DOI: 10.5281/zenodo.18050318
title: Multimodal Industrial Logistics Disruption and Risk Dataset

description: The released dataset represents a large-scale, high-resolution real-time industrial logistics monitoring dataset, designed to support research on disruption classification, risk assessment, and resilient supply-chain analytics.It consists of hourly records collected continuously from March 14, 2021, to September 29, 2025, capturing dynamic operational, environmental, and network-level conditions across complex logistics corridors. The temporal granularity and long observation horizon enable realistic modeling of short-term fluctuations, seasonal effects, and rare disruption events.

Each record corresponds to a shipment–route–facility state observed at a specific hour and integrates heterogeneous data streams commonly encountered in real-world logistics systems, including transportation management systems (TMS), warehouse management systems (WMS), IoT sensors, AIS telemetry, and external contextual sources. The dataset deliberately exhibits natural imbalance, noise, and non-stationarity, reflecting real operational environments rather than curated benchmark conditions.

All identifiers are anonymized and indexed to preserve privacy while maintaining realistic correlations between shipments, routes, facilities, and zones. The dataset is intended for real-time prediction, temporal modeling, multi-class classification, robustness analysis, and explainable AI studies in industrial logistics.

 

Feature Composition

The dataset contains over 80 attributes, organized into the following categories:

1. Identification and Spatial–Temporal Context





Timestamp (hourly resolution)




Shipment ID, Route ID, Facility ID, Zone ID




Latitude, Longitude



These features define the spatio-temporal state of logistics operations.

2. Shipment and Order Characteristics





Incoterm, Carrier Mode, Product Category




Hazardous Material Flag




Weight (kg), Volume (m³), Declared Value (USD)




Priority Flag, Temperature Control Indicator




Planned Transit Time, Time Buffer



They capture shipment criticality, handling requirements, and economic exposure.

3. Route Execution and Reliability Metrics





ETA Gap (hours), Dwell Time (minutes)




Stop Count (last 24 hours)




Lane Reliability Score




Alternate Path Count




Route Vulnerability Score



These variables describe execution efficiency and routing resilience.

4. Facility and Port Conditions





Yard Utilization (%), Gate Turn Time (minutes)




Berth Occupancy (%)




Customs Backlog Index




Labor Action Flag



They reflect congestion, processing delays, and workforce disruptions.

5. IoT and Telematics Signals





Vehicle Speed (km/h), Heading




Reefer Temperature Deviation




Fuel Level (%)




Engine Fault Code Count



These streams model real-time asset health and mobility behavior.

6. Environmental and Geopolitical Indicators





Temperature (°C), Precipitation (mm), Wind Speed (km/h)




Storm Alert Level, Flood Risk Index




Wildfire Risk, Earthquake Index




Conflict Intensity Index




Piracy Corridor Flag



They enable climate- and risk-aware logistics analysis.

7. Market and Capacity Signals





Spot Freight Rate (USD/TEU)




Vehicle Capacity Utilization (%)




Driver Availability Index




Demand Index




Bullwhip Index



These attributes capture economic pressure and capacity imbalance.

8. Sustainability and Efficiency Metrics





Planned and Actual CO₂e Emissions (kg)




CO₂e Gap (%)




Idle Time (minutes)




Detour Distance (%)




Consolidation Ratio




Empty Run Ratio



They support sustainability impact assessment and green logistics optimization.

9. Network and Graph Topology Features





Node Degree




Betweenness Centrality




Community ID



These features encode structural dependencies within logistics networks.

10. Calendar and Data Quality Indicators





Day of Week, Hour of Day, Season




Holiday Flag




Data Source Type




Data Completeness (%)




Latency (seconds)



They characterize temporal patterns and data reliability.



Target Labels

The dataset provides multiple unbalanced target variables for supervised learning:





Disruption Type (10 classes):Normal, Weather, Port Congestion, Customs Delay, Supplier Shutdown, Labor Strike, Cyber Incident, Route Blockage, Fuel Shortage, Demand Shock




Severity Level (5 levels): Ordinal scale from negligible to extreme




Zone Risk Level (4 levels): Low to critical regional risk




Sustainability Impact Class (4 levels): Environmental impact severity