1. 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 SIET 2013 Venezia, September 18th – 20th, 2013

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

3. AIM 1,129 Milan citizens have been surveyed (Green Move project). 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

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 VariableDescription GenderDummy variable: 1 “ if male, 0 “ if female. AgeAge of the respondent EducationDummy variable: 1 “ if the respondent achieved a bachelor degree, “0 otherwise N. of owned carsNumber of cars owned by the family Oil priceDummy variable: 1“ if the respondent has changed his/her travel patterns, 0“ otherwise. District of residenceDistrict where the respondent lives. Dummy variable. Modal choice: -LPT, Bike, Foot, Motorcycle, Car (driver), Car (passenger) Six dummy variables suggesting the main modal choice adopted by the respondent. Daily travel by car for: -reaching the workplace,or the LPT stop -moving within the neighbourhood or outside -leisure in the city, other motives Six dummy variables underlying why the respondent uses the car daily or very often. Car sharing memberDummy variable: 1“ if the respondent is or has been member of CS services, 0 “ otherwise. Area C tool and travel behaviour changeDummy variable: 1 “ if the respondents have reduced the car use consequently the Area C introduction, 0“ otherwise Socio economic Travel behaviour Green Attitude

9. Descriptive statistics (1) 53.4% potential sharers

10. Descriptive statistics (2)

11. 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

12. Binomial logit model

13. Results Group 1 GROUP 0: Those not interested to join a P2P CS system

14. Results Group 2 GROUP 0: Those not interested to join a P2P CS system

15. 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).

16. 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.

17. 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.

18. Questions and suggestions are welcome Ilaria Mariotti DAStU – Politecnico di Milano ilaria.mariotti@polimi.it