Connecting time-use, travel and shopping behavior: Results of a multi-stage household survey

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Connecting time-use, travel and shopping behavior Results of a multi-stage household survey

Author(s):

Schmid, Basil Publication Date:

2019-09-24 Permanent Link:

https://doi.org/10.3929/ethz-b-000366670

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Connecting time-use, travel and shopping behavior: Results of a multi-stage household survey

Basil Schmid IVTETH Zurich

PhD Thesis Defence

Zurich, September 24, 2019

Introduction

• Post-Car World (SNF project): Travel behavior in a (quasi-)car-less hypothetical environment (Schmid et al., 2019a)

=⇒ Pre-Post-Car World

• How do respondents trade-off different attributes in their (short- and long-term) choice and scheduling behavior?

• How are individual preferences for time-use, travel and shopping behavior related to each other?

• What is the role innovative mode sharing systems such as carsharing (CS) and carpooling (CP)?

– Valuation of level-of-service attributes

Fieldwork and response behavior

• Canton of Zurich (2015/16)

• 7’500 households invited by mail

• 50 CHF incentive payment

≈ 5% net cooperation rate

≈ 55% response rate (after recruitment)

• Final dataset:

≈ 300 households (red dots)

≈ 450 respondents

Survey methods: Three-stage household survey

• Revealed preference (RP; stage I) data: One-week reporting period on ...

– Travel behavior and time-use

– ICT usage (shopping, entertainment, etc.) – Daily and long-term expenditures

• Stated preference (SP; stage II) data on ...

– Mode choice – Route choice

– Shopping channel choice

• Stated adaptation (SA; stage III) data on ...

– Daily scheduling – Mobility tool ownership

The value of (travel) time: VTTS, VoL and VTAT

• Decomposition of the value of travel time savings (VTTS) into two components (DeSerpa, 1971; Jara-Diaz and Guevara, 2003)

VTTS_i,n

| {z }

typically>0

=VoL_n

| {z }

>0

−VTAT_i,n

| {z }

<or>0

– VTTS: Money value of indirect utility obtained from decrease in travel time – VoL: Money value to reduce travel time in favor of other activities;

opportunity value of time

– VTAT: Money value of the (dis)pleasure of travel time itself; depends on the conditions/comfort of travel

VTTS, VoL and VTAT: Policy relevance

• VTTS: Biggest share of user benefits in cost-benefit analyses

– Decisive role in the ranking of investment projects that affect travel time – Empirical basis for modeling and forecasting of travel behavior

=⇒ Identify mode and user-type effects in the VTTS

• VoL: Assessing the options under a budget constraint

VTTS [CHF/h] VoL [CHF/h] VTAT [CHF/h] Invest in ... Implication

20 5 –15 Conditions VTTS↓

20 20 0 Cond./speed VTTS/travel time↓

20 35 15 Speed Travel time↓

VoL and VTTS: Overview and main model variables

• VoL (H¨ossinger et al., 2019): Time assigned to activities (incl. work) and expenditures assigned to goods consumption, wage rate and fixed income (RP)

• VTTS (Schmid et al., 2019b): Travel choices (RP/SP) among a set of available alternatives with corresponding attributes (travel cost, travel time, etc.)

Mode Individual (user) Trip Random

VTTS VTTS/VoL VTTS VTTS/VoL

MIV Sex Distance Error components (VTTS)

PT Age Trip purpose Scale (VTTS)

CS Kids in HH Daily weather Taste (VTTS/VoL)

CP Couple Weekend vs. weekday

Bike Residential location area Walk Education

Personal income

SP route choice: Example choice situation

Mode-specific VTTS and VoL [CHF/h]

0102030405060VTTS and VoL [CHF/h]

Walk

Bike

MIV

PT

CS CP

VoL

VoL, VTTS and VTAT: Main findings

• VoL ≈52% of wage rate (49.5 CHF/h)

– Zurich has strong preference for goods consumption relative to leisure

• VTAT negative for car modes (MIV, CS and CP), positive for PT and bike – Conditions/quality of travel higher in PT and bike

– E.g. MIV vs. PT: Mode effect always dominates user-type effects

• VoL and VTTS are only weakly related

– Income has no effect on the VTTS, but on VoL

– Individual travel preferences (VTTS) mostly uncoupled with the opportunity value of time (VoL)

– Anomaly for Zurich? $$$$-income region with small share of mobility expenditures ...

In-store or online shopping?

