Exploring Travelers’ Characteristics Affecting their Intention to Shift to Bike-Sharing Systems due to a Sophisticated Mobile App †
Abstract
:1. Introduction
- 1st generation, “Free Bike Systems” or “White Bikes”: its name originates from the “White Bike system in Amsterdam. The failure of this generation is attributed to the vigorous phenomena of vandalism and theft, which are the result of complete anonymity and lack of surveillance.
- 2nd generation, “Coin-Deposit Systems”: they owe their name to the fact that bike rental in this generation’s systems was carried out through coin deposit in the stations. The coin was returned to the user after completing the rental. These systems firstly appeared in Danish cities in 1991 and the establishment of the Bycyklen system in Copenhagen was a landmark. As in the case of the first generation systems, the anonymity of the users resulted in frequent phenomena of vandalism and theft.
- 3rd generation, “Information Technology-Based Systems”: through the utilization of information technology, it was sought to collect data about the rentals and the users. Bike-sharing systems launched smart cards or mobile phone applications, through which the rental process was being done. Advertising companies (Clear Channel, JCDecaux) had a significant contribution to the growth of such systems and the establishment of the Velo’v system in Lyon can be considered as an important milestone.
- 4th generation, “Demand Responsive, Multi-Modal Systems”: the characteristics of this (current) generation are not clearly defined, but it is anticipated that the main attributes that will differentiate it from the third generation are the following: (a) integration with other sharing systems (e.g., car-sharing, scooter-sharing) and public transport, (b) further utilization of technology (e.g., usage of GPS devices for bicycle tracking, usage of electric-assist bicycles), (c) improved methods and algorithms for the re-distribution of bicycles, (d) flexible/mobile stations, which can be relocated according to the usage patterns or total absence of stations (dockless systems). It should be mentioned that the international experience has shown that specific regulations are needed for the dockless systems since they can cause important defects within the built environment [5,6].
- Infrastructure: the existence of appropriate infrastructure and in particular bicycle lane networks providing safe and comfortable movement, in any destination, is a key factor for bicycle use [13], especially for commuters with limited cycling experience [14]. Except for the bicycle lane networks, it is also of great importance to form safe and secure parking spaces for bicycles, to provide cyclists priority at signalized intersections and to implement restrictions for private cars [15].
- Trips: bicycles are considered to be preferable for short daily trips, but not shorter than one kilometer, since in this case it can be easily replaced by walking [16]. Also, it has been shown that when the final destination is near to the city center, bicycle use is more likely to be preferred [17].
- Travelers: in countries with limited cycling experience and less developed cycling culture, it has been identified that males and especially those of a young age are more likely to choose cycling for commuting. However, it should be mentioned that in well-established cycling cities, no differentiations based on gender or age can be found [16,18]. Private car ownership and attitudes towards cycling are also two aspects of great importance [17].
2. Materials and Methods
2.1. Case Study
2.2. Survey Design
- Section I: It is exactly the same in the two questionnaires. The questions that it contains concern the following: (a) gender, (b) age, (c) address, (d) occupation, (e) level of education, (f) household income, (g) exercise frequency, (h) private car ownership, (i) bicycle ownership.
- Section II: It is also exactly the same in the two questionnaires. In this section the respondents are asked to describe their most usual trip within the day (origin, destination, purpose, hour, transport mode), to reveal the frequency of using a private car or bicycle, to describe their most usual trip with bicycle (origin, destination, purpose, hour, percentage of the journey carried out through bicycle lanes), and to state the conditions that are needed to be met for using a bicycle more frequently for commuting.
- Section III: The differences between the two questionnaires are identified in this section. Existing users of the ThessBike system were called to complement: (a) an area that they would like to be served by a bike-sharing station, (b) the most usual purpose of their trip when using the ThessBike system, (c) a grade for five different attributes (number and location of stations, quality of bicycles, rental process, rental cost, provided information) of the ThessBike system, (d) a grade for the usefulness of thirteen different services that a bike-sharing app can provide and (e) if a state-of-the-art app, which will provide all the thirteen services, would guide them to use the ThessBike system more often. On the other hand, potential users were asked to fill in: (a) an area that they would like to be served by a bike-sharing station, (b) a grade about the effect that specific factors have in their decision not to use the system, (c) a grade for the usefulness of thirteen different services that a bike-sharing app can provide and (d) if an app, that will provide all the thirteen services, would guide them to register to the ThessBike system. It should be mentioned that for the evaluation of the ThessBike attributes, the usefulness of the app services and the effect of the factors in not using the system, a five-point Likert scale was used [24].
3. Results
3.1. Descriptive Statistics
3.1.1. Existing Users
3.1.2. Potential Users
3.2. Classification Tree
3.2.1. Classification Tree Development and Evaluation
3.2.2. Interpretation of the Classification Tree Results
- the group of people that has the highest probability to shift towards the bike-sharing system owing to the mobile app, is the one that includes potential users aged between 18 and 54 (green box),
- the group of people that has the lowest probability to shift towards the bike-sharing system owing to the mobile app, is the one that includes people older than 54 years old, who are retired or they are occupied as private employees (red box),
- regarding the existing users, the group of people that has the highest probability to be positively affected by the mobile app is the one that includes people aged between 18 and 54 that they are occupied as state employees or they are students.
