Percolation-based health management of complex traffic systems

Abstract

System health management, which aims to ensure the safe and efficient operation of systems by reducing uncertain risks and cascading failures during their lifetime, is proposed for complex transportation systems and other critical infrastructures, especially under the background of the New Infrastructure Projects launched in China. Previous studies proposed numerous approaches to evaluate or improve traffic reliability or efficiency. Nevertheless, most existing studies neglected the core failure mechanism (i.e., spatio–temporal propagation of traffic congestion). In this article, we review existing studies on traffic reliability management and propose a health management framework covering the entire traffic congestion lifetime, from emergence, evolution to dissipation, based on the study of core failure modes with percolation theory. Aiming to be “reliable, invulnerable, resilient, potential, and active”, our proposed traffic health management framework includes modeling, evaluation, diagnosis, and improvement. Our proposed framework may shed light on traffic management for megacities and urban agglomerations around the world. This new approach may offer innovative insights for systems science and engineering in future intelligent infrastructure management.

Graphical abstract

Keywords

traffic health / health management / critical infrastructure / systems science and engineering

References

[1] Albert R, Jeong H, Barabási A L (2000). Error and attack tolerance of complex networks. Nature, 406(6794): 378–382

[2] Añez J, de La Barra T, Pérez B (1996). Dual graph representation of transport networks. Transportation Research Part B: Methodological, 30(3): 209–216

[3] Arcidiacono V, Cimellaro G P, Reinhorn A M, Bruneau M (2012). Community resilience evaluation including interdependencies. In: 15th World Conference on Earthquake Engineering. Lisbon, 24–28

[4] Asakura Y, Kashiwadani M (1991). Road network reliability caused by daily fluctuation of traffic flow. In: 19th PTRC Summer Annual Meeting. Brighton: University of Sussex, 73–84

[5] Barabási A L, Albert R (1999). Emergence of scaling in random networks. Science, 286(5439): 509–512

[6] Barrett C B, Constas M A (2014). Toward a theory of resilience for international development applications. Proceedings of the National Academy of Sciences of the United States of America, 111(40): 14625–14630

[7] Barthélemy M (2011). Spatial networks. Physics Reports, 499(1–3): 1–101

[8] Behrends S, Lindholm M, Woxenius J (2008). The impact of urban freight transport: A definition of sustainability from an actor’s perspective. Transportation Planning and Technology, 31(6): 693–713

[9] Berdica K (2002). An introduction to road vulnerability: What has been done, is done and should be done. Transport Policy, 9(2): 117–127

[10] Boin A, McConnell A (2007). Preparing for critical infrastructure breakdowns: The limits of crisis management and the need for resilience. Journal of Contingencies and Crisis Management, 15(1): 50–59

[11] Bonissone P, Hu X, Subbu R (2009). A systematic PHM approach for anomaly resolution: A hybrid neural fuzzy system for model construction. In: Annual Conference of the PHM Society. San Diego, CA, 1

[12] Braess D, Nagurney A, Wakolbinger T (2005). On a paradox of traffic planning. Transportation Science, 39(4): 446–450

[13] Bruneau M, Chang S E, Eguchi R T, Lee G C, O’Rourke T D, Reinhorn A M, Shinozuka M, Tierney K, Wallace W A, von Winterfeldt D (2003). A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra, 19(4): 733–752

[14] Bunde A, Havlin S (1991). Fractals and Disordered Systems. Berlin: Springer

[15] Calvert S C, Snelder M (2018). A methodology for road traffic resilience analysis and review of related concepts. Transportmetrica A: Transport Science, 14(1–2): 130–154

[16] Cao D, Wu J, Dong X, Sun H, Qu X, Yang Z (2021). Quantification of the impact of traffic incidents on speed reduction: A causal inference based approach. Accident: Analysis and Prevention, 157: 106163

[17] Carrion C, Levinson D (2013). Valuation of travel time reliability from a GPS-based experimental design. Transportation Research Part C: Emerging Technologies, 35: 305–323

[18] Chang S E, Shinozuka M (2004). Measuring improvements in the disaster resilience of communities. Earthquake Spectra, 20(3): 739–755

