EMS Disaster Logistics Dataset

This dataset contains a large-scale, spatio-temporal record of emergency medical service (EMS) operations under disaster and high-stress conditions, designed to support research in predictive logistics, disaster healthcare analytics, intelligent transportation systems, and emergency response optimization. The dataset captures the complex interaction between emergency demand, disaster severity, transportation accessibility, healthcare resource availability, and operational delays, reflecting real-world EMS decision environments rather than idealized or balanced conditions. The data are structured as a multivariate time series with a 5-minute temporal resolution, enabling fine-grained analysis of rapid system dynamics that occur during disaster escalation and recovery phases. Temporal and Spatial Coverage Time span: January 1, 2023 – October 31, 2025 [Half Year sample is uploaded] Temporal resolution: 5 minutes Spatial granularity: Regional EMS service zones (24 regions) Geographical attributes: Latitude and longitude provided per region to enable spatial modeling and visualization All records are anonymized and aggregated at the regional level to preserve operational confidentiality while maintaining analytical fidelity. Target Variable expected_response_time_min (minutes):Continuous response time from emergency call initiation to EMS arrival. This variable serves as the primary dependent variable for regression-based forecasting of emergency response performance, particularly under disaster-induced stress. Feature Categories and Descriptions 1. Spatio-Temporal Context These features capture routine temporal patterns and regional context: region_id: Unique identifier for each EMS response region timestamp: ISO-formatted date and time (5-minute alignment) latitude, longitude: Fixed spatial coordinates per region hour_of_day: Hour index (0–23) day_of_week: Day index (0–6) is_weekend: Binary indicator time_since_disaster_onset_min: Minutes elapsed since disaster onset in the region 2. Disaster and Infrastructure Conditions These variables describe disaster intensity and infrastructure degradation: disaster_type: Dominant disaster category affecting the region disaster_severity_score: Continuous severity index (0–1) affected_population_est: Estimated impacted population road_accessibility_ratio: Fraction of usable road infrastructure power_availability_ratio: Power grid availability ratio communication_availability_ratio: Communication network availability 3. Emergency Healthcare Demand These features reflect EMS workload and case severity: total_emergency_calls: Number of emergency calls critical_case_ratio: Proportion of life-threatening cases trauma_case_count: Trauma-related emergencies medical_case_count: Non-trauma medical emergencies pediatric_case_ratio: Pediatric case proportion elderly_case_ratio: Elderly case proportion 4. Logistics and Mobility State These attributes capture transportation and mobility constraints: avg_travel_time_min: Average ambulance travel time traffic_congestion_index: Normalized congestion level road_closure_count: Number of road closures fuel_supply_ratio: Availability of fuel resources vehicle_operational_ratio: Fraction of functional EMS vehicles 5. Medical Resource Availability These variables describe healthcare capacity and constraints: available_ambulances: Ambulances currently available busy_ambulances: Ambulances engaged in service hospital_beds_available: Available hospital beds blood_units_available: Available blood supply units medical_kits_available: Emergency medical kits staff_availability_ratio: Fraction of available medical staff 6. System Load and Stress Indicators Derived indicators capturing operational strain: demand_supply_ratio: Emergency demand relative to ambulance availability ambulance_utilization_rate: Proportion of ambulances in use hospital_capacity_utilization: Fraction of occupied hospital capacity resource_replenishment_eta_min: Estimated resource replenishment delay coordination_delay_min: Inter-agency coordination delay 7. Historical Performance Context Temporal memory features enabling predictive modeling: avg_response_time_prev_window: Previous response time (lag-1) missed_cases_prev_window: Estimated unmet demand allocation_delay_prev_window: Prior dispatch allocation delay rolling_demand_mean: Rolling mean of emergency demand rolling_demand_std: Rolling demand variability Dataset Characteristics Highly imbalanced distributions: Reflecting real EMS operations where extreme delays and disaster conditions are rare but critical Non-stationary behavior: Temporal dynamics change rapidly during disasters Heavy-tailed response times: Capturing worst-case operational stress Chronologically ordered: Suitable for time-series forecasting without information leakage The dataset intentionally preserves these properties rather than artificially smoothing or rebalancing them, ensuring realism for disaster-aware predictive modeling.