• Goals, methods and main hypotheses (Schmid and Axhausen, 2019):

– Exploring key determinants for choice btw. in-store and online shopping

=⇒ Comparison of the value of delivery time (VDTS) and the VTTS – Search goods (standard electronic appliances): More easily evaluated from

externally provided information

– Experience goods (groceries): Preferably inspected physically – Incorporating attitudes in decision process

• Experimental framing:

– Home-based round trip for in-store alternative

– No private cars; no multi-channel shopping; no social motives – Goods quality is assumed to be identical

In-store or online shopping: Example choice situations

Ordering me / shopping me Travel me to store

Size / weight of the goods basket

Your choice

Situaon 1 _Order Travel to store

CHF 5.20

600.00 54 39

48

540.00 min. 1 week

CHF

CHF

min. min.

min.

CHF Delivery me (incl.

possible delays)

Shopping cost Delivery cost / travel cost

Situaon 2

5.00 CHF

0.00 9.10

600.00 66 21

58

600.00 2-3 days

CHF

CHF

min. min.

min.

CHF Purpose: Electronic appl.

Ordering me / shopping me Travel me to store

Size / weight of the goods basket

Your choice

Order Travel to

store

Delivery me (incl.

possible delays)

Shopping cost Delivery cost / travel cost Purpose: Electronic appl.

13

In-store or online shopping? Main findings I

• VTTS vs. VDTS:

Median VTTS shopping trips (groceries) [CHF/h] 54.4 VTTS shopping trips (electronics) [CHF/h] 29.3 VDTS delivery time (groceries) [CHF/day] 9.8 VDTS delivery time (electronics) [CHF/day] 1.2

• Context-dependent cost sensitivity: Delivery vs. shopping vs. travel cost

– Develop effective retailing strategies (i.e. delivery as part of shopping cost)

• Pro-online shopping attitudes higher shopping cost sensitivity:

Larger choice set when considering both shopping channels

• Socioeconomic profile of online/in-store shoppers

– Forecasting: (1) direct effects and (2) mediated via attitudes ...

Adaptations in car usage and ownership

• Goals, methods and main hypotheses:

– Assessing radical pricing effects from an activity-based perspective

=⇒ 4 adaptation scenarios (daily scheduling and mobility tool ownership experiment) with increasing variable MIV costs

– Understand and quantify the transition towards a Post-Car World

=⇒ Comparison of the MIV cost elasticities between the two experiments (1%increasein cost → x %decrease in distance traveled)

– Substitution effects may eventually lead to a decreasing cost elasticity

• Experimental framing:

– Future policies to reduce MIV usage; promotion of of shared mobility – Road tolls, congestion and fuel taxes

Daily scheduling experiment: Example scenario

Mobility tool ownership experiment: Example scenario

Adaptations in car usage: Main findings

• Daily scheduling experiment: ≈ −0.4→ medium-run (upper-bound?)

• Mobility tool ownership experiment: ≈ −0.15 → long-run (lower-bound?)

=⇒ Reflects substitution patterns towards more energy-efficient cars

• Hypothesized pathway of cost elasticities:

Summary and conclusions

• Main contributions of the thesis:

– Unique survey design

– Connecting the value of leisure, travel and delivery time using sophisticated modeling approaches with clear suggestions for policy and practice

– Quantitative assessment of car usage from a mobility pricing perspective

• Main limitations:

– Sampling bias (participation choice and response behavior) – Data resolution (activities) and quality (expenditures)

– Hypothetical bias (no private cars, experimental framing, etc.)

Questions?

Literature

DeSerpa, A. C. (1971) A theory of the economics of time,The Economic Journal,81(342) 828–846.

H¨ossinger, R., F. Aschauer, S. Jokubauskaite, B. Schmid, S. Peer, R. Gerike, S. R. Jara-Diaz and K. W. Axhausen (2019) A joint time assignment and expenditure allocation model: Value of leisure and value of time assigned to travel for specific population segments,Transportation(in press).

Jara-Diaz, S. R. and C. A. Guevara (2003) Behind the subjective value of travel time savings: The perception of work, leisure and travel,Journal of Transport Economics and Policy,37(1) 29–46.

Jara-Diaz, S. R., M. A. Munizaga, P. Greeven, R. Guerra and K. W. Axhausen (2008) Estimating the value of leisure from a time allocation model, Transportation Research Part B: Methodological,42(10) 946–957.