3.3. Binary Logit Model
3.3.1. Binary Logit Model Development and Evaluation
- Akaike information criterion (AIC) value: the selected model achieves the lowest AIC value, which is 161.37.
- Pseudo R2: McFadden’s R2 suggests that the selected model can explain about 20% of the variance of the dependent variable.
- Prediction accuracy: the accuracy of the model has been found to be equivalent to 78.68% (true positive rate = 95.68%, true negative rate = 25.71%).
- Receiver Operating Characteristic (ROC) curve: Figure 3 plots the sensitivity (true positive rate) against the specificity (false positive rate). The area under the curve (AUC) is an index of accuracy since it shows the ability of the model to distinguish the two classes. The greater this value (maximum AUC value equals 1) the better the prediction power of the model. The AUC value of the selected model is equal to 0.7864, indicating that there is approximately a 79% chance that the model will achieve to distinguish between positive class and negative class.
3.3.2. Interpretation of the Binary Logit Model Results
4. Discussion
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- European Cyclists’ Federation. Factsheet–The Rise of Bicycle Sharing Schemes; European Cyclists’ Federation: Brussels, Belgium, 2012. [Google Scholar]
- Shaheen, S.; Cohen, A.; Zohdy, I. Shared Mobility: Current Practices and Guiding Principles; U.S. Department of Transportation, Federal Highway Administration: Washington, DC, USA, 2016.
- De Maio, P. Bike-sharing: History, Impacts, Models of Provision, and Future. J. Public Transp. 2009, 12, 41–56. [Google Scholar] [CrossRef]
- Shaheen, S.; Guzman, S.; Zhang, H. Bikesharing in Europe, the Americas, and Asia: Past, Present, and Future. Transp. Res. Rec. J. Transp. Res. Board 2010, 2143, 159–167. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Y.; Lin, D.; Mi, Z. Electric fence planning for dockless bike-sharing services. J. Clean. Prod. 2018, 206, 383–393. [Google Scholar] [CrossRef]
- Ma, Y.; Lan, J.; Thornton, T.; Mangalagiu, D.; Zhu, D. Challenges of collaborative governance in the sharing economy: The case of free-floating bike sharing in Shanghai. J. Clean. Prod. 2018, 197, 356–365. [Google Scholar] [CrossRef]
- Godavarthy, R.P.; Taleqani, A.R. Winter bikesharing in US: User willingness, and operator’s challenges and best practices. Sustain. Cities Soc. 2017, 30, 254–262. [Google Scholar] [CrossRef]
- Zhao, J.; Deng, W.; Song, Y. Ridership and effectiveness of bikesharing: The effects of urban features and system characteristics on daily use and turnover rate of public bikes in China. Transp. Policy 2014, 35, 253–264. [Google Scholar] [CrossRef]
- Stefansdottir, H.; Næss, P.; Ihlebæk, C. Built environment, non-motorized travel and overall physical activity. Travel Behav. Soc. 2018, 16, 201–213. [Google Scholar] [CrossRef]
- Cervero, R.; Denman, S.; Jin, Y. Network design, built and natural environments, and bicycle commuting: Evidence from British cities and towns. Transp. Policy 2018, 74, 153–164. [Google Scholar] [CrossRef]
- Dill, J.; Voros, K. Factors Affecting Bicycling Demand Initial Survey Findings from the Portland, Oregon, Region. Transp. Res. Rec. J. Transp. Res. Board 2007, 2031, 9–17. [Google Scholar] [CrossRef]
- Menghini, G.; Carrasco, N.; Schüssler, N.; Axhausen, K.W. Route choice of cyclists in Zurich. Transp. Res. Part A 2010, 44, 754–765. [Google Scholar] [CrossRef]
- Buehler, R.; Dill, J. Bikeway networks: A review of effects on cycling. Transp. Rev. 2015, 36, 9–27. [Google Scholar] [CrossRef]
- Taylor, D.; Mahmassani, H. Analysis of Stated Preferences for Intermodal Bicycle-Transit Interfaces. Transp. Res. Rec. J. Transp. Res. Board 1996, 1556, 86–95. [Google Scholar] [CrossRef]
- Martino, A.; Maffii, S.; Raganato, P. The Promotion of Cycling. Directorate General for Internal Policies, Policy Department B: Structural and Cohesion Policies; Technical Report; European Parliament: Brussels, Belgium, April 2010. [Google Scholar]
- Barberan, A.; Monzon, A. How did bicycle share increase in Vitoria-Gasteiz? Transp. Res. Procedia 2016, 18, 312–319. [Google Scholar] [CrossRef]
- Barberan, A.; Abreu e Silva, J.; Monzon, A. Factors influencing bicycle use: A binary choice model with panel data. Transp. Res. Procedia 2017, 27, 253–260. [Google Scholar] [CrossRef]
- Aldred, R.; Woodcock, J.; Goodman, A. Does More Cycling Mean More Diversity in Cycling? Transp. Rev. 2016, 36, 28–44. [Google Scholar] [CrossRef] [Green Version]
- Urbanczyk, R. PRESTO Cycling Policy Guide: Promotion of Cycling; Executive Agency for Competitiveness and Innovation (EACI): Brussels, Belgium, 2010. [Google Scholar]
- Savan, B.; Cohlmeyer, E.; Ledsham, T. Integrated strategies to accelerate the adoption of cycling for transportation. Transp. Res. Part F 2017, 46, 236–249. [Google Scholar] [CrossRef]
- Hellenic Statistical Authority. Available online: www.statistics.gr (accessed on 10 September 2019).
