Analytics
JAKARTA TRAFFIC CONGESTION ANALYSIS 2024
This is a comprehensive data analysis project examining traffic congestion patterns across 10 major roads in Jakarta during 2024. The analysis combines synthetic data generation with rigorous statistical analysis and actionable business intelligence.
Year :
2026

Data Columns (15 total)
| Column | Type | Description | Range |
|--------|------|-------------|-------|
| timestamp | datetime | Date and time (30-min intervals) | 2024-01-01 to 2024-12-31 |
| road_name | categorical | 10 major Jakarta roads | 10 unique roads |
| direction | categorical | Traffic direction | inbound/outbound |
| avg_speed_kmh | float | Average vehicle speed | 5-60 km/h |
| congestion_index | float | Traffic congestion level | 0-10 scale |
| travel_time_minutes | float | Travel time per 5km segment | 5-60 minutes |
| vehicle_volume | integer | Vehicles per interval | 10-1,300 vehicles |
| weather | categorical | Weather condition | clear/cloudy/light_rain/heavy_rain |
| is_holiday | boolean | National public holiday flag | true/false |
| is_ramadan | boolean | Ramadan period (Mar 11-Apr 9) | true/false |
| is_eid | boolean | Eid period (Apr 8-14) | true/false |
| day_of_week | categorical | Day of week | Mon-Sun |
| hour | integer | Hour of day | 0-23 |
| temperature_celsius | float | Air temperature | 25-36°C |
| rainfall_mm | float | Rainfall amount | 0-8mm |

Realistic Patterns Embedded
✓ Morning rush hour peaks (07:00-09:00): 7.19/10 congestion
✓ Evening rush hour peaks (17:00-19:00): 8.33/10 congestion
✓ Weekend reduction: 30.1% less congested than weekdays
✓ Heavy rain impact: +2.35 points congestion (statistically significant, p<0.001)
✓ Ramadan shift: Peak moves from 08:00 to 15:00-17:00
✓ Eid period (H-3 to H+3): Vehicle volume drops 45-55%
✓ Road variations: Casablanca & TB Simatupang 18-25% more congested
✓ Late night (00:00-05:00): Free-flow conditions across all roads
TECHNICAL SPECIFICATIONS
### Libraries Used
- **pandas:** Data manipulation and analysis
- **numpy:** Numerical computations and random generation
- **matplotlib/seaborn:** Visualization and charting
- **scipy:** Statistical hypothesis testing (t-test)
### Data Generation Methodology
- Synthetic data created to match realistic Jakarta traffic patterns
- Temporal patterns: Rush hours, daily/weekly/monthly cycles
- Weather correlation: Rainfall-congestion relationship
- Special events: Ramadan (Mar 11-Apr 9), Eid (Apr 8-14)
- Road heterogeneity: Casablanca/TB Simatupang higher congestion
- Reproducible: Fixed random seed for consistent results
### Statistical Analysis
- **T-test:** Confirmed weather impact on congestion (p<0.001)
- **Correlation analysis:** Rainfall vs congestion relationship
- **Grouped aggregations:** Hourly, daily, monthly patterns
- **Comparative analysis:** Weekday vs weekend, Ramadan vs normal
### Visualization Standards
- **Resolution:** 300 DPI (publication-quality)
- **Colors:** Red-Yellow-Green palette (intuitive: red=congestion, green=free-flow)
- **Titles & Labels:** Clear, descriptive, professional
- **Legends:** Included for all multi-series charts
- **Grid lines:** Subtle for readability


