Arbeidspakke 4. Undersøkelse av tiltakseffekter og transporttilbud

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1 Arbeidspakke 4. Undersøkelse av tiltakseffekter og transporttilbud Farideh Ramjerdi November 23, 2016

2 Objective of WP4 To examine policies (including incentives) that are likely to have the greatest impact on travel behaviour of commuters such that they switch to more environmentally friendly modes. Page 2

3 Range of policies Clean car Car Public Transport Cycle Walk Working at distance Page 3

4 Identification of policies (1) Car use Travel time with car Car variable costs (fuel and toll cost) Parking cost & parking distance Public transport In vehicle time Public transport fees Public transport frequency No. of transfers Walking distance to/from station Seating place Page 4

5 Identification of policies (2) Cycling Cycle time Cycle path Changing facilities & shower and at work Parking Monetary incentive Walking Walk time Monetary incentive Working at distance Attributes of car (travel time, running cost, parking cost & distance) Attributes of public transport services (PT fee and level of service) No. of days working at a distance Page 5

6 Identification of policies (3) Promotion of clean cars (electric vehicles): As the main or as the second household car Purchase price Variable cost Battery range Refuelling time Depreciation relative to conventional cars At Fornebu A new T-bane line A new ferry service Page 6

7 Choice of methodologies (1) Stated Preference (SP) technique is used in this study SP relies on choice among hypothetical alternatives described by different attributes. A respondent chooses the alternative that best suits her/him. Descriptions of alternatives have to be plausible, i.e., related to the actual attributes of different modes available to them Attributes in a SP experiment relates to a policy (or policies) The number of SP experiment has to be limited (3 to 4) Page 7

8 Choice of methodologies (2) The SP data collected will be analysed using discrete choice theory Discrete choice theory is an economic/statistical method for predicting choices between a finite number of alternatives Assumptions: Individuals are always choosing the alternative with the highest utility The utility for each alternative has a deterministic component (to be estimated); the rest is noise The deterministic component is made up from alternative specific attributes and individual characteristics A model with a high explanatory power has a large deterministic component relative to the noise Page 8

9 Structure of the questionnaire Work category, arrangement, etc. Home & Work locations Accessibility to public transport (distance to stations at home & work, frequency, no. of transfers) Accessibility to car (parking cost & distance at work, car type) Estimated travel time for different modes Frequencies of commuting mode choice (Car, PT, Cycle, Walk) Respondent perceptions, attitudes, habits related to different modes Information related to working at a distance SP studies (4 in total, 3 per respondents) Socioeconomic data Page 9

10 The study A pilot study was conducted in June 2014 and the questionnaire and the SP experiments were modified accordingly The main SP study are conducted among commuters working in 6 selected locations: 1. Alna Nedre Linderud Nedre Kalbanken 2. Blindren 3. Nydalen 4. Sentrum 5. Fornebu 6. A-Hus Page 10

11 Recruitments, data collection The respondents at 6 different areas were recruited Internet survey The study was conducted between August 2014 and June 2015 About 50% of respondents were intercepted at PT stations and the other 50% at parking locations About 80% of the respondents were employed at the intercepted locations, the others were visiting these locations from other areas in Oslo Location Respondents 1 ALNA NEDRE LINDERUD NEDRE KALBAKKEN BLINDERN NYDALEN SENTRUM FORNEBU A-HUS 289 Page 11

12 Descriptive analysis W_location Age Gender Income N_Car Alna 43,98, ,26 Blindren 44,07, ,08 Nydalen 44,71, ,30 Sentrum 45,66, ,36 Fornebu 47,29, ,35 A-Hus 43,56, ,43 W_location Mode_Car_pm Mode_PT_pm Mode_Bike_pm Model_Walk_pm Alna Blindren Nydalen Sentrum Fornebu A-Hus W_location WH_Dist Time_Car Time_PT PT_Dist_H PT_Dist_W Alna 20,30 32,68 52, Blindren 13,32 35,67 41, Nydalen 18,32 33,20 46, Sentrum 17,23 40,18 39, Fornebu 21,99 39,07 49, A-Hus 16,08 23,92 56, Page 12

13 Descriptive analysis Work location Education Basic 12 Years 13 years 16 years 16+ Alna Blindren Nydalen Sentrum Fornebu A-Hus W_location DW_DaysAllow DW_Practice DW_InternetH DW_HomeOfficeH Alna Blindren Nydalen Sentrum Fornebu A-Hus Page 13

14 Example of an SP1 experiment Bil Kollektivtransport Sykkel Gå Reisetid (minutter) Ingen Parkering 150 Kr/dag sykkelparkering Parkering avstand Sitteplass Avgang mer enn 1,5 km ikke sitteplass hvert kvarter Gangtid til /fra holdeplass 15 Bytter (antall) 0 Sykkelsti Drifstoff + bompenger, kr 25 Månedskortpris, kr 412 sykkelsti hele vegen Økonomisk støtte støtte 50 kr/ dag ingen støtte Ditt valg Page 14

