Spatial partition for heterogeneous city networks composed of factors that influence the distribution of the macroscopic fundamental diagram
Abstract
Using a Macroscopic Fundamental Diagram (MFD) to implement partition control is effective in improving mobility in heterogeneous city networks. As one of the most complex issues in partition control, accurate sub-region partition is critical for control effectiveness. Current partition methods focus on the link density and precondition of existing MFD and disregard the factors that influence MFD distribution. To overcome this drawback, this study uses the characteristic value of the link and the intersection connected to the link as the analysis object and proposes an MFD sub-region partitioning method for large-scale networks. Firstly, the influences of road state parameters on MFD distribution are classified into traffic flow parameters, network physical properties, network operation mechanisms and emergencies. Simulation experiments are conducted to determine the degree to which these classifications affect MFD distribution. Secondly, a partition method combined with the link density and influence parameters of MFD is developed. The method is used for a preliminarily division of a road network through Minimum Spanning Tree (MST) and depth partition by the Normalised Cut (Ncut) algorithm. Finally, a case study is conducted in an actual city centre network, and results show that the developed method is superior to the single method based simply on link density.
Keyword : partition control, sub-region partition, macroscopic fundamental diagram, minimum spanning tree, normalised cut algorithm
How to Cite
Ding, H., Yuan, H., Zheng, X., Ma, H., & Bai, H. (2023). Spatial partition for heterogeneous city networks composed of factors that influence the distribution of the macroscopic fundamental diagram. Transport, 38(3), 152–164. https://doi.org/10.3846/transport.2023.20424
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Ma, Y.-Y.; Yang, X.-G.; Zeng, Y. 2010. Urban traffic signal control network parting using spectral method, Systems Engineering – Theory & Practice 30(12): 2290–2296. (in Chinese).
Mazloumian, A.; Geroliminis, N.; Helbing D. 2010. The spatial variability of vehicle densities as determinant of urban network capacity, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368(1928): 4627–4647. https://doi.org/10.1098/rsta.2010.0099
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Ramezani, M.; Haddad, J.; Geroliminis, N. 2015. Dynamics of heterogeneity in urban networks: aggregated traffic modeling and hierarchical control, Transportation Research Part B: Methodological 74: 1–19. https://doi.org/10.1016/j.trb.2014.12.010
Saeedmanesh, M.; Geroliminis, N. 2016.Clustering of heterogeneous networks with directional flows based on “snake” similarities, Transportation Research Part B: Methodological 91: 250–269. https://doi.org/10.1016/j.trb.2016.05.008
Saeedmanesh, M.; Geroliminis, N. 2017. Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks, Transportation Research Part B: Methodological 105: 193–211. https://doi.org/10.1016/j.trb.2017.08.021
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Wang, B.; Jia, K. 2010. Synthetic algorithm of vehicle flow detection based on video analysis, Computer and Communications (1): 20–25. (in Chinese).
Wu, Z.; Leahy, R. 1993. An optimal graph theoretic approach to data clustering: theory and its application to image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11): 1101–1113. https://doi.org/10.1109/34.244673
Xu, F.-F.; He Z.-C.; Sha Z.-R. 2013. Impacts of traffic management measures on urban network microscopic fundamental diagram, Journal of Transportation Systems Engineering and Information Technology (2): 185–190. (in Chinese).
Zhang, L.; Garoni, T. M.; De Gier, J. 2013. A comparative study of macroscopic fundamental diagrams of arterial road networks governed by adaptive traffic signal systems, Transportation Research Part B: Methodological 49: 1–23. https://doi.org/10.1016/j.trb.2012.12.002
Zhao, T.-T.; Li, Z.-H.; Huang, B.-Y.; Mu, B.-P.; Zhang, Y. 2014. Exploring the influence of traveller information on macroscopic fundamental diagrams, IET Intelligent Transport Systems 8(1): 58–67. https://doi.org/10.1049/iet-its.2011.0234
Zhou, Z.; Lin, S.; Xi, Y.; Li, D.; Zhang, J. 2016. A hierarchical urban network control with integration of demand balance and traffic signal coordination, IFAC-PapersOnLine 49(3): 31–36. https://doi.org/10.1016/j.ifacol.2016.07.006
Ampountolas, K.; Zheng, N.; Geroliminis, N. 2017. Macroscopic modelling and robust control of bi-modal multi-region urban road networks, Transportation Research Part B: Methodological 104: 616–637. https://doi.org/10.1016/j.trb.2017.05.007
Chen, X. 2013. Research on Clustering Algorithm Based on Minimum Spanning Tree. MSc Thesis. Chongqing University, China. 57 p. (in Chinese).
