All Issue

2018 Vol.36, Issue 2
April 2018. pp. 67-85
Abstract
차량의 자율주행기술과 차량간 무선통신을 통한 정보공유 군집주행 서비스가 실현되고 있다. 군집주행이란 여러대의 차량이 최소한의 안전거리만 유지한 채 일정한 간격을 두고 주행하는 기술이다. 이러한 군집주행은 도로의 용량을 증대시키고, 안전성을 향상시키며, 연료소비를 줄일 수 있는 잠재력을 가지고 있어 교통류 운영효율성, 안전성, 환경성 문제를 해결할 수 대안으로 주목받고 있다. 그러나 군집주행차량과 주변의 일반차량간의 적절한 상호작용이 가능할 때 교통류의 성능은 최적화 될 수 있다. 특히 교통운영 관리자는 화물차가 군집주행을 할 경우 유입연결로에서 비자율차가 안전하게 진입할 수 있도록 군집간간격과 군집크기와 같은 군집주행 파라미터를 조정하여 안전성과 운영효율성을 극대화시킬 수 있어야 한다. 본 연구에서는 고속도로 유입연결로 구간에서 교통류 퍼포먼스를 극대화 시킬 수 있는 화물차 군집 운영전략을 수립할 수 있는 방안을 제시하였다. 운영효율성을 평가하기 위한 지표는 주행속도로 설정하였으며, 안전성 평가를 위해서 비자율차의 차량추종 관계 대비 상충상황에 노출되는 빈도를 나타내는 비자율차 상충률의 개념을 정의하여 적용하였다. 또한 분석결과를 이용하여 최적 군집운영 조건을 판단하는 방법론을 제시하였으며, 군집간간격이 50m이고 군집크기가 6대인 운영시나리오가 최적의 성능을 유도할 수 있음을 확인하였다. 본 연구에서 제시한 운영전략 수립 방안에 따라 운영효율성과 안전성을 고려한 교통상황별 적정 군집주행 파라미터를 도출할 수 있으며, 이는 군집운영 전략을 지원할 수 있는 자료로 활용할 수 있을 것으로 기대된다.


Vehicle platooning through wireless communication and automated driving technology has become realized. Platooning is a technique in which several vehicles travel at regular intervals while maintaining a minimum safety distance. Truck platooning is of keen interest because it contributes to preventing truck crashes and reducing vehicle emissions, in addition to the increase in truck flow capacity. However, it should be noted that interactions between vehicle platoons and adjacent manually-driven vehicles (MV) significantly give an impact on the performance of traffic flow. In particular, when vehicles entering from on-ramp attempt to merge into the mainstream of freeway, proper interactions by adjusting platoon size and inter-platoon spacing are required to maximize traffic performance. This study developed a methodology for establishing operational strategies for truck platoonings on freeway on-ramp areas. Average speed and conflict rate were used as measure of effectiveness (MOE) to evaluate operational efficiency and safety. Microscopic traffic simulation experiments using VISSIM were conducted to evaluate the effectiveness of various platooning scenarios. A decision making process for selecting better platoon operations to satisfy operations and safety requirements was proposed. It was revealed that a platoon operating scenario with 50m inter-platoon spacing and the platoon consisting of 6 vehicles outperformed other scenarios. The proposed methodology would effectively support the realization of novel traffic management concepts in the era of automated driving environments.


