Over recent years, we have seen a significant rise in electric mobility to overcome the anthropogenic emissions by conventional gasoline vehicles. However, the prerequisite of smoothening of peaks and imbalances through bidirectional charging is concatenated with the undesired impacts on reliability and security of power system operation when there are probable intrusions in the eXtreme Fast Charging (XFC) station, hence destabilizing the charging networks. So this paper applies STRIDE based threat modeling to analyze and identify multiple potential threats endured by the cyber-physical system (CPS) of XFC station by using weighted attack defense tree. Potential mitigation strategies are then suggested for the identified severe threats. In addition, this paper also develops a stochastic probabilistic tool, the Hidden Markov Model (HMM) for modeling the security attacks for a given range of identified attack vectors and hence employing an appropriate defense strategy against the malicious hacker. Also, a weighted attack defense tree has been developed to generate various attack scenarios. In the end, the results of the proposed work are substantiated and validated if it is able to considerably improve overall charging efficiency and cyber-physical security of the charging station network.

Smart mobility is an imperative facet of smart cities, and the transition of conventional automotive systems to connected and automated vehicles (CAVs) is envisioned as one of the emerging technologies on urban roads. The existing AV mobility environment is perhaps centered around road users and infrastructure, but it does not support future CAV implementation due to its proximity with distinct modules nested in the cyber layer. Therefore, this paper conceptualizes a more sustainable CAV-enabled mobility framework that accommodates all cyber-based entities. Further, the key to a thriving autonomous system relies on accurate decision making in real-time, but cyberattacks on these entities can disrupt decision-making capabilities, leading to complicated CAV accidents. Due to the incompetence of the existing accident investigation frameworks to comprehend and handle these accidents, this paper proposes a 5Ws and 1H-based investigation approach to deal with cyberattack-related accidents. Further, this paper develops STRIDE threat modeling to analyze potential threats endured by the cyber-physical system (CPS) of a CAV ecosystem. Also, a stochastic anomaly detection system is proposed to identify the anomalies, abnormal activities, and unusual operations of the automated driving system (ADS) functions during a crash analysis.

This study identifies the critical factors that affect motorcycle crash severity based on Korean motorcycle crash data in 2009. Motorcyclists, the environment, roadways, other vehicles involved in the crashes, and traffic flow characteristics were used as variables for identifying critical factors. Multivariable statistical methods were used to analyze the data, including categorical principal components analysis (CatPCA) and nonlinear canonical correlation analysis (NLCCA). The results indicate that the following factors are the most critical in increasing motorcycle crash severity: age (motorcyclists in their teens and over fifty years old), motorcycle speed over 30 km/h, speed over 50 km/h for other vehicles involved in the crash, crashes with heavy vehicles such as buses and trucks, crashes on roadways less than six meters wide, crashes at curved sections, crashes at basic roadway segments without any speed control facilities, and head-on crashes. These findings are expected to serve as a valuable reference for formulating remedial policy measures to decrease the severity of motorcycle crashes on roadways in the Seoul metropolitan area of South Korea.

Power substations are the crucial nodes of an interconnected grid, serving as the points where power is transferred from the transmission/distribution grids to the loads. However, interconnected cyberphysical systems and communication-based operations at substations lead to many cybersecurity vulnerabilities. Therefore, more sophisticated cybersecurity vulnerability analyses and threat modeling are required during productization phases, and system hardening is mandatory for the commercialization of products. This paper shows the design and methods to test the cybersecurity of multicast messages for digital substations. The proposed vulnerability assessment methods are based on the semantics of IEC61850 Generic Object Oriented Substation Event (GOOSE) and Sampled Value (SV), and cybersecurity features from IEC62351-6. Different case scenarios for cyberattacks are considered to check the vulnerabilities of the device under test (DUT) based on the IEC62351-6 standard. In order to discover security vulnerabilities in a digital substation, the proposed cybersecurity tester will generate malicious packets that compromise the regular functionality. The results show that the proposed cybersecurity testing module is able to detect potential vulnerabilities in multicast messages and the authentication methods (e.g., message authentication code) of multicast communications. Both commercial and simulated devices are used for the case studies.

As the number of autonomous vehicles increases, the number of accidents also increases every year. These incidents include general car/traffic accidents and may introduce new potential issues such as cybersecurity and sensor errors of autonomous vehicles. The existing traffic accident investigation method has limitations in identifying the cause of the autonomous vehicle accident. Some states in the US (e.g., California and Texas) introduced limited items of Lv. 2 autonomous vehicle accidents. For instance, “vehicle level” and “autonomous mode/conventional mode” are being investigated to identify the cause of autonomous vehicle accidents. Therefore, it is crucial to propose accident investigation items and procedures in preparation for various autonomous vehicles that may occur in the future. In order to address these issues, this study collected reports used in existing traffic accident investigations, autonomous driving‐related reports and literature, and accident videos involving autonomous driving to build investigation items. First, we reviewed the items required for investigation in the event of a conventional vehicle accident and added additional investigation items deemed necessary to be reviewed in addition to the existing reports. Second, based on the conventional vehicle accident investigation items, this study derived the autonomous driving traffic accident investigation items. Finally, an accident involving autonomous vehicle(s) investigation process was established that can be used by the police and various investigation jurisdictions. The results of this paper can improve the understanding of the cause of future traffic accidents involving autonomous vehicles.

Identification of recurrent bottlenecks is an effective way to hone an appropriate investment in current facilities to relieve congestion. Furthermore, it would enable the ranking or prioritisation of bottlenecks since bottleneck removal and its associated impact alleviation are hampered by limited sources. It is imperative that transportation jurisdiction understand and identify the basis for ranking bottlenecks by exploring: how often they are active; how long it takes the congestion to disappear; and how many miles of road are affected. Previous bottleneck identification schemes have focused on identifying congestion with little attention to distinguishing the recurrent level at the same ‘bottleneck’ location. In contrast to traditional schemes, a data‐driven approach for identifying recurrent bottlenecks is introduced, using probe vehicle speed reports. The historical spatiotemporal characteristics of bottlenecks are investigated through a comprehensive analysis of 2253 miles of all state‐wide interstates in North Carolina. Using the characteristics determined the recurrent bottleneck locations with a historical time span of bottleneck activation are revealed and tested. The findings of the proposed identification schemes generate critical information in order to quantify and diagnose a bottleneck and its associated impact area.