자율주행차 혼합 환경의 교통흐름 및 지속 가능성 평가를 위한 거시적 시뮬레이션 연구
Macroscopic Simulation Study for Assessing Traffic Flow and Sustainability in Mixed Autonomous Vehicle Environments
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초록
본 연구는 혼합 교통 환경에서 자율주행차(AV)와 사람이 운전하는 차량(HDV)이 교통 흐름과 환경 지속 가능성에 미치는 영향을 평가하기 위한 통합 거시적 시뮬레이션 프레임워크를제안한다. 화성시 사례 연구를 통해 다양한 AV 보급률, AV의 자동화 수준, 차량-인프라 간 통신(V2I) 시나리오를 설정했다. 그 결과, AV 도입과 V2I 구현은 차량주행거리 및 차량주행시간과 같은 교통흐름 지표에는 제한적인 영향을 미쳤지만, 배출량을 크게 줄였다. 특히, AV 중심환경에서 이산화탄소 배출량은 HDV중심 환경에 비해 최대 50%까지 감소했으며, 미세먼지도비슷한 감소율을 보였다. 본 연구는 AV 보급과 V2I 인프라의 증가가 지속 가능성에 미치는긍정적 영향을 정량적으로 제시했으며, 정책결정자와 도시계획가에게 실질적인 인사이트를제공했다. 제안된 프레임워크는 다양한 정책 시나리오를 분석할 수 있는 기반을 마련하며, 동적 교통패턴 및 실제 데이터로 확장하여 보다 발전된 정책 설계를 할 수 있다.
Abstract
This study proposes an integrated macroscopic simulation framework to evaluate the impact of autonomous vehicles (AVs) and human-driven vehicles (HDVs) on traffic flow and sustainability in mixed traffic environments. A case study explored varying AV adoption rates, automation levels, and vehicle-to-infrastructure (V2I) communication implementations. The results showed that while AV adoption and V2I deployment had limited effects on traffic flow indicators such as vehicle kilometers traveled and vehicle hours traveled, they significantly reduced emissions. CO2 emissions decreased by up to 50% in AV-dominant scenarios compared to HDV-dominant ones. This study highlights the substantial sustainability benefits of increasing AV adoption and expanding V2I infrastructure, offering practical insights for policymakers. The proposed framework provides a foundation for analyzing diverse policy scenarios and can be further enhanced with real-world data for advanced policy development.
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