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Bike Sharing Systems in Munich: a (Non)Users' Behavioural, Socio–Demographic and Psychographic Analysis

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Bike Sharing Systems in Munich: a (Non)Users' Behavioural, Socio–Demographic and Psychographic Analysis Technical University of Munich Chair of Urban Structure and Transport Planning Master’s Thesis Bike Sharing Systems in Munich: A (non)users’ behavioural, socio–demographic and psychographic analysis. Author: Michael St¨ockle Supervised by: Prof. Dr.-Ing. Gebhard Wulfhorst M.Sc. David Dur´an Rodas M.Sc. Maximilian Pfertner January 24, 2020 II Abstract The operation of Bike Sharing Systems is associated with benefits like reducing emissions and improving the health conditions of the users. In order to further increase BSS ridership, marketing strategies and incentives have to be developed. This requires a detailed knowledge of both users and non-users of the systems. The purpose of this research was to gather this information by identifying the peoples’ attributes that are associated to BSS usage. For this purpose, a survey was developed based on market segmentation theory, containing questions on the socio-demographic, behavioural, attitudinal and psychographic attributes of the participants. The survey was executed in Munich, Germany from October 24 to December 16, 2019 and generated a sample of 408 responses. The attributes associated to BSS usage were explored with a multiple logistic regression and with the LASSO selection method. Attributes significantly associated to BSS usage were identified in all categories of the questionnaire. Attributes with a negative influence on BSS usage include, among others: being female, considering tradition important, having a high school education or never having used car sharing or e-scooters. Attributes with a positive influence on BSS usage include, among others: being between 30 to 39 years old, having an household net income of 6000 to 7000 Euro, using the private bike frequently, and having a rather negative opinion towards car driving. III IV Contents 1 Introduction 1 1.1 Problemstatement ................................ 1 1.2 Need........................................ 1 1.3 Objectivesandresearchquestions . ...... 2 1.4 Thesisframeworkandreportstructure . ...... 2 2 Literature review 5 2.1 BikeSharingSystems(BSS) . .. 5 2.1.1 BikeSharingSystems(BSS)ingeneral . ... 5 2.1.2 BSSworldwide .............................. 8 2.1.3 BSSinGermany ............................. 10 2.1.4 PositiveimpactsofBSS ......................... 11 2.2 Marketsegmentation .............................. 13 2.2.1 Introductiontomarketsegmentation . .... 13 2.2.2 Typesofmarketsegmentation . 14 2.3 AttributesinfluencingtheBSSusage . ..... 16 2.3.1 Socio-demographicattributes . ... 16 2.3.2 Psychographicattributes . .. 17 2.3.3 Attitudinalattributes. .. 18 2.3.4 Behaviouralattributes . 18 2.3.5 Geographicattributes . 19 2.4 Relatedwork ................................... 19 3 Methodology 21 3.1 DataCollection:Survey . .. 23 3.1.1 Definitionofsurveyparameters . .. 23 3.1.2 Surveydesign ............................... 25 3.1.3 PilotSurvey................................ 30 3.1.4 Onlinesurveyexecution . 33 3.1.5 Offlinesurveydesignanddistribution . .... 34 3.2 DataAnalysis................................... 35 3.2.1 Data cleaning and BSS users determination . .... 35 3.2.2 Surveystatistics.............................. 36 3.2.3 ExploratoryDataAnalysis(EDA). 36 3.2.4 MultipleLogisticRegression(MLR) . ... 38 V VI CONTENTS 3.2.5 LASSO .................................. 39 4 Case of study: Munich 41 4.1 KeyfactsonMunich ............................... 41 4.2 BSSinMunich .................................. 42 5 Results 49 5.1 DataCollection:Survey . .. 49 5.1.1 Surveyparameters ............................ 49 5.1.2 Surveyquestionnaire . 50 5.1.3 Pilotsurvey ................................ 50 5.1.4 Onlinesurveyexecution . 51 5.1.5 Offlinesurveyexecution . 54 5.2 Surveydataanalysis .............................. 55 5.2.1 Data cleaning and BSS users determination . .... 55 5.2.2 Surveystatistics.............................. 56 5.3 ExploratoryDataAnalysis(EDA). .. 60 5.3.1 GeneralfindingsonBSSinMunich . 61 5.3.2 Socio-demographic attributes and BSS usage . ..... 63 5.3.3 BehaviouralattributesandBSSusage. .... 64 5.3.4 TrippurposeandBSSusage . 66 5.3.5 AttitudinalattributesandBSSusage . .... 68 5.3.6 PsychographicattributesandBSSusage . .... 71 5.4 Multiple logistic regression (MLR) results . ......... 72 5.4.1 Socio-demographicattributes . ... 72 5.4.2 Behaviouralattributes . 75 5.4.3 Trippurpose ............................... 77 5.4.4 Attitudinalattributes. .. 81 5.4.5 Psychographicattributes . .. 84 5.5 LASSOresults................................... 86 5.5.1 Socio-demographicattributes . ... 86 5.5.2 Behaviouralattributes . 87 5.5.3 Trippurpose ............................... 