KEY INSIGHTS & RECOMMENDATIONS
### [INSIGHT #1] Critical Rush Hours Dominate Daily Congestion
**Finding:** Morning rush averages 7.19/10, evening rush 8.33/10 – **7x worse than late night**
**Implication:** Two concentrated periods drive most disruptions and productivity loss
**Recommendation:** Staggered work hours (6:30-7:30 AM, 4:30-5:30 PM), work-from-home incentives, employer coordination
### [INSIGHT #2] TB Simatupang and Casablanca Persistently Critical
**Finding:** Casablanca highest (4.92/10), MT Haryono lowest (3.60/10) – **18-25% variation between roads**
**Implication:** Strategic corridors have infrastructure bottlenecks
**Recommendation:** Dedicated bus lanes, road widening, smart traffic signals, congestion pricing to redistribute traffic
### [INSIGHT #3] Weather Significantly Amplifies Congestion
**Finding:** Heavy rain adds **+2.35 points** to congestion index (statistically significant, p<0.001)
**Implication:** Nov-Mar wet season predictably worsens traffic with 25-40% longer travel times
**Recommendation:** Weather-responsive traffic management, drainage improvements, dynamic toll pricing
### [INSIGHT #4] Ramadan Shifts Peak Congestion Timing
**Finding:** Peak moves from 08:00 to **15:00-17:00**, reaching 8.33/10
**Implication:** Traditional work schedules invalid; logistics unprepared for timing shift
**Recommendation:** 2-week awareness campaigns before Ramadan, business delivery schedule adjustments, staffing increases
### [INSIGHT #5] Eid Holiday Period Shows Dramatic Traffic Relief
**Finding:** Vehicle volume drops **45-55%**, congestion proportionally reduced
**Implication:** Vehicle supply directly drives congestion; demand management viable
**Recommendation:** Permanent interventions: work-from-home (15-20% vehicle reduction), odd-even plates, park-and-ride hubs
### [INSIGHT #6] Weekends Demonstrate 30-40% Traffic Reduction Potential
**Finding:** Weekend congestion **30.1% lower** (3.24/10 vs 4.64/10 weekday)
**Implication:** Lower volumes eliminate congestion; professional/commercial traffic dominates peaks
**Recommendation:** Expand teleworking, dedicated freight corridors (06:00-07:00, 21:00-23:00), commuter separation
### [INSIGHT #7] Late Night Hours Offer Untapped Traffic Capacity
**Finding:** Hours 00:00-05:00: only **1.20/10 congestion**, 2-4% road utilization
**Implication:** Infrastructure handles demand when distributed; timing=problem, not capacity
**Recommendation:** 24-hour city incentives, night-freight corridors with toll rebates, night-shift employment options
1. DATA OVERVIEW & CLEANING
✅ Shape and dtypes verified
- ✅ Zero missing values
- ✅ Data validation checks (speed bounds, positive values, etc.)
- ✅ Coverage summary: 366 days, 10 roads, consistent patterns
- ✅ Average congestion: 4.24/10 overall, 8.24/10 at peak hours
Summary :
2. EXPLORATORY DATA ANALYSIS (EDA)
Temporal Analysis
- **Heatmap:** Congestion by hour × day of week (identifies peak times)
- **Line Chart:** Hourly trends comparing weekday vs weekend
- **Monthly Trend:** Yearly congestion pattern with Ramadan/Eid highlighted
#### Per-Road Analysis
- **Ranking Chart:** Roads ranked worst-to-best by average congestion
- Worst: Casablanca (4.92/10)
- Best: MT Haryono (3.60/10)
- **Distribution Box Plot:** Congestion variability per road
- **Heatmap:** Road × hour matrix for peak identification
#### Weather Analysis
- **Bar Chart:** Congestion by weather condition
- Heavy rain: 6.19/10
- Clear: 3.84/10
- Difference: +2.35 (highly significant)
- **Scatter Plot:** Rainfall vs congestion with regression trend
- **Statistical Test:** T-test confirms weather impact (p<0.001)
#### Special Event Analysis
- **Ramadan vs Non-Ramadan:** Peak time shift visualization
- **Eid vs Normal Days:** Vehicle volume comparison showing 50% reduction
- **Friday Analysis:** Behavioral differences from other weekdays


