Connected Autonomous Vehicles Intersection Capacity Analysis Considering HCM 7th: Case Study of Jeju Innovation City

Yegi Yoo; Inhi Kim

doi:10.7470/jkst.2022.40.6.879

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2022 Vol.40, Issue 6 Preview Page Next Page

Article

Connected Autonomous Vehicles Intersection Capacity Analysis Considering HCM 7^th: Case Study of Jeju Innovation City HCM 7^th을 고려한 커넥티드 자율주행 교차로 용량 분석: 제주 혁신도시 사례연구

31 December 2022. pp. 879-894

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Abstract

With the development of autonomous driving technology, recent vehicles are equipped with advanced driver assistance systems to assist in driving and prevent car accidents. Due to the technological development and introduction of autonomous vehicles, driving behavior on roads in the future will change dramatically. It will lead people not to drive themselves. However, even if autonomous cars are introduced, a mixed traffic environment must be considered because human-driven vehicles will not disappear shortly. In the transportation sector, it is essential to analyze the traffic capacity by considering factors such as traffic flow, speed limit, and exclusive lanes according to the mixed rate of autonomous vehicles. However, predicting and analyzing what has not yet happened is challenging. In addition, research on autonomous driving is not simple to study on the road because of technical problems, human casualties, and serious damage, so it is generally analyzed using a test bed or simulation in a designated space. Until now, there were no clear standards for the introduction of autonomous vehicles, which made it difficult to analyze them accurately. In this study, in order to solve this problem, the newly released 7th edition of the Highway Capacity Manual was adopted to analyze the capacity change according to the market share of autonomous vehicles, the ratio of heavy vehicles, and changes in traffic volume. As a result, as the market share of autonomous vehicles increased, it positively affected capacity increase. When all vehicles were composed of autonomous vehicles, a capacity increase effect of up to 68.30% was analyzed compared to the case of conventional vehicles. The introduction of autonomous vehicles has a positive impact on road capacity, and it is also necessary to prepare policy and institutional measures that can affect the future.

Keywords

capacity

connected and automated vehicles

mixed traffic flow

signalized intersections

simulation

자율주행 기술이 발전하면서, 최근 차량에는 첨단운전자보조시스템이 차량에 탑재되어 운전을 보조하고 사고를 예방하는데 도움을 준다. 자율주행 차량의 기술발전 및 도입으로 인해 다가올 미래에는 사람이 차량을 운전하지 않을 것이며 이에 따라 도로 위의 주행 행태도 변할 것으로 예상된다. 그러므로 자율주행이 실제 도로에 도입되기 이전에 예상할 수 있는 도로용량변화에 대처하는 것은 교통흐름의 효율성 및 안전성을 위하여 매우 중요하다. 특히, 일반차량과 혼재된 상황이 불가피하기 때문에 혼재시 발행할 수 있는 여러 상황을 사전에 인지해야 한다. 교통부문에서 자율주행의 도입 시 교통류, 제한속도, 전용차선 등의 요소를 고려하여 교통 용량을 분석하는 것은 필수적이다. 그러나 아직 일어나지 않은 일을 예측해서 분석하는 것은 어려운일이다. 또한, 자율주행에 관한 연구는 아직 기술의 문제, 인명피해, 재물 등의 심각한 손해가 발생하기 때문에 실제 도로에서 분석이 이루어지기 어려워 일정 공간의 테스트베드 또는 시뮬레이션을 사용하여 분석한다. 하지만 그간 자율주행 차량 도입에 따른 명확한 기준이 존재하지 않아 정확한 분석에 어려움이 있었다. 본 연구에서는 이러한 점을 해소하고자 최근 새롭게 발표된 미국의 도로용량편람 7판을 적용하여 자율주행 차량의 시장 점유율, 중차량의 비율, 교통량에 따라 변화하는 용량을 분석하였다. 그 결과, 자율주행 차량의 점유율이 높아질수록 용량증대에 긍정적인 영향을 미쳤다. 모든 차량이 자율주행 차량으로 구성되었을 때, 일반차량으로 구성되어 있는 경우보다 최대 68.30%의 용량증대 효과가 분석되었다. 자율주행 차량의 도입은 도로용량에 긍정적인 영향을 미쳤으며, 미래에 미칠 수있는 정책적, 제도적 방안을 마련하는 것 또한 필요하다.