Daganzo, C. F.; Geroliminis, N. 2008. An analytical approximation for the macroscopic fundamental diagram of urban traffic, Transportation Research Part B: Methodological 42(9): 771–781. https://doi.org/10.1016/j.trb.2008.06.008
Ding, H.; Guo F.; Zheng X.; Zhang W. 2017. Traffic guidance–perimeter control coupled method for the congestion in a macro network, Transportation Research Part C: Emerging Technologies 81: 300–316. https://doi.org/10.1016/j.trc.2017.06.010
Ding, H.; Zhang, Y.; Zheng, X.; Yuan, H.; Zhang, W. 2018. Hybrid perimeter control for two-region urban cities with different states, IEEE Transactions on Control Systems Technology 26(6): 2049–2062. https://doi.org/10.1109/TCST.2017.2746061
Feng, A. R. 2014. Research on Image Segmentation Based on Normalized Cuts. MSC Thesis. Chongqing University, China. (in Chinese).
Gayah, V. V.; Daganzo, C. F. 2011. Clockwise hysteresis loops in the macroscopic fundamental diagram: an effect of network instability, Transportation Research Part B: Methodological 45(4): 643–655. https://doi.org/10.1016/j.trb.2010.11.006
Godfrey, J. W. 1969. The mechanism of a road network, Traffic Engineering and Control 11: 323–327.
Gonzales, E. J.; Chavis, C.; Li, Y.; Daganzo, C. F. 2009. Multimodal Transport Modeling for Nairobi, Kenya: Insights and Recommendations with an Evidence-Based Model. Working Paper UCB-ITS-VWP-2009-5. UC Berkeley Center for Future Urban Transport, Berkeley, CA, US. 41 p.
Geroliminis, N.; Daganzo, C. F. 2007. Macroscopic modeling of traffic in cities, in TRB 86th Annual Meeting Compendium of Papers CD-ROM, 21–25 January 2007, Washington DC, US. 21 p.
Geroliminis, N.; Haddad, J.; Ramezani, M. 2013. Optimal perimeter control for two urban regions with macroscopic fundamental diagrams: a model predictive approach, IEEE Transactions on Intelligent Transportation Systems 14(1): 348–359. https://doi.org/10.1109/TITS.2012.2216877
Geroliminis N.; Sun J. 2011a. Hysteresis phenomena of a Macroscopic Fundamental Diagram in freeway networks, Transportation Research Part A: Policy and Practice 45(9): 966–979. https://doi.org/10.1016/j.tra.2011.04.004
Geroliminis, N.; Sun, J. 2011b. Properties of a well-defined macroscopic fundamental diagram for urban traffic, Transportation Research Part B: Methodological 45(3): 605–617. https://doi.org/10.1016/j.trb.2010.11.004
Haddad, J. 2017. Optimal perimeter control synthesis for two urban regions with aggregate boundary queue dynamics, Transportation Research Part B: Methodological 96: 1–25. https://doi.org/10.1016/j.trb.2016.10.016
Haddad, J.; Geroliminis, N. 2012. On the stability of traffic perimeter control in two-region urban cities, Transportation Research Part B: Methodological 46(9): 1159–1176. https://doi.org/10.1016/j.trb.2012.04.004
Haddad, J.; Mirkin, B. 2017. Coordinated distributed adaptive perimeter control for large-scale urban road networks, Transportation Research Part C: Emerging Technologies 77: 495–515. https://doi.org/10.1016/j.trc.2016.12.002
Hajiahmadi, M.; Haddad J.; De Schutter, B.; Geroliminis, N. 2013. Optimal hybrid macroscopic traffic control for urban regions: perimeter and switching signal plans controllers, in 2013 European Control Conference (ECC), 17–19 July 2013, Zurich, Switzerland, 3500–3505. https://doi.org/10.23919/ECC.2013.6669572
Hajiahmadi, M.; Haddad, J.; De Schutter, B.; Geroliminis, N. 2015. Optimal hybrid perimeter and switching plans control for urban traffic networks, IEEE Transactions on Control Systems Technology 23(2): 464–478. https://doi.org/10.1109/TCST.2014.2330997
Ji, Y.; Geroliminis, N. 2012. On the spatial partitioning of urban transportation networks, Transportation Research Part B: Methodological 46(10): 1639–1656. https://doi.org/10.1016/j.trb.2012.08.005
Ji, Y.; Luo, J.; Geroliminis, N. 2013. Dynamic partitioning of urban transportation networks, in 13th Swiss Transport Research Conference: STRC 2013, 24–26 April 2013, Monte Verita, Ascona, Switzerland. 9 p. Available from Internet: https://www.strc.ch/2013/Ji_EtAl.pdf
