混入智能车的下匝道瓶颈路段交通流建模与仿真分析

董长印; 王昊; 王炜; 李烨; 华雪东

doi:10.7498/aps.67.20172752

摘要

以下匝道瓶颈路段为研究背景，以手动驾驶汽车和两类智能车为研究对象，包括自适应巡航（ACC）汽车和协同自适应巡航（CACC）汽车，建立了混入智能车的混合交通流模型.在车辆的纵向控制层面，分别构建了手动驾驶汽车改进舒适驾驶元胞自动机规则和智能车的跟驰模型；基于车辆下匝道行驶特性，引入车辆感知范围R、换道控制区域LLC、换道冒险因子λ等参数，建立了控制车辆横向运动的自由换道和强制换道模型.通过对混合交通流模型进行数值仿真发现，CACC车辆混入率PCACC、车辆感知范围R、换道区域长度LLC和换道冒险程度λ均对下匝道交通系统产生影响.当CACC车辆混入率低于0.5时，CACC退化为ACC的概率增大，系统稳定性下降，交通拥堵呈恶化趋势；当CACC车辆混入率大于0.5时，车辆运行速度显著提升，拥堵消散能力提高.增大车辆感知范围、加长换道区域长度、提高换道冒险程度，都能够有效缓解改善下匝道瓶颈路段主线的拥挤状况，而对匝道运行效率影响并不明显.

关键词:

Abstract

With the rapid development of vehicular technology, hi-tech manufacturing facilities are equipped in intelligent vehicles to improve road capacity and traffic safety. However, freeway diverge segment has significant influence on current traffic flow, and could affect the heterogeneous traffic flow consisting of manual and intelligent vehicles. The primary objective of this study is to evaluate how intelligent vehicles affect traffic flow at an off-ramp bottleneck.In order to depict the car-following dynamics of manual vehicles, the modified comfortable model, one of the most classic cellular automata models, is employed to distinguish intelligent vehicles. In this paper, intelligent vehicles consist of adaptive cruise control (ACC) vehicles cooperative adaptive cruise control (CACC) vehicles. The ACC and CACC model are proposed by partners for advanced transportation technology (PATH), which are validated by real experimental data. Besides, vehicles equipped with CACC will degrade ACC vehicle if the leading vehicle is driven manually. From the perspective of vehicle's lateral movement, two novel lane-changing models, including the discretionary lane-change (DLC) model and mandatory lane-change (MLC) model, are developed to model the future behaviors of intelligent vehicles. A risk factor λ is introduced into the DLC model to distinguish vehicles from conventional ones. Based on environment perception technology, a five-step MLC decision-making model is designed specifically for intelligent vehicles exiting to off-ramp. It is comprised of environment perception, safe gap computation, measured gap ranking, measured gap classification and lane-changing gap selection. Based on the proposed hybrid traffic flow model, numerical simulations are conducted to study the influences of intelligent vehicles on the traffic flow near an off-ramp. Apart from the market penetration of intelligent vehicles, parameters considered in this paper include the demands of mainlines and off-ramp, range of environment perception, length of lane-changing area, and level of lane-changing risk.Analytical studies and simulation results are as follows. 1) The integration of car-following model and lane-changing model for the off-ramp system enables vehicles to have reasonable dynamic characteristics. 2) The capacity ascends to the peak after an initial decrease as CACC vehicle penetration increases. The maximum capacity obtained in 100% CACC vehicle scenario is improved by over 50%, compared with that in 50% CACC penetration scenario. 3) Enlarging the ranges of environment perception and lane-changing areas, and enhancing the lane-changing risk can significantly dissipate congestion upstream of the off-ramp and improve the efficiency of mainlines. However, they have little influence on traffic flow at off-ramp. 4) The worst performance of the system occurs in the scenario of 50% CACC penetration, where deterioration caused by degraded ACC vehicles suggests that enough patience and public confidence should be paid for the development of intelligent vehicles.

Keywords:

作者及机构信息

1.
东南大学交通学院, 城市智能交通江苏省重点实验室, 南京 210096;

2.
现代城市交通技术江苏高校协同创新中心, 南京 210096;

3.
东南大学建筑学院, 南京 210096

通信作者: 王昊, haowang@seu.edu.cn

基金项目: 国家自然科学基金（批准号：51478113，51508122）、江苏省博士后科研资助计划（批准号：1701082B）和东南大学优秀博士学位论文培养基金（批准号：YBJJ1734）资助的课题.

