Spitsmijden Reward Experiments. Jasper Knockaert VU University Amsterdam

Transcription

1 Spitsmijden Reward Experiments Jasper Knockaert VU University Amsterdam

2 Overview Spitsmijden? Experimental design Data collection Behavioural analysis: departure time choice (and trade off with (reliability of) travel time and reward)

3 Motivation Taxation is wildly unpopular Why not try a subsidy? How do people react on a reward?

4 Spitsmijden? Concept: positive stimuli (~rewards) to change travellers behaviour (to increase social welfare) Co-operation of private sector, public sector and academic institutions Series of experiments: Road (4) or Public Transport (6) Trial + Surveys (including conjoint choice exp.) Technical demonstrations

5 Spitsmijden Road 3 road projects on A12 Gouda The Hague: 1.Autumn 2006: 310 participants, 10 weeks 2.Sep 2007 May 2008: 700 participants, 9 months 3.November 2008 Dec 2010: 5000 participants at end of project New project starting up in Eindhoven Den Bosch area As an example here a presentation of the experimental setup of the Autumn 2006 trial

6 Spitsmijden 06: Where & When? EVI Zwaardslootseweg (N206) THE HAGUE EVI Zoetermeerse rijbaan EVI A12 EVI Katwijkerlaan Reference: 2 weeks Reward: 10 weeks No Reward: 1 week Sep 18 Okt Dec 4 Dec 11

7 Spitsmijden 06: What? Money: 3 types of reward, 232 participants h 8h 9h 10h

8 Spitsmijden 06: What? Yeti smartphone (108 participants): Traffic information Saving credits to keep phone at end of experiment credit no credit credit information only 7h 8h 9h 10h

9 Spitsmijden 06: What? Reward adapted on basis of initial behaviour Less frequent drivers are rewarded less frequent Rewards' regimes varied over participants over the duration of the experiment Exclude the bias caused by learning impact or effects of change of season (Bikes,.)

10 Data acquisition Automated observations: Fixed points: cameras and RFID OBUs GPS logging (selection of participants) Internet based surveys: conjoint choice experiments travel diary (modal choice & trip motive) Data from other available sources (e.g. induction loop traffic observations for travel time)

11 Departure time choice Tested for a number of panel discrete choice model specifications Classical approach in linear utility functions with reward level, (reliablity of) travel time and schedule delay as independent variables Deriving travel time and schedule delay valuations

12 Impact (money) 45% 40% 35% 30% 25% 20% Voormeting Voormeting 3 Euro Voormeting Euro Voormeting 7 euro Voormeting 3 Euro euro Euro Variabele 7 euro beloning Variabele beloning Nameting 15% 10% 5% 0% Car before 7.30 Met auto voor 7.30 Car Met Met auto auto Car Met Met auto auto Car Met auto Met auto Car after 9.30 Met auto na 9.30 Met auto na 9.30 Other car 1 Andere auto uit gezin Andere auto uit gezin Other car 2 Andere auto buiten gezin Andere auto buiten gezin Carpool Passagier in carpool Passagier in carpool Public Transp. Bike OV OV Fiets Fiets Andere vervoerwijze Other mode Andere vervoerwijze Thuiswerken Telecommute Thuiswerken Andere werklocatie Other location Andere werklocatie

13 Impact (departure time) Dispersion of trips during rush hour Spreiding ritten over de spits 25% 20% 15% 10% Met Reward beloning Zonder beloning No Reward 5% 0% 06:00 07:00 08:00 09:00 10:00 11:00

14 A nested logit choice model commute Car offpeak Car peak Bike... Telecommute 7h-7h15 7h15-7h h45-10h

15 A nested logit choice model Null LL: Final LL: ρ2 : 0.194

16 A nested logit choice model Description Coeff. estimate Robust Asympt. std. error t-stat p-value Constant for bike Constant for other Constant for telework Constant for car (not 7h 10h) Constant for carpool Constant for public transport β euro [euro] β sde [hour] β sdl [hour] β sde^2 [hour 2 ] β sdl^2 [hour 2 ] β traveltime [hour] β weather [max temp in C] β yeti Inclusive value 1/λ for car 7 10h

17 A mixed logit choice model Account for repeated choice setting Random coefficient for car travel 7h 10h: Fixed Mean (zero) Normally distributed Constant across observations for same respondent Null LL: Final LL: ρ2 : 0.236

