ChecklistGuro Real-Time Traffic Congestion Impact Analysis
Optimize urban mobility with our Real-Time Traffic Congestion Impact Analysis workflow. Gain actionable insights into how traffic surges affect public transit schedules, identify bottleneck patterns, and implement proactive routing adjustments to ensure seamless, reliable passenger transport and reduced operational delays.
Start
Start of the Workflow/Process.
1. Fetch Sensor Data
Retrieve real-time vehicle count and speed data from IoT traffic sensors in the target zone.
2. Fetch Historical Baseline
Retrieve historical average traffic patterns for the same day and time from the historical data model.
3. Calculate Congestion Index
Calculate the deviation percentage between real-time speed and historical baseline speed.
4. Aggregate Zone Density
Calculate the average vehicle density across all active sensors within the analyzed area.
5. Calculate Delay Impact
Estimate the total extra travel time (in minutes) caused by the current congestion.
6. Create Analysis Report Entry
Generate a new record in the Analysis Data Model containing the calculated congestion metrics and timestamp.
7. Update Traffic Status
Update the 'Current Status' field in the Road Segment Data Model (e.g., from 'Green' to 'Red').
8. Assign Traffic Engineer Review
Create a task for the Duty Engineer to manually verify high-severity congestion anomalies.
9. Create a task for the field unit if congestion is caused by a detected incident/accident.
Create a task for the field unit if congestion is caused by a detected incident/accident.
10. Alert Transport Authorities
Send an automated email alert to the Department of Transportation regarding critical congestion levels.
11. Notify Emergency Services
Send an SMS alert to emergency responders if the analysis detects a sudden drop in speed indicating a possible crash.
12. Generate Daily Congestion Summary
Create a visual PDF report summarizing the daily congestion trends and impact levels for stakeholders.
13. Cleanup Temporary Sensor Logs
Delete temporary high-frequency raw data entries that are no longer needed after analysis completion.
End
End of the Workflow/Process.
Start of the Workflow/Process.
Retrieve real-time vehicle count and speed data from IoT traffic sensors in the target zone.
Retrieve historical average traffic patterns for the same day and time from the historical data model.
Calculate the deviation percentage between real-time speed and historical baseline speed.
Calculate the average vehicle density across all active sensors within the analyzed area.
Estimate the total extra travel time (in minutes) caused by the current congestion.
Generate a new record in the Analysis Data Model containing the calculated congestion metrics and timestamp.
Update the 'Current Status' field in the Road Segment Data Model (e.g., from 'Green' to 'Red').
Create a task for the Duty Engineer to manually verify high-severity congestion anomalies.
Create a task for the field unit if congestion is caused by a detected incident/accident.
Send an automated email alert to the Department of Transportation regarding critical congestion levels.
Send an SMS alert to emergency responders if the analysis detects a sudden drop in speed indicating a possible crash.
Create a visual PDF report summarizing the daily congestion trends and impact levels for stakeholders.
Delete temporary high-frequency raw data entries that are no longer needed after analysis completion.
End of the Workflow/Process.
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