Ji, Y.; Luo, J.; Geroliminis, N. 2014. Empirical observations of congestion propagation and dynamic partitioning with probe data for large-scale systems, Transportation Research Record: Journal of the Transportation Research Board 2422: 1–11. https://doi.org/10.3141/2422-01
Keyvan-Ekbatani, M.; Yildirimoglu, M.; Geroliminis, N.; Papageorgiou, M. 2015. Multiple concentric gating traffic control in large-scale urban networks, IEEE Transactions on Intelligent Transportation Systems 16(4): 2141–2154. https://doi.org/10.1109/TITS.2015.2399303
Knoop, V. L.; Van Lint, H.; Hoogendoorn, S. P. 2015. Traffic dynamics: its impact on the macroscopic fundamental diagram, Physica A: Statistical Mechanics and its Applications 438: 236–250. https://doi.org/10.1016/j.physa.2015.06.016
Kouvelas, A.; Saeedmanesh, M.; Geroliminis, N. 2017. Enhancing model-based feedback perimeter control with data-driven online adaptive optimization, Transportation Research Part B: Methodological 96: 26–45. https://doi.org/10.1016/j.trb.2016.10.011
Li, G.; Zhao, Y. 2012. Urban traffic signal control network partitioning based on macro-traffic flow theory, in The 7th China Intelligent Transportation Conference Excellent Proceedings, 26–28 September 2012, Beijing, China, 67–72. (in Chinese).
Ma, Y.-Y.; Yang, X.-G.; Zeng, Y. 2010. Urban traffic signal control network parting using spectral method, Systems Engineering – Theory & Practice 30(12): 2290–2296. (in Chinese).
Mazloumian, A.; Geroliminis, N.; Helbing D. 2010. The spatial variability of vehicle densities as determinant of urban network capacity, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368(1928): 4627–4647. https://doi.org/10.1098/rsta.2010.0099
Peng, B.; Zhang, L.; Zhang, D. 2013. A survey of graph theoretical approaches to image segmentation, Pattern Recognition 46(3): 1020–1038. https://doi.org/10.1016/j.patcog.2012.09.015
Peng, J. X. 2013. Macroscopic Characteristics of Dense Road Networks. MSC Thesis. University of Hong Kong, Pok Fu Lam, Hong Kong. 107 p. Available from Internet: https://hub.hku.hk/handle/10722/195994
Ramezani, M.; Haddad, J.; Geroliminis, N. 2015. Dynamics of heterogeneity in urban networks: aggregated traffic modeling and hierarchical control, Transportation Research Part B: Methodological 74: 1–19. https://doi.org/10.1016/j.trb.2014.12.010
Saeedmanesh, M.; Geroliminis, N. 2016.Clustering of heterogeneous networks with directional flows based on “snake” similarities, Transportation Research Part B: Methodological 91: 250–269. https://doi.org/10.1016/j.trb.2016.05.008
Saeedmanesh, M.; Geroliminis, N. 2017. Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks, Transportation Research Part B: Methodological 105: 193–211. https://doi.org/10.1016/j.trb.2017.08.021
Shi, J.; Malik, J. 2000. Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8): 888–905. https://doi.org/10.1109/34.868688
Wang, B.; Jia, K. 2010. Synthetic algorithm of vehicle flow detection based on video analysis, Computer and Communications (1): 20–25. (in Chinese).
Wu, Z.; Leahy, R. 1993. An optimal graph theoretic approach to data clustering: theory and its application to image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11): 1101–1113. https://doi.org/10.1109/34.244673
Xu, F.-F.; He Z.-C.; Sha Z.-R. 2013. Impacts of traffic management measures on urban network microscopic fundamental diagram, Journal of Transportation Systems Engineering and Information Technology (2): 185–190. (in Chinese).
Zhang, L.; Garoni, T. M.; De Gier, J. 2013. A comparative study of macroscopic fundamental diagrams of arterial road networks governed by adaptive traffic signal systems, Transportation Research Part B: Methodological 49: 1–23. https://doi.org/10.1016/j.trb.2012.12.002
Zhao, T.-T.; Li, Z.-H.; Huang, B.-Y.; Mu, B.-P.; Zhang, Y. 2014. Exploring the influence of traveller information on macroscopic fundamental diagrams, IET Intelligent Transport Systems 8(1): 58–67. https://doi.org/10.1049/iet-its.2011.0234
Zhou, Z.; Lin, S.; Xi, Y.; Li, D.; Zhang, J. 2016. A hierarchical urban network control with integration of demand balance and traffic signal coordination, IFAC-PapersOnLine 49(3): 31–36. https://doi.org/10.1016/j.ifacol.2016.07.006