Analyze Driving Behavior

curl -X GET "https://api.bookovia.com/v1/analytics/behavior/trip_1234567890abcdef?analysis_depth=comprehensive&include_predictions=true" \
  -H "X-API-Key: bkv_test_your_api_key_here"

{
  "trip_id": "trip_1234567890abcdef",
  "analysis_timestamp": "2024-04-13T12:30:00Z",
  "driver_id": "driver_456",
  "vehicle_id": "vehicle_123",
  "behavior_profile": {
    "driving_style": "moderate",
    "consistency_rating": "consistent",
    "risk_tolerance": "low",
    "experience_indicators": {
      "smoothness_score": 87,
      "anticipation_ability": "high",
      "emergency_response": "appropriate",
      "traffic_flow_integration": "excellent"
    },
    "adaptability_score": 82,
    "behavioral_strengths": [
      "smooth_acceleration",
      "consistent_speed_management",
      "appropriate_following_distance"
    ],
    "behavioral_concerns": [
      "occasional_harsh_braking",
      "late_lane_changes"
    ]
  },
  "behavior_metrics": {
    "acceleration_analysis": {
      "average_acceleration_rate": 1.2,
      "max_acceleration_rate": 3.1,
      "acceleration_smoothness": 89,
      "patterns": {
        "gradual_acceleration_percentage": 78,
        "moderate_acceleration_percentage": 18,
        "aggressive_acceleration_percentage": 4
      },
      "fuel_efficiency_impact": "positive",
      "comfort_rating": "high",
      "acceleration_events": {
        "gentle": 45,
        "moderate": 12,
        "harsh": 2
      }
    },
    "braking_analysis": {
      "average_deceleration_rate": -1.8,
      "max_deceleration_rate": -4.2,
      "braking_smoothness": 76,
      "anticipation_quality": "good",
      "patterns": {
        "gradual_braking_percentage": 68,
        "moderate_braking_percentage": 24,
        "harsh_braking_percentage": 8
      },
      "brake_wear_indicator": "normal",
      "safety_margin": "adequate",
      "braking_events": {
        "gentle": 32,
        "moderate": 8,
        "harsh": 3
      },
      "panic_braking_incidents": 0
    },
    "cornering_analysis": {
      "average_lateral_g": 0.25,
      "max_lateral_g": 0.68,
      "cornering_smoothness": 92,
      "speed_adaptation": "excellent",
      "patterns": {
        "smooth_cornering_percentage": 85,
        "moderate_cornering_percentage": 13,
        "sharp_cornering_percentage": 2
      },
      "stability_indicators": {
        "understeer_tendency": "minimal",
        "oversteer_incidents": 0,
        "traction_loss_events": 0
      }
    },
    "speed_analysis": {
      "speed_compliance_rate": 94,
      "average_speed_deviation": 2.1,
      "speed_consistency": 88,
      "cruise_control_usage": 45,
      "patterns": {
        "below_limit_percentage": 72,
        "at_limit_percentage": 22,
        "above_limit_percentage": 6
      },
      "speed_transitions": {
        "smooth_transitions": 89,
        "abrupt_changes": 3
      },
      "efficiency_indicators": {
        "optimal_speed_percentage": 67,
        "eco_speed_score": 84
      }
    },
    "following_distance_analysis": {
      "average_following_time": 2.8,
      "minimum_following_time": 0.9,
      "safe_distance_percentage": 82,
      "tailgating_incidents": 2,
      "patterns": {
        "conservative_following": 45,
        "appropriate_following": 37,
        "close_following": 15,
        "tailgating": 3
      },
      "reaction_time_estimate": 1.4,
      "safety_buffer_adequacy": "good"
    },
    "lane_discipline": {
      "lane_keeping_accuracy": 95,
      "lane_change_frequency": 8,
      "lane_change_smoothness": 87,
      "turn_signal_usage": 92,
      "patterns": {
        "proper_lane_changes": 85,
        "late_lane_changes": 12,
        "abrupt_lane_changes": 3
      },
      "highway_discipline": "excellent",
      "city_discipline": "good"
    }
  },
  "environmental_adaptation": {
    "weather_response": {
      "rain_adaptation": {
        "speed_reduction": 8,
        "following_distance_increase": 15,
        "braking_adjustment": "appropriate"
      },
      "low_visibility_response": {
        "speed_adjustment": "conservative",
        "caution_level": "high"
      }
    },
    "traffic_adaptation": {
      "heavy_traffic_behavior": {
        "patience_level": "high",
