Post on 30-Dec-2015
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Investigating the determinants of a Peer-to-peer (P2P) car
sharing. The case of Milan
Ilaria Mariotti
Paolo Beria
Antonio Laurino
DAStU, Politecnico di MilanoSIET 2013
Venezia, September 18th – 20th , 2013
STRUCTURE
• Aim
• Literature review on P2P
• Data and methodology
• Descriptive statistics
• Econometric analysis
• Discussion and conclusions
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
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 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.
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
Dependent variableAnswers Answers – Multinomial logit Answers – binomial logit
Yes, with all people that joined the service
Yes, with all people joining the service
1 Yes
1
Yes, but only with an entourage of people I choose
Yes, with the people I know (friends, neighbors and colleagues)
2 Yes, but only with my neighbours
Yes, but only with my colleagues
No, because the car is a personal effect No
0
No
0
No, because I want the car always available No, because I do not need to deprive me of my car * question: “Would you be interested, under these conditions (…) to share your car (or one of your cars) at the time of the day you indicate?”
Explanatory variablesVariable Description
Gender Dummy variable: 1 “ if male, 0 “ if female.
Age Age of the respondent
Education Dummy variable: 1 “ if the respondent achieved a bachelor degree, “0 otherwise
N. of owned cars Number of cars owned by the family
Oil price Dummy variable: 1“ if the respondent has changed his/her travel patterns, 0“ otherwise.
District of residence District 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 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
Descriptive statistics (1)•53.4% potential sharers
35%
55%
6%4%
All P2P members Group of people
Neighbours Colleagues
Descriptive statistics (3)
Potential sharers Non- sharers
LPT 26.6 24.1 Bike 11.2 6.4 Foot 15.5 15.4 Motorcycle 6.7 5.1 Car-driver 35.9 42.7 Car-passenger 4.0 6.2
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
Model 1 Model 2 Model 3
Age -0.0124*** -0.0121** -0.0123**
Gender 0.2174* 0.2158 0.1980
Degree 0.2701*** 0.2705** 0.2502*
Number of owned cars 0.2794*** 0.2853*** 0.2856***
LPT 0.3652*** 0.2915* 0.3217*
Bike 0.6610*** 0.6638*** 0.6579***
Foot 0.1597 0.1688 0.1663
Motorcycle 0.3271 0.3107 0.3104
Car (driver) -0.0058 -0.0067 0.000
Car (passenger) -0.1482 -0.1637 -0.0949
Carsharing Member 0.9872*** 0.9772*** 0.9994***
Area C- car use reduction 0.3317*** 0.3397*** 0.3473*** Oil price increase -car use reduction
0.5079*** 0.5066*** 0.5306***
To reach the workplace -0.0998 -0.1132
LTP stop 0.4661** 0.4410*
Neighbourhood 0.0927 0.1050
Leisure in the city -0.0729 -0.0677
Constant -0.8179*** -0.8079*** -0.7691**
n. obs. 1129 1129 1129
Log Likelihood -730.3661 -727.9935 -722.9772
PseudoR2 0.0636 0.0666 0.0730
Results Group 1
GROUP 0:Those not interested to join a P2P CS system
Group 1: all members Model 1 Model 2 Model 3
Age -0.001 -0.000 -0.0010
Gender 0.568*** 0.581*** 0.5601***
Degree 0.428*** 0.437*** 0.3936***
Number of owned cars 0.374*** 0.377*** 0.3850***
LPT 0.609*** 0.516*** 0.5282***
Bike 0.931*** 0.942*** 0.9268***
Foot 0.003 0.021 0.0072
Motorcycle 0.499 0.489 0.4720
Car (driver) 0.214 0.226 0.2449
Car (passenger) 0.302 0.305 0.3823
CS Member 0.950*** 0.931*** 0.9593***
Area C- car use reduction 0.207 0.212 0.2189
Oil price increase -car use reduction 0.403*** 0.406*** 0.4362***
To reach the workplace -0.205 -0.2114
LTP stop 0.562** 0.5230*
Neighbourhood 0.265 0.2747
Leisure in the city -0.043 -0.0262
Constant -2.8898*** -2.9049*** -2.8665***
Results Group 2
GROUP 0:Those not interested to join a P2P CS system
Group 2: Friends, neighbours Model 1 Model 2 Model 3
Age -0.0186*** -0.0184*** -0.0185***
Gender 0.0255 0.0191 0.0039
Degree 0.1834 0.1817 0.1679
Number of owned cars 0.2192*** 0.2263*** 0.2227***
LPT 0.2264 0.1652 0.2022
Bike 0.5014*** 0.4990*** 0.4949***
Foot 0.2253 0.2293 0.2309
Motorcycle 0.2241 0.2024 0.2035
Car (driver) -0.1246 -0.1337 -0.1359
Car (passenger) -0.4143 -0.4354 -0.3788
CS Member 0.9938*** 0.9871*** 1.0102***
Area C- car use reduction 0.3979*** 0.4055*** 0.4147***
Oil price increase -car use reduction 0.5673*** 0.5669*** 0.5903***
To reach the job place -0.0391 -0.0559
LTP stop 0.3984 0.3836
Neighbourhood -0.0053 -0.0104
Leisure in the city -0.0819 -0.0834
Constant -07010 -0.6882 -0.6434
n. obs. 1129 1129 1129
Log Likelihood -1107.8923 -1104.2871 -1096.0491
PseudoR2 0.0548 0.0579 0.0649
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).
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.
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.