Molloy, J., B. Schmid, F. Becker and K. W. Axhausen (2019) mixl: An open-source R package for estimating complex choice models on large datasets, Working Paper, 1408, Institute for Transport Planning and Systems (IVT), ETH Zurich, Zurich.

Petrin, A. and K. Train (2010) A control function approach to endogeneity in consumer choice models,Journal of Marketing Research,47(1) 3–13.

Schmid, B. and K. W. Axhausen (2019) In-store or online shopping of search and experience goods: A hybrid choice approach,Journal of Choice Modelling,31, 156–180.

Schmid, B., M. Balac and K. W. Axhausen (2019a) Post-Car World: Data collection methods and response behavior in a multi-stage travel survey, Transportation,46(2) 425–492.

Schmid, B., F. Aschauer, S. Jokubauskaite, S. Peer, R. H¨ossinger, R. Gerike, S. R. Jara-Diaz and K.W. Axhausen (2019b) A pooled RP/SP mode, route and destination choice model to investigate mode and user-type effects in the value of travel time savings,Transportation Research Part A:

Policy and Practice,124, 262–294.

Appendix: Response behavior, by survey wave

Pre-Test Wave 1 Wave 2 Wave 3 Stage I survey period: Jan. 2015 Jul. 2015 Oct. 2015 Apr. 2016

Number of households invited: 800 1600 3500 1600

Contacted by phone: 270 676 1110 546

Rejected participation: 203 543 919 428

Accepted participation: 67 133 191 118

Response burden scores of stage I: 3500 1700 1700 1700

Response rate stage I: 52.2% 54.8% 64.9% 66.9%

Response burden scores of stage II: 530 350 350 350

Response rate stage II: 52.2% 54.8% 61.7 % 63.6%

Response burden scores of stage III: 270 500 500 –

Estimated total response time 360 min. 215 min. 215 min. 170 min.

Final response rate: 52.2% 54.1% 60.2% 63.6%

Final cooperation rate: 4.9% 5.2% 3.8% 5.3%

Appendix: Response behavior for different incentive levels

• Sample selection participation choice Probit model – Incentive payments were varied in the pre-test

– 100 vs. 50 CHF: Increase in initial participation probability by 28%-points – Negative effect on completion conditional on participation of –33%-points

=⇒ net-effect on completion not statistically different from zero

Reject Participate Drop-out Complete Total

N [%] [%] [%] [%] [%]

Incentive 50 CHF 989 57.9 42.1 37.5 63.5 26.7

70/80 CHF 58 62.1 37.9 50.0 50.0 19.0

100 CHF 34 44.1 55.9 52.6 47.4 26.5

Sample N 1081 624 457 156 284 284

Appendix: Response burden and response rates @ IVT

020406080100Response Rate [%]

0 500 1000 1500 2000 2500 3000 3500 4000

Response Burden Score [−]

Prior recruitment and incentive Fit & 95 % CI Prior recruitment, no incentive Fit

No prior recruitment, no incentive Fit

PCW (Pre−Test) PCW (Wave I)

PCW (Wave II) PCW (Wave III)

a)

a) a)

a) a) a)

b) b)

c) c)

Appendix: Time-use model specification

• Log-linear additive function with diminishing marginal utility (Jara-Diaz et al., 2008; H¨ossinger et al., 2018)

– T_w: Time assigned to work – T_i: Time assigned to activities

– Ej: Expenditures assigned to goods consumption

• θ andψ represent the baseline utility parameters max U = θ_wlog(Tw) +

I

X

i=1

θ_ilog(T_i) +

J

X

j=1

ψ_jlog(E_j)

s.t. τ−Tw−

I

X

i=1

T_i = 0 (µ) T_i −T_i^min ≥0, ∀i ∈A^c (κ_i)

w ·Tw+Y −

J

XEj ≥0 (λ) Ej −E_j^min≥0, ∀j ∈G^c (ηj)

Appendix: Mode/route choice as utility maximization

• Assume linear function of travel time and cost that one wants to minimize (mode-independent)

• Slopes are given by individual preferences → VTTS_n

• Choice = mode/route with highest utility

Appendix: Conditional indirect utility function

• An individual behaves according to maxU(Xj,Qi)

s.t.^X

j

P_jX_j +c_i ≤I

• Find optimal consumption Xj based on the discrete choicei with costci → conditional demand X_j(P,I−c_i,Q_i) with available incomeI−c_i

