E-GO: An Innovative Electric Car Sharing Project in Roma Tre University

Transcription

1 E-GO: An Innovative Electric Car Sharing Project in Roma Tre University Stefano Carrese, Tommaso Giacchetti, Marialisa Nigro, Sergio Maria Patella Department of Engineering, Roma Tre University Dipartimento di

2 E GO car sharing project timeline Five years project three stages: first stage: service planning, infrastructural implementation, financial feasibility and demand analysis; second stage: supervision of the starting phase and effective users response; third stage: opening of the service to a wider area of Rome /21

3 1 Generation of EV Car Sharing 2 Generation of EV Car Sharing 3/21 Dipartimento di E- GO Car Sharing First Stage (1) Designing phase: LISELEC 1999, La Rochelle. France 25 Peugeot 106s and 25 Citroen Saxos Now named Yélomobile CAR 2 GO 2011, Amsterdam. Netherlands 300 Electric Smart Fortwo Working Orix Car Share 1997, Japan 400 Nissan vehicles Turned in hybrids and gasoline vehicles Autolib 2011, Paris. France 3000 Bolloré Bluecar and 1200 stations Sizing problem UCR Intellishare 1999, Riverside campus, California 25Honda EV Plus Ended in 2010 Share ngo 2015, Italy 1500 ZD (Florence, Milan, Rome) Working

4 Dipartimento di E- GO Car Sharing First Stage (2) Designed as a one way station based car sharing: 20 Renault Twizy 2 seats, 60 Km 27 recharge st. in Campus 100 in the city 10 Renault Zoe 4 seats, 180 Km Ex-ante survey - On-line RP and SP survey to 1,200 potential users: 960 respondents RP section: socio-economic characteristics and mobility habits; behavioural attitudes (green attitude, out of pocket, sharing attitude) SP section: evaluation 3 fee scenarios eco-friendly and money-saving attributes are those that most influence EV Car Sharing choice (Burkhardt e Millard-Ball, 2006) 4/21

5 Mobility characteristics Distances: 34% of the sample performs a short-distance home-university trip (less or equal then 5 km); 28% a medium-distance trip (more than 5 km, but less or equal than 15 km); 16% a long-distance trip (more than 15 km but less or equal than 25 km). The remaining ones perform a trip longer than 25 km; Modal shift: off-site students mostly choose the public transport alternative for their trips (67%); for on-site students there is a comparable choice between the automobile and the public transport; Other users: higher values of private vehicle (70%) Other: 92% of respondents has a driving license; public transport subscription is not diffused: about 52% of the sample declared to not have any kind of subscription. 5/21

6 Adoption of the new car sharing system For home-university trips: 80% (students and off-site students); 83% (others); 71% CS integrated with public transport The adoption decreases with the increase of the distances to be travelled (85% d < 5km; 55% d > 50 km); Frequency: 3 days/week For other trip purposes: 86%; 92% (off-site students); No difference between male/female; Reduction of the adoption of CS with the increase of the household income: Out of pocket: less than 50 per month for the 80% of the students. 6/21

7 Green attitude How much users are environmental conscious and open minded about new sustainable transport modes? degree of care about energy and environmental sustainability issues GA is not linked to the user s gender, the economic conditions and to the level of education; about 70% of respondents over 45 years old declared to be aware of environment; public transport users show higher GA values. 7/21

8 Sharing attitude Cohousing and car sharing car pooling use: SA decreases with the increase of age and income; Males are slightly more opened to share goods and services than females from our sample (respectively 60% full sharing attitude and 47% full sharing attitude); Green Attitude and SA are positively correlated. 8/21

9 9/21 Fee scenarios: The most chosen one. It is a good offer during the working day, thus it is more oriented to catch demand for potential trips inside the University SP survey Dipartimento di After the proposed fees, respondents are invited to express their willingness to subscribe the proposed service: Working day Evening - nigh time Special time windows Weekend Easy morning Easy evening Flexible 8:00-20:00 [0,21 /min] 20:00-8:00 flat [18 ] 9:00-19:00 [0,25 /min] 19:00-9:00 flat [15 ] - - From Saturday 11:00-to Monday 8:00 flat [25 ] From Saturday 11:00-to Monday 9:00 flat [23 ] about the 24% of the respondents declared to be interested in using the proposed service for home-university trips; the 28% for not home-work trips closed to University 9:00-19:30 [0,23 /min] 19:30-9:00 flat [16 ] Lunch break 12:00-14:00 flat [6 ] From Friday 19:30 to Monday 9:00 flat [30 ]

10 E- GO Car Sharing First Stage: moving to free floating Pilot test: from mid-september 2016 to the end of November 2016: 100 users resulted available from the ex-ante survey involved in using the service for free; Critical issues: Recharge stations not available; Security and policy issues: if the recharge station is kept by an E-GO vehicle: damages by owners of other electric vehicles; Safety issues. Second survey - On-line RP and SP survey Demand modelling (Zhou et al., 2009, Katzev, 2003, Lane, 2005, Efthymiou et al. 2016, De Luca et al. 2016) 10/21

11 11/21 Dipartimento di Stated modal shift with the free-floating E-GO Home-work trips: Other activities:

12 12/21 Dipartimento di Deriving demand models for Home-Work trips MNL CAR CS PT Calibration Results: Rho - square Adjusted rho - square Init log likelihood Final log likelihood Systematic Utilities: V CAR = ASC car *one + β t t_car + β cost * cost_car V PT = ASC PT *one + β t * t_pt + β cost * cost_pt V CS = ASC CS *one + β t * t_cs + β cost * cost_cs

13 Deriving demand models for other activities (1) NL Systematic Utility : V CAR = ASC car *one + β cost * cost_car CAR CS PT V PT = ASC PT *one + β cost * cost_pt Calibration Results V CS = ASC CS *one + β cost * cost_cs + β t_dest t_dest Rho - square Adjusted rho - square Init log likelihood Final log likelihood Private Public

14 Deriving demand models for other activities (2) CNL Systematic Utility: V CAR = ASC car *one + β cost * cost_car CAR CS PT V PT = ASC PT *one + β cost * cost_pt + β freq * freq V CS = ASC CS *one + β cost * cost_cs Calibration Results: Rho - square Adjusted rho - square Init log likelihood Final log likelihood Private Public Private_S1_CAR Private_S3_CS Public_S2_PT Public_S3_CS

15 15/21 Sensitivity analysis: E-GO fee Home-Work Trips Other activities Trips e.g. 30 minutes Trip Starting Cost 10% 20% 30% 8.10 Increase Decrease

16 E-GO and change the mode for the main trip 16/21 Home-work trips with auto mode: 33% shall use public transport (modifying modal shift), if E-GO is available for other activities trips.

17 E-Go Car Sharing Now Second stage: from January 2017 the service is: Free floating (operative area) and counts for 400 members. 17/21

18 E-Go Car Sharing available infrastructures Engineering recharge park 1.Book the car 2.Report damages Reserved slot Recharge point 3.Start the rental 4.Leave the car 18/21

19 19/21 Dipartimento di E-Go Car Sharing governance & management Roma Tre e-go management team Innovation and new business team Fleet Management (1 resource) Recharge points maintenance Refloating team (2 resources) Call center service Reserch team (Prof. Stefano Carrese) Website & social communication Marketing team (20 ambassador) Fleet insurance & leasing cost Registration office Fleet Management support

20 20/21 Marketing Dipartimento di

21 21/21 Dipartimento di Electric Car Sharing..and more