Dissipative Structure Properties of Traffic Flow in Expressway Weaving Areas

Weihua ZHANG; Lijun XIONG; Qingtong JI; Huiwen LIU; Fan ZHANG; Huiting CHEN

doi:10.7307/ptt.v36i4.525

Authors

Weihua ZHANG Hefei University of Technology, School of Automotive and Transportation Engineering
Lijun XIONG Hefei University of Technology, School of Automotive and Transportation Engineering
Qingtong JI Hangzhou Hikvision DIGITAL Technology Co., Ltd
Huiwen LIU Hefei University of Technology, School of Civil and Hydraulic Engineering
Fan ZHANG Hefei University of Technology, School of Automotive and Transportation Engineering
Huiting CHEN Hefei University of Technology, School of Automotive and Transportation Engineering

DOI:

https://doi.org/10.7307/ptt.v36i4.525

Keywords:

urban expressway, dissipative structure, weaving area, length of weaving area, weaving flow ratio, congestion duration

Abstract

Expressway weaving areas meet dissipative structure characteristics. When traffic states reach a certain range, they exhibit self-organising criticality, and slight changes may trigger unpredictable congestion. This paper examines the correlation between the dissipative structure of the weaving area and key traffic parameters. The range of dissipative structure states in the weaving area is defined through the dissipative structure concept with three-phase traffic flow theory and real traffic data. Based on the fundamental diagram and measured traffic data, the weaving area dissipative structure model characterising the relationship between critical state changes in traffic volume is constructed and validated. Finally, the Cell Transmission Model simulation was used to examine the characteristic relationship between the weaving area dissipative structure state duration, the weaving area length and the weaving flow ratio. The results show that the length of the dissipative structure state is maintained when the traffic flow is self-organised into a free-flow or a congested state positively correlates with the length of the weaving area. Higher weaving flow ratios lead to shorter dissipative structure state durations during congestion formation, and the exact opposite during congestion evacuation. This paper is important for analysing the congestion mechanism and managing congestion.

References

Huang Z, Chen K. Research on the minimum spacing of expressway ramps with first entry and exit. Journal of China & Foreign Highway. 2016;36(4):335–340. DOI: 10.14048/j.issn.1671-2579.2016.04.075.

Pan X. Discussing the scheme for the insufficient spacing between the entrances and exits of expressways. Engineering Technology Research. 2019;4(4):207-208.

Cai X, et al. Study on capacity model of weaving areas of arterial in mountainous city. China Intelligent Transportation Association. Proceedings of the 14th Annual Conference on Intelligent Transportation in China. 2019.429:446. DOI:10.26914/c.cnkihy.2019.013377.

Marczak F, Leclercq L, Buisson C. A macroscopic model for freeway weaving sections. Computer-Aided Civil and Infrastructure Engineering. 2015;30(6):464-477. DOI: 10.1111/mice.12119.

Highway Capacity Manual. Transportation Research Board, The National Academics of Sciences, Engineering, and Medicine. Washington, D.C. 2016.

Kashani A, Shirgir B. Development of maximum weaving length model based on HCM 2016. Transportation research record. 2021;2675(4):135-145. DOI: 10.1177/0361198120973667.

Lee JY, et al. Estimating the effects of freeway weaving section length on level of service based on microsimulation. Transportation Research Record. 2023;2677(11):205-218. DOI: 10.1177/03611981231165019.

Nagel K, Rasmussen S, Barrett CL. Network traffic as a self-organized critical phenomena. Los Alamos National Lab (LANL), Los Alamos, NM (United States), 1996.

Le RS. Non-Equilibrium Thermodynamics and Dissipation structures. Tsinghua University Press; 1986.

Dănilă B, Harko T, Mocanu G. Self-organized criticality in a two-dimensional cellular automaton model of a magnetic flux tube with background flow. Monthly Notices of the Royal Astronomical Society. 2015;453(3):2982-2991. DOI: 10.1093/mnras/stv1821.

