Abstract
The continuous development in technology allows to have fully autonomous vehicles (AVs) on the market. Travelers are interested in maximizing their utility onboard by involving themselves in multitasking. Does choosing a particular type of multitasking determine what type of transport mode to be used? Several studies are conducted on conventional transport modes (CTMs); however, scarcely can be found on AVs. A stated preference (SP) survey including sociodemographic and trip characteristics as well as discrete choice experiments (DCEs) is used. The impact of multitasking on travel behavior is assessed, where multitasking is divided into six types of activities based on the characteristics of an activity (i.e., active, or passive activity). The random utility theory approach including the discrete choice modeling is applied, where transport choice models for the shared autonomous vehicle (SAV) and public transport (PT) are developed considering time, cost, and multitasking availability. The results demonstrate that each onboard activity has a different impact on the transport mode choice, and the social media activity has the largest positive, while the only writing activity shows a negative impact on the transport mode choice. Moreover, the impact of travel time on multitasking is higher than that of the travel cost. Additionally, the changes in the travel time and the travel cost do not show strong and high differences between onboard activities. SAV is more affected by a change in the onboard activities, the travel time, and the travel cost than PT. In conclusion, the results show that inside urban areas, PT is more likely to be used than SAV concerning the multitasking possibility, the travel time, and the travel cost.
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References
Becker, G.: A theory of the allocation of time. Econ. J. 75(299), 493 (1965). https://doi.org/10.2307/2228949
Belenky, P.: The value of travel time savings: departmental guidance for conducting economic evaluations, revision 2. Department of Transportation (2011)
Mackie, P.J., Jara-Dıaz, S., Fowkes, A.: The value of travel time savings in evaluation. Transp. Res. Part E Logist. Transp. Rev. 37(2–3), 91–106 (2001). https://doi.org/10.1016/S1366-5545(00)00013-2
Varghese, V., Jana, A.: Impact of ICT on multitasking during travel and the value of travel time savings: Empirical evidences from Mumbai, India. Travel Behav. 12, 11–22 (2018). https://doi.org/10.1016/j.jtrangeo.2012.02.007
Steck, F., et al.: How autonomous driving may affect the value of travel time savings for commuting. Transp. Res. Rec. J. Transp. Res. Board 2672(46), 10 (2018). https://doi.org/10.1177/0361198118757980
Kouwenhoven, M., de Jong, G.: Value of travel time as a function of comfort. J. Choice Model. 28, 97–107 (2018). https://doi.org/10.1016/j.jocm.2018.04.002
Cherlow, J.R.: Measuring values of travel time savings. J. Consum. Res. 7(4), 360–371 (1981). https://doi.org/10.1086/208826
Litman, T.: Valuing transit service quality improvements. J. Public Transp. 11(2), 3 (2008). https://doi.org/10.5038/2375-0901.11.2.3
Coppola, P., Esztergár-Kiss, D.: Autonomous Vehicles and Future Mobility. Elsevier, Amsterdam (2019)
Berliner, R.M., et al.: Travel-based multitasking: modeling the propensity to conduct activities while commuting. In: Transportation Research Board 94th Annual Meeting, Washington DC, United States (2015)
Ettema, D., Verschuren, L.: Multitasking and value of travel time savings. Transp. Res. Rec. J. Transp. Res. Board 2010(1), 19–25 (2007). https://doi.org/10.3141/2010-03
Keseru, I., et al.: Multitasking on the go: an observation study on local public transport in Brussels. Travel Behav. Soc. 18, 106–116 (2020). https://doi.org/10.1016/j.tbs.2019.10.003
Malokin, A., Circella, G., Mokhtarian, P.L.: How do activities conducted while commuting influence mode choice? Using revealed preference models to inform public transportation advantage and autonomous vehicle scenarios. Transp. Res. Part A Policy Pract. 124, 82–114 (2019). https://doi.org/10.1016/j.tra.2018.12.015
Rhee, K.-A., et al.: Analysis of effects of activities while traveling on travelers’ sentiment. Transp. Res. Rec. J. Transp. Res. Board 2383(1), 27–34 (2013). https://doi.org/10.3141/2383-04
Mokhtarian, P.L., Papon, F., Goulard, M., Diana, M.: What makes travel pleasant and/or tiring? an investigation based on the French National Travel Survey. Transportation 42(6), 1103–1128 (2014). https://doi.org/10.1007/s11116-014-9557-y
Etzioni, S., et al.: Modeling cross-national differences in automated vehicle acceptance. Sustainability 12(22), 9765 (2020). https://doi.org/10.3390/su12229765
Malokin, A., Circella, G., Mokhtarian, P.L.: Do multitasking millennials value travel time differently? a revealed preference study of Northern California commuters. In: Transportation Research Board 96th Annual Meeting, Washington DC, United States (2017)
Banerjee, I., Kanafani, A.: The value of wireless internet connection on trains: implications for mode-choice models. In: UC Berkeley, U.o.C.T. Center, Editor, Berkeley (2008)
Evans, A.W.: On the theory of the valuation and allocation of time. Scot. J. Polit. Econ. 19(1), 1–17 (1972). https://doi.org/10.1111/j.1467-9485.1972.tb00504.x
Jara-Díaz, S.R.: Allocation and valuation of travel time savings. Handb. Transp. 1, 303–319 (2000). https://doi.org/10.1108/9780857245670-018
Singleton, P.A.: Discussing the “positive utilities” of autonomous vehicles: will travellers really use their time productively? Transp. Rev. 39(1), 50–65 (2019). https://doi.org/10.1080/01441647.2018.1470584
