Resilience in Intelligent Transportation Systems (ITS)

Alexander A. Ganin, Avi C. Mersky, Andrew S. Jin, Maksim Kitsak, Jeffrey M. Keisler, Igor Linkov

Research output: Contribution to journalArticleScientificpeer-review

112 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)318-329
Number of pages12
JournalTransportation Research. Part C: Emerging Technologies
Publication statusPublished - 2019
Externally publishedYes


  • Resilience
  • Smartness
  • Network science
  • Modeling
  • Cyber-Physical Systems


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