Authors and contacts

1.
Jiangsu Key Laboratory of Urban Intelligent Traffic System, School of Transportation, Southeast University, Nanjing 210096, China;

2.
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China;

3.
School of Architecture, Southeast University, Nanjing 210096, China

Corresponding author: Wang Hao, haowang@seu.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 51478113, 51508122), the Jiangsu Planned Projects for Postdoctoral Research Funds, China (Grant No. 1701082B), and the Scientific Research Foundation of Graduate School of Southeast University, China (Grant No. YBJJ1734).

参考文献

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[32]	Yu S, Shi Z 2015 Physica A 428 206
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[40]	Wei L Y, Wang Z L, Wu R H 2014 Acta Phys. Sin. 63 044501 (in Chinese) [魏丽英, 王志龙, 吴荣华 2014 63 044501]
[41]	Zhang W H, Yan R, Feng Z X, Wang K 2016 Acta Phys. Sin. 65 064501 (in Chinese) [张卫华, 颜冉, 冯忠祥, 王锟 2016 65 064501]
[42]	Zhu W X, Zhang H M 2017 Physica A 496 274
[43]	Zhu W X, Zhang J Y 2017 Physica A 467 107
[44]	Tang T Q, Huang H J, Shang H Y 2017 Physica A 468 322
[45]	Tang T Q, Wang T, Chen L, Shang H Y 2017 Physica A 486 720
[46]	Zhang J, Tang T Q, Yu S W 2018 Physica A 492 1831
[47]	Jiang R, Wu Q S 2003 J. Phys. A: Math. Gen. 36 381
[48]	Jiang R, Wu Q S 2005 Eur. Phys. J. B 46 581
[49]	Jiang R, Wu Q S 2006 Phys. Lett. A 359 99
[50]	Jiang R, Hu M B, Jia B, Wang R, Wu Q S 2007 Eur. Phys. J. B 58 197
[51]	Naus G J, Vugts R P, Ploeg J, van de Molengraft M J, Steinbuch M 2010 IEEE Trans. Veh. Technol. 59 4268
[52]	Dong C Y 2016 M. S. Thesis (Nanjing: Southeast University) (in Chinese) [董长印 2016 硕士学位论文 (南京: 东南大学)]
[53]	Kang R, Yang K 2013 Acta Phys. Sin. 62 238901 (in Chinese) [康瑞, 杨凯 2013 62 238901]
[54]	Liu X H, Ko H T, Guo M M, Wu Z 2016 Chin. Phys. B 25 048901
[55]	Zhang Y, Ioannou P A 2017 IEEE Trans. Intell. Transp. Syst. 18 1812
[56]	Gorter M 2015 M. S. Thesis (Delft: Delft University of Technology)

