Transcript Investigating the determinants of a Peer-to

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
STRUCTURE
• Aim
• Literature review on P2P
• Data and methodology
• Descriptive statistics
• Econometric analysis
• Discussion and conclusions
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).
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 variable
Answers
Yes, with all people that joined the
service
Yes, but only with an entourage of
people I choose
Yes, but only with my neighbours
Yes, but only with my colleagues
No, because the car is a personal effect
No, because I want the car always
available
No, because I do not need to deprive me
of my car
Answers – Multinomial logit
Yes, with all people
joining the service
Answers – binomial logit
1
Yes, with the
people I know
(friends, neighbors
and colleagues)
2
No
0
Yes
1
No
0
* 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 variables
Socio economic
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
Number of cars owned by the family
Green Attitude Travel behaviour
Variable
District of residence
N. of owned cars
Oil price
Modal choice:
- LPT, Bike, Foot, Motorcycle, Car
(driver), Car (passenger)
Daily travel by car for:
- reaching the workplace,or the LPT stop
- moving within the neighbourhood or
outside
- leisure in the city, other motives
Car sharing member
Area C tool and travel behaviour change
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.
Six dummy variables underlying why the respondent
uses the car daily or very often.
Dummy variable: 1“ if the respondent is or has been
member of CS services, 0 “ otherwise.
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
4%
6%
35%
55%
All P2P members
Group of people
Neighbours
Colleagues
Descriptive statistics (3)
Respondents’ travel behavior
LPT
Bike
Foot
Motorcycle
Car-driver
Car-passenger
Potential sharers
26.6
11.2
15.5
6.7
35.9
4.0
Non- sharers
24.1
6.4
15.4
5.1
42.7
6.2
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
-0.0124***
-0.0121**
-0.0123**
Gender
0.2174*
0.2158
0.1980
Degree
0.2701***
0.2705**
0.2502*
Number of owned cars
LPT
0.2794***
0.3652***
0.2853***
0.2915*
0.2856***
0.3217*
Bike
Foot
0.6610***
0.1597
0.6638***
0.1688
0.6579***
0.1663
Motorcycle
0.3271
0.3107
0.3104
Car (driver)
-0.0058
-0.0067
0.000
Car (passenger)
Carsharing Member
-0.1482
0.9872***
-0.1637
0.9772***
-0.0949
0.9994***
Area C- car use reduction
Oil price increase -car use
reduction
To reach the workplace
0.3317***
0.3397***
0.3473***
0.5079***
0.5066***
0.5306***
-0.0998
-0.1132
-0.8179***
0.4661**
0.0927
-0.0729
-0.8079***
0.4410*
0.1050
-0.0677
-0.7691**
1129
1129
1129
-730.3661
-727.9935
-722.9772
0.0636
0.0666
0.0730
Age
LTP stop
Neighbourhood
Leisure in the city
Constant
n. obs.
Log Likelihood
PseudoR2
Results
Group 1
GROUP 0:
Those not
interested
to join a P2P
CS system
Model 1
Model 2
Model 3
-0.001
-0.000
-0.0010
Gender
Degree
0.568***
0.428***
0.581***
0.437***
0.5601***
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
Motorcycle
0.003
0.499
0.021
0.489
0.0072
0.4720
Car (driver)
0.214
0.226
0.2449
0.302
0.950***
0.305
0.931***
0.3823
0.9593***
0.207
0.212
0.2189
0.403***
0.406***
-0.205
0.4362***
-0.2114
0.562**
0.265
0.5230*
0.2747
-0.043
-2.9049***
-0.0262
-2.8665***
Group 1: all members
Age
Car (passenger)
CS Member
Area C- car use reduction
Oil price increase -car use reduction
To reach the workplace
LTP stop
Neighbourhood
Leisure in the city
Constant
-2.8898***
Results
Group 2
GROUP 0:
Those not
interested
to join a P2P
CS system
Model 1
Model 2
Model 3
-0.0186***
-0.0184***
-0.0185***
Gender
0.0255
0.0191
0.0039
Degree
0.1834
0.1817
0.1679
Number of owned cars
LPT
0.2192***
0.2264
0.2263***
0.1652
0.2227***
0.2022
Bike
0.5014***
0.4990***
0.4949***
Foot
0.2253
0.2293
0.2309
Motorcycle
Car (driver)
0.2241
-0.1246
0.2024
-0.1337
0.2035
-0.1359
Car (passenger)
-0.4143
-0.4354
-0.3788
CS Member
Area C- car use reduction
0.9938***
0.3979***
0.9871***
0.4055***
1.0102***
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
Neighbourhood
0.3984
-0.0053
0.3836
-0.0104
Leisure in the city
Constant
-07010
-0.0819
-0.6882
-0.0834
-0.6434
1129
1129
1129
-1107.8923
-1104.2871
-1096.0491
0.0548
0.0579
0.0649
Group 2: Friends, neighbours
Age
n. obs.
Log Likelihood
PseudoR2
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: 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.
Questions and suggestions are welcome
Ilaria Mariotti
DAStU – Politecnico di Milano
[email protected]