Decision making in a transportation mode experiment
The rise of rapidly improving Artificial Intelligence technology has resulted in various governmental institutions and organisations’ heightened interest in the integration of Autonomous Vehicles (AVs) into the transportation network. As such, this paper seeks to provide insights on individuals’ dec...
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sg-ntu-dr.10356-1475642023-03-05T15:44:25Z Decision making in a transportation mode experiment Kwa, Wei Jun Loong, Pin Sheng Lua, Shu Chyi Jonathan Tan Yohanes Eko Riyanto School of Social Sciences j.tan@ntu.edu.sg, YERIYANTO@ntu.edu.sg Social sciences::Economic theory The rise of rapidly improving Artificial Intelligence technology has resulted in various governmental institutions and organisations’ heightened interest in the integration of Autonomous Vehicles (AVs) into the transportation network. As such, this paper seeks to provide insights on individuals’ decision making of transportation mode choices by utilising a Driving Simulation (DS) experiment as a platform for decision-theoretic analysis, comparing between driving themselves (Self), delegating to other drivers (NAV), or AVs. Effects of individualistic characteristics such as gender, self-performance, driver license and private vehicle ownership, and behavioural biases like driver’s overconfidence, risk preference, and trust in technology on decision-making in a transport context were also analysed using Discrete Choice Model (DCM) to postulate probability and preference of transport choices. For this research, a wider variance to AV’s performance was specially allocated to replicate uncertainty of AV’s current technology. Effects of individual’s learning and experience on transportation mode choice were also studied using Adaptive Learning Models where short-term recency and long-term memorisation effects were validated at 1% significance level. Results indicated individuals’ reliance on recent mode choice and private benefits obtained, and memorisation of choices across a period. However, they lacked the abilities to remember the benefits received for each individual trip. Results from the DCM indicated previous trip value, trust, private vehicle ownership, and self-performance to be statistically significant, but there was a lack of evidence to suggest the same for driver license ownership, risk preference, and gender. The results from previous trip value and trust also provided a ranked preference of AV > NAV > Self while private vehicle ownership and self-performance only indicated Self > AV and NAV. These suggest over-reliance and biasness towards AVs and thus, provides policymakers with insights regarding factors and policies to consider for mass adoption of AVs. Bachelor of Arts in Economics 2021-04-06T08:49:56Z 2021-04-06T08:49:56Z 2021 Final Year Project (FYP) Kwa, W. J., Loong, P. S. & Lua, S. C. (2021). Decision making in a transportation mode experiment. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/147564 https://hdl.handle.net/10356/147564 en application/pdf Nanyang Technological University |
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Social sciences::Economic theory Kwa, Wei Jun Loong, Pin Sheng Lua, Shu Chyi Decision making in a transportation mode experiment |
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The rise of rapidly improving Artificial Intelligence technology has resulted in various governmental institutions and organisations’ heightened interest in the integration of Autonomous Vehicles (AVs) into the transportation network. As such, this paper seeks to provide insights on individuals’ decision making of transportation mode choices by utilising a Driving Simulation (DS) experiment as a platform for decision-theoretic analysis, comparing between driving themselves (Self), delegating to other drivers (NAV), or AVs. Effects of individualistic characteristics such as gender, self-performance, driver license and private vehicle ownership, and behavioural biases like driver’s overconfidence, risk preference, and trust in technology on decision-making in a transport context were also analysed using Discrete Choice Model (DCM) to postulate probability and preference of transport choices. For this research, a wider variance to AV’s performance was specially allocated to replicate uncertainty of AV’s current technology.
Effects of individual’s learning and experience on transportation mode choice were also studied using Adaptive Learning Models where short-term recency and long-term memorisation effects were validated at 1% significance level. Results indicated individuals’ reliance on recent mode choice and private benefits obtained, and memorisation of choices across a period. However, they lacked the abilities to remember the benefits received for each individual trip.
Results from the DCM indicated previous trip value, trust, private vehicle ownership, and self-performance to be statistically significant, but there was a lack of evidence to suggest the same for driver license ownership, risk preference, and gender. The results from previous trip value and trust also provided a ranked preference of AV > NAV > Self while private vehicle ownership and self-performance only indicated Self > AV and NAV. These suggest over-reliance and biasness towards AVs and thus, provides policymakers with insights regarding factors and policies to consider for mass adoption of AVs. |
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Jonathan Tan |
author_facet |
Jonathan Tan Kwa, Wei Jun Loong, Pin Sheng Lua, Shu Chyi |
format |
Final Year Project |
author |
Kwa, Wei Jun Loong, Pin Sheng Lua, Shu Chyi |
author_sort |
Kwa, Wei Jun |
title |
Decision making in a transportation mode experiment |
title_short |
Decision making in a transportation mode experiment |
title_full |
Decision making in a transportation mode experiment |
title_fullStr |
Decision making in a transportation mode experiment |
title_full_unstemmed |
Decision making in a transportation mode experiment |
title_sort |
decision making in a transportation mode experiment |
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Nanyang Technological University |
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2021 |
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https://hdl.handle.net/10356/147564 |
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