Understanding Changes in Decision-making Processes to Adopt First-/Last-Mile Automated Bus Service
Time: Tue 2021-04-20 09.30
Location: Videolänk https://ntu-sg.zoom.us/j/93722171218 Kod: 653614, Du som saknar dator /datorvana kontakta Yusak Susilo email@example.com / Use the e-mail address if you need technical assistance, Singapore (English)
Subject area: Transport Science, Transport Systems
Doctoral student: Pei Nen Esther Chee , Systemanalys och ekonomi, School of Civil and Environmental Engineering - Nanyang Technological University, Integrated Transport Research Lab (ITRL)
Opponent: Professor Kay Axhausen, ETH Zürich
Supervisor: Professor Yusak Octavius Susilo, University of Natural Resources and Life Sciences, Vienna, Austria; Professor Wong Yiik Diew, NTU, Singapore
This research investigates the changes in the interactions of objective factors (factors which are independent of perception e.g. socio-demographic characteristics), subjective factors (psychological factors which are inherent in individual), and user’s behavioural responses towards a new technology/service in response to actual experience with the technology/service. In specific, this research investigates the changes in decision-making parameters to adopt first-/last-mile automated bus service over three important adoption stages, as specified in diffusion of innovations theory: 1. decision (before any actual ride experiences), 2. implementation (first trial) and 3. continuation (after the first trial).
Findings show that objective factors have both direct and indirect influences on users’ behavioural responses towards a new technology. There are dominant perceptions towards the technology among the same group of people with the same socio-demographic characteristics/travel characteristics/familiarity with the technology. However, the relationships between the objective factors with the associated perceptions are not stable when the users have gathered incomplete information about the technology. Decision made without complete information about the technology is subjected to logical fallacy. Consequently, the decision made is irrational and subject to changes after an individual gains complete information about the new technology/service through actual experience with the new technology/service.
Actual ride experiences provide users complete information about the technology. As compared to the behavioural responses of first-time users, the behavioural responses of the adopters (users who continued with the service after the first trial) are stable and consistent. Also, reinforcement learning process was found to exist in the adopters of the first-/last mile automated bus service. The findings give strong evidence that users’ level of information about a new technology/service has a significant impact on the stability of the representation of both the objective and subjective factors.
In conclusion, users’ level of information about a new technology/service has a crucial impact on the reliability of the representation of the objective factors and subjective factors identified from the travel behaviour studies of new technology/service like automated vehicle/bus. The decision made when users have incomplete information about a new technology is irrational and subject to changes. Hence, the representation of the objective factors and subjective factors identified from hypothetical studies in which the respondents lack real experience with automated vehicle/bus technology is unreliable. On the other hand, actual experience provides users with complete information about the new technology/service and allows users to learn about the technology/service. As a result, users can make a more rational decision hence the representation of the identified objective factors and subjective factors is reliable.
As another contribution of this research, a systematic approach to obtain objective measurements from real-world pedestrian-automated bus interactions using LiDAR data is also demonstrated in this thesis. The approach developed can be used as the fundamental framework to measure the safety risk between a crossing pedestrian with an automated vehicle/bus. Also, the developed method is helpful to the designing of control algorithm of an automated bus due to the attainments of the actual pedestrians’ risk thresholds when they interact with an automated bus in the real-world setting.