Resilience in Intelligent Transportation Systems (ITS)

Abstract Many cities are adopting increasingly advanced Intelligent Transportation Systems (ITS). These systems combine connectivity, coordination, adaptivity, and automated response for transportation policy optimization, thus increasing “smartness” and efficiency. However, the control and sensing systems of implemented ITS can open new vulnerabilities, especially to cyber-attacks. Currently vulnerability is managed within the framework of traditional risk assessment that assesses potential failures of the system in response to specified threats. Emerging technologies by their nature have threats that are not fully known, therefore, resilience, defined as the system's ability to recover and adapt to both known and unknown threats, is an emerging area that holds promise for assessing threats to ITS. To illustrate the applicability of resilience to ITS, we conducted a study of network efficiency and resilience in response to random and targeted disruptions of ITS systems in 10 urban areas. Disruptions were generated to affect either intersections or roadways controlled by ITS under different threat scenarios. Modeled attacks, under worst case scenarios, disrupted 20% of intersections causing on average 14.6% more additional delays than the same severity attacks on roadways. Additionally, locking traffic signal states was found to cause more disruption than fully disabling signals. Thus, as cities adopt ITS and other smart systems resulting in potentially unknown vulnerabilities, it is important to consider resilience of transportation infrastructure affected by potential cyber-attacks.

[1]  Arun Prakash,et al.  Machine-to-Machine (M2M) communications: A survey , 2016, J. Netw. Comput. Appl..

[2]  Zachary A. Collier,et al.  Security Metrics in Industrial Control Systems , 2015, ArXiv.

[3]  Mohammad Shahidehpour,et al.  Assessing and mitigating cybersecurity risks of traffic light systems in smart cities , 2016, IET Cyper-Phys. Syst.: Theory & Appl..

[4]  Hani S. Mahmassani,et al.  50th Anniversary Invited Article - Autonomous Vehicles and Connected Vehicle Systems: Flow and Operations Considerations , 2016, Transp. Sci..

[5]  Rajkumar Buyya,et al.  A survey on vehicular cloud computing , 2014, J. Netw. Comput. Appl..

[6]  U. Berardi,et al.  Smart Cities: Definitions, Dimensions, Performance, and Initiatives , 2015 .

[7]  Eric Horvitz,et al.  Predicting Travel Time Reliability using Mobile Phone GPS Data , 2017 .

[8]  Herbert S. Wilf,et al.  Generating functionology , 1990 .

[9]  Vicente Milanés Montero,et al.  Genetic optimization of a vehicle fuzzy decision system for intersections , 2012, Expert Syst. Appl..

[10]  J F Hughes,et al.  Measuring the resilience of transport infrastructure , 2014 .

[11]  Patrick Weber,et al.  OpenStreetMap: User-Generated Street Maps , 2008, IEEE Pervasive Computing.

[12]  Silvia Giordano,et al.  Modelling the smart city performance , 2012 .

[13]  Tao Zhang,et al.  Defending Connected Vehicles Against Malware: Challenges and a Solution Framework , 2014, IEEE Internet of Things Journal.

[14]  Arputharaj Kannan,et al.  Dual Authentication and Key Management Techniques for Secure Data Transmission in Vehicular Ad Hoc Networks , 2016, IEEE Transactions on Intelligent Transportation Systems.

[15]  D. Schrank,et al.  2015 Urban Mobility Scorecard , 2015 .

[16]  Akbar Ghaffar Pour Rahbar,et al.  Detection of malicious vehicles (DMV) through monitoring in Vehicular Ad-Hoc Networks , 2011, Multimedia Tools and Applications.

[17]  Mayada Omer,et al.  Assessing resilience in a regional road-based transportation network , 2013 .

[18]  Igor Linkov,et al.  Resilience and efficiency in transportation networks , 2017, Science Advances.

[19]  Igor Linkov,et al.  Operational resilience: concepts, design and analysis , 2015, Scientific Reports.

[20]  Murat Arcak,et al.  A Compartmental Model for Traffic Networks and Its Dynamical Behavior , 2014, IEEE Transactions on Automatic Control.

[21]  Lina Kattan,et al.  Variable speed limit: A microscopic analysis in a connected vehicle environment , 2015 .

[22]  Kevin Heaslip,et al.  CPS: an efficiency-motivated attack against autonomous vehicular transportation , 2013, ACSAC.

[23]  B. Obama Presidential Policy Directive 21: Critical Infrastructure Security and Resilience , 2013 .

[24]  Vikash V. Gayah,et al.  Crash Risk Assessment Using Intelligent Transportation Systems Data and Real-Time Intervention Strategies to Improve Safety on Freeways , 2007, J. Intell. Transp. Syst..

[25]  Georges Voronoi Nouvelles applications des paramètres continus à la théorie des formes quadratiques. Deuxième mémoire. Recherches sur les parallélloèdres primitifs. , 1908 .

[26]  Denis Gillet,et al.  Fluent coordination of autonomous vehicles at intersections , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[27]  Ning Lu,et al.  Smart-grid security issues , 2010, IEEE Security & Privacy.

[28]  Mosa Ali Abu-Rgheff,et al.  An Efficient and Lightweight Intrusion Detection Mechanism for Service-Oriented Vehicular Networks , 2014, IEEE Internet of Things Journal.

[29]  Alexandre M. Bayen,et al.  On Cybersecurity of Freeway Control Systems: Analysis of Coordinated Ramp Metering Attacks , 2015 .

[30]  Steven E Shladover,et al.  Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow , 2012 .

[31]  Steven E. Shladover,et al.  Potential Cyberattacks on Automated Vehicles , 2015, IEEE Transactions on Intelligent Transportation Systems.

[32]  Kara M. Kockelman,et al.  Locating Electric Vehicle Charging Stations , 2013 .

[33]  Liang Liu,et al.  Estimating Origin-Destination Flows Using Mobile Phone Location Data , 2011, IEEE Pervasive Computing.

[34]  Dipak Ghosal,et al.  Security vulnerabilities of connected vehicle streams and their impact on cooperative driving , 2015, IEEE Communications Magazine.