15 SP1 Sample size: 9911 Excluded observations: 829 Init log likelihood: Final log likelihood: Estimated parameters Value t-test Robust t-test Car_Cost Car_Time Car_ParkCost Car_ParkDist PT_Cost PT_Time PT_Frequency PT_Transfer PT_Seat availability PT_Walk Time Bike_Time Mixed Traffic Bike_Time Cycle Path Bike_Safe Parking Bike_Shower facility at work Bike_Incentive Walk_Time Walk_Incentive Value of time (NOK/hr) Car 70 PT 45 Bike Mixed 76 Bike Cycle path 56 Walk 137 Page 15

16 Example of an SP2 experiment (Fornebu) Bil Dagens kollektivtilbud Ferge Ny T-bane Reisetid (minutter) Bilkostnad (drivstoff og bompenger for en 30 Parkeringskostnad Gratis Avstand til parkeringsplass 500 meter Billettpris for én reise kollektivt Bytter (antall) Frekvens (avgang..) hvert 10. minutt hver time hvert kvarter Page 16

17 SP2 Number of estimated parameters: 22 Sample size: 366 Excluded observations: 1015 Init log-likelihood: Final log-likelihood: Estimated parameters Value t-test Robust t-test Car_Cost Car_Time Car_ParkCost Car_ParkDist PT_Cost PT_Time PT_Freq TBane_Cost TBane_Time TBane_Freq Ferry_Cost Ferry_time Ferry_Freq Transfer VOT (NOK/hr) Car 134 PT 99 T-Bane 81 Ferry 67 Page 17

18 SP3: Working at distance CAR Alt 1 Alt2 Car cost +Toll Parking cost Parking distance No. of days 2 3 Page 18

19 SP3: Working at distance Public Transport Alt 1 Alt2 PT cost PT Time Frequency No. of Transfers 0 1 No days 2 1 Page 19

20 SP3 Car SP3 PT Number of estimated parameters: 6 Number of estimated parameters: 7 Sample size: 3582 Sample size: 3732 Excluded observations: 7158 Excluded observations: 7008 Init log likelihood: Init log likelihood: Final log likelihood: Final log likelihood: Estimated parameters Value Robust t-test Estimated parameters Value Robust t-test Car Cost PT_Cost Parking Cost PT_Time Parking Dist PT_Frequency Working at Home (days/month)^ PT_Transfer Working at Home (days/month) Working at Home (days/month)^ Working at Home (days/month) VOT 93 Page 20

21 SP4 Vehicle type choice as the second car in the household Electric car Gasoline/Deisel car Purchase price kr Kr Variable car cost 0,5 kr/km 3,5 kr/km Range 100 km 500 km Refueling time 8 hr 5 min Depreciation relative to a conventional car 0.9 Your Choice Page 21

22 SP4 Vehicle type choice as the main car in the household Electric car Gasoline/Deisel car Purchase price kr Kr Variable car cost 0,5 kr/km 3,5 kr/km Range 100 km 500 km Refueling time 8 hr 5 min Depreciation relative to a conventional car kr Page 22

23 SP4 Main Car SP4 Second car Number of estimated parameters: 8 Number of estimated parameters: 8 Sample size: 7014 Sample size: 7013 Excluded observations: 3726 Excluded observations: 3727 Init log likelihood: Init log likelihood: Final log likelihood: Final log likelihood: Estimated parameters Value Robust t-test Estimated parameters t-test Robust t-test G_D Car Price G_D Car Price G_D Car Variable Cost G_D Car Variable Cost El-Car, Price El-Car_Price El-Car Variable Cost El-Car Variable Cost El-Car Range ElCar_Range El-Car Refueling Time El-Car Refueling Time El-Car Depreciation El-Car Depreciation Page 23

24 Effective policies Increase in parking cost and parking distance Increase PT use, cycle & walk (when feasible) Increase in working at distance (when possible) Improvements in PT services Decrease in car use & cycle and walk Cycle path, secure cycle parking, shower facilities at work Increase cycling (when feasible) Monetary incentive for cycling and walking Increase cycling and walking (when feasible) Subsidies for El-car purchase More El-car Page 24

25 Location specific policies Car related policies Public Transport policies Work Parking Distance to location Parking cost distance PT Station Transfer Frequency Alna X XX XXX XXX XX Blindren XXX XXX X X X Nydalen XX XXX XXX XX XX Sentrum X XX XX XX X Fornebu X XXX XXX XX X A-Hus XX XXX XXX XXX XX Cycle policies Walk WorK Secure Changing Monetary Moneraty Locatio Cycle lane parking facilities incentive incentice Alna X X X X X Blindren XXX XXX XX X X Nydalen XX XX X X X Sentrum X X X X X Fornebu XX X XX X X A-Hus XXX XXX XX X X Page 25

26 Thank you Page 26

Predicted response of Prague residents to regulation measures

Predicted response of Prague residents to regulation measures Markéta Braun Kohlová, Vojtěch Máca Charles University, Environment Centre marketa.braun.kohlova@czp.cuni.cz; vojtech.maca@czp.cuni.cz June