References
  1. Amoozadeh M., Deng H., Chuah C. N., Zhang H. M., Ghosal D. (2015), Platoon Management With Cooperative Adaptive Cruise Control Enabled by VANET, Vehicular Communications, 2(2), 110-123.10.1016/j.vehcom.2015.03.004
  2. Aria E., Olstam J., Schwietering C. (2016), Investigation of Automated Vehicle Effects on Driver's Behavior and Traffic Performance, Transp. Res. Procedia, 15, 761-770.10.1016/j.trpro.2016.06.063
  3. Choi S., Kim M., Oh C., Lee K. (2013), Effects of Weather and Traffic Conditions on Truck Accident Severity on Freeways, J. Korean Soc. Civ. Eng., 33(3), Korean Society of Civil Engineering, 180-185.10.12652/Ksce.2013.33.3.1105
  4. Davis L. C. (2004), Effect of Adaptive Cruise Control Systems on Traffic Flow, Physical Review E, 69(6), 066110.10.1103/PhysRevE.69.066110
  5. Di Bernardo M., Salvi A., Santini S., Valente A. S. (2015), Third-order Consensus in Vehicles Platoon With Heterogeneous Time-varying Delays, IFAC-PapersOnLine, 48(12), 358-363.10.1016/j.ifacol.2015.09.404
  6. Eckhardt J. (2016), European Truck Platooning Challenge 2016, The Hague, Delta3.
  7. Fernandes P., Nunes U. (2012), Platooning With IVC-enabled Autonomous Vehicles: Strategies to Mitigate Communication Delays, Improve Safety and Traffic Flow, IEEE Transactions on Intelligent Transportation Systems, 13(1), 91-106.10.1109/TITS.2011.2179936
  8. Gouy M., Wiedemann K., Stevens A., Brunett G., Reed N. (2014), Driving Next to Automated Vehicle Platoons: How Do Short Time Headways Influence Non-platoon Drivers' Longitudinal Control?, Transp. Res. Part F: Traffic Psychol. Behav., 27, 264-273.10.1016/j.trf.2014.03.003
  9. Hyundai, http://www.hyundai.com/kr/showroom.do?carCd1=TR003, 2017.07.27.
  10. Impacts of Connected and Autonomous Vehicles on Traffic Flow Technical Report (2016), UK Department of Transportation.
  11. Jeong E., Oh C., Lee G., Cho H. (2014), Safety Impacts of Intervehicle Warning Information Systems for Moving Hazards in Connected Vehicle Environments, Transp. Res. Rec.: J. Transp. Res. Board, 2424, 11-19.10.3141/2424-02
  12. Joo S. H., Oh C. (2016), An Integrated Simulation Approach for Evaluating Speed Management Strategies Considering Public Health, J. Korean Soc. Transp., 34(6), Korean Society of Transportation, 548-559.
  13. Kesting A., Treiber M., Schönhof M., Helbing D. (2008), Adaptive Cruise Control Design for Active Congestion Avoidance, Transp. Res. Part C: Emerg. Technol., 16(6), 668-683.10.1016/j.trc.2007.12.004
  14. Larsson E., Sennton G., Larson J. (2015), The Vehicle Platooning Problem: Computational Complexity and Heuristics, Transp. Res. Part C: Emerg. Technol., 60, 258-277.10.1016/j.trc.2015.08.019
  15. Lee S. Y., Oh C. (2017), Lane Change Behavior of Manual Vehicles in Automated Vehicle Platooning Environments, J. Korean Soc. Transp., 35(4), Korean Society of Transportation, 332-347.
  16. Lei C., Van Eenennaam E. M., Wolterink W. K., Karagiannis G., Heijenk G., Ploeg J. (2011), Impact of Packet Loss on CACC String Stability Performance, In ITS Telecommunications (ITST), 2011 11th International C.
  17. Lou Y., Li P., Hong X. (2016), A Distributed Framework for Network-wide Traffic Monitoring and Platoon Information Aggregation Using V2V Communications, Transp. Res. Part C: Emerg. Technol., 69, 356-374.10.1016/j.trc.2016.06.003
  18. MAN Truck Yemen, https://www.truck.man.eu/ye/en/man-world/man-in-yemen/press-and-media/European-Truck-Platooning- Challenge-2016-289793.html, 2017.07.25.
  19. Ministry of Land, Infrastructure and Transport (2013), Korea Highway capacity manual.
  20. Moon S., Moon I., Yi K. (2009), Design, Tuning, and Evaluation of a Full-range Adaptive Cruise Control System With Collision Avoidance, Control Engineering Practice, 17(4), 442-455.10.1016/j.conengprac.2008.09.006
  21. Park I., Lee J., Lee J., Hwang K. (2015), Impacts of Automated Vehicles on Freeway Traffic-flow- Focused on Seoul-Singal Basic Sections of GyeongBu Freeway, J. Korea Inst. Intell. Transp. Syst. 14(6), Korea Institute of Intelligent Transport Systems, 21-36.10.12815/kits.2015.14.6.021
  22. Santini S., Salvi A., Valente A. S., Pescapé A., Segata M., Cigno R. L. (2017), A Consensus-Based Approach for Platooning With Intervehicular Communications and Its Validation in Realistic Scenarios, IEEE Transactions on Vehicular Technology, 66(3), 1985-1999.10.1109/TVT.2016.2585018
  23. Segata M., Bloessl B., Joerer S., Sommer C., Gerla M., Cigno R. L. et al. (2014), Towards Inter-vehicle Communication Strategies for Platooning Support, In Communication Technologies for Vehicles.
  24. Shladover S., Su D., Lu X. Y. (2012), Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow, Transp. Res. Rec.: J. Transp. Res. Board, 2324, 63-70.10.3141/2324-08
  25. Su D., Ahn S. (2016), An Adaptive Vehicle Platoon Formation Mechanism for Road Capacity Improvement, KIPS Tr. Comp. and Comm. Sys., 5(10), Korea Information Processing Society, 327-330.
  26. Suh S., Lee S., Oh C., Choi S. (2017), Impacts of Automated Vehicle Platoons on Car-following Behavior of Manually- Driven Vehicles, J. Korea Inst. Intell. Transp. Syst. 16(4), Korea Institute of Intelligent Transport Systems, 107-121.10.12815/kits.2017.16.4.107
  27. Traffic Monitoring System, http://www.road.re.kr/analysis/analysis_01.asp, 2017.09.14.
  28. Treiber M., Hennecke A., Helbing D. (2000), Congested Traffic States in Empirical Observations and Microscopic Simulations, Physical review E, 62(2), 1805.10.1103/PhysRevE.62.1805
  29. Tuchner A., Haddad J. (2015), Vehicle Platoon Formation Using Interpolating Control, IFAC-PapersOnLine, 48(14), 414-419.10.1016/j.ifacol.2015.09.492
  30. Van Arem B., Van Driel C.J.G., Visser R. (2006), The Impact of Cooperative Adaptive Cruise Control on Traffic-flow Characteristics, IEEE Trans. Intell. Transp. Syst., 7, 429-436.10.1109/TITS.2006.884615
  31. VanderWerf J., Shladover S., Kourjanskaia N., Miller M., Krishnan H. (2001), Modeling the Effects of Driver Control Assistance Systems on Traffic, Transportation Research Record 1748, 167-174.10.3141/1748-21
  32. Zhao L., Sun J. (2013), Simulation Framework for Vehicle Platooning and Car-following Behaviors Under Connected- vehicle Environment, Procedia-Social and Behavioral Sciences, 96, 914-924.10.1016/j.sbspro.2013.08.105
  33. Zheng Y., Li S. E., Wang J., Cao D., Li K. (2016), Stability and Scalability of Homogeneous Vehicular Platoon: Study on the Influence of Information Flow Topologies, IEEE Transactions on Intelligent Transportation Systems, 17(1), 14-26.10.1109/TITS.2015.2402153
  34. Zhong Z., Lee J., Zhao L. (2017), Evaluations of Managed Lane Strategies for Arterial Deployment of Cooperative Adaptive Cruise Control, Transportation Research Board 96th Annual Meeting.
Information