Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/nagpritam/identification-of-trucks-and-potential-risky-driver-using-databricks-spark-api-

The project intended to identify trucks based on their model, fuel consumption, driving behaviors and past records of violations/accidents
https://github.com/nagpritam/identification-of-trucks-and-potential-risky-driver-using-databricks-spark-api-

databricks hadoop hive powerbi python3 spark

Last synced: 4 months ago
JSON representation

The project intended to identify trucks based on their model, fuel consumption, driving behaviors and past records of violations/accidents

Host: GitHub
URL: https://github.com/nagpritam/identification-of-trucks-and-potential-risky-driver-using-databricks-spark-api-
Owner: Nagpritam
Created: 2024-06-22T14:52:55.000Z (7 months ago)
Default Branch: master
Last Pushed: 2024-06-22T16:27:43.000Z (7 months ago)
Last Synced: 2024-09-28T07:01:27.059Z (4 months ago)
Topics: databricks, hadoop, hive, powerbi, python3, spark
Homepage:
Size: 4.26 MB
Stars: 0
Watchers: 1
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

# Project Title

## ANT SAFE WHEELS

### Ensuring Safety and Compliance at ANT

#### Project Introduction

Accidents caused by large trucks are a significant concern in the United States. Az National Trucking (ANT) aims to mitigate risks associated with trucking operations by identifying drivers exhibiting high-risk behaviors. The organization seeks to ensure compliance with regulations, enhance safety measures, and reduce insurance risks.

#### Objective

Identify drivers with risk factors of 7.0 or higher to trigger alerts to management and insurance and analyze trucking data to visualize driver risk levels for informed decision-making.
Strengthen adherence to FMCSA regulations and internal policies to elevate safety standards across the fleet.
### Project Architectural Diagram

![Project Architectural Diagram](https://github.com/Nagpritam/Big-Data-Project/blob/master/architecture.png)

* This Project utilises Spark Via Databricks Notebooks, Hadoop file format, Hive Tables and Power BI for Data Visualization

### Spark Transformations

![Spark Transformations](https://github.com/Nagpritam/Big-Data-Project/blob/master/spark-transformation.png)

### Analysis

![Count of Models](https://github.com/Nagpritam/Big-Data-Project/blob/master/countofmodels.png)

#### 1. Count of Models (Bubble Chart)
* Displays the number of vehicles for each model.
* Larger bubbles represent a higher count of vehicles.
* Ford has the largest bubble, indicating it has the most

![MPG by Model](https://github.com/Nagpritam/Big-Data-Project/blob/master/MPGbymodel.png)

#### 2. MPG by Model (Heat Map)
* Shows the miles per gallon for each vehicle model.
* Darker shades represent higher MPG values.
* Crane appears to have the highest MPG, as indicated by its dark shade.

![Risk Factor By Model](https://github.com/Nagpritam/Big-Data-Project/blob/master/Riskfactorbymodel.png)

### 3. Risk Factor By Model (Bar Graph):
* Depicts the risk factor associated with each vehicle model.
* Oshkosh has the highest risk factor while Navistar has the least
* The risk factors are relatively similar across models, ranging around 7 to just under 10.

![Violation Analysis](https://github.com/Nagpritam/Big-Data-Project/blob/master/violations.png)

### 4. Violation Analysis (Pie Chart):
* Lane Departure: The most frequent violation with 152 occurrences, indicating potential issues with driver attention or fatigue.
* Unsafe Following Distance: Nearly as common, with 150 instances, pointing to risks of rear-end collisions due to inadequate spacing between vehicles.
* Overspeed and Unsafe Tail Distance: Speeding is noted in 90 instances, a widespread issue, while unsafe tail distance, though less frequent at 65 cases, still poses a significant risk in poor driving conditions.

![Risky drivers](https://github.com/Nagpritam/Big-Data-Project/blob/master/riskydrivers.png)

### 5. Top 10 Risky drivers
Namely A97 Driver is the most unsafe driver

![Location of Violations](https://github.com/Nagpritam/Big-Data-Project/blob/master/Maps_violations.png)

### 6. Location of Violations
Geographical Spread of Violations: Violations are widespread across California, with concentrations in urban areas like San Francisco and San Diego.
* Types of Violations: The map indicates two main types of violations: overspeed and unsafe following distance, with unsafe following distance being less common