Virtual Operation Support Team as a Tool for Threat Mapping and Improving Scenario Modeling in the Field of Road Critical Infrastructure †
Abstract
1. Introduction
2. Background and Materials
3. Framework for VOST Implementation
3.1. The Role of VOSTs in Crisis Management
3.2. Indicators and Metrics
- The assessed critical infrastructure element is selected and described in detail.
- The element is classified into the appropriate category (group) of infrastructure elements.
- The environment and domain for which the indicators will be defined are determined.
- Specific indicators of the resilience disruption of the given element are identified.
3.3. Threats Affecting Bridge Structures
3.4. Vost Communication Protocols with the Crisis Staff
3.5. Network Metrics
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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| Phase | Key Factors | Roles of the Virtual Operations Support Team |
|---|---|---|
| Prevention | Anticipation, detection, risk management, security measures, innovation processes, and educational and development processes. | Timely identification of trends, verification of signals, and inputs for information campaigns and plan adjustments. |
| Preparedness | Crisis preparedness, robustness, redundancy, material resources, financial resources, human resources, and recovery processes. | Scenario development, digital risk maps, testing of communication channels, and training of both the public and crisis staffs. |
| Response | Responsiveness, detection, security measures, human resources, material resources, and redundancy. | Rapid collection and sorting of information, triage, support for staff decision-making, and coordinated communication with the public, including countering disinformation. |
| Recovery | Renewability, recovery processes, adaptability, financial resources, and innovation processes. | Process analysis, evaluation of experiences (lessons learned), mapping of damages and needs, and proposals for systemic changes. |
| Tool | Advantages | Disadvantages | Reference |
|---|---|---|---|
| Traditional methods (telephone, radio) | Reliable, clear, and direct communication | Limited reach, slow response time | [18,19,39] |
| Social media (Twitter, Facebook) | Fast information, wide reach, real-time updates | Risk of disinformation, need for continuous monitoring | [8,18,19,39,40] |
| VOSTs (virtual operations support teams) | Effective coordination, real-time data | Dependence on technologies, required sufficient digital literacy | [22,41,42,43] |
| Title and Author | Observed Variables | Data Interpretation | Characteristics/Limitations |
|---|---|---|---|
| Hazus–FEMA’s methodology for estimating potential losses from disasters | Flood depth/hazard intensity; object exposure; building typology | Estimation of physical and economic impacts, risk mapping, and prioritization of interventions | Standardized GIS framework for earthquake/flood/cyclone scenarios; output quality strongly depends on the quality of input data |
| Travel-time reliability & PTI [46] | Speed; traffic flow; frequency of congestion; Planning Time Index (PTI) | Identification of bottlenecks and anomalies in time and space on the transport network | Requires detailed operational data; indicates consequences of disruption, not the cause |
| Resilience-based identification of critical roads [45] | Traffic flow; speed; density; network topology OpenStreetMap (OSM)/GIS | Simulation of outages and localization of segments with the highest impact on the network | The network is often simplified due to computational demands; calibration to local operating conditions is necessary |
| GIS identification of critical links [47] | Road density; accessibility; buffer zones; points of interest (POI) | Identification of critical links in the road network based on spatial structure | Static view without real-time data; suitable for preliminary identification of weak points |
| Runoff-sensitive road sections [48] | Total precipitation; surface runoff; digital terrain model; drainage capacity | Localization of segments sensitive to water accumulation during flash rainfall | Requires high-quality elevation data and hydrological calibration; suitable for flash flood scenarios |
| Bridge accident factors [49] | Accident data; bridge parameters; traffic intensity | Identification of bridges with above-average accident rates and risk characteristics | Context-specific method (developed for Norway); requires robust and consistent accident records |
| GIS safety management–roadway geometry [50] | Road curvature; sequence of curves; GPS/ real-time kinematic (RTK) data; speed | Identification of segments with higher risk based on geometric parameters | Field data collection (e.g., using cameras and RTK) is demanding; however, it significantly improves the accuracy of risk assessment for segments |
| Multi-criteria pavement condition assessment [51] | C1 flatness; C2 roughness; C3 bearing capacity; C4 surface degradation | Prioritization of maintenance and indication of the most degraded road sections | Works as part of multi-criteria analysis ELimination Et Choix Traduisant la REalité (ELECTRE), does not provide a complete picture on its own; requires high-quality inventory data |
| UAV imagery + PCI for pavement management [52] | Pavement Condition Index (PCI); heavy vehicle loading; CBR | Localization of pavement surface damage and proposal of repair priorities | Requires the deployment of drones and favorable conditions for imaging; high data resolution significantly increases the accuracy of defect identification |
| City-scale resilience & event detection from GPS data [53] | Travel times; speeds; deviations from expected values | Detection of extraordinary events and evaluation of their impact on mobility at the city-wide scale | Requires a large volume of GPS data; risk of false alarms due to noise deviations |
| Corridor mobility analysis from GPS [54] | Segmentation of the transport corridor; temporal profiles of travel time | Identification of critical periods of mobility decline and detection of sections with significant delays | Dependent on the availability and quality of GPS data; method suitable for operational traffic monitoring |
| Triangulation of flood risk methods [55] | Hydrological data; topographic data; object exposure; infrastructure | Combined flood risk indicators and identification of sensitive network sections | Integration-intensive method; its advantage is the synthesis of multiple data sources for a more robust identification of endangered sites |
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Ryska, O.; Gamonova, P. Virtual Operation Support Team as a Tool for Threat Mapping and Improving Scenario Modeling in the Field of Road Critical Infrastructure. Eng. Proc. 2025, 116, 33. https://doi.org/10.3390/engproc2025116033
Ryska O, Gamonova P. Virtual Operation Support Team as a Tool for Threat Mapping and Improving Scenario Modeling in the Field of Road Critical Infrastructure. Engineering Proceedings. 2025; 116(1):33. https://doi.org/10.3390/engproc2025116033
Chicago/Turabian StyleRyska, Ondrej, and Patricie Gamonova. 2025. "Virtual Operation Support Team as a Tool for Threat Mapping and Improving Scenario Modeling in the Field of Road Critical Infrastructure" Engineering Proceedings 116, no. 1: 33. https://doi.org/10.3390/engproc2025116033
APA StyleRyska, O., & Gamonova, P. (2025). Virtual Operation Support Team as a Tool for Threat Mapping and Improving Scenario Modeling in the Field of Road Critical Infrastructure. Engineering Proceedings, 116(1), 33. https://doi.org/10.3390/engproc2025116033

