Competing Risks Models for the Assessment of Intelligent Transportation Systems Devices: A Case Study for Connected and Autonomous Vehicle Applications

Intelligent transportation system (ITS) has become a crucial section of transportation and traffic management systems in the past decades. As a result, transportation agencies keep improving the quality of transportation infrastructure management information for accessibility and security of transportation networks. The goal of this paper is to evaluate the impact of two competing risks: “natural deterioration” of ITS devices and hurricane-induced failure of the same components. The major devices employed in the architecture of this paper include closed circuit television (CCTV) cameras, automatic vehicle identification (AVI) systems, dynamic message signals (DMS), wireless communication systems and DMS towers. From the findings, it was evident that as ITS infrastructure devices age, the contribution of Hurricane Category 3 as a competing failure risk is higher and significant compared to the natural deterioration of devices. Hurricane Category 3 failure vs. natural deterioration indicated an average hazard ratio of 1.5 for CCTV, AVI and wireless communications systems and an average hazard ratio of 2.3 for DMS, DMS towers and portable DMS. The proportional hazard ratios of the Hurricane Category 1 compared to the devices was estimated as <0.001 and that of Hurricane Category 2 < 0.5, demonstrating the lesser impact of the Hurricane Categories 1 and 2. It is expedient to envisage and forecast the impact of hurricanes on the failure of wireless communication networks, vehicle detection systems and other message signals, in order to prevent vehicle to infrastructure connection disruption, especially for autonomous and connected vehicle systems.

[1]  J. Sobanjo,et al.  Competing risks models for the deterioration of highway pavement subject to hurricane events , 2019, Structure and Infrastructure Engineering.

[2]  John D. Andrews,et al.  Importance measures for noncoherent-system analysis , 2003, IEEE Trans. Reliab..

[3]  Yue Li,et al.  Age-dependent fragility and life-cycle cost analysis of wood and steel power distribution poles subjected to hurricanes , 2016 .

[4]  Yaping Wang,et al.  A Multi-Objective Optimization of Imperfect Preventive Maintenance Policy for Dependent Competing Risk Systems With Hidden Failure , 2011, IEEE Transactions on Reliability.

[5]  Jack W. Baker,et al.  Efficient Analytical Fragility Function Fitting Using Dynamic Structural Analysis , 2015 .

[6]  Sherali Zeadally,et al.  Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies , 2015, IEEE Wireless Communications.

[7]  Farouk Yalaoui,et al.  New methods to minimize the preventive maintenance cost of series-parallel systems using ant colony optimization , 2005, Reliab. Eng. Syst. Saf..

[8]  Reginald DesRoches,et al.  Retrofitted Bridge Fragility Analysis for Typical Classes of Multispan Bridges , 2009 .

[9]  Toshiyuki Yamamoto,et al.  Competing-Risks-Duration Model of Household Vehicle Transactions with Indicators of Changes in Explanatory Variables , 1999 .

[10]  Carmine Zoccali,et al.  When do we need competing risks methods for survival analysis in nephrology? , 2013, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[11]  Anil K. Agrawal,et al.  Deterioration Rates of Typical Bridge Elements in New York , 2008 .

[12]  Jason J. Jung,et al.  Internet of agents framework for connected vehicles: A case study on distributed traffic control system , 2017, J. Parallel Distributed Comput..

[13]  Adrian Barbu,et al.  Prediction of the crack condition of highway pavements using machine learning models , 2019 .

[14]  Praprut Songchitruksa,et al.  Interlinking Vissim and ns-3 for Connected Vehicle Simulation: Case Study of Intelligent Dilemma Zone Avoidance , 2017 .

[15]  J. Sobanjo,et al.  Multilevel competing risks model for the performance assessment of highway pavement , 2018, International Journal of Pavement Engineering.

[16]  Ruimin Li,et al.  Competing risks analysis on traffic accident duration time , 2015 .

[17]  John O Sobanjo,et al.  Modeling the Risk of Advanced Deterioration in Bridge Management Systems , 2013 .

[18]  Adrian Barbu,et al.  Pavement Crack Rating Using Machine Learning Frameworks: Partitioning, Bootstrap Forest, Boosted Trees, Naïve Bayes, and K -Nearest Neighbors , 2019, Journal of Transportation Engineering, Part B: Pavements.

[19]  Gary R. Consolazio,et al.  Numerically Efficient Dynamic Analysis of Barge Collisions with Bridge Piers , 2005 .

[20]  Yaping Wang,et al.  Modeling the Dependent Competing Risks With Multiple Degradation Processes and Random Shock Using Time-Varying Copulas , 2012, IEEE Transactions on Reliability.

[21]  J.-K. Chan,et al.  Modeling repairable systems with failure rates that depend on age and maintenance , 1993 .

[22]  G. Dimitrakopoulos,et al.  Intelligent Transportation Systems , 2010, IEEE Vehicular Technology Magazine.

[23]  Yilin Zhao,et al.  Mobile phone location determination and its impact on intelligent transportation systems , 2000, IEEE Trans. Intell. Transp. Syst..

[24]  Fei-Yue Wang,et al.  Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.

[25]  Steven E. Shladover,et al.  Connected and automated vehicle systems: Introduction and overview , 2018, J. Intell. Transp. Syst..

[26]  Elisabeth Uhlemann,et al.  Introducing Connected Vehicles [Connected Vehicles] , 2015, IEEE Vehicular Technology Magazine.

[27]  Venkat Venkatasubramanian,et al.  Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities , 2005, Comput. Chem. Eng..

[28]  J.L. Martins de Carvalho,et al.  Towards the development of intelligent transportation systems , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[29]  Sung-Hee Kim,et al.  Investigating the performance of as-built and overlaid pavements: a competing risks approach , 2015 .

[30]  M Gail,et al.  A review and critique of some models used in competing risk analysis. , 1975, Biometrics.

[31]  V T Farewell,et al.  The analysis of failure times in the presence of competing risks. , 1978, Biometrics.

[32]  E. Gockenbach,et al.  Component Reliability Modeling of Distribution Systems Based on the Evaluation of Failure Statistics , 2007, IEEE Transactions on Dielectrics and Electrical Insulation.