키워드

국도로용량

커넥티드 자율주행 차량

혼합교통류

신호교차로

시뮬레이션

References

Faisal A., Kamruzzaman M., Yigitcanlar T., Currie G. (2019), Understanding Autonomous Vehicles, Journal of Transport and Land Use, 12(1), 45-72. 10.5198/jtlu.2019.1405

Friedrich B. (2016), The Effect of Autonomous Vehicles on Traffic, In Autonomous Driving, 317-334, Springer. 10.1007/978-3-662-48847-8_16

He J., Tang Z., Fu X., Leng S., Wu F., Huang K. et al. (2019), Cooperative Connected Autonomous Vehicles (CAV): Research, Applications and Challenges, Paper Presented at the 2019 IEEE 27th International Conference on Network Protocols (ICNP). 10.1109/ICNP.2019.8888126

Jiang H., Hu J., An S., Wang M., Park B. B. (2017), Eco Approaching at an Isolated Signalized Intersection under Partially Connected and Automated Vehicles Environment, Transportation Research Part C: Emerging Technologies, 79, 290-307. 10.1016/j.trc.2017.04.001

Jiang Y., Wang S., Yao Z., Zhao B., Wang Y. (2021), A Cellular Automata Model for Mixed Traffic Flow Considering the Driving Behavior of Connected Automated Vehicle Platoons, Physica A: Statistical Mechanics and its Applications, 582, 126262. 10.1016/j.physa.2021.126262

Jin H., Kim S., Kim T. (2022), A Study on Safety of Urban Road Traffic Flow of Autonomous Vehicles in Adverse Conditions, J. Korean Soc. Transp., 40(2), Korean Society of Transportation, 161-177. 10.7470/jkst.2022.40.2.161

Jin I. G., Avedisov S. S., He C. R., Qin W. B., Sadeghpour M., Orosz G. (2018), Experimental Validation of Connected Automated Vehicle Design among Human-driven Vehicles, Transportation Research Part C: Emerging Technologies, 91, 335-352. 10.1016/j.trc.2018.04.005

Kim H., Baek S., Yun D., Park J. (2021), A Study on the Scenario Development for the Analysis of Mixed Traffic Flow Characteristics Following the Introduction of Freeway Exclusive Lanes for Autonomous Vehicles, J. Korean Soc. Transp., 39(6), Korean Society of Transportation, 838-848. 10.7470/jkst.2021.39.6.838

Kopelias P., Demiridi E., Vogiatzis K., Skabardonis A., Zafiropoulou V. (2020), Connected & Autonomous Vehicles-Environmental Impacts: A Review, Science of The Total Environment, 712, 135237. 10.1016/j.scitotenv.2019.13523731927439

Papadoulis A., Quddus M., Imprialou M. (2019), Evaluating the Safety Impact of Connected and Autonomous Vehicles on Motorways, Accident Analysis & Prevention, 124, 12-22. 10.1016/j.aap.2018.12.01930610995

Sinha A., Chand S., Wijayaratna K. P., Virdi N., Dixit V. (2020), Comprehensive Safety Assessment in Mixed Fleets with Connected and Automated Vehicles: A Crash Severity and Rate Evaluation of Conventional Vehicles, Accident Analysis & Prevention, 142, 105567. 10.1016/j.aap.2020.10556732361477

Talebpour A., Mahmassani H. S. (2016), Influence of Connected and Autonomous Vehicles on Traffic Flow Stability and Throughput. Transportation Research Part C: Emerging Technologies, 71, 143-163. 10.1016/j.trc.2016.07.007

Transportation Research Board National Academies of Sciences, Engineering, Medicine (2022), Highway Capacity Manual 7th Edition: A Guide for Multimodal Mobility Analysis, Washington, DC: The National Academies Press.

Virdi N., Grzybowska H., Waller S. T., Dixit V. (2019), A Safety Assessment of Mixed Fleets with Connected and Autonomous Vehicles Using the Surrogate Safety Assessment Module, Accident Analysis & Prevention, 131, 95-111. 10.1016/j.aap.2019.06.00131233998

Wu Y., Wang D. Z., Zhu F. (2022), Influence of CAVs Platooning on Intersection Capacity under Mixed Traffic, Physica A: Statistical Mechanics and its Applications, 593, 126989. 10.1016/j.physa.2022.126989

Yao Z., Zhao B., Yuan T., Jiang H., Jiang Y. (2020), Reducing Gasoline Consumption in Mixed Connected Automated Vehicles Environment: A Joint Optimization Framework for Traffic Signals and Vehicle Trajectory, Journal of cleaner production, 265, 121836. 10.1016/j.jclepro.2020.121836

Ye L., Yamamoto T. (2019), Evaluating the Impact of Connected and Autonomous Vehicles on Traffic Safety, Physica A: Statistical Mechanics and its Applications, 526, 121009. 10.1016/j.physa.2019.04.245

Information

Publisher :Korean Society of Transportation
Publisher(Ko) :대한교통학회
Journal Title :Journal of Korean Society of Transportation
Journal Title(Ko) :대한교통학회지
Volume : 40
No :6
Pages :879-894
Received Date : 2022-10-05
Revised Date : 2022-11-16
Accepted Date : 2022-11-29
DOI :https://doi.org/10.7470/jkst.2022.40.6.879

Journal of Korean Society of Transportation ISSN:1229-1366(Print) 2234-4217(Online) 대한교통학회지

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