• Optimize overi → the conditional indirect utility function Vi is the maximum utility the individual would achieve if alternative i was chosen

Vi ≡U(Xj(P,I−ci,Qi),Qi)

Appendix: Subjective value of a characteristic

• The marginal (unconditional) utility of income equals minus the marginal (conditional) dis-utility of costc_i

• The subjective value of an increasein quality characteristick in alternative i is defined as the marginal rate of substitution (MRS) between this characteristic and money (e.g. increase in cost ci) at constant utility

SV_k,i = ∂V_i/∂Q_k,i

∂V/∂I =−∂V_i/∂Q_k,i

∂Vi/∂ci

• E.g. average willingness to pay for reductionin travel time VTTSi = β_tt,i

βtc

Appendix: Time-use data

Category PCW [h] MAED [h] ATUS [h] Variable

Working 36.2 37.8 36.9 Tw

Wage rate [CHF/h] 49.5 14.5 – w

Leisure – 28.9 32.3 Tf1

Out-of-home leisure 16.4 – – Tf1

In-home leisure 9.6 – – Tf2

Eating – 9.3 8.6 Tf2

Shopping – 2.1 2.0 Tf2

Committed time – 80.6 80.1 Tc

Travel 10.6 9.3 8.1 Tc

Shopping 1.9 – – Tc

Other 0.8 – – Tc

At home 92.5 – – Tc

Total (time constraint) 168.0 168.0 168.0 τ

Appendix: Expenditures data

Category PCW [%] MAED [%] HABE [%] Variable

Hotel, restaurants and holidays 11.4 6.2 7.5 Ef1

Leisure 2.9 7.8 3.9 Ef1

Clothes and accessories 5.3 5.6 3.1 Ef2

Electronics 2.2 3.6 2.0 Ef2

Taxes 23.1 – 10.8 Ec

Housing 18.6 23.2 18.7 Ec

Food 10.2 17.3 9.1 Ec

Health (incl. insurance) 7.0 2.4 9.8 Ec

Mobility 5.0 12.7 6.8 Ec

Communication 1.6 – 2.2 Ec

Furniture 1.6 2.4 1.3 Ec

Education 1.3 2.0 1.6 Ec

Services 1.8 3.1 2.0 Ec

Insurances¹ 3.0 8.2 17.6 Ec

Other expenditures 4.9 4.7 3.5 Ec

Avg. weekly expenditures [CHF] 1931.0 560.1 2309.7 P_Ef

j+Ec Avg. weekly labor income [CHF] 1995.6 550.7 2296.8 w·Tw

Appendix: VoL model estimation

• Four baseline utility parameters to be estimated: Λx,n∈ {θ_w, θTf1, ψEf1,Ψ} Λx,n=±exp(ζx +Znβx+ρx,n)

• The VoL for each individual is given by

VoLdn= ∂U/∂Ti

∂U/∂E_j = µ

λ = w·Tw^∗+Y −Ec Ψbn(τ−Tw^∗−Tc)

• Ψ^bn: Conditionals of the preference parameter for freely chosen goods relative time

• µ andλ: Marginal utility of increasing available time/money (s.t. satiation)

• Decomposition of the VoL in a preference and data-driven component:

log(VoL^dn) = −logΨ^bn

+ logw·Twd^∗+Y −Ec−logτ −Twd^∗−Tc

Appendix: Discrete choice model specification

• Including time-use residuals to account for endogeneity (Petrin and Train, 2010)

• Mixed Logit model estimated in willingness-to-pay space (Schmid et al., 2019b)

VTTS^i,n,t = (VTTSi +Pn,tρVTTS,i+ZnκVTTS,i+ηVTTS,i,n)distn,t

dist

θVTTS,i

• The scale parameter is defined as

ψen,t = exp (βscale +Znκscale+ηscale,n)dist_n,t dist

θscale

>0 ∀ n,t

• The VTTS for each mode and individual is given by the conditionals VTTS\i,n

• Estimated in R 3.4.1 using mixl-package (Molloy et al., 2019)

Appendix: Travel choice data

• Discrete choice model: Pooled RP/SP panel data

• 12’594 choice observations, 362 individuals

02468Percent [%]

0 20 40 60 80 100

a) # choice observations per respondents

0510152025Percent [%]

Walk (MC_RP)Bike (MC_RP)MIV (MC_RP)PT (MC_RP)Walk (MC_SP)Bike (MC_SP)CP (MC_SP)CS (MC_SP)PT (MC_SP)

