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CROWDSOURCED ROAD SAFETY ANALYSIS
TERRA
-by team NEXXUS
AGENDA
I N T R O D U C T I O N
O B J E C T I V E
A I M
I M P O R TA N C E
W O R K I N G
T E C H N O L O G Y S TA C K
F I N A L T I P S & TA K E A WAY S
INTRODUCTION
 Crowdsourced road safety analysis uses real-time data from
public reports .
 Traffic incidents cause congestion
 Traditional monitoring is costly and slow
 FCD helps but lacks real time accuracy
OBJECTIVE
 Collect and analyze real-time road safety data.
 Identify accident-prone areas.
 Provides predictive Insights
 Assist authorities in implementing data-driven
road policies
AIM
 Improving Road Infrastructure and Traffic Management
 Increased Safety
 Create a smart traffic management system using AI and data
analytics.
 Enhance drivers and pedestrian safety.
IMPORTANCE
WHY IS IT IMPORTANT?
WHY IS IT IMPORTANT?
 Helps identify accident-prone zones
 traffic hazards.
 Enhances road infrastructure planning
 traffic management.
 Reduces accidents by alerting drivers
 Authorities in advance.
Mr. Gadkari said every year India sees
4,80,000 accidents, which lead to
1,88,000 deaths of people aged
between 18 and 45 years. India is
losing 3% of its GDP due to around five
lakh road accidents annually in the
country, Union Minister Nitin Gadkari
said on Tuesday (March 25, 2025).
HOW IT WORKS
Data Collection
Reports from users, sensors, and
historical records.
Data Processing
Filtering and validating reports.
Analysis
AI & ML detect patterns and risks.
Risk Identification
Heatmaps and insights pinpoint problem areas.
Notifications
Authorities and drivers receive real-time
alerts.
S
S
E
C
O
R
P
PROCESS
UNDERSTANDING IMPLEMENTATION
TERRA.pptx for geospatial analysis project presentation
TECHNOLOGY
STACK
 Data Sources: Mobile apps, IoT sensors,
GPS data.
 Processing: Python, Pandas, OpenCV (for
image analysis).
 Machine Learning: TensorFlow, Scikit-
learn for predictions.
 Visualization: Power BI, Tableau, Google
Maps API.
 Communication: APIs, Cloud platforms
(AWS, Azure, GCP).
THANK YOU
Project by-
NEXXUS
Subhranshu Pattnayak spnayakshubh97@gmail.com 17175
Amit Chaturvedi amit2020.mau@gmail.com 17804
Roshan Sharma roshansharma0395@gmail.com 17151

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TERRA.pptx for geospatial analysis project presentation

  • 1. CROWDSOURCED ROAD SAFETY ANALYSIS TERRA -by team NEXXUS
  • 2. AGENDA I N T R O D U C T I O N O B J E C T I V E A I M I M P O R TA N C E W O R K I N G T E C H N O L O G Y S TA C K F I N A L T I P S & TA K E A WAY S
  • 3. INTRODUCTION  Crowdsourced road safety analysis uses real-time data from public reports .  Traffic incidents cause congestion  Traditional monitoring is costly and slow  FCD helps but lacks real time accuracy
  • 4. OBJECTIVE  Collect and analyze real-time road safety data.  Identify accident-prone areas.  Provides predictive Insights  Assist authorities in implementing data-driven road policies
  • 5. AIM  Improving Road Infrastructure and Traffic Management  Increased Safety  Create a smart traffic management system using AI and data analytics.  Enhance drivers and pedestrian safety.
  • 6. IMPORTANCE WHY IS IT IMPORTANT?
  • 7. WHY IS IT IMPORTANT?  Helps identify accident-prone zones  traffic hazards.  Enhances road infrastructure planning  traffic management.  Reduces accidents by alerting drivers  Authorities in advance. Mr. Gadkari said every year India sees 4,80,000 accidents, which lead to 1,88,000 deaths of people aged between 18 and 45 years. India is losing 3% of its GDP due to around five lakh road accidents annually in the country, Union Minister Nitin Gadkari said on Tuesday (March 25, 2025).
  • 9. Data Collection Reports from users, sensors, and historical records. Data Processing Filtering and validating reports. Analysis AI & ML detect patterns and risks. Risk Identification Heatmaps and insights pinpoint problem areas. Notifications Authorities and drivers receive real-time alerts. S S E C O R P
  • 12. TECHNOLOGY STACK  Data Sources: Mobile apps, IoT sensors, GPS data.  Processing: Python, Pandas, OpenCV (for image analysis).  Machine Learning: TensorFlow, Scikit- learn for predictions.  Visualization: Power BI, Tableau, Google Maps API.  Communication: APIs, Cloud platforms (AWS, Azure, GCP).
  • 13. THANK YOU Project by- NEXXUS Subhranshu Pattnayak spnayakshubh97@gmail.com 17175 Amit Chaturvedi amit2020.mau@gmail.com 17804 Roshan Sharma roshansharma0395@gmail.com 17151