Analysis of Driving Behavior and Capacity Estimation Using Actual Traffic Data of Autonomous Vehicles at Signalized Intersections

Seongkil Choi; Chihyun Shin

doi:10.7470/jkst.2023.41.5.624

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2023 Vol.41, Issue 5 Preview Page

Article

Analysis of Driving Behavior and Capacity Estimation Using Actual Traffic Data of Autonomous Vehicles at Signalized Intersections 자율주행자동차 운행 데이터 실측을 통한 신호교차로 주행행태 및 용량 분석

31 October 2023. pp. 624-637

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Abstract

Technology development is underway for the introduction of autonomous vehicles (AVs). South Korean government has set a goal of introducing autonomous buses and shuttles by 2025, and plans to commercialize fully automated AVs by 2027. When AVs are introduced to roads, significant impact on roadway capacity is most likely. AVs could contribute to increase road capacity by minimizing human fallibility and enhancing various response times. However, in the early stages of coexistence, the road capacity may decrease due to differences in driving behavior between human driving vehicles (HDVs) and AVs. Researches have been focused on the impact of AVs deployment on capacity, delay, travel time, and other factors on urban roadways. However, most of these researches are literature-based and use theoretically assumed driving parameters for analysis. In this study, we have used drones, street-corner camcorders and methods of pairing probes with AVs and riding-AV to collect actual AV driving data in the AV demonstration districts. We have utilized AV-generated driving data as well. Based on the collected data, comparisons of the driving data between AVs and HDVs were conducted, along with an analysis of the saturation flow rate and capacity according to the mixed rate of AVs. The analysis showed that HDVs are superior to AVs in terms of driving behavior, with the exception of perceived reaction time to a signal departure. In addition, it was found that the saturation flow rate decreased by 23.02% when the market penetration rate of AVs in the signalized intersection reaches up to 80%. These analysis results indicate that the driving behavior parameter values are set conservatively because AVs currently in service put safety first. If those parameter values of AVs are improved, however, it is expected to see the growth in the capacity value of urban intersections.

Keywords

autonomous vehicles

driving behavior

road capacity

saturation flow rates

signalized intersections

운전자가 필요 없는 자율주행자동차(Autonomous Vehicles, AV)의 도입으로 인해 도로의 용량은 변화할 것으로 분석된다. AV는 사전에 정의된 알고리즘에 따라 체계적인 행동으로 인해 도로 용량을 증대시킬 수 있으나, 혼재된 초기에는 비자율주행자동차(Human Driving Vehicles, HDV)와의 주행행태 차이로 인하여 교통 혼잡이 발생할 수 있다. 이와 관련된 도로 용량, 지체, 통행시간 등 효과 분석에 관한 연구가 진행되고 있지만, 대부분의 연구에서 적용된 AV의 주행행태는 이상적인 값을 이용한 연구들이 대부분인 실정이다. 이에 본 연구에서는 AV의 상용화를 위한 초기 단계에서 AV의 혼입에 따른 신호교차로의 용량을 산정하기 위해 자율주행 시범운행지구에서 운행되고 있는 AV를 대상으로 현장에서 영상기록장치, 드론, probe car, 동승 조사를 이용하여 수집된 데이터와 자율주행 기록장치 데이터를 통해 주행행태 값을 도출하고 AV와 HDV의 주행행태를 비교·분석하였다. 분석 결과, AV는 정지선에서 신호에 반응하여 출발하는 인지반응시간을 제외한 주행행태 값은 HDV에 비해 낮은 것으로 분석되었다. 이로 인해 신호교차로에서 AV의 구성 비율이 0%에서 80%로 증가하였을 때 포화교통류율은 23.02% 감소하고 용량은 28.31% 감소하였다. 이러한 결과는 현재 운행 중인 AV가 안전성을 최우선하고 있기 때문에 주행행태를 보수적으로 설정한 것으로 보인다. 향후 AV의 주행행태가 개선되고 보급률이 증가한다면, 신호교차로의 용량은 크게 증대될 것으로 분석된다. 이는 AV 보급률의 규모 있는 증가는 상당한 시간의 경과를 수반하고, 그 시간의 경과는 AV 차량의 성능과 운행 파라미터의 값의 개선 및 향상을 의미하기 때문이다.

키워드

자율주행자동차

주행행태

도로 용량

포화교통류율

신호교차로

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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 : 41
No :5
Pages :624-637
Received Date : 2023-07-11
Revised Date : 2023-07-25
Accepted Date : 2023-08-17
DOI :https://doi.org/10.7470/jkst.2023.41.5.624

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

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