[19] Chen A, Yang C, Kongsomsaksakul S, Lee M (2007). Network-based accessibility measures for vulnerability analysis of degradable transportation networks. Networks and Spatial Economics, 7(3): 241–256

[20] Chen A, Yang H, Lo H K, Tang W H (1999). A capacity related reliability for transportation networks. Journal of Advanced Transportation, 33(2): 183–200

[21] Chen Y, Cai Y, Hou Y, An J, Li X, Wei P (2015). “Health-index” evaluation method of metropolitan bus lines. Journal of Chang’an University (Natural Science Edition), 35(S1): 1–6, 16 (in Chinese)

[22] Chen Y, Liang Y, Du H (2003). The application of reliability in the road network performance evaluation. China Civil Engineering Journal, (1): 36–40 (in Chinese)

[23] Chickering D M (2002). Learning equivalence classes of Bayesian-network structures. The Journal of Machine Learning Research, 2: 445–498

[24] Cohen R, Erez K, Ben-Avraham D, Havlin S (2000). Resilience of the internet to random breakdowns. Physical Review Letters, 85(21): 4626–4628

[25] Cohen R, Erez K, Ben-Avraham D, Havlin S (2001). Breakdown of the internet under intentional attack. Physical Review Letters, 86(16): 3682–3685

[26] Connors R D, Watling D P (2015). Assessing the demand vulnerability of equilibrium traffic networks via network aggregation. Networks and Spatial Economics, 15(2): 367–395

[27] Cox A, Prager F, Rose A (2011). Transportation security and the role of resilience: A foundation for operational metrics. Transport Policy, 18(2): 307–317

[28] Crucitti P, Latora V, Porta S (2006). Centrality measures in spatial networks of urban streets. Physical Review E, 73(3): 036125

[29] Cui H, You T, Li X, Zhu M (2019). Optimization and reinforcement of regional highway network seismic based on connectivity reliability. Science Technology and Engineering, 19(14): 346–350 (in Chinese)

[30] Daganzo C F (2002). A behavioral theory of multi-lane traffic flow, Part II: Merges and the onset of congestion. Transportation Research Part B: Methodological, 36(2): 159–169

[31] Duan D, Lv C, Si S, Wang Z, Li D, Gao J, Havlin S, Stanley H E, Boccaletti S (2019). Universal behavior of cascading failures in interdependent networks. Proceedings of the National Academy of Sciences of the United States of America, 116(45): 22452–22457

[32] Erdős P, Rényi A (1959). On random graphs I. Publicationes Mathematicae Debrecen, 6: 290–297

[33] Fang C, Dong P, Fang Y, Zio E (2020). Vulnerability analysis of critical infrastructure under disruptions: An application to China Railway High-speed. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 234(2): 235–245

[34] Feder A, Fred-Torres S, Southwick S M, Charney D S (2019). The biology of human resilience: Opportunities for enhancing resilience across the life span. Biological Psychiatry, 86(6): 443–453

[35] Francis R, Bekera B (2014). A metric and frameworks for resilience analysis of engineered and infrastructure systems. Reliability Engineering & System Safety, 121: 90–103

[36] Freckleton D, Heaslip K, Louisell W, Collura J (2012). Evaluation of resiliency of transportation networks after disasters. Transportation Research Record: Journal of the Transportation Research Board, 2284(1): 109–116

[37] Ganin A A, Kitsak M, Marchese D, Keisler J M, Seager T, Linkov I (2017). Resilience and efficiency in transportation networks. Science Advances, 3(12): e1701079

[38] Gao J, Barzel B, Barabási A L (2016). Universal resilience patterns in complex networks. Nature, 530(7590): 307–312

[39] Geroliminis N, Daganzo C F (2008). Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings. Transportation Research Part B: Methodological, 42(9): 759–770

[40] Granger C W J (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3): 424–438

[41] Gu Y, Fu X, Liu Z, Xu X, Chen A (2020). Performance of transportation network under perturbations: Reliability, vulnerability, and resilience. Transportation Research Part E: Logistics and Transportation Review, 133: 101809

[42] Guan W, Wu J, Gao Z (2020). Transportation network systems engineering. Journal of Transportation Systems Engineering and Information Technology, 20(6): 9–21 (in Chinese)