87 5.5.4 Attitudinalattributes. .. 88 5.5.5 Psychographicattributes . .. 89 6 Discussion 91 6.1 Discussionofresults ............................. .. 91 6.1.1 Surveyresults............................... 91 6.1.2 Exploratory Data Analysis and Modelling results . ...... 92 6.2 Strengths and Limitations of the research . ........ 93 7 Conclusions and further research 95 7.1 Conclusions .................................... 95 7.2 Recommendationsforfurtherresearch . ...... 96 CONTENTS VII Appendices 107 A Residential location of BSS users and non-users in Munich 109 B Online survey questionnaire 111 VIII CONTENTS List of Figures 1.1 Thesisstructure................................. 4 2.1 Overview of the different financing, ownership and operation concepts of BSS 8 2.2 NumberofbikesinBSSworldwide . 9 2.3 Worldwide market volume of bike sharing services 2013-2021......... 9 2.4 Share of onliners who used BSS through apps or websites during the last 12 months....................................... 10 2.5 MostfrequentlyusedBSSinGermany2019 . .... 11 3.1 Methodologystructure . .. 22 3.2 ResultofaMLR ................................. 39 3.3 CodeusedfortheLASSOandthesecondMLR . 40 4.1 DBCallaBikebicycle .............................. 42 4.2 MVGRadbicycleatabikestation . .. 43 4.3 DonkeyRepublicbicycle . .. 44 4.4 JumpBikebicycle ................................ 44 4.5 OperationareaofBSSinMunich . .. 46 5.1 Residential location of survey participants . .......... 57 5.2 Brand awareness of the different BSS in Munich, N=408 . ...... 61 5.3 ReasonsfornotusingBSS . 62 5.4 Socio-demographic attributes and BSS usage, N=408 . ........ 63 5.5 Behavioural attributes and BSS usage, N=408 . ...... 65 5.6 TrippurposeandBSSusage,N=408 . 67 5.7 TrippurposeandBSSusage,N=408 . 68 5.8 Attitudinal attributesand BSS usage, N=408 . ...... 69 5.9 Psychographic attributes and BSS usage, N=408 . ....... 71 5.10 MLR significant socio-demographic variables-part 1 . ............ 73 5.11 MLR significant socio-demographic variables-part 2 . ............ 74 5.12 MLR significant behavioural variables . ........ 76 5.13 MLR significant trip purpose variables - part 1 . ......... 78 5.14 MLR significant trip purpose variables - part 2 . ......... 79 5.15 MLR significant trip purpose variables - part 3 . ......... 80 5.16 MLR significant attitudinal variables - part 1. .......... 82 5.17 MLR significant attitudinal variables - part 2. .......... 83 IX X LIST OF FIGURES 5.18 MLR significant psychographic variables . ........ 85 5.19 Socio-demographic attributes associated to BSS usage ............. 86 5.20 Behavioural attributes associated to BSS usage . .......... 87 5.21 Trip purpose attributes associated to BSS usage . .......... 87 5.22 Attitudinal attributes associated to BSS usage . ........... 88 5.23 Attitudinal variables influencing BSS usage . .......... 89 A.1 Residential location of survey participants in Munich . ........... 110 List of Tables 3.1 Evaluationtableforpilotsurvey. ...... 32 4.1 BSSinMunichasofDecember2019 . 47 5.1 Facebook groups where the survey was shared and member numbers. 52 5.2 Surveyparticipationandresponserate . ....... 54 5.3 Impactofdatacleaningonsamplesize . ..... 56 5.4 Comparison of the sample with MID and city of Munich data . ....... 59 XI XII LIST OF TABLES List of Abbreviations AIO Activities-Interests-Opinions ASZ Alten- und Servicezentrum BSS Bike Sharing Systems DB Deutsche Bahn AG EDA Exploratory Data Analysis ESS European Social Survey FFBS Free-floating Bike Sharing GHG Greenhouse Gases GIS Geographic Information System GLM Generalized Linear Model HBS Hybrid Bike Sharing LASSO Least absolute shrinkage and selection operator MID Mobilit¨at in Deutschland MLR Multiple Logistic Regression MVG M¨unchner Verkehrsgesellschaft MVV M¨unchner Verkehrs- und Tarifverbund PT Public Transport PVQ Portraits Value Questionnaire SBBS Station-based Bike Sharing SEPA Single Euro Payment Area SBI Strategic Business insights SVS Schwartz Value Survey SWM Stadtwerke M¨unchen TUM Technische Universit¨at M¨unchen VSBBS Virtually station-based Bike Sharing WGS World Geodetic System XIII XIV LIST OF TABLES Acknowledgements First of all, I would like to thank my supervisors Prof. Dr.-Ing. Gebhard Wulfhorst, M.Sc. David Dur´an Rodas and M.Sc. Maximilian Pfertner for their big support during this thesis. “Thank you!” also to everyone who helped spreading my survey. Without you, it would not have been possible to reach so many answers. In this context, special thanks go to the employees, the inhabitants and the guests of the “Alten- und Servicezentrum Maxvorstadt”, who made it possible for me to conduct a part of my survey there. Furthermore, I am grateful to all the people who supported me during the thesis, my family and my friends. XV XVI LIST OF TABLES Chapter 1 Introduction 1.1 Problem statement Between 1995 and 2017, car traffic in Germany has increased by almost 18% Umweltbun- desamt
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