How do activities conducted while commuting influence mode choice? Using revealed preference models to inform public transportation advantage and autonomous vehicle scenarios

Aliaksandr Malokin, Giovanni Circella, Patricia L. Mokhtarian, 2019, in Transportation Research Part A

doi:10.1016/j.tra.2018.12.015
Location Northern California
Population Other (specify)
Sample size 2229
Factor analysis type exploratory factor analysis, Oblique rotation
Stepwise regression no
Removal of insignificant variables yes
Reviewed by SH

Abstract

From early studies of time allocation onward, it has been acknowledged that the “productive” nature of travel could affect its utility. Currently, at the margin an individual may choose transit over a shorter automobile trip, if thereby she is able to use the travel time more productively. On the other hand, recent advancements toward partly/fully automated vehicles are poised to revolutionize the perception and utilization of travel time in cars, and are further blurring the role of travel as a crisp transition between location-based activities. To quantify these effects, we created and administered a survey to measure travel multitasking attitudes and behaviors, together with general attitudes, mode-specific perceptions, and standard socioeconomic traits (N = 2229 Northern California commuters). In this paper, we present a revealed preference mode choice model that accounts for the impact of multitasking attitudes and behavior on the utility of various alternatives. We find that the propensity to engage in productive activities on the commute, operationalized as using a laptop/tablet, significantly influences utility and accounts for a small but non-trivial portion of the current mode shares. For example, the model estimates that commuter rail, transit, and car/vanpool shares would respectively be 0.11, 0.23, and 1.18 percentage points lower, and the drive-alone share 1.49 percentage points higher, if the option to use a laptop or tablet while commuting were not available. Conversely, in a hypothetical autonomous vehicles scenario, where the car would allow a high level of engagement in productive activities, the drive-alone share would increase by 1.48 percentage points. The results empirically demonstrate the potential of a multitasking propensity to reduce the disutility of travel time. Further, the methodology can be generalized to account for other properties of autonomous vehicles, among other applications.

Factors

Variable Pattern loading
Ability to do things I need/want while traveling (Convenience) standardized single item

