Analytics
Statistical analysis · Z-score normalization · Correlation & distribution research
Mean (μ)
643
detections / behavior
Median
519
center of distribution
Std Dev (σ)
495
spread across behaviors
IQR
441
Q3 − Q1 range
Top z-score
+2.43σ
RB-01
HIGH severity
67%
of all detections
Behavior Z-Score Analysis
Standard deviations from mean (μ = 643, σ = 495) · each bar colored by behavior class
z > 1σ0–1σbelow avg
↳ Insight:RB-01 (Helmet Non-Compliance) is the strongest outlier at 2.43σ above mean — nearly 2.4× the standard deviation, indicating disproportionate prevalence compared to other behaviors. 2 behavior(s) exceed the +1σ threshold, confirming a right-skewed distribution.
Pareto Analysis (80/20 Rule)
Count per class (bars) + cumulative % (line) · dashed = 80% threshold
↳ Insight:Only 6 of 10 behaviors account for ≥80% of all detections (84.5%). Targeted interventions on RB-01, RB-02, RB-03, RB-04, RB-05, RB-06 alone could address the vast majority of school-zone violations.
30-Day Distribution (Box Plot)
Whiskers = min/max · box = IQR · line = median · ◆ = mean
↳ Insight:RB-01 has the widest IQR (21 detections/day), indicating high daily variability — likely tied to enforcement presence. Behaviors with narrow IQRs (RB-05, RB-06) show more predictable patterns, suitable for automated alert thresholds.
30-Day Detection Trend
Daily counts per behavior class · March – April 2026
↳ Insight:Multi-behavior trends reveal synchronised spikes on school days (Mon–Fri) with dips on weekends, confirming school-zone correlation. RB-01 consistently leads all classes with a daily mean of 61.4 detections. Correlated oscillations across behaviors suggest shared causal factors such as peak commuter traffic volume.
Inter-Behavior Correlation Matrix
Pearson r from 30-day daily series · ■ positive · ■ negative · diagonal = self (r = 1.0)
↳ Insight:Strongest pair: RB-04 ↔ RB-10 (r = 0.92). High positive correlations suggest behavioral co-occurrence — riders who commit one violation are statistically more likely to commit another simultaneously, supporting a "risk profile clustering" hypothesis.
95% Confidence Intervals
Daily mean detection rate ± 95% CI (t-dist, df = 29) per behavior
↳ Insight:RB-01 shows the widest 95% CI (±5.01/day), indicating high day-to-day variability and lower statistical confidence. Narrower CIs on lower-frequency behaviors reflect more stable detection patterns, improving the reliability of trend-based predictions.
Risk Impact Matrix
Detection frequency index (x) vs. risk impact score (y) · bubble size = relative volume
↳ Insight:RB-01 and RB-02 occupy the critical quadrant (high frequency + high risk) — requiring immediate, scaled intervention. RB-07 (Pedestrian Conflict) sits in the high-risk/lower-frequency quadrant, demanding pre-emptive structural solutions despite lower current detection volume.
Normal Distribution of Detection Counts
Theoretical N(μ=643, σ=495) · each behavior marked at its observed count
RB-01
RB-02
RB-03
RB-04
RB-05
RB-06
RB-07
RB-08
RB-09
RB-10
↳ Insight:The distribution is strongly right-skewed — RB-01 and RB-02 fall 2.43σ and 1.13σ above the mean, far outside the expected normal range. 7 behaviors fall below average, confirming that a minority of violation types drive the majority of risk.
School Comparison
Submissions by behavior class per school (stacked)
↳ Insight:Lê Quý Đôn and Nguyễn Thị Minh Khai show the highest absolute detection volume. The consistent helmet non-compliance share (≈38%) across all schools suggests a systemic cultural factor rather than site-specific infrastructure, supporting a city-wide behavioral intervention strategy.
Time-of-Day Distribution
Detection frequency by hour · school day average
↳ Insight:Two sharp peaks emerge at 07:30 (234 detections) and 17:30 (234 detections), precisely coinciding with school arrival and dismissal windows. Off-peak periods drop 73% below peak, confirming that enforcement and AI-monitoring resources should be concentrated within ±30 minutes of school bell times.
Behavior Statistical Summary
Sorted by z-score · normalization metrics for comparative research analysis
| Code | Behavior | Severity | Total | Mean / Day | Z-Score | Percentile | % Share |
|---|---|---|---|---|---|---|---|
| RB-01 | Helmet Non-Compliance | HIGH | 1.842 | 61.4 | +2.43σ | 100th | 28.7% |
| RB-02 | Phone Use While Riding | HIGH | 1.203 | 40.1 | +1.13σ | 89th | 18.7% |
| RB-03 | Wrong-Way Riding | HIGH | 731 | 24.4 | +0.18σ | 78th | 11.4% |
| RB-04 | Lane Violation | MODERATE | 614 | 20.5 | -0.06σ | 67th | 9.6% |
| RB-05 | Red Light Running | MODERATE | 548 | 18.3 | -0.19σ | 56th | 8.5% |
| RB-06 | Double/Triple Riding | MODERATE | 490 | 16.3 | -0.31σ | 44th | 7.6% |
| RB-07 | Pedestrian Conflict | HIGH | 378 | 12.6 | -0.53σ | 33th | 5.9% |
| RB-08 | Abrupt Stop in Flow | MODERATE | 290 | 9.7 | -0.71σ | 22th | 4.5% |
| RB-09 | Overloading | MODERATE | 187 | 6.2 | -0.92σ | 11th | 2.9% |
| RB-10 | Speeding in School Zone | HIGH | 142 | 4.7 | -1.01σ | 0th | 2.2% |