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.

Questions and suggestions are welcome

Ilaria Mariotti

DAStU – Politecnico di Milano ilaria.mariotti@polimi.it