[43] Guo J, Gao Y, Wen H (2007). Assessment methodology of connect reliability based on substituted route. Journal of Highway and Transportation Research and Development, (7): 91–94, 136 (in Chinese)

[44] Guo S, Zhou D, Fan J, Tong Q, Zhu T, Lv W, Li D, Havlin S (2019). Identifying the most influential roads based on traffic correlation networks. EPJ Data Science, 8: 28

[45] Headey D, Barrett C B (2015). Opinion: Measuring development resilience in the world’s poorest countries. Proceedings of the National Academy of Sciences of the United States of America, 112(37): 11423–11425

[46] Henry D, Ramirez-Marquez J E (2012). Generic metrics and quantitative approaches for system resilience as a function of time. Reliability Engineering & System Safety, 99: 114–122

[47] Herman R, Lam T, Prigogine I (1972). Kinetic theory of vehicular traffic: Comparison with data. Transportation Science, 6(4): 440–452

[48] Holling C S (1973). Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4(1): 1–23

[49] Hollnagel E, Woods D D, Leveson N (2006). Resilience Engineering: Concepts and Precepts. Aldershot: Ashgate Publishing Ltd.

[50] Ibeas Á, Dell’olio L, Alonso B, Sainz O (2010). Optimizing bus stop spacing in urban areas. Transportation Research Part E: Logistics and Transportation Review, 46(3): 446–458

[51] Ip W H, Wang D (2011). Resilience and friability of transportation networks: Evaluation, analysis and optimization. IEEE Systems Journal, 5(2): 189–198

[52] Ji X, Xie J, Wu J (2019). Assessment method of expressway resilience considering different intrusion scenes. Journal of Safety Science and Technology, 15(1): 12–19 (in Chinese)

[53] Johnson S B, Gormley T, Kessler S, Mott C, Patterson-Hine A, Reichard K, Scandura Jr P (2011). System Health Management: With Aerospace Applications. Chichester: John Wiley & Sons

[54] Khan S, Yairi T (2018). A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107: 241–265

[55] Kim S, Yeo H (2016). A flow-based vulnerability measure for the resilience of urban road network. Procedia: Social and Behavioral Sciences, 218: 13–23

[56] Lam W H K, Shao H, Sumalee A (2008). Modeling impacts of adverse weather conditions on a road network with uncertainties in demand and supply. Transportation Research Part B: Methodological, 42(10): 890–910

[57] Lam W H K, Xu G (1999). A traffic flow simulator for network reliability assessment. Journal of Advanced Transportation, 33(2): 159–182

[58] Li D, Fu B, Wang Y, Lu G, Berezin Y, Stanley H E, Havlin S (2015). Percolation transition in dynamical traffic network with evolving critical bottlenecks. Proceedings of the National Academy of Sciences of the United States of America, 112(3): 669–672

[59] Li D, Jiang Y, Kang R, Havlin S (2014). Spatial correlation analysis of cascading failures: Congestions and blackouts. Scientific Reports, 4(1): 5381

[60] Li D, Kosmidis K, Bunde A, Havlin S (2011). Dimension of spatially embedded networks. Nature Physics, 7(6): 481–484

[61] Liang Z, Wakahara Y (2014). Real-time urban traffic amount prediction models for dynamic route guidance systems. EURASIP Journal on Wireless Communications and Networking, (1): 85

[62] Lighthill M J, Whitham G B (1955). On kinematic waves I: Flood movement in long rivers. Proceedings of the Royal Society of London, Series A: Mathematical and Physical Sciences, 229(1178): 281–316

[63] Liu H, Pu Y (2004). Road network capacity reliability based on link travel time reliability. Journal of Southwest Jiaotong University, (5): 573–576 (in Chinese)

[64] Lo H K, Tung Y K (2003). Network with degradable links: Capacity analysis and design. Transportation Research Part B: Methodological, 37(4): 345–363

[65] Mancuso A, Compare M, Salo A, Zio E (2021). Optimal Prognostics and Health Management-driven inspection and maintenance strategies for industrial systems. Reliability Engineering & System Safety, 210: 107536

[66] Marseguerra M, Zio E, Podofillini L (2002). Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation. Reliability Engineering & System Safety, 77(2): 151–165

