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Investigating the determinants of a Peer-to-peer (P 2 P) car sharing. The case of Milan Ilaria Mariotti Paolo Beria Antonio Laurino DASt. U, Politecnico di Milano SIET 2013 Venezia, September 18 th – 20 th , 2013
STRUCTURE • Aim • Literature review on P 2 P • Data and methodology • Descriptive statistics • Econometric analysis • Discussion and conclusions
AIM • Investigate the main determinants to join a P 2 P 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).
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
Literature review (2) • Literature on P 2 P system is scanty ▫ Hampshire and Gaites (2011) emphasise the higher accessibility that P 2 P 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.
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 P 2 P carsharing is investigated by means of a descriptive statistics, which results are corroborated by a binomial logit model and a multinomial logit model
Explanatory variables Socio economic Description Gender Dummy variable: 1 “ if male, 0 “ if female. Age of the respondent Education Green Attitude Travel behaviour Variable Dummy variable: 1 “ if the respondent achieved a bachelor degree, “ 0 otherwise Number of cars owned by the family Dummy variable: 1“ if the respondent has changed his/her travel patterns, 0“ otherwise. District where the respondent lives. Dummy variable. Six dummy variables suggesting the main modal choice adopted by the respondent. District of residence Modal choice: -LPT, Bike, Foot, Motorcycle, Car (driver), Car (passenger) Daily travel by car for: Six dummy variables underlying why the respondent -reaching the workplace, or the LPT stop uses the car daily or very often. -moving within the neighbourhood or outside -leisure in the city, other motives Car sharing member Dummy variable: 1“ if the respondent is or has been member of CS services, 0 “ otherwise. Area C tool and travel behaviour change Dummy variable: 1 “ if the respondents have reduced the car use consequently the Area C introduction, 0“ otherwise N. of owned cars Oil price
Descriptive statistics (1) • 53. 4% potential sharers
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
Binomial logit model
Results Group 1 GROUP 0: Those not interested to join a P 2 P CS system
Results Group 2 GROUP 0: Those not interested to join a P 2 P CS system
Results (1) The probability to join a P 2 P 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).
Results (2) When comparing the users willing to share their own car with all members of the P 2 P 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.
CONCLUSIONS • Relevance of the three groups of determinants: socioeconomic, travel behavior and green attitude. • Potential users are sensitive to CS systems – being or having being members of the Milan CS –, and are costsensitive (i. e. oil price increase and Area C policy tool). Besides, they prefer to ride the bike or use the LPT to travel.
Thank you Questions and suggestions are welcome Ilaria Mariotti DASt. U – Politecnico di Milano ilaria. [email protected] it