Autonomous Vehicles: Integrating Technology Acceptance and Traffic Flow Models Using Fuzzy Logic

The uncertainties surrounding autonomous vehicles (AVs) have four main aspects: technology, market penetration, governmental readiness, and public perception. Public perception will affect AV adoption rates and hence future traffic flows. Despite this underlying connection, the extant literature explores these concepts separately, creating inherently weak transport models that do not account for the public perception of AVs. This study proposes integrating technology acceptance and traffic flow models to improve future travel demand modeling practices. The study includes a fuzzy rule-based framework that can capture the uncertainty in public perception that might arise due to a lack of first-rate experience with AVs. The study will also discuss the impact of information on public perception–perceived usefulness, perceived safety, perceived ease of use, and behavioral intention–of AVs. The study will take place in the United Arab Emirates (UAE), which ranks high among AV-ready countries.

Recent AV test runs in Abu Dhabi and Sharjah; Dubai Self-Driving Transport Strategy; and the country’s commitment to ICT technologies: 5G networks, AI, and blockchain outline the future of AV transport in the UAE. The expected theoretical contributions of the study include a reinterpretation of the Unified Theory of Acceptance and Use of Technology (UTAUT2) in the context of AVs. The study will also likely improve the accuracy of AV-based transport flow models. Practically, the study is relevant to those decision-makers in the government and private sectors who deal with travel demand models. The envisioned fuzzy-based system will be available to the decision-makers and the public to find the impact of AV adoption on the network traffic flows.

Autonomous Vehicles: Integrating Technology Acceptance and Traffic Flow Models Using Fuzzy Logic