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2026 Vol.44, Issue 1 Preview Page

Article

Estimating Capacity Adjustment Factors for Highway Basic Segments under Autonomous Vehicle Penetration

자율주행차량 혼입을 고려한 고속도로 기본구간 용량 보정계수 산정 연구

Seongchae BAEK1
,
Junhyeong MOON2
,
Jeongin YUN3
,
Chihyun SHIN4
,
Jinwoo LEE5
,
Jejin PARK6*
백 성채1
,
문 준형2
,
윤 정인3
,
신 치현4
,
이 진우5
,
박 제진6*
1Ph.D. Candidate, Department of Architecture and Civil Engineering, Chonnam National University, Gwangju 61186, Korea
2Ph.D. Student, Department of Urban & Transportation Engineering, Kyonggi University, Suwon 15442, Korea
3Ph.D. Candidate, Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
4Professor, Department of Urban & Transportation Engineering, Kyonggi University, Suwon 15442, Korea
5Associate Professor, Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
6Associate Professor, Department of Civil Engineering, Chonnam National University, Gwangju 61186, Korea
1전남대학교 건축토목공학과 박사수료
2경기대학교 도시·교통공학과 박사과정
3한국과학기술원 건설및환경공학과 박사과정
4경기대학교 도시·교통공학과 교수
5한국과학기술원 건설및환경공학과 부교수
6전남대학교 토목공학과 부교수
*Corresponding Author
28 February 2026. pp. 86-98
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Abstract

This study quantifies the impacts of autonomous vehicle (AV) penetration and driving behavior on the capacity of freeway basic segments and develops Highway Capacity Manual compatible capacity adjustment factors (CAFs) for Korean practice. Using field data, we selected and calibrated representative basic segments (speed limits: 100/120km/h), then ran 25 VISSIM scenarios (AV penetration α=0-1; behaviors: Cautious/Normal/Aggressive/CAV). Baseline calibration relied on RMSE, MAE, bias, and standard deviation. Capacity was defined as the per-lane maximum flow (pcphpl) from single-regime Greenshields fits. For the 100km/h site, CAV yielded the largest gains, reaching 2,804 pcphpl at α=1.0 (CAF=1.268), while Aggressive and Normal produced more modest improvements with CAFs of 1.083 and 1.038 at α=1.0, respectively. In contrast, Cautious decreased monotonically with penetration, reducing capacity to 1,584 pcphpl (CAF=0.72) at α=1.0. At 120km/h, CAV again provided the strongest benefits, reaching 2,836 pcphpl (CAF=1.24 at α=1.0), whereas Aggressive and Normal showed moderate gains (maximum CAFs =1.08 and 1.05). Cautious reduced capacity further to 1,368 pcphpl (CAF=0.60). Mixed-behavior CAF curves consistently lay between the CAV upper and Cautious lower bounds, offering realistic planning ranges for mixed traffic. The contributions are threefold: (i) a rule-based baseline calibration procedure for representative sites, (ii) empirically derived CAF curves and values by penetration and behavior, and (iii) a clear pathway to incorporate AV effects into KHCM-style analyses.

Keywords
autonomous/connected vehicles
capacity adjustment factor (CAF)
freeway basic segment
mixed traffic
vissim

본 연구는 자율주행차의 혼입률과 주행행태가 고속도로 기본구간 용량에 미치는 영향을 정량화하고, 국내 도로용량편람 체계에 호환되는 용량 보정계수(CAF)를 제시한다. 대표 기본구간(제한속도 100/120km/h)을 실측 자료 기반으로 선별·정산한 뒤, VISSIM에서 혼입률(α=0-1)과 주행행태(Cautious, Normal, Aggressive, CAV)를 교차 설계한 25개 시나리오를 수행하였다. 정산은 RMSE·MAE·Bias·반복 표준편차를 이용해 기준 시나리오를 확정하고, 용량은 Greenshields 단일체제 모형에서 얻은 차로당 최대교통류(pcphpl)로 정의하였다. 100km/h 대상지에서는 CAV가 α=1.0에서 2,804 pcphpl(CAF=1.268)에 도달하여 가장 큰 이득을 보였고, Aggressive와 Normal은 각각 CAF=1.083, 1.038(α=1.0 기준) 수준의 완만한 개선을 나타냈다. 반대로 Cautious는 혼입률이 증가할수록 일관되게 감소하여, α=1.0에서 1,584 pcphpl(CAF=0.72)까지 용량이 축소되었다. 120km/h 대상지에서도 CAV 효과가 가장 크게 나타나 α=1.0에서 2,836 pcphpl(CAF=1.24)에 도달하였으며, Aggressive와 Normal은 최대 CAF가 각각 약 1.08, 1.05 수준의 중간 정도 개선 효과를 보였다. Cautious는 α=1.0에서 1,368 pcphpl(CAF=0.60)까지 용량을 저하시켰다. 혼합행태 시나리오의 CAF 곡선은 전 구간에서 CAV 상한과 Cautious 하한 사이에 위치하여, 현실적인 혼합교통류에서 기대 가능한 계획·운영 범위를 제시한다. 본 연구는 (i) 대표 구간의 규칙 기반 정산 절차, (ii) 혼입률·주행행태별 CAF 곡선·값, (iii) KHCM/도로용량편람에 AV 효과를 접목하는 적용 경로를 제시함으로써 향후 기준 개정의 정량적 근거를 제공한다.

키워드
자율주행차
용량 보정계수
고속도로 기본구간
혼합교통류
미시 시뮬레이션
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Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 44
  • No :1
  • Pages :86-98
  • Received Date : 2025-11-03
  • Revised Date : 2025-11-20
  • Accepted Date : 2025-11-28
  • DOI :https://doi.org/10.7470/jkst.2026.44.1.086
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