More Projects
Analytics
JAKARTA TRAFFIC CONGESTION ANALYSIS 2024
This is a comprehensive data analysis project examining traffic congestion patterns across 10 major roads in Jakarta during 2024. The analysis combines synthetic data generation with rigorous statistical analysis and actionable business intelligence.
Year :
2026

Data Columns (15 total)
| Column | Type | Description | Range |
|--------|------|-------------|-------|
| timestamp | datetime | Date and time (30-min intervals) | 2024-01-01 to 2024-12-31 |
| road_name | categorical | 10 major Jakarta roads | 10 unique roads |
| direction | categorical | Traffic direction | inbound/outbound |
| avg_speed_kmh | float | Average vehicle speed | 5-60 km/h |
| congestion_index | float | Traffic congestion level | 0-10 scale |
| travel_time_minutes | float | Travel time per 5km segment | 5-60 minutes |
| vehicle_volume | integer | Vehicles per interval | 10-1,300 vehicles |
| weather | categorical | Weather condition | clear/cloudy/light_rain/heavy_rain |
| is_holiday | boolean | National public holiday flag | true/false |
| is_ramadan | boolean | Ramadan period (Mar 11-Apr 9) | true/false |
| is_eid | boolean | Eid period (Apr 8-14) | true/false |
| day_of_week | categorical | Day of week | Mon-Sun |
| hour | integer | Hour of day | 0-23 |
| temperature_celsius | float | Air temperature | 25-36°C |
| rainfall_mm | float | Rainfall amount | 0-8mm |

Realistic Patterns Embedded
✓ Morning rush hour peaks (07:00-09:00): 7.19/10 congestion
✓ Evening rush hour peaks (17:00-19:00): 8.33/10 congestion
✓ Weekend reduction: 30.1% less congested than weekdays
✓ Heavy rain impact: +2.35 points congestion (statistically significant, p<0.001)
✓ Ramadan shift: Peak moves from 08:00 to 15:00-17:00
✓ Eid period (H-3 to H+3): Vehicle volume drops 45-55%
✓ Road variations: Casablanca & TB Simatupang 18-25% more congested
✓ Late night (00:00-05:00): Free-flow conditions across all roads
TECHNICAL SPECIFICATIONS
### Libraries Used
- **pandas:** Data manipulation and analysis
- **numpy:** Numerical computations and random generation
- **matplotlib/seaborn:** Visualization and charting
- **scipy:** Statistical hypothesis testing (t-test)
### Data Generation Methodology
- Synthetic data created to match realistic Jakarta traffic patterns
- Temporal patterns: Rush hours, daily/weekly/monthly cycles
- Weather correlation: Rainfall-congestion relationship
- Special events: Ramadan (Mar 11-Apr 9), Eid (Apr 8-14)
- Road heterogeneity: Casablanca/TB Simatupang higher congestion
- Reproducible: Fixed random seed for consistent results
### Statistical Analysis
- **T-test:** Confirmed weather impact on congestion (p<0.001)
- **Correlation analysis:** Rainfall vs congestion relationship
- **Grouped aggregations:** Hourly, daily, monthly patterns
- **Comparative analysis:** Weekday vs weekend, Ramadan vs normal
### Visualization Standards
- **Resolution:** 300 DPI (publication-quality)
- **Colors:** Red-Yellow-Green palette (intuitive: red=congestion, green=free-flow)
- **Titles & Labels:** Clear, descriptive, professional
- **Legends:** Included for all multi-series charts
- **Grid lines:** Subtle for readability