Ma Q, et al. Traffic guidance self-organization method for neighbor weaving segments based on self-organized critical state. IEEE Access, 2020;8:171784-171795. DOI: 10.1109/ACCESS.2020.3025006.

Ma Q, et al. Self-organizing traffic control using carrying capacity modelling in adjacent weaving sections. IET Intelligent Transport Systems. 2023;17(2):327-340. DOI: 10.1049/itr2.12261.

Zeng G, et al. Switch between critical percolation modes in city traffic dynamics. Proc. Natl. Acad. Sci. 2019;116(1):23-28. DOI:10.48550/arXiv.1709.03134.

Zhang L, et al. Scale-free resilience of real traffic jams. Proc. Natl. Acad. Sci. 2019;116(18):8673-8678. DOI:10.1073/pnas.1814982116.

Zeng G, et al. Multiple metastable network states in urban traffic. Proc. Natl. Acad. Sci. 2020;117 (30):17528-17534. DOI: 10.1073/pnas.1907493117.

Laval JA. Self-organized criticality of traffic flow: Implications for congestion management technologies. Transportation Research Part C: Emerging Technologies. 2023;149:104056. DOI: 10.1016/j.trc.2023.104056.

Porter E, Hamdar SH, Daamen W. Pedestrian dynamics at transit stations: an integrated pedestrian flow modeling approach. Transportmetrica A Transport Science. 2018;14(5-6):468-483. DOI: 10.1080/23249935.2017.1378280.

Fujita A, et al. Traffic flow in a crowd of pedestrians walking at different speeds. Physical Review E. 2019;99(6):062307. DOI:10.1103/PhysRevE.99.062307.

Chen T, Chen S. Study of dissipation structure properties of urban road traffic systems. Journal of Civil Engineering. 2004;01(2004):74-77. DOI:10.3321/j.issn:1000-131X.2004.01.014.

Nie W, Shao C, Yang L. Exploration of dissipation structure and entropy change theory in regional traffic system. Highway Traffic Science and Technology. 2016;10(2006):95-98. DOI: 10.1061/JHTRCQ.0000175.

Mo X. Research on the mechanism and control of self-organized operation of urban road traffic flow. PhD dissertation. Jilin University; 2014.

Kerner BS. Three-phase traffic theory and highway capacity. Physica A: Statistical Mechanics and its Applications. 2004;333(1):379-440. DOI:10.1016/j.physa.2003.10.017.

Yang G, et al. Impacts of traffic flow arrival pattern on the necessary queue storage space at metered onramps. Transportmetrica A: Transport Science. 2018;14(7):543-561. DOI: 10.1080/23249935.2017.1387875.

Peng L. Open ITS consortium opendata 7.0 - Canadian Whitemud Drive highway data. 2021. https://www.openits.cn/openData1/700.jhtml. [Accessed: 2022-08-19].

Daganzo CF. The cell transmission model, part II: Network traffic. Transportation Research Part B: Methodological. 1995;29(2):79-93. DOI: 10.1016/0191-2615(94)00022-R.

Zhang Y, Ni Q. A coordinated traffic control on urban expressways with modified particle swarm optimization. KSCE Journal of Civil Engineering. 2017;21:501-511. DOI: 10.1007/s12205-017-1505-x.

An X, et al. Effect of lane allocation on operational efficiency at weaving areas based on a cellular automaton model. IET Intelligent Transport Systems. 2019;13(5):851-859. DOI: 10.1049/iet-its.2018.5143.

Stewart J, Baker M, Van Aerde M. Evaluating weaving section designs using INTEGRATION. Transportation Research Record. 1996;555(1):33-41. DOI: 10.1177/0361198196155500.

Marczak F, Daamen W, Buisson C. Empirical analysis of lane changing behavior at a freeway weaving section. Traffic Management. 2016;3:139-151. DOI: 10.1002/9781119307822.ch10.

Chansu R, Bongsoo SON. An evaluation of design standard for freeway weaving section length for operational efficiency. Journal of Korean Society of Transportation. 2021;39(1):62-77. DOI: 10.7470/jkst.2021.39.1.062.