施引文献

[1]	Chowdhury D, Santen L, Schadschneider A 2000 Phys. Rep. 329 199
[2]	Helbing D 2001 Rev. Mod. Phys. 73 1067
[3]	Jin S, Wang D H, Tao P F, Li P F 2010 Physica A 389 4654
[4]	Jing M, Deng W, Wang H, Ji Y J 2012 Acta Phys. Sin. 61 244502 (in Chinese) [敬明, 邓卫, 王昊, 季彦婕 2012 61 244502]
[5]	Gupta A K, Redhu P 2013 Physica A 392 5622
[6]	Lei Y, Zhong K S, Tong L 2014 Phys. Lett. A 378 348
[7]	Zhou T, Sun D H, Kang Y R, Li H M, Tian C 2014 Commun. Nonlinear Sci. Numer. Simul. 19 3820
[8]	Sun D H, Zhang M, Tian C 2014 Mod. Phys. Lett. B 28 1450091
[9]	Sun D H, Kang Y R, Li H M 2015 Acta Phys. Sin. 64 154503 (in Chinese) [孙棣华, 康义容, 李华民 2015 64 154503]
[10]	Werf J V, Shladover S, Miller M, Kourjanskaia N 2002 Transp. Res. Rec. 1800 78
[11]	Davis L C 2004 Phys. Rev. E 69 066110
[12]	Xiao L Y, Gao F 2011 IEEE Trans. Intell. Transp. 12 1184
[13]	Mark V, Schleicher S, Gelau C 2011 Accident Anal. Prev. 43 1134
[14]	Davis L C 2012 Phys. Lett. A 376 2658
[15]	Siebert F W, Oehl M, Pfister H R 2014 Trans. Res. F 25 65
[16]	Zhao D B, Hu Z H, Xia Z P, Alippi C, Zhu Y H, Wang D 2014 Neurocomputing 125 57
[17]	Yuan Y M, Jiang R, Hu M B, Wu Q S, Wang R 2009 Physica A 388 2483
[18]	Milanés V, Villagrá J, Pérez J, González C 2012 IEEE Trans. Ind. Electron. 59 620
[19]	Milanés V, Shladover S E, Spring J, Nowakowski C, Kawazoe H, Nakamura M 2014 IEEE Trans. Intell. Transp. Syst. 15 296
[20]	Jin I G, Orosz G Tang T Q, Yu Q, Yang S C, Ding C 2015 Mod. Phys. Lett. B 29 1550157
[21]	Tang T Q, Yu Q, Yang S C, Ding C 2015 Mod. Phys. Lett. B 29 1550157
[22]	Tang T Q, Chen L, Yang S C, Shang H Y 2015 Physica A 430 148
[23]	Ge H X, Zheng P J, Wang W, Cheng R J 2015 Physica A 433 274
[24]	Ge H X, Cheng R J, Li Z P 2008 Physica A 387 5239
[25]	Tang T Q, Li J G, Yang S C, Shang H Y 2015 Physica A 419 293
[26]	Hua X D, Wang W, Wang H 2016 Acta Phys. Sin. 65 010502 (in Chinese) [华雪东, 王炜, 王昊 2016 65 010502]
[27]	Hua X D, Wang W, Wang H 2016 Acta Phys. Sin. 65 084503 (in Chinese) [华雪东, 王炜, 王昊 2016 65 084503]
[28]	Sau J, Monteil J, Billot R, Faouzi N E E 2014 Transp. B: Transp. Dyn. 2 60
[29]	van Arem B, van Driel C J G, Visser R 2006 IEEE Trans. Intell. Transp. Syst. 7 429
[30]	Qin Y Y, Wang H, Wang W, Wan Q 2017 Acta Phys. Sin. 66 094502 (in Chinese) [秦严严, 王昊, 王炜, 万千 2017 66 094502]
[31]	Tang T Q, Xu K W, Yang S C, Ding C 2016 Physica A 441 221
[32]	Yu S, Shi Z 2015 Physica A 428 206
[33]	Wang M, Daamen W, Hoogendoorn S P, van Arem B 2016 IEEE Trans. Intell. Transp. Syst. 17 1459
[34]	Jerath K, Brennan S N 2012 IEEE Trans. Intell. Transp. Syst. 13 1782
[35]	Shladover S, Su D, Lu X Y 2012 Transp. Res. Rec. 2324 63
[36]	Milanés V, Shladover S E 2014 Transp. Res. C 48 285
[37]	Nagel K, Wolf D, Wagner P 1996 Phys. Rev. E 58 1425
[38]	Kesting A, Treiber M, Schonhof M, Helbing D 2008 Transp. Res. C 16 668
[39]	Hua X D, Wang W, Wang H 2011 Acta Phys. Sin. 60 084502 (in Chinese) [华雪东, 王炜, 王昊 2011 60 084502]
[40]	Wei L Y, Wang Z L, Wu R H 2014 Acta Phys. Sin. 63 044501 (in Chinese) [魏丽英, 王志龙, 吴荣华 2014 63 044501]
[41]	Zhang W H, Yan R, Feng Z X, Wang K 2016 Acta Phys. Sin. 65 064501 (in Chinese) [张卫华, 颜冉, 冯忠祥, 王锟 2016 65 064501]
[42]	Zhu W X, Zhang H M 2017 Physica A 496 274
[43]	Zhu W X, Zhang J Y 2017 Physica A 467 107
[44]	Tang T Q, Huang H J, Shang H Y 2017 Physica A 468 322
[45]	Tang T Q, Wang T, Chen L, Shang H Y 2017 Physica A 486 720
[46]	Zhang J, Tang T Q, Yu S W 2018 Physica A 492 1831
[47]	Jiang R, Wu Q S 2003 J. Phys. A: Math. Gen. 36 381
[48]	Jiang R, Wu Q S 2005 Eur. Phys. J. B 46 581
[49]	Jiang R, Wu Q S 2006 Phys. Lett. A 359 99
[50]	Jiang R, Hu M B, Jia B, Wang R, Wu Q S 2007 Eur. Phys. J. B 58 197
[51]	Naus G J, Vugts R P, Ploeg J, van de Molengraft M J, Steinbuch M 2010 IEEE Trans. Veh. Technol. 59 4268
[52]	Dong C Y 2016 M. S. Thesis (Nanjing: Southeast University) (in Chinese) [董长印 2016 硕士学位论文 (南京: 东南大学)]
[53]	Kang R, Yang K 2013 Acta Phys. Sin. 62 238901 (in Chinese) [康瑞, 杨凯 2013 62 238901]
[54]	Liu X H, Ko H T, Guo M M, Wu Z 2016 Chin. Phys. B 25 048901
[55]	Zhang Y, Ioannou P A 2017 IEEE Trans. Intell. Transp. Syst. 18 1812
[56]	Gorter M 2015 M. S. Thesis (Delft: Delft University of Technology)

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