KEY INSIGHTS & RECOMMENDATIONS
### [INSIGHT #1] Critical Rush Hours Dominate Daily Congestion
**Finding:** Morning rush averages 7.19/10, evening rush 8.33/10 – **7x worse than late night**
**Implication:** Two concentrated periods drive most disruptions and productivity loss
**Recommendation:** Staggered work hours (6:30-7:30 AM, 4:30-5:30 PM), work-from-home incentives, employer coordination
### [INSIGHT #2] TB Simatupang and Casablanca Persistently Critical
**Finding:** Casablanca highest (4.92/10), MT Haryono lowest (3.60/10) – **18-25% variation between roads**
**Implication:** Strategic corridors have infrastructure bottlenecks
**Recommendation:** Dedicated bus lanes, road widening, smart traffic signals, congestion pricing to redistribute traffic
### [INSIGHT #3] Weather Significantly Amplifies Congestion
**Finding:** Heavy rain adds **+2.35 points** to congestion index (statistically significant, p<0.001)
**Implication:** Nov-Mar wet season predictably worsens traffic with 25-40% longer travel times
**Recommendation:** Weather-responsive traffic management, drainage improvements, dynamic toll pricing
### [INSIGHT #4] Ramadan Shifts Peak Congestion Timing
**Finding:** Peak moves from 08:00 to **15:00-17:00**, reaching 8.33/10
**Implication:** Traditional work schedules invalid; logistics unprepared for timing shift
**Recommendation:** 2-week awareness campaigns before Ramadan, business delivery schedule adjustments, staffing increases
### [INSIGHT #5] Eid Holiday Period Shows Dramatic Traffic Relief
**Finding:** Vehicle volume drops **45-55%**, congestion proportionally reduced
**Implication:** Vehicle supply directly drives congestion; demand management viable
**Recommendation:** Permanent interventions: work-from-home (15-20% vehicle reduction), odd-even plates, park-and-ride hubs
### [INSIGHT #6] Weekends Demonstrate 30-40% Traffic Reduction Potential
**Finding:** Weekend congestion **30.1% lower** (3.24/10 vs 4.64/10 weekday)
**Implication:** Lower volumes eliminate congestion; professional/commercial traffic dominates peaks
**Recommendation:** Expand teleworking, dedicated freight corridors (06:00-07:00, 21:00-23:00), commuter separation
### [INSIGHT #7] Late Night Hours Offer Untapped Traffic Capacity
**Finding:** Hours 00:00-05:00: only **1.20/10 congestion**, 2-4% road utilization
**Implication:** Infrastructure handles demand when distributed; timing=problem, not capacity
**Recommendation:** 24-hour city incentives, night-freight corridors with toll rebates, night-shift employment options
1. DATA OVERVIEW & CLEANING
✅ Shape and dtypes verified
- ✅ Zero missing values
- ✅ Data validation checks (speed bounds, positive values, etc.)
- ✅ Coverage summary: 366 days, 10 roads, consistent patterns
- ✅ Average congestion: 4.24/10 overall, 8.24/10 at peak hours
Summary :
2. EXPLORATORY DATA ANALYSIS (EDA)
Temporal Analysis
- **Heatmap:** Congestion by hour × day of week (identifies peak times)
- **Line Chart:** Hourly trends comparing weekday vs weekend
- **Monthly Trend:** Yearly congestion pattern with Ramadan/Eid highlighted
#### Per-Road Analysis
- **Ranking Chart:** Roads ranked worst-to-best by average congestion
- Worst: Casablanca (4.92/10)
- Best: MT Haryono (3.60/10)
- **Distribution Box Plot:** Congestion variability per road
- **Heatmap:** Road × hour matrix for peak identification
#### Weather Analysis
- **Bar Chart:** Congestion by weather condition
- Heavy rain: 6.19/10
- Clear: 3.84/10
- Difference: +2.35 (highly significant)
- **Scatter Plot:** Rainfall vs congestion with regression trend
- **Statistical Test:** T-test confirms weather impact (p<0.001)
#### Special Event Analysis
- **Ramadan vs Non-Ramadan:** Peak time shift visualization
- **Eid vs Normal Days:** Vehicle volume comparison showing 50% reduction
- **Friday Analysis:** Behavioral differences from other weekdays