Khaloei, M., Ranjbari, A., Mackenzie, D.: Analyzing the Shift in Travel Modes’ Market Shares with the Deployment of Autonomous Vehicle Technology (2019)
Zhong, H., et al.: Will autonomous vehicles change auto commuters’ value of travel time? Transp. Res. Part D: Transp. Environ. 83, 102303 (2020). https://doi.org/10.1016/j.trd.2020.102303
Horni, A., Nagel, K., Axhausen, K.W.: The Multi-agent Transport Simulation MATSim, p. 620. Ubiquity Press, London (2016)
Hamadneh, J., Esztergár-Kiss, D.: Impacts of shared autonomous vehicles on the travelers’ mobility. In: 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS). IEEE, Poland (2019). https://doi.org/10.1109/MTITS.2019.8883392
Hamadneh, J., Esztergár-Kiss, D.: Potential travel time reduction with autonomous vehicles for different types of travellers. Promet Traffic Transp. 33(1), 61–76 (2021). https://doi.org/10.7307/ptt.v33i1.3585
Perk, V.A., et al.: Improving value of travel time savings estimation for more effective transportation project evaluation. Florida Department of Transportation, Center for Urban Transportation Research (2012)
Cirillo, C., Axhausen, K.W.: Evidence on the distribution of values of travel time savings from a six-week diary. Transp. Res. Part A Policy Pract. 40(5), 444–457 (2006). https://doi.org/10.1016/j.tra.2005.06.007
DeSerpa, A.C.: A theory of the economics of time. Econ. J. 81(324), 828–846 (1971). https://doi.org/10.2307/2230320
Xumei, C., Qiaoxian, L., Guang, D.: Estimation of travel time values for urban public transport passengers based on SP survey. J. Transp. Syst. Eng. Inf. Technol. 11(4), 77–84 (2011). https://doi.org/10.1016/S1570-6672(10)60132-8
Janssen, C.P., Kenemans, J.L.: Multitasking in autonomous vehicles: ready to go? In: 3rd Workshop on User Experience of Autonomous Vehicles at AutoUI 2015. ACM Press, Nottingham (2015)
Ortega, J., et al.: Simulation of the daily activity plans of travelers using the park-and-ride system and autonomous vehicles: work and shopping trip purposes. Appl. Sci. 10(8), 2912 (2020). https://doi.org/10.3390/app10082912
Simoni, M.D., et al.: Congestion pricing in a world of self-driving vehicles: an analysis of different strategies in alternative future scenarios. Transp. Res. Part C Emerg. Technol. 98, 167–185 (2018). https://doi.org/10.1016/j.trc.2018.11.002
Gurumurthy, K.M., Kockelman, K.M.: Modeling Americans’ autonomous vehicle preferences: A focus on dynamic ride-sharing, privacy & long-distance mode choices. Technol. Forecast. Social Change 150, 119792 (2020). https://doi.org/10.1016/j.techfore.2019.119792
Lavieri, P.S., Bhat, C.R.: Modeling individuals’ willingness to share trips with strangers in an autonomous vehicle future. Transp. Res. Part A Policy Pract. 124, 242–261 (2019). https://doi.org/10.1016/j.tra.2019.03.009
Bozorg, S.L., Ali, S.M.: Potential Implication of Automated Vehicle Technologies on Travel Behavior and System Modeling (2016)
Kolarova, V., Steck, F., Bahamonde-Birke, F.J.: Assessing the effect of autonomous driving on value of travel time savings: a comparison between current and future preferences. Transp. Res. Part A Policy Pract. 129, 155–169 (2019). https://doi.org/10.1016/j.tra.2019.08.011
Wadud, Z., Huda, F.Y.: The potential use and usefulness of travel time in fully automated vehicles. In: Transportation Research Board 97th Annual Meeting, Washington DC, United States (2018)
Hauber, A.B., et al.: Statistical methods for the analysis of discrete choice experiments: a report of the ISPOR Conjoint analysis good research practices task force. Value Health 19(4), 300–315 (2016). https://doi.org/10.1016/j.jval.2016.04.004
Lancsar, E., Louviere, J.: Conducting discrete choice experiments to inform healthcare decision making. Pharmacoeconomics 26(8), 661–677 (2008). https://doi.org/10.2165/00019053-200826080-00004
Cascetta, E.: Random utility theory. In: Transportation Systems Analysis, pp. 89–167. Springer, Heidelberg (2009). https://doi.org/10.1007/978-0-387-75857-2_3
Hensher, D.A., Greene, W.H.: The mixed logit model: the state of practice. Transportation 30(2), 133–176 (2003). https://doi.org/10.1023/A:1022558715350
Hoffman, S.D., Duncan, G.J.: Multinomial and conditional logit discrete-choice models in demography. Demography 25(3), 415–427 (1988). https://doi.org/10.2307/2061541
Ben-Akiva, M.E., Lerman, S.R., Lerman, S.R.: Discrete Choice Analysis: Theory and Application to Travel Demand, vol. 9. MIT press, Cambridge (1985)
Schmitz, C.: LimeSurvey: an open source survey tool. LimeSurvey Project Hamburg, Germany (2012). http://www.limesurvey.org, http://www.limesurvey.org
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The research reported in this paper and carried out at the Budapest University of Technology and Economics has been supported by the National Research Development and Innovation Fund (TKP2020 Institution Excellence Subprogram, Grant No. BME-IE-MISC) based on the charter of bolster issued by the National Research Development and Innovation Office under the auspices of the Ministry for Innovation and Technology.
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Hamadneh, J., Esztergár-Kiss, D. (2021). Modeling of Onboard Activities: Public Transport and Shared Autonomous Vehicle. In: Krömker, H. (eds) HCI in Mobility, Transport, and Automotive Systems. HCII 2021. Lecture Notes in Computer Science(), vol 12791. Springer, Cham. https://doi.org/10.1007/978-3-030-78358-7_3
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