Alt. 1 (RC_CS)Alt. 2 (RC_CS)Alt. 3 (RC_CS)Alt. 1 (RC_PT)Alt. 2 (RC_PT)Alt. 3 (RC_PT)

b) Choice rates, by alternative (pooled data set)

Appendix: Time-use model estimation results

• All baseline utility main effects significant (p <0.01)

• θw <0: Utility of working time negative

• Utility of out-of-home leisure significantly higher than in-home leisure

BASE INTER TUMIX

# estimated parameters 4 24 28

# respondents 362 362 362

# MLHS draws − − 2000

R²: Tw^∗ equation 0.73 0.73 0.77 R²: Tf1^∗ equation 0.64 0.67 0.75 R²: Ef1^∗equation 0.30 0.41 0.57

LLfinal −5592 −5528 −5510

AICc 11193 11109 11080

Appendix: VoL decomposition

log(money) log(time) log(exprate) log(pref.) log(VoL) Age [years] +0.34^∗∗∗ −0.12^∗∗ +0.39^∗∗∗ +0.78^∗∗∗ −0.19^∗∗∗

Male +0.33^∗∗∗ −0.09 +0.36^∗∗∗ +0.49^∗∗∗ +0.00

High educ. +0.27^∗∗∗ −0.07 +0.29^∗∗∗ +0.19^∗∗∗ +0.16^∗∗∗

Urban res. loc. +0.04 +0.05 +0.00 −0.00 +0.01

Couple +0.03 −0.09^∗ +0.09 −0.02 +0.10^∗

Kids −0.10^∗ −0.12^∗∗ +0.00 −0.55^∗∗∗ +0.43^∗∗∗

Inc. [CHF] +0.62^∗∗∗ −0.10^∗ +0.63^∗∗∗ +0.38^∗∗∗ +0.37^∗∗∗

Money = available money component; time = available time component; exprate = expenditure rate;

pref. = preference component

Significance levels: ^∗∗∗:p<0.01,^∗∗:p<0.05,^∗:p<0.1

Appendix: Choice model estimation results

• All mode-specific VTTS main effects significant (p<0.01)

• Endogeneity: LR test significant (8 df; increase in LL by 34.8 units)

• Trip explain much more than respondent characteristics

RMNL TMNL UMNL MIXL

# est. parameters 30 52 66 79

# respondents 356 356 356 356

# choice observations 12594 12594 12594 12594

# Sobol draws − − − 5000

LLfinal −10875 −8791 −8617 −6548

AICc 21816 17704 17397 13301

Appendix: Mode effects in VTTS

• MIV: Reference mode

• Residential location area: Strongest power in disentangling the total mode effect

MIV–PT MIV–CS MIV–CP

Total ∆VTTSi−j 15.8 –0.1 –2.1

Interquartile range (IQR) (19.0) (20.1) (17.3)

Ntotal 233 173 98

Female ∆VTTSi−j 21.2 10.9 2.2

Na 117 82 49

Male ∆VTTSi−j 8.7 –7.9 –5.7

Nb 116 91 49

Agglo./rural ∆VTTSi−j 19.3 5.1 1.2

Na 147 111 70

Urban ∆VTTSi−j 5.9 –6.5 –10.8

Appendix: VTTS and VoL correlation analysis

VTTS walk VTTS bike VTTS MIV

log(preference) −0.04 −0.11 −0.41^∗∗∗

log(avail. money) −0.10 0.01 −0.21^∗∗∗

log(avail. time) −0.18^∗∗∗ 0.07 0.09

N 256 166 253

VTTS PT VTTS CS VTTS CP

log(preference) −0.23^∗∗∗ −0.16^∗∗ −0.36^∗∗∗

log(avail. money) 0.00 0.05 −0.18^∗∗

log(avail. time) 0.01 0.08 0.10

N 331 219 120

Significance levels: ^∗∗∗:p<0.01,^∗∗:p<0.05,^∗:p<0.1

Appendix: Shopping attitudes

onl1: I often order products on the Internet onl2: Online shopping is associated with risks

onl3: Credit card fraud is one of the reasons why I don’t like online shopping onl4: The Internet has more cons than pros

onl5: Online shopping facilitates the comparison of prices and products

onl6: The risk of receiving a wrong product is one of the main reasons why I don’t like online shopping

onl7: I like to follow the new developments in the tech industry onl8: All I need, I find in the shops

onl9: Number of different IT gadgets in possession

ple1: I like to visit shops, even if I don’t want to buy something, just for looking around ple2: A disadvantage of online shopping is that I cannot physically examine the products ple3: Shopping usually is an annoying duty

Appendix: Correlation patterns and attitudes

PRO−ONLINE PLEASURE Male Age Income High education Car available HH members Store access.