More Projects
Analytics
JAKARTA TRAFFIC CONGESTION ANALYSIS 2024
This is a comprehensive data analysis project examining traffic congestion patterns across 10 major roads in Jakarta during 2024. The analysis combines synthetic data generation with rigorous statistical analysis and actionable business intelligence.
Year :
2026

Data Columns (15 total)
| Column | Type | Description | Range |
|--------|------|-------------|-------|
| timestamp | datetime | Date and time (30-min intervals) | 2024-01-01 to 2024-12-31 |
| road_name | categorical | 10 major Jakarta roads | 10 unique roads |
| direction | categorical | Traffic direction | inbound/outbound |
| avg_speed_kmh | float | Average vehicle speed | 5-60 km/h |
| congestion_index | float | Traffic congestion level | 0-10 scale |
| travel_time_minutes | float | Travel time per 5km segment | 5-60 minutes |
| vehicle_volume | integer | Vehicles per interval | 10-1,300 vehicles |
| weather | categorical | Weather condition | clear/cloudy/light_rain/heavy_rain |
| is_holiday | boolean | National public holiday flag | true/false |
| is_ramadan | boolean | Ramadan period (Mar 11-Apr 9) | true/false |
| is_eid | boolean | Eid period (Apr 8-14) | true/false |
| day_of_week | categorical | Day of week | Mon-Sun |
| hour | integer | Hour of day | 0-23 |
| temperature_celsius | float | Air temperature | 25-36°C |
| rainfall_mm | float | Rainfall amount | 0-8mm |

Realistic Patterns Embedded
✓ Morning rush hour peaks (07:00-09:00): 7.19/10 congestion
✓ Evening rush hour peaks (17:00-19:00): 8.33/10 congestion
✓ Weekend reduction: 30.1% less congested than weekdays
✓ Heavy rain impact: +2.35 points congestion (statistically significant, p<0.001)
✓ Ramadan shift: Peak moves from 08:00 to 15:00-17:00
✓ Eid period (H-3 to H+3): Vehicle volume drops 45-55%
✓ Road variations: Casablanca & TB Simatupang 18-25% more congested
✓ Late night (00:00-05:00): Free-flow conditions across all roads
TECHNICAL SPECIFICATIONS
### Libraries Used
- **pandas:** Data manipulation and analysis
- **numpy:** Numerical computations and random generation
- **matplotlib/seaborn:** Visualization and charting
- **scipy:** Statistical hypothesis testing (t-test)
### Data Generation Methodology
- Synthetic data created to match realistic Jakarta traffic patterns
- Temporal patterns: Rush hours, daily/weekly/monthly cycles
- Weather correlation: Rainfall-congestion relationship
- Special events: Ramadan (Mar 11-Apr 9), Eid (Apr 8-14)
- Road heterogeneity: Casablanca/TB Simatupang higher congestion
- Reproducible: Fixed random seed for consistent results
### Statistical Analysis
- **T-test:** Confirmed weather impact on congestion (p<0.001)
- **Correlation analysis:** Rainfall vs congestion relationship
- **Grouped aggregations:** Hourly, daily, monthly patterns
- **Comparative analysis:** Weekday vs weekend, Ramadan vs normal
### Visualization Standards
- **Resolution:** 300 DPI (publication-quality)
- **Colors:** Red-Yellow-Green palette (intuitive: red=congestion, green=free-flow)
- **Titles & Labels:** Clear, descriptive, professional
- **Legends:** Included for all multi-series charts
- **Grid lines:** Subtle for readability