        "gap_acceptance": "conservative",
        "stress_indicators": "low"
      },
      "light_traffic_behavior": {
        "speed_utilization": "appropriate",
        "lane_usage_efficiency": "good"
      }
    },
    "road_type_behavior": {
      "highway_performance": {
        "merging_skill": "excellent",
        "lane_discipline": "very_good",
        "speed_management": "appropriate"
      },
      "city_performance": {
        "intersection_behavior": "cautious",
        "pedestrian_awareness": "high",
        "traffic_light_response": "appropriate"
      },
      "rural_performance": {
        "curve_negotiation": "skilled",
        "hazard_anticipation": "good",
        "speed_adjustment": "appropriate"
      }
    },
    "time_of_day_patterns": {
      "morning_behavior": {
        "alertness_level": "high",
        "aggression_level": "low",
        "consistency": "excellent"
      },
      "evening_behavior": {
        "fatigue_indicators": "minimal",
        "caution_level": "appropriate",
        "reaction_times": "normal"
      }
    }
  },
  "predictive_insights": {
    "risk_predictions": {
      "near_term_crash_risk": "low",
      "fatigue_risk_indicators": "minimal",
      "weather_vulnerability": "low",
      "traffic_stress_tolerance": "high"
    },
    "improvement_potential": {
      "braking_smoothness": {
        "current_level": 76,
        "achievable_improvement": 8,
        "timeframe": "4-6 weeks"
      },
      "fuel_efficiency": {
        "current_efficiency": "good",
        "potential_improvement": "5-8%",
        "focus_areas": ["acceleration_timing", "speed_consistency"]
      }
    },
    "behavioral_trends": {
      "compared_to_last_month": {
        "safety_trend": "improving",
        "consistency_trend": "stable",
        "efficiency_trend": "improving"
      },
      "learning_indicators": {
        "skill_development": "positive",
        "habit_formation": "good",
        "adaptation_rate": "normal"
      }
    }
  },
  "segment_analysis": {
    "highway_segments": {
      "percentage_of_trip": 65,
      "behavior_quality": "excellent",
      "key_observations": [
        "Maintained appropriate following distances",
        "Smooth lane changes with proper signaling",
        "Consistent speed management"
      ]
    },
    "city_segments": {
      "percentage_of_trip": 30,
      "behavior_quality": "good",
      "key_observations": [
        "Cautious intersection approach",
        "Occasional harsh braking in traffic",
        "Good pedestrian awareness"
      ]
    },
    "rural_segments": {
      "percentage_of_trip": 5,
      "behavior_quality": "very_good",
      "key_observations": [
        "Appropriate speed for road conditions",
        "Smooth curve negotiation"
      ]
    }
  },
  "coaching_recommendations": [
    {
      "category": "braking",
      "priority": "medium",
      "title": "Improve braking anticipation",
      "description": "Focus on earlier brake application to reduce harsh braking incidents",
      "specific_techniques": [
        "Increase scanning distance to 12-15 seconds ahead",
        "Practice gradual pressure application",
        "Use engine braking on downhill grades"
      ],
      "practice_scenarios": [
        "Traffic light approach in heavy traffic",
        "Following distance maintenance behind slow vehicles"
      ],
      "measurement_metrics": [
        "Reduce harsh braking events by 50%",
        "Increase braking smoothness score to 85+"
      ],
      "expected_timeframe": "3-4 weeks"
    },
    {
      "category": "lane_management",
      "priority": "low",
      "title": "Plan lane changes earlier",
      "description": "Improve lane change timing and planning",
      "specific_techniques": [
        "Begin planning lane changes 200-300m in advance",
        "Check mirrors and blind spots systematically",
        "Signal earlier and maintain signals longer"
      ],
      "expected_timeframe": "2-3 weeks"
    }
  ],
  "behavior_score": 84,
  "behavior_grade": "B+",
  "overall_assessment": "Driver demonstrates consistent, safe driving behaviors with minor areas for improvement. Shows good environmental awareness and appropriate risk management."
}