18 A mixed logit choice model Description Coeff. estimate Robust Asympt. std. error t-stat p-value Constant for bike Constant for other Constant for telework Constant for car (not 7h 10h) Constant for carpool Constant for public transport β euro [euro] β sde [hour] β sdl [hour] β sde^2 [hour 2 ] β sdl^2 [hour 2 ] β traveltime [hour] β weather [max temp in C] β yeti Var(car 7h 10h)

19 Conclusions Successful experiment Reward can be used as effective policy instrument Behavioural analysis: shadow prices per unit of time of schedule delay in our experiment are close to constant shifting departure time is likely to be a more important behavioural response to policies for congestion relief, compared to a modal shift or teleworking

20 What's next Work-in-progress: enhanced mixed logit covariates traffic information Imminent: combined stated & revealed preference Last but not least: time dynamic behaviour

21 Spitsmijden PT Project on rail line Utrecht The Hague Conjoint choice experiment with approx 700 participants (37% response rate) Cross nested logit model specification New projects are in preparation

22 Coinjoint choice experiment: design Each respondents answers six choice sets Each choice set has two (customised) choice alternatives for a regular season pass: keep current pass switch to an off-peak pass which has the option to be upgraded on a day-by-day basis (for a supplement) In case of switch, share of before/during/after peak travel is asked for

23 Appendix A. Choice screen stated preference questionnaire: You usually commute 5 days a week between Utrecht and The Hague. The Dutch railway company offers you the following tickets for these trips: A. A monthly season pass for the route Utrecht-The Hague for 360. B. An off-peak monthly season pass for the route Utrecht-The Hague for 330. This pass gives you a discount of 30 compared with option A, however, you are not allowed to travel with this pass from Monday to Friday between 7:00h-9:00h unless you buy a peak supplement of 3.50 per day. Which pass would you purchase? o A o B If you choose pass B: Could you indicate when and how often you would commute during your 5-day working week now that you have purchased an off-peak monthly season pass not valid between 7:00h-9:00h?: I would commute days a week before the peak I would commute days a week after the peak I would commute days a week during the peak and buy a peak supplement of 3.50 a piece. note: attributes of the choice set are indicated in bold letters. Attributes of the individual respondent are indicated in italic letters. 19

24 Behavioural analysis Utility specification (generic): regular: U peak = D+ε off-peak: U of-peak = β.x+ε with vector x: costs, schedule delay, comfort Number of choice alternatives in model? Keep regular pass: 1 option Choose off-peak pass: up to 56 combinations of travel before, during and after peak Different model specifications tested for

25 Behavioural analysis Estimated models (see paper): multinomial logit with 7 choice alternatives multinomial logit with 12 choice alternatives mixed logit with 12 choice alternatives multinomial logit with 57 choice alternatives cross-nested logit with 57 choice alternatives mixed logit with 57 choice alternatives

26 Behavioural analysis Cross-nested logit: 4 nests: before, during, after, other each choice alternative is attributed to the nests proportional to the corresponding share of trips e.g. a choice that implies 4 weekly trips, with 1 trip before peak, 3 trips during peak and none after peak 20% in nest before, 60% in nest during, 0% in nest after and 20% in nest other regular pass outside nests

27 Behavioural analysis Coefficient Keeping regular pass Schedule delay early (h/week) Schedule delay late (h/week) Not making the trip (trips/week) Train crowding discomft (trips/week) Discount and supplements ( /week) Nest before Nest during Nest after Nest other Value Robust Std err Robust t-test p-value 2,84 0,07 41,2 0,0% -0,525 0,031-17,0 0,0% -0,742 0,064-11,6 0,0% -2,61 0,36-7,32 0,0% -0,0495 0,0240-2,06 4,0% -0,125 0,005-23,5 0,0% 2,93 0,29 6,78 0,0% 4,94 1,60 2,46 1,0% 1,36 0,13 2,91 0,0% 1,21 0,13 1,63 10,0% Estimation statistics Number of observations Adjusted rho-square ,768 Schedule delay valuation Value Std err Early ( /h) 4,20 0,31 Late ( /h) 5,94 0,57

28 Work in progress Currently focussing on join RP-SP estimation of reliability of travel time Alternative specification with stated probabilities rather than choices (SP experiment) Exploring semi-parametric model specifications (Fosgerau 2007) Exploring possibilities to use GPS observations