- Nikiforiadis, A.; Basbas, S. Can pedestrians and cyclists share the same space? The case of a city with low cycling levels and experience. Sustain. Cities Soc. 2019, 46, 101453. [Google Scholar] [CrossRef]
- Vaitsis, P.; Basbas, S.; Nikiforiadis, A. How eudaimonic aspect of subjective well-being affect transport mode choice? The case of Thessaloniki, Greece. Soc. Sci. 2019, 8, 9. [Google Scholar] [CrossRef] [Green Version]
- Likert, R. A Technique for the Measurement of Attitudes. Arch. Psychol. 1932, 140, 1–55. [Google Scholar]
- Loh, W.Y. Classification and regression trees. WIREs Data Min. Knowl. Discov. 2011, 1, 14–23. [Google Scholar] [CrossRef]
- James, G.; Witten, D.; Hastie, T.; Tibshirani, R. An Introduction to Statistical Learning with Applications in R; Springer: Berlin/Heidelberg, Germany, 2013. [Google Scholar]
- Ripley, B. Tree: Classification and Regression Trees. R Package Version 1.0-39. Available online: https://CRAN.R-project.org/package=tree (accessed on 1 May 2019).
- Team. R.C. A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available online: http://www.R-project.org (accessed on 1 May 2019).
- Lesnoff, M.; Lancelot, R. aod: Analysis of Overdispersed Data. R Package Version 1.3.1. Available online: http://cran.r-project.org/package=aod (accessed on 1 May 2012).
- Burnham, K.P.; Anderson, D.R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach; Springer: Berlin/Heidelberg, Germany, 2002. [Google Scholar]
- Alexandri, D.; Iordanopoulos, P.; Chrysostomou, K.; Mitsakis, E. Impacts of advanced traveler information systems: The case of the city of Patras. In Proceedings of the 4th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Budapest, Hungary, 3–5 June 2015. [Google Scholar]
Estimate | Std. Error | z-Value | p-Value | |
---|---|---|---|---|
Intercept | −2.7973 | 1.1514 | −2.429 | 0.01512 |
user: no | −1.2866 | 0.4591 | −2.802 | 0.00508 |
age: 25–39 | 0.9286 | 1.1104 | 0.836 | 0.40302 |
age: 40–54 | 1.5090 | 1.1620 | 1.299 | 0.19407 |
age: 55–64 | 2.9870 | 1.2176 | 2.453 | 0.01416 |
age: >64 | 20.1348 | 1160.8338 | 0.017 | 0.98616 |
private car owner: no | 0.8164 | 0.5405 | 1.511 | 0.13090 |
Odds Ratios | 2.5% | 97.5% | |
---|---|---|---|
Intercept | 0.061 | 0.003 | 0.412 |
user: no | 0.276 | 0.106 | 0.655 |
age: 25–39 | 2.531 | 0.407 | 49.432 |
age: 40–54 | 4.522 | 0.641 | 93.485 |
age: 55-64 | 19.825 | 2.465 | 436.928 |
age: >64 | 5.551533 × 108 | 5.123544 × 10−49 | na |
private car owner: no | 2.262 | 0.777 | 6.629 |
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Nikiforiadis, A.; Chrysostomou, K.; Aifadopoulou, G. Exploring Travelers’ Characteristics Affecting their Intention to Shift to Bike-Sharing Systems due to a Sophisticated Mobile App. Algorithms 2019, 12, 264. https://doi.org/10.3390/a12120264
Nikiforiadis A, Chrysostomou K, Aifadopoulou G. Exploring Travelers’ Characteristics Affecting their Intention to Shift to Bike-Sharing Systems due to a Sophisticated Mobile App. Algorithms. 2019; 12(12):264. https://doi.org/10.3390/a12120264
Chicago/Turabian StyleNikiforiadis, Andreas, Katerina Chrysostomou, and Georgia Aifadopoulou. 2019. "Exploring Travelers’ Characteristics Affecting their Intention to Shift to Bike-Sharing Systems due to a Sophisticated Mobile App" Algorithms 12, no. 12: 264. https://doi.org/10.3390/a12120264