import Bookovia from '@bookovia/javascript-sdk';

const client = new Bookovia('bkv_test_your_api_key');

// Comprehensive behavior analysis
const behaviorAnalysis = await client.analytics.analyzeBehavior('trip_1234567890abcdef', {
  analysisDepth: 'comprehensive',
  includePredictions: true,
  includeComparisons: true,
  segmentAnalysis: true
});

console.log(`Driving Style: ${behaviorAnalysis.behaviorProfile.drivingStyle}`);
console.log(`Behavior Score: ${behaviorAnalysis.behaviorScore} (${behaviorAnalysis.behaviorGrade})`);

// Analyze specific behavior category
const brakingBehavior = behaviorAnalysis.behaviorMetrics.brakingAnalysis;
console.log(`Braking Smoothness: ${brakingBehavior.brakingSmootness}%`);
console.log(`Harsh Braking Events: ${brakingBehavior.brakingEvents.harsh}`);

// Driver coaching dashboard
const createCoachingDashboard = async (driverTrips) => {
  const behaviorAnalyses = await Promise.all(
    driverTrips.map(tripId => 
      client.analytics.analyzeBehavior(tripId, { 
        analysisDepth: 'standard',
        includePredictions: true 
      })
    )
  );
  
  // Aggregate behavior patterns
  const aggregatedBehaviors = {
    averageScore: 0,
    dominantStyle: '',
    consistentWeaknesses: [],
    improvementAreas: [],
    coachingPriority: 'low'
  };
  
  // Calculate averages
  aggregatedBehaviors.averageScore = behaviorAnalyses.reduce((sum, analysis) => 
    sum + analysis.behaviorScore, 0) / behaviorAnalyses.length;
  
  // Find consistent behavior patterns
  const behaviorStyles = behaviorAnalyses.map(a => a.behaviorProfile.drivingStyle);
  aggregatedBehaviors.dominantStyle = findMostCommon(behaviorStyles);
  
  // Identify recurring coaching needs
  const allRecommendations = behaviorAnalyses.flatMap(a => a.coachingRecommendations);
  const categoryFrequency = allRecommendations.reduce((acc, rec) => {
    acc[rec.category] = (acc[rec.category] || 0) + 1;
    return acc;
  }, {});
  
  aggregatedBehaviors.consistentWeaknesses = Object.entries(categoryFrequency)
    .filter(([category, count]) => count >= behaviorAnalyses.length * 0.6)
    .map(([category]) => category);
  
  // Determine coaching priority
  if (aggregatedBehaviors.averageScore < 70) {
    aggregatedBehaviors.coachingPriority = 'high';
  } else if (aggregatedBehaviors.averageScore < 85) {
    aggregatedBehaviors.coachingPriority = 'medium';
  }
  
  return aggregatedBehaviors;
};

// Real-time behavior monitoring for fleet
const monitorFleetBehavior = async (activeTrips) => {
  const alerts = [];
  
  for (const trip of activeTrips) {
    try {
      const analysis = await client.analytics.analyzeBehavior(trip.trip_id);
      
      // Check for concerning patterns
      if (analysis.behaviorProfile.riskTolerance === 'very_high') {
        alerts.push({
          tripId: trip.trip_id,
          driverId: trip.driver_id,
          type: 'high_risk_behavior',
          severity: 'high',
          description: 'Driver showing high risk tolerance behavior'
        });
      }
      
      // Check for fatigue indicators
      if (analysis.predictiveInsights?.riskPredictions?.fatigueRiskIndicators === 'high') {
        alerts.push({
          tripId: trip.trip_id,
          driverId: trip.driver_id,
          type: 'fatigue_risk',
          severity: 'medium',
          description: 'Driver showing signs of potential fatigue'
        });
      }
      
    } catch (error) {
      console.error(`Failed to analyze behavior for trip ${trip.trip_id}:`, error);
    }
  }
  
  return alerts;
};

// Behavior trend analysis
const analyzeBehaviorTrends = async (driverId, tripIds) => {
  const analyses = await Promise.all(
    tripIds.map(async tripId => {
      try {
        return await client.analytics.analyzeBehavior(tripId, {
          includeComparisons: true,
          includePredictions: true
        });
      } catch (error) {
        return null;
      }
    })
  );
  
  const validAnalyses = analyses.filter(a => a !== null);
  
  if (validAnalyses.length < 2) {
    return { error: 'Insufficient data for trend analysis' };
  }
  
  // Calculate trends
  const scores = validAnalyses.map(a => a.behaviorScore);
  const trendDirection = scores[scores.length - 1] > scores[0] ? 'improving' : 'declining';
  
  return {
    driverId,
    trendDirection,
    scoreImprovement: scores[scores.length - 1] - scores[0],
    averageScore: scores.reduce((sum, s) => sum + s, 0) / scores.length,
    consistentWeaknesses: findConsistentWeaknesses(validAnalyses),
    emergingStrengths: findEmergingStrengths(validAnalyses)
  };
};

// Helper functions
const findMostCommon = (array) => {
  const frequency = array.reduce((acc, item) => {
    acc[item] = (acc[item] || 0) + 1;
    return acc;
  }, {});
  
  return Object.entries(frequency)
    .sort(([,a], [,b]) => b - a)[0][0];
};

const findConsistentWeaknesses = (analyses) => {
  const allConcerns = analyses.flatMap(a => 
    a.behaviorProfile.behavioralConcerns || []
  );
  
  const frequency = allConcerns.reduce((acc, concern) => {
    acc[concern] = (acc[concern] || 0) + 1;
    return acc;
  }, {});
  
  return Object.entries(frequency)
    .filter(([, count]) => count >= analyses.length * 0.6)
    .map(([concern]) => concern);
};