1 / 17

Investigating the determinants of a Peer-to-peer (P2P) car sharing. The case of Milan

Investigating the determinants of a Peer-to-peer (P2P) car sharing. The case of Milan. Ilaria Mariotti Paolo Beria Antonio Laurino DAStU , Politecnico di Milano. STRUCTURE. Aim Literature review on P2P Data and methodology Descriptive statistics Econometric analysis

jessamine-bentley
Download Presentation

Investigating the determinants of a Peer-to-peer (P2P) car sharing. The case of Milan

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Investigating the determinants of a Peer-to-peer (P2P) car sharing. The case of Milan IlariaMariotti Paolo Beria Antonio Laurino DAStU, Politecnicodi Milano

  2. STRUCTURE • Aim • Literature review on P2P • Data and methodology • Descriptive statistics • Econometric analysis • Discussion and conclusions

  3. AIM • Investigate the main determinants to join a P2P car sharing system by means a descriptive statistics and two discrete choice models: binomial logit model and multinomial logit model 1,129 Milan citizens have been surveyed (Green Move project).

  4. Literature review (1) • Ex-post analyses on Car Sharing (CS) prevail • Main determinants to join CS: • density of users aged 25 – 45, single or living in small households • well educated with an income higher than the average • cost sensitive • environmentally conscious • good public transport service • CS mainly used for recreation/social activities

  5. Literature review (2) • Literature on P2P system is scanty • Hampshire and Gaites (2011) emphasise the higher accessibility that P2P scheme could entail, in particular in lower density areas, thanks to the almost total absence of the upfront costs that a traditional CS operator has to bear to buy its fleet. • Hampshire and Sinha (2011) analyze the main trade-off of balancing car utilization with reservation availability.

  6. Data and methodology • Dataset – Green Move survey conducted in 2012 among the inhabitants of the municipality of Milan (1,129 respondents) • The probability to undertake a P2P carsharing is investigated by means of a descriptive statistics, which results are corroborated by a binomial logit model and a multinomial logit model

  7. Dependent variable

  8. Explanatory variables Socio economic Travel behaviour GreenAttitude

  9. Descriptive statistics (1) • 53.4% potential sharers

  10. Descriptive statistics (3) Respondents’ travel behavior 9% of the potential sharers are or have been members of the Milan CS vs. 2.5% of the non users

  11. Binomial logit model

  12. Results Group 1 GROUP 0: Thosenotinterestedto join a P2P CS system

  13. Results Group 2 GROUP 0: Thosenotinterestedto join a P2P CS system

  14. Results (1) The probability to join a P2P CS is positively and significantly related to: • users’ education (bachelor degree), • car ownership (more than two cars), • travel behaviour (LPT and bike), • CS membership (previous or present), • cost sensitiveness (i.e. oil price increase).

  15. Results (2) When comparing the users willing to share their own car with all members of the P2P system (confident shares), it results that they tend to be: • male, • use the car daily to reach the LPT stop, • have reduced the car use because of the Area C, • are less willing to live in zone 9. While, those willing to share their own car only with a selected group of people, tend to be: • younger, • use the bike to travel, • are less willing to live in zone 7.

  16. CONCLUSIONS • Relevance of the three groups of determinants: socio-economic, travel behavior and green attitude. • Potential users are sensitive to CS systems – being or having being members of the Milan CS –, and are cost-sensitive (i.e. oil price increase and Area C policy tool). Besides, they prefer to ride the bike or use the LPT to travel.

  17. Thank you Questions and suggestions are welcome IlariaMariotti DAStU – Politecnico di Milano ilaria.mariotti@polimi.it

More Related