How Autonomous Driving May Affect the Value of Travel Time Savings for Commuting

Autonomous driving is being discussed as a promising solution for transportation-related issues and might bring some improvement for users of the system. For instance, especially high mileage commuters might compensate for some of their time spent traveling as they will be able to undertake other activities while going to work. At the same time, there are still many uncertainties and little empirical data on the impact of autonomous driving on mode choices. This study addresses the impact of autonomous driving on value of travel time savings (VTTS) and mode choices for commuting trips using stated-choice experiments. Two use cases were addressed – a privately owned, and a shared autonomous vehicle – compared with other modes of transportation. The collected data were analyzed by performing a mixed logit model. The results show that mode-related factors such as time elements, especially in-vehicle time and cost, play a crucial role for mode choices that include autonomous vehicles. The study provides empirical evidence that autonomous driving may lead to a reduction in VTTS for commuting trips. It was found that driving autonomously in a privately owned vehicle might reduce the VTTS by 31% compared with driving manually, and is perceived similarly to in-vehicle time in public transportation. Furthermore, riding in a shared autonomous vehicle is perceived 10% less negatively than driving manually. The study provides important insights into VTTS by autonomous driving for commuting trips and could be a base for future research to build upon.

[1]  J. Shires,et al.  An international meta-analysis of values of travel time savings. , 2009, Evaluation and program planning.

[2]  Brice G. Nichols,et al.  Using an Activity-Based Model to Explore Possible Impacts of Automated Vehicles , 2015 .

[3]  Mark Wardman,et al.  Meta-analysis of UK values of travel time: An update , 2011 .

[4]  D. Cox,et al.  An Analysis of Transformations , 1964 .

[5]  D. McFadden.  Conditional logit analysis of qualitative choice behavior , 1972 .

[6]  Nidhi Kalra,et al.  Autonomous Vehicle Technology: A Guide for Policymakers , 2014 .

[7]  K. Train,et al.  Mixed Logit with Repeated Choices: Households' Choices of Appliance Efficiency Level , 1998, Review of Economics and Statistics.

[8]  Kay W. Axhausen.  Ermittlung von Bewertungsansätzen für Reisezeiten und Zuverlässigkeit auf Basis der Schätzung eines Modells für modale Verlagerungen im nicht-gewerblichen und gewerblichen Personenverkehr für die Bundesverkehrswegeplanung , 2014 .

[9]  Stephane Hess,et al.  On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice , 2006 .

[10]  Staffan Algers,et al.  Mixed Logit Estimation of the Value of Travel Time , 1998 .

[11]  Claude Weis,et al.  Ermittlung von Bewertungsansätzen für Reisezeiten und Zuverlässigkeit auf der Basis eines Modells für modale Verlagerungen im nicht-gewerblichen und gewerblichen Personenverkehr für die Bundesverkehrswegeplanung: FE-Projekt-Nr. 96.996/2011 , 2014 .

[12]  Florian Heiss,et al.  Discrete Choice Methods with Simulation , 2016 .

[13]  S. Jara-Díaz.  Allocation and Valuation of Travel-Time Savings , 2007 .

[14]  Barbara Lenz,et al.  User Perspectives on Autonomous Driving:A Use-Case-Driven Study in Germany , 2016 .

[15]  Anthony Perl,et al.  Shared Autonomous Vehicles: Catalyst of New Mobility for the Last Mile? , 2016 .

[16]  David A. Hensher,et al.  Valuation of Travel Time Savings , 2011 .

[17]  Benjamin Kickhöfer,et al.  A Systemic View on Autonomous Vehicles , 2018, disP - The Planning Review.

[18]  Barbara Lenz,et al.  Autonomous Driving - The Impact of Vehicle Automation on Mobility Behaviour , 2016 .

[19]  Rico Krueger,et al.  Preferences for shared autonomous vehicles , 2016 .

[20]  K. Martens,et al.  A Stated-Choice Experiment on Mode Choice in an Era of Free-Floating Carsharing and Shared Autonomous Vehicles , 2017 .

[21]  Ahmed M El-Geneidy,et al.  Am stressed, must travel: The relationship between mode choice and commuting stress , 2015 .

[22]  K. Axhausen,et al.  Predicting the use of automated vehicles. [First results from the pilot survey] , 2017 .

[23]  Luis Ignacio Rizzi,et al.  The impact of traffic images on travel time valuation in stated-preference choice experiments , 2012 .

[24]  David A. Hensher,et al.  The Mixed Logit Model: the State of Practice and Warnings for the Unwary , 2001 .

[25]  A S Fowkes,et al.  Values of travel time savings in the UK: report to Department for Transport , 2003 .

[26]  Kay W. Axhausen,et al.  The Institute for Transport Planning and Systems , 2004 .

[27]  Joseph M. Stanford,et al.  Assessing the long-term effects of autonomous vehicles: a speculative approach , 2016 .

[28]  Yoram Shiftan,et al.  User preferences regarding autonomous vehicles , 2017 .

[29]  Melvyn Weeks,et al.  Discrete choice methods with simulation, Kenneth E. Train, Cambridge University Press, 2003, ISBN: 0-521-81696-3, pp. 334 , 2003 .

[30]  Daniel J. Fagnant,et al.  Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations , 2015 .

[31]  M. D. Yapa,et al.  Valuation of travel attributes for using automated vehicles as egress transport of multimodal train trips , 2015 .

[32]  John M. Rose,et al.  Designing Stated Choice Experiments: State of the Art , 2007 .

[33]  Lars Kröger,et al.  Autonomous car- and ride-sharing systems: A simulation-based evaluation of various supply options for different regions , 2017 .

[34]  Itf.  Urban Mobility System Upgrade: How shared self-driving cars could change city traffic , 2015 .