Models

Dependent variable propensity to use a laptop, netbook, or tablet computer
Model type binary logit
Sample size 265.0
R2 0.9051
Adjusted R2
Pseudo R2 nan
AIC nan
BIC nan
Log-likelihood at zero -183.684
Log-likelihood at constants -16.426
Log-likelihood at convergence -16.426
Variable Coefficient p-value
Constant -4.47 0.0
Dependent variable propensity to use a laptop, netbook, or tablet computer
Model type binary logit
Sample size 197.0
R2 0.3326
Adjusted R2
Pseudo R2 nan
AIC nan
BIC nan
Log-likelihood at zero -136.55
Log-likelihood at constants -136.426
Log-likelihood at convergence -84.128
Variable Coefficient p-value
Constant 0.313 0.705
Has to/would like to work on commute 1.148 0.0
Would like to take same route -0.543 0.008
Female -1.36 0.002
Age -0.049 0.001
Hourly Waged (=1 if ‘yes’,=0 otherwise) -3.276 0.01
Travel distance, mi 0.026 0.0
Dependent variable propensity to use a laptop, netbook, or tablet computer
Model type binary logit
Sample size 811.0
R2 0.509
Adjusted R2
Pseudo R2 nan
AIC nan
BIC nan
Log-likelihood at zero -562.142
Log-likelihood at constants -292.922
Log-likelihood at convergence -272.025
Variable Coefficient p-value
Constant -2.268 0.0
Pro-technology 0.549 0.0
Polychronicity 0.241 0.045
Has to/would like to work on commute 0.368 0.001
Dependent variable propensity to use a laptop, netbook, or tablet computer
Model type binary logit
Sample size 389.0
R2 0.5449
Adjusted R2
Pseudo R2 nan
AIC nan
BIC nan
Log-likelihood at zero -269.634
Log-likelihood at constants -186.341
Log-likelihood at convergence -113.711
Variable Coefficient p-value
Constant -4.408 0.0
Travel is wasted time 0.564 0.001
Has to/would like to work on commute 1.262 0.0
Has to/would like to do recreation on commute 0.685 0.002
Has to/would like to multitask at work -0.456 0.021
Has to/would like to be available to people 0.486 0.009
Would like to take same route -0.383 0.042
Annual household per capita income, $000 -0.021 0.001
Travel distance, mi 0.029 0.0
Dependent variable propensity to use a laptop, netbook, or tablet computer
Model type binary logit
Sample size 1001.0
R2 0.799
Adjusted R2
Pseudo R2 nan
AIC nan
BIC nan
Log-likelihood at zero -693.84
Log-likelihood at constants -158.328
Log-likelihood at convergence -132.445
Variable Coefficient p-value
Constant -2.178 0.0
Multitasking is normative 0.401 0.03
Time spent working -0.372 0.045
Has to/would like to work on commute 0.77 0.0
Has to do recreation on commute 0.946 0.0
would like to do recreation on commute -0.389 0.091
Vehicle age -0.102 0.013
Dependent variable Mode: Driving alone (base), Biking, Commuter rail, Transit, Shared ride
Model type multinomial logit
Sample size 2229.0
R2 0.5756
Adjusted R2
Pseudo R2 nan
AIC nan
BIC nan
Log-likelihood at zero -2655.817
Log-likelihood at constants -1555.064
Log-likelihood at convergence -1127.247
Biking
Variable Coefficient p-value
Constant -5.327 0.0
In-vehicle travel time, min -0.163 0.006
out-of-vehicle travel time, min -0.048 0.0
one-way commute cost, ln($) -1.175 0.0
pro-active modes 2.088 0.0
Mode convenience 0.455 0.0
Mode benefit/cost 0.368 0.0
Mode comfort 0.405 0.0
Mode multitaskability 0.098 0.023
Propensity to use a laptop/tablet/netbook 1.24 0.0
Commuter rail
Variable Coefficient p-value
Constant -2.959 0.0
In-vehicle travel time, min -0.016 0.004
out-of-vehicle travel time, min -0.048 0.0
one-way commute cost, ln($) -1.175 0.0
Pro-transit 0.954 0.001
Mode convenience 0.455 0.0
Mode benefit/cost 0.368 0.0
Mode comfort 0.405 0.0
Mode multitaskability 0.098 0.023
Propensity to use a laptop/tablet/netbook 1.24 0.0
Driving alone
Variable Coefficient p-value
In-vehicle travel time, min -0.016 0.004
out-of-vehicle travel time, min -0.048 0.0
one-way commute cost, ln($) -1.175 0.0
Mode convenience 0.455 0.0
Mode benefit/cost 0.368 0.0
Mode comfort 0.405 0.0
Mode multitaskability 0.098 0.023
Propensity to use a laptop/tablet/netbook 1.24 0.0
Shared ride
Variable Coefficient p-value
Constant -2.752 0.0
Female 0.393 0.009
Limitation on walking 0.166 0.003
In-vehicle travel time, min -0.016 0.004
out-of-vehicle travel time, min -0.048 0.0
one-way commute cost, ln($) -1.175 0.0
Pro-transit 0.201 0.014
Polychronicity 0.191 0.006
Mode convenience 0.455 0.0
Mode benefit/cost 0.368 0.0
Mode comfort 0.405 0.0
Mode multitaskability 0.098 0.023
Propensity to use a laptop/tablet/netbook 1.24 0.0
Transit
Variable Coefficient p-value
Constant 0.785 0.343
Driver's license -1.89 0.022
Race: white 0.532 0.014
In-vehicle travel time, min -0.016 0.004
out-of-vehicle travel time, min -0.048 0.0
out-of-vehicle travel time, min -0.048 0.0
one-way commute cost, ln($) -1.175 0.0
one-way commute cost, ln($) -1.175 0.0
Pro-transit 0.825 0.0
Mode convenience 0.455 0.0
Mode convenience 0.455 0.0
Mode benefit/cost 0.368 0.0
Mode benefit/cost 0.368 0.0
Mode comfort 0.405 0.0
Mode comfort 0.405 0.0
Mode multitaskability 0.098 0.023
Mode multitaskability 0.098 0.023
Propensity to use a laptop/tablet/netbook 1.24 0.0
Propensity to use a laptop/tablet/netbook 1.24 0.0

The Attitudes and Travel Database is produced with support from the Center for Teaching Old Models New Tricks at Arizona State University, a University Transportation Center sponsored by the US Department of Transportation through Grant No. 69A3551747116.

sha256:a08d9e369743bf7e6d1c40d27347318209b40a7fb1543813fdcf31b898918815