Married Non−working Urban res. loc.

PLEASURE Male Age

Income Car available

Married Non−working

c > +0.3 +0.3 > c > +0.2 +0.1 > c > +0.2

−0.1 < c < −0.2

−0.2 < c < −0.3 c < −0.3

Appendix: Hybrid choice modeling framework

Appendix: HCM structural model I

The utility equations for shopping channeli ∈ {O,S}and individualn ∈ {1,2, ...,N}in choice scenariot ∈ {1,2, ...,Tn} with choice attributesXi,n,t and the latent variables LV_z,nwith z ∈ {online shopping attitudes,pleasure of shopping} are given by

UO,n,t =XO,n,tβO +LVz,nµLVz +Zz,nΘz+f1,O,n,t+f3,n,t+ψO,n+O,n,t

U_S,n,t =X_S,n,tβ_S+f_1,S,n,t+f2,n,t+_S,n,t where

f_1,i,n,t = (β_cost +Z_online,n∆online,cost +ψ_cost,n)·cost_i,n,t+ ϕ_{LVonline,cost} ·LV_online,n·cost_i,n,t

f2,n,t = (βtime,S +Zpleasure,n∆pleasure,time,S+ψtime,S,n)·timeS,n,t+ ϕ_{LVpleasure,time}·LV_pleasure,n·time_S,n,t

f = (α^M,L+α ·male +α ·age )·size^M,L

Appendix: HCM structural model II and measurement model

The LV structural equations for latent variablez are linear functions of observed socioeconomic characteristicsZ_z,n for individualn:

LV_z,n=Z_z,nρ_z+η_LVz,n η_LVz,n ∼N(0, σ_LV² _z)

The latent variable measurement equations with responses to the attitudinal questions (items)Iw,n with w ∈ {onl1,onl2, ...,ple3}are given by

Iw,n=Iw +τIwLVz,n+νw,n

ν_w,n∼N(0, σ_I²_w)

Appendix: Calculation of valuation indicators

First obtain the conditional estimates of the shopping cost coefficientλ^_cost,n, which is then used to calculate the weighted average marginal disutility of costCn for each individual:

λ^r_cost,n=β^bcost+Zonline,n∆bonline,cost +ψ_cost,n^r +ϕbLVonline,costLV^r_online,n

λ^cost,n= PR

r=1

hQ

i

QTn

t=1P(c_i,n,t = 1|X_i,n,t,Z_z,n,Ω^b,Σ^b,Γ^r)^ci,n,tλ^r_cost,nⁱ PR

r=1

Q

i

QTn

t=1P(ci,n,t = 1|X_i,n,t,Zz,n,Ω^b,Σ^b,Γ^r)^ci,n,t were Γ^r corresponds toLV_online,n^r andψ^r_i,n. Finally,

C_n=

Tn

X

t=1

1 T_n

λ^cost,nP

icosti,n,t+β^bdelivery cost^G,E delivery cost^G,E_n,t P

icost_i,n,t+delivery cost^G_n,t^,E Travel and delivery time are fixed coefficients = =

Appendix: Daily scheduling data

• Average MIV costs: 0.17 CHF/km (base scenario) → 1.77 CHF/km (scenario 4)

• Average distance traveled: 60 km (base scenario) →11 km (scenario 4)

012345Average MIV km cost [CHF/km]

B S1 S2 S3 S4

050100150200250300Traveled distance by MIV [km]

B S1 S2 S3 S4

Appendix: Mobility tool ownership data

• Average MIV costs: 0.25 CHF/km (base scenario) → 1.14 CHF/km (scenario 4)

• Average distance traveled: 9’900 km (base scenario)→ 6’610 km (scenario 4)

0.511.522.5Average MIV km cost [CHF/km]

B S1 S2 S3 S4

010,00020,00030,00040,00050,00060,000Traveled distance by MIV [km]

B S1 S2 S3 S4

Appendix: Daily distance; by mode

020406080100Daily distance [in % relative to base scenario]

