In the second year of this multi-phase research effort, our team continued to refine and operationalise freight disruption measures originally developed in Year 1. These measures are grounded in large-scale freight telematics data supplied by Robinsight, enabling an unprecedented view into real-world freight movements. The research was carried out in partnership with leading U.S. transportation research institutions, ensuring both academic rigour and practical applicability.

Scope and Objectives
The core goal for Year 2 was to strengthen the measures’ reliability and broaden their use in practical planning contexts. This involved:

• Metric Refinement: Enhancing algorithms to better capture the scale and duration of freight disruptions, from localised closures to regional supply chain shocks.
• Data Integration: Aligning telematics trip data with infrastructure inventories, weather and incident databases, and commodity flow datasets to create richer, multi-dimensional disruption profiles.
• Geographic Expansion: Testing the measures in multiple regions, including the Tennessee freight network and Pacific Northwest corridors, to ensure robustness across different network types and operating conditions.

Methodology
Our approach combined large-scale spatial analytics with disruption scenario modelling:

1. Telematics Data Ingestion and Processing: Millions of GPS trip records from heavy freight vehicles were cleaned, standardised to a common road network, and aggregated to meaningful spatial units such as corridors or network segments.
2. Event Detection and Classification: Using a combination of traffic speed anomalies, dwell time increases, and route diversion patterns, we identified disruptions ranging from minor slowdowns to major network failures.
3. Resilience Measure Calculation: Key metrics — such as recovery time, detour efficiency, and network redundancy — were computed for each disruption event.
4. Cross-Validation: Results were validated against known historical disruptions and compared to baseline “normal” operating patterns.
5. Scenario Modelling: The refined measures were integrated into hypothetical disruption scenarios to evaluate potential vulnerabilities and inform investment priorities.

Outputs and Deliverables

• Validated Freight Disruption Measures: A refined set of indicators that can be applied consistently across regions and data sources.
• Regional Case Studies: Detailed application of the measures in Tennessee and the Pacific Northwest, including maps, charts, and statistical summaries.
• Public Resources: An open-access GitHub repository containing anonymised test datasets and example code for researchers and practitioners.
• Academic and Industry Engagement: Presentations at major transportation conferences, peer-reviewed journal submissions, and integration of the methodology into graduate-level transportation engineering courses.

Impact
By providing empirically validated, data-driven disruption measures, this work allows agencies to:

• Quantify the resilience of freight corridors with real-world operating data.
• Identify critical network links where investments in redundancy or hardening will yield the greatest benefit.
• Integrate resilience considerations into freight planning, asset management, and emergency preparedness.

This project marks a significant step toward mainstreaming freight telematics in resilience planning. The refined measures are already influencing discussions within U.S. state DOTs and are positioned to become a standard reference for freight disruption analysis.

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