[67] Masten A S (2016). Resilience in developing systems: The promise of integrated approaches. European Journal of Developmental Psycho-logy, 13(3): 297–312

[68] Mine H, Kawai H (1982). Mathematics for Reliability Analysis. Tokyo: Asakura-shoten (in Japanese)

[69] Mumby P J, Hastings A, Edwards H J (2007). Thresholds and the resilience of Caribbean coral reefs. Nature, 450(7166): 98–101

[70] Nagel K, Schreckenberg M (1992). A cellular automaton model for freeway traffic. Journal de Physique I, 2(12): 2221–2229

[71] Newell G F (1961). Nonlinear effects in the dynamics of car following. Operations Research, 9(2): 209–229

[72] Ouyang M, Dueñas-Osorio L, Min X (2012). A three-stage resilience analysis framework for urban infrastructure systems. Structural Safety, 36–37: 23–31

[73] Papageorgiou M, Diakaki C, Dinopoulou V, Kotsialos A, Wang Y (2003). Review of road traffic control strategies. Proceedings of the IEEE, 91(12): 2043–2067

[74] Peng Y, Dong M, Zuo M (2010). Current status of machine prognostics in condition-based maintenance: A review. International Journal of Advanced Manufacturing Technology, 50(1–4): 297–313

[75] Peters J, Janzing D, Schölkopf B (2017). Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA: MIT Press

[76] Ponce-Campos G E, Moran M S, Huete A, Zhang Y, Bresloff C, Huxman T E, Eamus D, Bosch D D, Buda A R, Gunter S A, Scalley T H, Kitchen S G, McClaran M P, McNab W H, Montoya D S, Morgan J A, Peters D P C, Sadler E J, Seyfried M S, Starks P J (2013). Ecosystem resilience despite large-scale altered hydroclimatic conditions. Nature, 494(7437): 349–352

[77] Rao A M, Rao K R (2012). Measuring urban traffic congestion: A review. International Journal for Traffic and Transport Engineering, 2(4): 286–305

[78] Renschler C S, Frazier A E, Arendt L A, Cimellaro G P, Reinhorn A M, Bruneau M (2010). A framework for defining and measuring resilience at the community scale: The PEOPLES resilience framework. Technical Report MCEER-10-0006. Buffalo, NY: University at Buffalo, State University of New York

[79] Richards P I (1956). Shock waves on the highway. Operations Research, 4(1): 42–51

[80] Rose A (2007). Economic resilience to natural and man-made disasters: Multidisciplinary origins and contextual dimensions. Environmental Hazards, 7(4): 383–398

[81] Saedi R, Saeedmanesh M, Zockaie A, Saberi M, Geroliminis N, Mahmassani H S (2020). Estimating network travel time reliability with network partitioning. Transportation Research Part C: Emerging Technologies, 112: 46–61

[82] Shi J, Chen L, Qiao F, Yu L, Li Q, Fan G (2020). Simulation and analysis of the carrying capacity for road networks using a grid-based approach. Journal of Traffic and Transportation Engineering, 7(4): 498–506

[83] Stauffer D, Aharony A (1992). Introduction to Percolation Theory. 2nd ed. London: CRC

[84] Sun H, Wu J, Wang W, Gao Z (2014). Reliability-based traffic network design with advanced traveler information systems. Information Sciences, 287: 121–130

[85] Talebpour A, Mahmassani H S, Elfar A (2017). Investigating the effects of reserved lanes for autonomous vehicles on congestion and travel time reliability. Transportation Research Record: Journal of the Transportation Research Board, 2622(1): 1–12

[86] Tan Y, Wu J, Deng H (2011). Progress in invulnerability of complex networks. Journal of University of Shanghai for Science and Technology, 33(6): 653–668, 508 (in Chinese)

[87] Taylor M A P, Sekhar S V C, D’Este G M (2006). Application of accessibility based methods for vulnerability analysis of strategic road networks. Networks and Spatial Economics, 6(3–4): 267–291

[88] Wang C (2016). Research on health status evaluation system of road traffic in old city area. Urban Roads Bridges & Flood Control, (12): 143–147, 18 (in Chinese)

[89] Wang J, Li Z, Bai G, Zuo M (2020). An improved model for dependent competing risks considering continuous degradation and random shocks. Reliability Engineering & System Safety, 193: 106641