Base Scenario 1 Scenario 2 Scenario 3 Scenario 4

Appendix: Yearly distance; by mode

020406080100Yearly distance [in % relative to base scenario]

Base Scenario 1 Scenario 2 Scenario 3 Scenario 4

Small car Medium car Large car

Appendix: Adaptations in mobility tool ownership

020406080100a) Car fleet [% rel. to base scenario] _{Base Scenario 1 Scenario 2 Scenario 3 Scenario 4}

Small car Medium car Large car

MPV Company car

020406080100b) Fuel type [% rel. to base scenario] Base Scenario 1 Scenario 2 Scenario 3 Scenario 4

Gasoline Diesel Gas

Electric Hybrid

20406080100 020406080100120d) Costs [% rel. to base scenario]

Base Scenario 1 Scenario 2 Scenario 3 Scenario 4

Appendix: Modeling framework adaptations in car usage

Exponential regression model accounting for panel structure (EMIX):

yn,t = expα+Znρ+ψα,n+β^en·log(xn,t)+ζn,t

βen= −exp(βcost +Znκ+ψβ,n)·

distn,0

dist₀ ωdist

A draw ofψis calculated as ψ^r_α,n ψ_β,n^r

!

= σα 0

σ_α,β σ_β

!

· η_α^r η^r_β

!

From this follows that Var(ψα,n) =σ_α², Var(ψ_β,n) =σ²_α,β+σ_β² and Cov(ψ_α,n, ψ_β,n) =σ_α·σ_α,β

Appendix: Estimation results (adaptation in car usage)

BASE DIST USER EMIX

# estimated parameters 3 4 13 16

# respondents 163 163 163 163

# choice observations 741 741 741 741

# draws − − − 4000

R² 0.11 0.57 0.63 0.82

LLfinal −4010.39 −3747.20 −3696.99 −3573.68

AICc 8026.93 7502.65 7422.42 7183.08

# estimated parameters 3 4 9 12

# households 165 165 165 165

# choice observations 821 821 821 821

# draws − − − 4000

R² 0.02 0.46 0.54 0.95

LLfinal −2885.36 −2642.64 −2577.22 −2015.49

Appendix: Cost elasticities (adaptation in car usage)

• All cost elasticity main effects significant (p <0.01)

• EMIX: Substantial amount of unobserved heterogeneity

• Daily scheduling: Elasticities are larger in all model specifications

• Results strongly affected by the inclusion of covariates; decreasing elasticities for increasing distance traveled in the base scenario

• EMIX: Similar relative, but decreasing magnitudes compared to BASE model

BASE DIST USER EMIX N

Medianb^{n −0.47 −0.27 −0.26 −0.37 163}

Meanb^{n −0.47 −0.34 −0.36 −0.42}

IQR (0.0) (0.30) (0.35) (0.29)

Medianb^{n −0.16 −0.24 −0.23 −0.13 165}

Meanb^{n −0.16 −0.29 −0.27 −0.19}

IQR (0.0) (0.27) (0.25) (0.12)

Appendix: Further research (related to current dataset)

• Mobility tool ownership RP model

– Investigate preferences and substitution patterns

– Integrate in the mode/route choice model (self-selection) – Modification of availability conditions

• Savings rate in time-use model – Savings now part ofEf2

– Decrease in the median VoL by about 7%

• Interrelations between travel behavior and ICT usage – Based on diary data (RP)

– Comparison/validation of results obtained from the shopping choice model

• Inclusion of attitudes (now only in shopping choice model) – Better representation of the decision process?

Appendix: Further research (general)

• Finer grained distinction of activities and expenditures – Committed vs. freely chosen

– Secondary activities (during travel, at home, etc.)

• Better tailored research design to disentangle mode and user-type effects – Self-selection still present (at the individual and/or trip-level)?

– Suggestion: Additional route choice tasks for different modes/trip purposes

• Context-dependency of results

– Example: Travel vs. shopping vs. delivery cost sensitivity – Important for valuation studies

• More sophisticated research design to investigate shopping channel preferences – More relaxed assumptions (product categories, trip chaining, etc.)

Appendix: Main lessons learned

• Focus groups: Which attributes are important?

• Reduction in response burden, more specific research design

• Better address respondents who are less willing to participate (saliency effects)

• Inclusion of traditional modes (e.g. car) in SP experiments

• Within-subject designs (trip purpose, distance, shopping purpose, etc.)

• Avoid necessity to impute based on external datasets (e.g. fixed income)