|Table of Contents|

Macroscopic traffic flow model of expressway on-ramp bottlenecks(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2019年03期
Page:
122-133
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Macroscopic traffic flow model of expressway on-ramp bottlenecks
Author(s):
SUN Jian1;  YIN Ju-yuan1;  LI Tao-ning1; 2
China; 2. Shanghai Bureau of Public Security Traffic Police Corps, Shanghai 200070, China)
Keywords:
intelligent transportation system;  macroscopic traffic flow model;  cell transmission model;  on-ramp bottleneck;  parameter calibration;  fine modeling
PACS:
U491.112
DOI:
-
Abstract:
Based on on-ramp merging mode and fundamental diagram form, an adjusted cell transmission model(CTM)was proposed. On-ramp state variables were introduced to track the traffic state of on-ramps, and new on-ramp merging rules were defined.The dual capacity fundamental diagram was introduced to the adjusted CTM in order to adapt to the varying capacities under different traffic conditions. The nelder-mead method and genetic algorithm were combined, and a hybrid multi-objective parameter optimization method was proposed. Three simulation scenarios were established,the performances of adjusted CTM and the hybrid multi-objective parameter optimization method were evaluated. Simulation result shows that for the prediction of the occurrence time and ending time of congestion on the upstream of on-ramp, compared with the original CTM, the adjusted CTM improves the accuracy by 22.3 and 10.8 min, respectively. For the simulation of the propagation and dissipation of congestion at the on-ramp merging section, the result of adjusted CTM is closer to the actual propagation/dissipation rules.As for the simulation of the early-onset breakdown traffic characteristic on the test segment, the fitting errors of adjusted CTM for the maximum pre-queue flow and queue discharge flow are below 4%, which are less than the values of original CTM. In the term of model simulation accuracy, compared with the original CTM, the various indexes of adjusted CTM are better, the simulated speed error of the former is 10.42 km·h-1, which is 25.4% lower than the value of the latter. Compared with the traditional genetic algorithm, the hybrid multi-objective parameter optimization method can reduce the total calculation times, and the total consumed time of parameter calibration shortens by 29.3%. 4 tabs, 11 figs, 31 refs.

References:

[1] KOTSIALOS A, PAPAGEORGIOU M. The importance of traffic flow modeling for motorway traffic control[J]. Networks and Spatial Economics, 2001, 1(1/2): 179-203.
[2] DAGANZO C F. The cell transmission model: a dynamic
representation of highway traffic consistent with the hydrodynamic theory[J]. Transportation Research Part B: Methodological, 1994, 28(4): 269-287.
[3] SZETO W Y. Enhanced lagged cell-transmission model for dynamic traffic assignment[J]. Transportation Research Record, 2008(2085): 76-85.
[4] MUÑOZ L, SUN Xiao-tian, HOROWITZ R, et al. Traffic density estimation with the cell transmission model[C]∥IEEE. Proceedings of the 2003 American Control Conference. New York: IEEE, 2003: 3750-3755.
[5] SUMALEE A, ZHONG R X, PAN T L, et al. Stochastic cell transmission model(SCTM): a stochastic dynamic traffic model for traffic state surveillance and assignment[J]. Transportation Research Part B: Methodological, 2011, 45(3): 507-533.
[6] MUÑOZ L, SUN Xiao-tian, SUN Deng-feng, et al. Methodological calibration of the cell transmission model[C]∥IEEE. Proceedings of the 2004 American Control Conference. New York: IEEE, 2004: 798-803.
[7] GOMES G, HOROWITZ R. Optimal freeway ramp metering using the asymmetric cell transmission model[J]. Transportation Research Part C: Emerging Technologies, 2006, 14(4): 244-262.
[8] RONCOLI C, PAPAGEORGIOU M, PAPAMICHAIL I.
Traffic flow optimisation in presence of vehicle automation and communication systems—Part Ⅰ: a first-order multi-lane model for motorway traffic[J]. Transportation Research Part C: Emerging Technologies, 2015, 57: 241-259.
[9] SHIOMI Y, TANIGUCHI T, UNO N, et al. Multilane first-order traffic flow model with endogenous representation of lane-flow equilibrium[J]. Transportation Research Procedia, 2015, 7: 398-419.
[10] 杨 泳,严余松,户佐安,等.城市快速路改进型元胞传输模型及仿真[J].公路交通科技,2015,32(6):135-141.
YANG Yong, YAN Yu-song, HU Zuo-an, et al. An improved cell transmission model for urban expressway and simulation[J]. Journal of Highway and Transportation Research and Development, 2015, 32(6): 135-141.(in Chinese)
[11] 杨 泳,户佐安.改进型CTM模型匝道控制下拥堵传播规律研究[J].武汉理工大学学报(交通科学与工程版),2015,39(5):915-919.
YANG Yong, HU Zuo-an. Research on ramp metering traffic congestion propagation mechanism with improved CTM model[J]. Journal of Wuhan University of Technology(Transportation Science and Engineering), 2015, 39(5): 915-919.(in Chinese)
[12] 林 琴,龙科军.基于改进CTM模型的城市快速路交通流仿真[J].长沙理工大学学报(自然科学版),2018,15(4):52-58.
LIN Qin, LONG Ke-jun. Urban expressway traffic flow simulation based on improved CTM model[J]. Journal of Changsha University of Science and Technology(Natural Science), 2018, 15(4): 52-58.(in Chinese)
[13] 陈 庚.基于改进型CTM模型的城市快速路交通事件仿真[J].交通科技,2016(6):146-149.
CHEN Geng. Urban expressway traffic incident simulation based on improved CTM model[J]. Transportation Science and Technology, 2016(6): 146-149.(in Chinese)
[14] 陈永恒,刘鑫山,熊 帅,等.冰雪条件下快速路汇流区可变限速控制[J].吉林大学学报(工学版),2018,48(3):677-687.
CHEN Yong-heng, LIU Xin-shan, XIONG Shuai, et al. Variable speed limit control under snow and ice conditions for urban expressway in junction bottleneck area[J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(3): 677-687.(in Chinese)
[15] 黄 超,陈日强.基于元胞传输模型的高速公路交通流仿真模型[J].中国交通信息化,2017(7):127-130,135.
HUANG Chao, CHEN Ri-qiang. Freeway traffic flow simulation model based on cellular transmission model[J]. China ITS Journal, 2017(7): 127-130, 135.(in Chinese)
[16] 邹 娟.基于改进元胞传输模型的交通信号优化控制[D].武汉:武汉科技大学,2018.
ZOU Juan. Traffic signal optimization control based on improved cell transmission model[D]. Wuhan: Wuhan University of Science and Technology, 2018.(in Chinese)
[17] 龚 䶮,李苏剑,邢恩辉.拥堵程度时变的随机路网可变元胞传输模型[J].计算机工程与应用,2015,51(3):6-11.
GONG Yan, LI Su-jian, XING En-hui. Novel variable cell transmission model for stochastic road network with time-varying congestion degree[J]. Computer Engineering and Applications, 2015, 51(3): 6-11.(in Chinese)
[18] SPILIOPOULOU A, KONTORINAKI M, PAPAGEORGIOU M, et al. Macroscopic traffic flow model validation at congested freeway off-ramp areas[J]. Transportation Research Part C: Emerging Technologies, 2014, 41: 18-29.
[19] CASSIDY M J, BERTINI R L. Some traffic features at freeway bottlenecks[J]. Transportation Research Part B: Methodological, 1999, 33(1): 25-42.
[20] CASSIDY M J, RUDJANAKANOKNAD J. Increasing
capacity of an isolated merge by metering its on-ramp[J]. Transportation Research Part B: Methodological, 2005, 39(10): 896-913.
[21] SUN Jian, ZHANG Juan, ZHANG H. Investigation of the early-onset breakdown phenomenon at urban expressway bottlenecks in Shanghai[J]. Transportmetrica B: Transport Dynamics, 2014, 2(3): 215-228.
[22] HU Jia-qi, SUN Jian, ZHAO Li. Some flow features at urban
expressway on-ramp bottlenecks in Shanghai[C]∥TRB. 93rd Annual Meeting of the Transportation Research Board. Washington DC: TRB, 2014: 1-17.
[23] 李宙峰,城市快速路宏观交通流建模与仿真[D].上海:同济大学,2016.
LI Zhou-feng. Macroscopic traffic flow modeling and simulation for urban expressway[D]. Shanghai: Tongji University, 2016.(in Chinese)
[24] JIN Wen-long, GAN Qi-jian, LEBACQUE J P. A kinematic wave theory of capacity drop[J]. Transportation Research Part B: Methodological, 2015, 81: 316-329.
[25] MUÑOZ L, SUN Xiao-tian, HOROWITZ R, et al. A piecewise-linearized cell transmission model and parameter calibration methodology[J]. Transportation Research Record, 2006(1965): 183-191.
[26] GREWAL M S, PAYNE H J. Identification of parameters in a freeway traffic model[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1976, 6(3): 176-185.
[27] KAMIYAMA D, TAMURA K, YASUDA K. Down-hill
simplex method based differential evolution[C]∥IEEE. Proceedings of SICE Annual Conference. New York: IEEE, 2010: 1641-1646.
[28] POOLE A J, KOTSIALOS A. METANET model validation using a genetic algorithm[J]. IFAC Proceedings Volumes, 2012, 45(24): 7-12.
[29] SPILIOPOULOU A, PAPAMICHAIL I, PAPAGEORGIOU M, et al. Macroscopic traffic flow model calibration using different optimization algorithms[J].Operational Research, 2017, 17: 145-164.
[30] BAN Xue-gang, CHU Lian-yu, BENOUAR H. Bottleneck
identification and calibration for corridor management planning[J]. Transportation Research Record, 2007(1999): 40-53.
[31] SONG Rui, SUN Jian. Calibration of a micro-traffic simulation model with respect to the spatial-temporal evolution at expressway on-ramp bottlenecks[J]. Simulation, 2016, 92(6): 535-546.

Memo

Memo:
-
Last Update: 2019-06-27