International Choice Modelling Conference, International Choice Modelling Conference 2017

Font Size: 
Capturing drivers’ adaptive time use behavior inside autonomous vehicles and willingness-to-pay as well as affective experiences
YING JIANG, Junyi Zhang

Last modified: 28 March 2017


Many people have to be patient with driving because of inefficient use of limited time, traffic congestion, and various driving risks. Recent years have observed rapid developments of Autonomous Vehicles (AVs), which are capable of sensing their environment and navigating without human interventions. AVs could not only improve driving safety dramatically, but also be developed as a “moving home” or a “moving office”, allowing drivers to make more efficient use of time with better feelings, in comparison with traditional vehicles. However, understanding such potential improvements involve various difficulties because the AVs market is still immature. No relevant studies can be found in literature. This study attempts to fill this research gap by clarifying drivers’ time use behavior inside AVs and willingness-to-pay under different levels of AVs and their diffusion rates in the market, where the influence of social interactions is reflected. For this purpose, a day reconstruction method (DRM) is first applied to capture drivers’ affective experience and time use (multitasking behavior) during use of their current vehicles and during use of public transport systems (if any). Then, an ASP-off-RP survey is developed to capture time use behavior inside autonomous vehicles and willingness-to-pay under different levels of AVs and their diffusion rates in the market (ASP: adaptive stated preference; RP revealed preference). Finally, several econometric models are built to investigate influential factors to drivers’ time use behavior inside autonomous vehicles and willingness-to-pay as well as potential changes in affective experiences by shifting from use of traditional vehicles to use of AVs.

DRM, proposed by Kahneman et al. (2004), assesses how people spend their time and how they experience the various activities and settings of their lives in a reliable way. In the DRM survey, respondents are asked to first write down a set of activity episodes occurring in the preceding day as well as their feelings experienced, as a short diary. Next, respondents are asked to describe each episode by indicating: when the episode began and ended; what they were doing; where they were; and with whom they were interacting. DRM could allow for interpersonal and temporal comparisons of affect. It allows us to better capture drivers’ affective experience and time use on multitasking during use of their current vehicles. In this study, “happy”, “warm”, “enjoyable”, “impatient”, “frustrated”, “depressed”, “hassled”, “angry”, “worried”, “tired”, and “stressful” are adopted to represent feelings experienced during travel.

The SP approach has been widely applied to capture consumers’ preferences for not-yet-existing alternatives (AVs are such an example); however, it suffers from biases due to unrealistic scenarios assumed in the survey. To enhance the survey reliability, an SP-off-RP approach (Train and Wilson, 2008) has been developed, where SP alternatives and their levels of attributes are constructed based on those that respondents chose in an actual market setting. In the SP-off-RP survey, respondents face the same number of alternatives in the SP task as in the RP task, and there is a one-to-one correspondence of the SP alternatives to the RP alternatives. In this study, AVs are treated as a vehicle updated from respondents’ current vehicles. In this sense, the target of this study is the current car drivers. There are two types of alternatives in the SP-off-RP survey: one is about vehicles (from the current vehicle to an updated vehicle, i.e., AV) and another is about time use. As for time use, the concept of adaptive SP survey (Fowkes and Shinghal, 2002; Tilahun et al., 2007) is further applied, where respondents are asked to report possible changes in their activity participation and time use behaviors during travel responding to different levels of AVs and their diffusion rates in the market, from their current multitasking time use captured in the above DRM survey. The resulting survey is called ASP-off-RP survey. Social interactions are represented in the form of shares of AVs with different functions in the market.

As for the aforementioned econometric models, a time use model is first built to reflect trade-offs between activities within limited time based on the concept of multilinear utility, where zero-consumption on time use is reflected. This model is used to understand drivers’ multitasking behaviors during travel and their adaptive time use behaviors when using AVs. Second, a discrete-continuous choice model is built to jointly represent ownership of AVs and willingness-to-pay, where drivers’ current affective experiences are introduced as a part of explanatory variables. Third, a multiple ordered probit model is built to describe affective experiences during the current travel and it is further used to predict changes in affective experiences during use of AVs. In the above modeling analysis, different levels of AVs and their diffusion rates in the market are treated as common explanatory variables.

Currently, we already finished the questionnaire design based on our experiences of using the traditional SP survey (Jiang and Zhang, 2014), the SP-off-RP survey (Yu et al., 2013), and the DRM survey (Zhang, 2009). We also finished the model development based on our previous modeling practices (Zhang et al., 2005 (time use model); Zhang, 2009 (multiple ordered probit model); Yu et al., 2012 (discrete-continuous choice model)). We will finish the implementation of the new survey in Japan on the Internet by the end of August, 2016. Analysis results will be reported afterwards.

In summary, drivers’ time use behavior inside autonomous vehicles and willingness-to-pay will be investigated by incorporating the influence of social interactions and potential changes in affective experiences during use of AVs will be examined, based on innovative but operational survey and modeling approaches. Policy implications of analysis results to mitigate negative affects during travel (indirectly associated with driving safety) and future research challenges will also be discussed.


1)      Fowkes, A.S., Shinghal, N., 2002. The Leeds Adaptive Stated Preference Methodology. Institute of Transport Studies, University of Leeds, Working Paper 558 (; Accessed July 16, 2016).

2)      Jiang, Y., Zhang, J., 2014. How drivers adapt to traffic accidents and dynamic travel information: Stated preference survey in Japan. Transportation Research Record 2413, 74-83.

3)      Kahneman, D., Krueger, A.B., Schkade, D.A., Schwarz, N., Stone, A.A., 2004. A survey method for characterizing daily life experience: The day reconstruction method. Science 306, 1776-1780.

4)      Tilahun, N.Y., Levinson, D.M., Krizek, K.J., 2007. Trails, lanes, or traffic: Valuing bicycle facilities with an adaptive stated preference survey. Transportation Research Part A 41, 287-301.

5)      Train, K., Wilson, W.W., 2008. Estimation on stated-preference experiments constructed from revealed-preference choices. Transportation Research Part B 42, 191-203.

6)      Yu, B., Zhang, J., Fujiwara, A., 2012. A household time use and energy consumption model with multiple behavioral interactions and zero-consumption. Environment and Planning B: Planning and Design 40 (2), 330-349.

7)      Yu, B., Zhang, J., Fujiwara, A., 2013. Rebound effects caused by the improvement of vehicle energy efficiency: An analysis based on an SP-off-RP survey. Transportation Research Part D: Transport and Environment 24, 62-68.

8)      Zhang, J., 2009. Subjective well-being and activity-travel behavior analysis: Applying day reconstruction method to explore affective experience during travel. Proceedings of The 14th International Conference of Hong Kong Society for Transportation Studies, Hong Kong, China, December 10-12, Vol. 2, 439-449.

9)      Zhang, J., Timmermans, H.J.P., Borgers, A., 2005. A model of household task allocation and time use. Transportation Research Part B 39, 81-95.

Conference registration is required in order to view papers.