictures

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

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

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

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.

Thank you for your Attention! Tamás MÁTRAI PhD Student, BME tamas.matrai@mail.bme.hu +36-20-260-6171 +36-1-463-3742