Developing a mobility system testing environment (a “Living Lab”) of large-enough urban area with multiresolution modeling capabilities, and realistic real-world data inputs. A modeled network of intersections for signal-control testing (from the city of Irvine) and the wider area around it known as Autonomicity will be developed with simulation capabilities for security breaches on a variety of sensor, controller and communication components of the network. The activity system models used for realistic system conditions can be from an even larger network of Orange County that can be modeled at a mesoscopic level using models such as DYNAMART and
POLARIS. This platform will address the network system-level impacts of security breaches from vulnerabilities in the sensor and control systems for vehicular traffic, both in current conditions and the future scenarios of cooperative driving automation (CDA) that are expected in on-demand passenger delivery systems becoming popular now (ridehailing, shared-ride, mobility-as-a-service), and package delivery systems. This research will build on the Transportation Mobility Living Laboratory (TML2) at UCI. TML2 includes a system of infrastructure-based LiDAR that can track all road users across a network of 25 intersections in the City of Irvine to
support safety, efficiency, and energy advances through CDA. Incidents as well as security breaches can result due to inaccuracies of sensor technology. This research will focus on identifying and mitigating the vulnerabilities of the TML2 system to maintain system performance under accidental and nefarious disruptions. System effects of existing traffic sensor and control system breaches: We will develop failure scenarios resulting from potential vulnerabilities and study their system effects (delays, accident probabilities) using simulated microscopic vehicular trajectories in the presence of V2X and CDA applications. The research focus will be on
selected case studies such as on
1. automated lane centering security,
2. safety policy enforcement for Autonomous Driving, and
3. sensor redundancy design for recovery of the transportation system, and
4. IOTbased on-demand passenger delivery system vulnerabilities.
The simulated system developed in the above tasks will be used for predicting transportation domain-specific system effects and designing sufficient redundancy
while facing security breaches and attacks. Dynamic multiple time-period modeling will be used to quantify the lost efficiency in selected Autonomicity network contexts, and this will be used in the design problem:
Multi-objective Performance Evaluation: The platform will incorporate concepts of evaluating the modeled performance outputs using a variety of performance objectives such as
1. degradation and system resilience on temporal measures of performance in the immediate, medium-term and long-term perspectives,
2. vulnerability exposure of different user-classes in the broader activity system and the associated access to transportation srevices,
3. system degradation and resilience on various energy, and environmental impacts with measures such as fuel consumptions, emissions, and their surrogate measures such as VMT (vehicle miles traveled) and VHT (Vehicle Hours Traveled). Disaggregating the components of output measures (e.g., VMT) for various user and vehicle classes can provide richer insights on the impact of vulnerability and security breaches on each class of users.
Keywords and Index Terms:
Simulation platform, transport network performance modeling, agent-based models, cyber-security and system
vulnerability, disruption impacts

The ongoing growth and economic benefits of America’s largest container ports are threatened by negative externalities associated with port operations, particularly increasing congestion and harmful emissions caused by drayage truck and rail modes serving the ports and traveling to inland transloading, rail yard, warehouse and distribution center facilities. For example, the San Pedro Bay Ports (SPBP) of Los Angeles and Long Beach in Southern California, the largest container port complex in the US and one of the largest in the world, is critical to the nation’s future intermodal logistics system and vital to our economic growth and standard of living. One proposed solution to these issues is to transition the heavy duty (HD) drayage fleet (as well as long haul fleets) to autonomous vehicles (AVs), in order to increase supply chain capacity, throughput, safety, resilience and sustainability.

In this research, we plan to extend our Freight Mobility Living Laboratory (FML2) research of Year 1, which explored the feasibility of LiDAR technology for traffic monitoring in an infrastructure-based, side- fire LiDAR configuration. LiDAR-based microscopic longitudinal and lateral trajectories were obtained for HD vehicles at 0.1 second resolution, enabling site-based detection of anomalies in vehicle behavior. In Year 2, we will develop combined LiDAR and automated license plate reader (ALPR)-based models that will re-identify a potentially cyber-compromised HD AV (based on its anomalous trajectories) over long distances across a complex metropolitan highway network. The LiDAR-based approach to truck tracking is resilient and possesses inherent advantages over other competing technologies: Because LiDAR is an active sensor technology which measures light pulses emitted from the unit itself, it is unaffected by lighting conditions and offers an advantage over traditional cameras where glare, shadows and low-light conditions are known to adversely affect performance. And while automated license plate reader (ALPR) technology has demonstrated high accuracies in plate reads, it cannot be relied upon solely for vehicle reidentification as it performs best when a truck possesses a plate that is present and not obscured, which may not be the case for a cyber-compromised operator that is actively avoiding surveillance. Two existing FML2 study sites spanning over 40-miles across the Los Angeles freeway network – on Interstate I-710 near the SPBP, and on the SR-60 freeway en route to the Inland Empire logistics center, will be used in this study. Data will be collected and processed at both locations to develop a LiDAR point-cloud and ALPR-based long distance tracking and re-identification model to investigate proof-of-concept for corridor and network application of these technologies.