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An Interactive survey to explore a driver’s decisions in connected and incentivised on-ramp merging

The University of Queensland
Professor Zuduo Zheng (Aggregated by) Professor Zuduo Zheng (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.48610/429d67e&rft.title=An Interactive survey to explore a driver’s decisions in connected and incentivised on-ramp merging&rft.identifier=RDM ID: 13f947f4-5813-4ab5-ba99-611c66745b06&rft.publisher=The University of Queensland&rft.description=Emerging connected and automated vehicle (CAV) systems enable cooperative traffic behaviours through information and incentive exchange. This study presents a stated preference (SP) survey dataset designed to capture driver responses to incentivised cooperation in on-ramp merging scenarios. The survey integrates video-based representations of microsimulated traffic conditions with a behavioural choice framework grounded in Cumulative Prospect Theory (CPT). An adaptive staircase procedure was used to elicit individual certainty equivalents for cooperation decisions, supporting estimation of value and probability weighting constructs. The dataset includes SP responses, socio-demographic attributes, driving characteristics, and validated psychological measures such as risk-taking and driving anger. A mixed recruitment approach combining free distribution methods and a quota-controlled Qualtrics panel was used, with data screening applied to ensure quality and demographic representation aligned with Australian population benchmarks. Accompanying materials include survey instruments, visual stimuli, and documentation to support reuse. The dataset enables analysis of driver decision-making under uncertainty and supports the development of behavioural and traffic modelling applications.&rft.creator=Professor Zuduo Zheng&rft.creator=Professor Zuduo Zheng&rft.date=2026&rft_rights= https://guides.library.uq.edu.au/deposit-your-data/license-reuse-data-agreement&rft_subject=eng&rft_subject=Weighting&rft_subject=Documentation&rft_subject=Incentive&rft_subject=Preference&rft_subject=Quality (philosophy)&rft_subject=Representation (politics)&rft_subject=Population&rft_subject=Data collection&rft_subject=Intelligent mobility&rft_subject=Transportation, logistics and supply chains&rft_subject=COMMERCE, MANAGEMENT, TOURISM AND SERVICES&rft.type=dataset&rft.language=English Access the data
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School of Civil Engineering

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Emerging connected and automated vehicle (CAV) systems enable cooperative traffic behaviours through information and incentive exchange. This study presents a stated preference (SP) survey dataset designed to capture driver responses to incentivised cooperation in on-ramp merging scenarios. The survey integrates video-based representations of microsimulated traffic conditions with a behavioural choice framework grounded in Cumulative Prospect Theory (CPT). An adaptive staircase procedure was used to elicit individual certainty equivalents for cooperation decisions, supporting estimation of value and probability weighting constructs. The dataset includes SP responses, socio-demographic attributes, driving characteristics, and validated psychological measures such as risk-taking and driving anger. A mixed recruitment approach combining free distribution methods and a quota-controlled Qualtrics panel was used, with data screening applied to ensure quality and demographic representation aligned with Australian population benchmarks. Accompanying materials include survey instruments, visual stimuli, and documentation to support reuse. The dataset enables analysis of driver decision-making under uncertainty and supports the development of behavioural and traffic modelling applications.

Issued: 16 06 2026

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Subjects
Commerce, Management, Tourism and Services | Data collection | Documentation | Incentive | Intelligent Mobility | Population | Preference | Quality (philosophy) | Representation (politics) | Transportation, Logistics and Supply Chains | Weighting | eng |

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ACN 633 798 857