[90] Wang T, Wu L (2010). Research on invulnerability of urban transit network based on complex network. Application Research of Computers, 27(11): 4084–4086 (in Chinese)

[91] Wu J, Gao Z, Sun H, Huang H (2006). Congestion in different topologies of traffic networks. Europhysics Letters, 74(3): 560–566

[92] Xiong Z, Yao Z, Shao C (2004). Travel time reliability in road network associated with road section. China Safety Science Journal, (10): 84–87, 1 (in Chinese)

[93] Xu L, Gao Z (2006). Urban transport network design based on link capacity reliability. China Journal of Highway and Transport, (2): 86–90 (in Chinese)

[94] Xu M, Allenby B R, Crittenden J C (2011). Interconnectedness and resilience of the US economy. Advances in Complex Systems, 14(5): 649–672

[95] Yang H, Bell M G H (1998). Models and algorithms for road network design: A review and some new developments. Transport Reviews, 18(3): 257–278

[96] Yang H, Huang H (2005). Mathematical and Economic Theory of Road Pricing. Oxford: Elsevier

[97] Yang H, Wang X (2011). Managing network mobility with tradable credits. Transportation Research Part B: Methodological, 45(3): 580–594

[98] Yang L, Qian D (2012). Vulnerability analysis of road networks. Journal of Transportation Systems Engineering and Information Technology, 12(1): 105–110

[99] Youn B D, Hu C, Wang P (2011). Resilience-driven system design of complex engineered systems. Journal of Mechanical Design, 133(10): 101011

[100] Zeng G, Gao J, Shekhtman L, Guo S, Lv W, Wu J, Liu H, Levy O, Li D, Gao Z, Stanley H E, Havlin S (2020). Multiple metastable network states in urban traffic. Proceedings of the National Academy of Sciences of the United States of America, 117(30): 17528–17534

[101] Zeng G, Li D, Guo S, Gao L, Gao Z, Stanley H E, Havlin S (2019). Switch between critical percolation modes in city traffic dynamics. Proceedings of the National Academy of Sciences of the United States of America, 116(1): 23–28

[102] Zeng S, Pecht M G, Wu J (2005). Status and perspectives of Prognostics and Health Management technologies. Acta Aeronautica et Astronautica Sinica, (5): 626–632 (in Chinese)

[103] Zhang L, Zeng G, Li D, Huang H, Stanley H E, Havlin S (2019). Scale-free resilience of real traffic jams. Proceedings of the National Academy of Sciences of the United States of America, 116(18): 8673–8678

[104] Zhang X (2017). Day-to-day road network vulnerability identification based on network efficiency. Journal of Transportation Systems Engineering and Information Technology, 17(2): 176–182 (in Chinese)

[105] Zhao J, Li D, Sanhedrai H, Cohen R, Havlin S (2016). Spatio–temporal propagation of cascading overload failures in spatially embedded networks. Nature Communications, 7(1): 10094

[106] Zhao L, Huang D, Song J (2012). Fractal characteristics of mountain cities’ traffic flow with sub-health state. Control Engineering of China, 19(4): 583–586 (in Chinese)

[107] Zheng Z, Chen Y, Zhu D, Sun H, Wu J, Pan X, Li D (2021). Extreme unbalanced mobility network in bike sharing system. Physica A, 563: 125444

[108] Zhou L, Yang X, Wang H, Wu J, Chen L, Yin H, Qu Y (2020a). A robust train timetable optimization approach for reducing the number of waiting passengers in metro systems. Physica A, 558: 124927

[109] Zhou Z, Ouyang J, Jing G (2020b). Urban transportation decision-making evaluation index system based on healthy development concept. Urban Transport of China, 18(4): 101–106 (in Chinese)

[110] Zio E (2009). Reliability engineering: Old problems and new challenges. Reliability Engineering & System Safety, 94(2): 125–141

[111] Zio E (2016). Challenges in the vulnerability and risk analysis of critical infrastructures. Reliability Engineering & System Safety, 152: 137–150

[112] Zobel C W (2011). Representing perceived tradeoffs in defining disaster resilience. Decision Support Systems, 50(2): 394–403

RIGHTS & PERMISSIONS

2021 Higher Education Press