Magnetometer Traffic Event Log — Engineering Interpretation Report

ITAR Screen Result = CLEAR

Rationale: The uploaded file contains roadway traffic monitoring event summaries (vehicle/pedestrian/bicycle/animal detections and magnetic metrics). No defense articles, weapons performance data, targeting, guidance, military platform signatures, or controlled technical details were observed.

Item classification accuracy can exceed 95% under optimal conditions; actual confidence depends on sampling rate, sensor sensitivity, placement, environmental magnetic noise, and data completeness.

Executive Summary

Total duration analyzed: 59 min 30 s (from 2026-03-05T13:00:00+00:00 to 2026-03-05T13:59:30+00:00).

Number of detected events: 120.

Overall data quality assessment: MODERATE. This dataset is an event-summary log (not raw waveforms), so confidence is based on physics-based plausibility and separability patterns (magnitude/duration/speed) rather than waveform morphology. Several detections have implausible speeds for pedestrians/bicycles/animals, reducing confidence for those specific events.

Data Characteristics

File format detected: JSON array of event records.

Data type detection: Event-summary / detection log (pre-extracted events; no continuous waveform samples).

Magnetic axes: 3-axis fields reported (mag_x_uT, mag_y_uT, mag_z_uT) plus a provided scalar (signal_magnitude_uT).

Sampling rate: Not provided and cannot be inferred reliably from event summaries alone.

Noise level assessment: Not directly measurable without raw waveform. Indirectly, low-magnitude classes (pedestrian/bicycle/animal/motorcycle) show signal magnitudes mostly under ~20 µT, suggesting a detection threshold in that range for small objects.

Event Class Summary

Heavy-vehicle share (Bus + Dump Truck + Semi Truck): 35.8% of all events. Lane 2 is the heaviest lane in this sample (heavy share ≈ 50.8%).

Detected Events

Because there are 120 events, this section lists grouped results and the subset of low-plausibility events. Full per-event details can be produced if required.

A) High-confidence heavy vehicles (separability strong)

Heavy-vehicle detections typically show large signal magnitudes (often >90 µT here). These are generally separable from light vehicles and non-vehicles in this dataset.

B) Plausibility flags (speed vs class mismatch)

These events have internal inconsistencies for the labeled object class (e.g., pedestrian speeds >15 mph). They are the primary drivers of reduced confidence.

Limitations & Assumptions

• No raw waveform time series was provided, so I cannot evaluate SNR, baseline drift, clipping/saturation, axle/peak structure, or event overlap. Confidence is therefore “plausibility/separability” only.

• Units appear to be microtesla (µT), but the definition of signal_magnitude_uT (peak deviation vs baseline, RMS, etc.) is not documented in-file.

• “speed_mph” appears to be a derived attribute; several pedestrian/bicycle/animal speeds are physically implausible, suggesting either mislabeling, unit mismatch, or association errors across lanes.

• Without site metadata (sensor placement depth, lane geometry, mounting orientation, calibration method), class separability thresholds are site-specific and may not generalize.

Recommendations

Traffic-magnetometer system improvements (data quality):

• Capture raw waveforms for periodic calibration runs (even 10–30 seconds per lane) to quantify noise floor, baseline drift, and to validate event segmentation.

• Add/verify ground truth for a small sample (video audit, axle counter, or manual spot checks) to calibrate confidence and correct class-speed mismatches.

• Validate the speed estimation method against a known reference (dual-loop spacing, radar, or LIDAR) and confirm unit handling.

Roadway structure / construction guidance (traffic-management, land surface):

Based on this sample, heavy vehicles are a substantial fraction (≈36% overall; lane 2 ≈51%). If this proportion is representative of typical daily traffic, pavement design should assume a relatively high ESAL loading (exact ESALs require axle-load distributions and AADT). A conservative flexible pavement concept section for heavy-lane service is:

• Surface: 2.0–2.5 in dense-graded asphalt (wearing course).

• Binder/intermediate: 4–6 in asphalt (may be split into two lifts).

• Base: 8–12 in crushed aggregate base (or asphalt-treated base for higher rut resistance).

• Subbase (as needed): 6–12 in stabilized granular subbase depending on frost depth and subgrade moisture susceptibility.

• Subgrade: proof-roll + compaction; improve with lime/cement stabilization or geogrid if CBR is low or moisture is high.

Key construction details to support magnetometer installations and traffic loading:

• Ensure longitudinal and transverse drainage (edge drains where needed) to protect the base/subgrade; rutting risk increases sharply with trapped moisture.

• For in-pavement magnetometers: place conduit/pull-boxes outside wheel paths when possible; if sensors are in wheel paths, specify sawcut/backfill materials compatible with surrounding HMA to avoid reflective cracking.

• Verify compaction targets (HMA density, base density) and smoothness; poor compaction near sensor cuts is a frequent failure point.

Note: Final layer thicknesses must be designed per local DOT/AASHTO procedures using AADT, growth rate, reliability, climate, subgrade resilient modulus/CBR, and axle-load spectra.