KEY INSIGHTS & RECOMMENDATIONS
### [INSIGHT #1] Critical Rush Hours Dominate Daily Congestion
**Finding:** Morning rush averages 7.19/10, evening rush 8.33/10 – **7x worse than late night**
**Implication:** Two concentrated periods drive most disruptions and productivity loss
**Recommendation:** Staggered work hours (6:30-7:30 AM, 4:30-5:30 PM), work-from-home incentives, employer coordination
### [INSIGHT #2] TB Simatupang and Casablanca Persistently Critical
**Finding:** Casablanca highest (4.92/10), MT Haryono lowest (3.60/10) – **18-25% variation between roads**
**Implication:** Strategic corridors have infrastructure bottlenecks
**Recommendation:** Dedicated bus lanes, road widening, smart traffic signals, congestion pricing to redistribute traffic
### [INSIGHT #3] Weather Significantly Amplifies Congestion
**Finding:** Heavy rain adds **+2.35 points** to congestion index (statistically significant, p<0.001)
**Implication:** Nov-Mar wet season predictably worsens traffic with 25-40% longer travel times
**Recommendation:** Weather-responsive traffic management, drainage improvements, dynamic toll pricing
### [INSIGHT #4] Ramadan Shifts Peak Congestion Timing
**Finding:** Peak moves from 08:00 to **15:00-17:00**, reaching 8.33/10
**Implication:** Traditional work schedules invalid; logistics unprepared for timing shift
**Recommendation:** 2-week awareness campaigns before Ramadan, business delivery schedule adjustments, staffing increases
### [INSIGHT #5] Eid Holiday Period Shows Dramatic Traffic Relief
**Finding:** Vehicle volume drops **45-55%**, congestion proportionally reduced
**Implication:** Vehicle supply directly drives congestion; demand management viable
**Recommendation:** Permanent interventions: work-from-home (15-20% vehicle reduction), odd-even plates, park-and-ride hubs
### [INSIGHT #6] Weekends Demonstrate 30-40% Traffic Reduction Potential
**Finding:** Weekend congestion **30.1% lower** (3.24/10 vs 4.64/10 weekday)
**Implication:** Lower volumes eliminate congestion; professional/commercial traffic dominates peaks
**Recommendation:** Expand teleworking, dedicated freight corridors (06:00-07:00, 21:00-23:00), commuter separation
### [INSIGHT #7] Late Night Hours Offer Untapped Traffic Capacity
**Finding:** Hours 00:00-05:00: only **1.20/10 congestion**, 2-4% road utilization
**Implication:** Infrastructure handles demand when distributed; timing=problem, not capacity
**Recommendation:** 24-hour city incentives, night-freight corridors with toll rebates, night-shift employment options
1. DATA OVERVIEW & CLEANING
✅ Shape and dtypes verified
- ✅ Zero missing values
- ✅ Data validation checks (speed bounds, positive values, etc.)
- ✅ Coverage summary: 366 days, 10 roads, consistent patterns
- ✅ Average congestion: 4.24/10 overall, 8.24/10 at peak hours
Summary :
2. EXPLORATORY DATA ANALYSIS (EDA)
Temporal Analysis
- **Heatmap:** Congestion by hour × day of week (identifies peak times)
- **Line Chart:** Hourly trends comparing weekday vs weekend
- **Monthly Trend:** Yearly congestion pattern with Ramadan/Eid highlighted
#### Per-Road Analysis
- **Ranking Chart:** Roads ranked worst-to-best by average congestion
- Worst: Casablanca (4.92/10)
- Best: MT Haryono (3.60/10)
- **Distribution Box Plot:** Congestion variability per road
- **Heatmap:** Road × hour matrix for peak identification
#### Weather Analysis
- **Bar Chart:** Congestion by weather condition
- Heavy rain: 6.19/10
- Clear: 3.84/10
- Difference: +2.35 (highly significant)
- **Scatter Plot:** Rainfall vs congestion with regression trend
- **Statistical Test:** T-test confirms weather impact (p<0.001)
#### Special Event Analysis
- **Ramadan vs Non-Ramadan:** Peak time shift visualization
- **Eid vs Normal Days:** Vehicle volume comparison showing 50% reduction
- **Friday Analysis:** Behavioral differences from other weekdays







