Tengizchevroil: Ensuring Safer Motor Vehicle Operations ...

Tengizchevroil: Ensuring Safer Motor Vehicle Operations for a fleet of 5000

Daniel Barragan – TCO Business Intelligence Advisor

Bekmurat Spayev – TCO Data Scientist

2018 Tengizchevroil

Intelligence Motor Vehicle Safety Project

Objective

2

“World-class in process safety, personal safety & health, environment

impact, reliability and efficiency”

IT

Digitalization

Motor

Vehicle

Safety (MVS)

• Use technology to increase driver safety

• Encourage safe driver behavior

• Provide accurate and accessible information and data

Zero

Fatalities

TCO Key

Focus Area

Motor Vehicle SafetyTechnology Project

2018 Tengizchevroil


Opportunities

3

To Improve Motor Vehicle Safety, Prevent Injuries, & Save Lives

Interactive Business Intelligence

• automated tools instead of manual reports

• geo-spatial visualizations

Timely Fleet Information and Reports

• shorten feedback loop to drivers

• uncover developing or underway issues

Real-Time Situational Awareness

• alerts & notifications

• real-time geo-spatial portal

Provide new insights

• violation detection

• traffic congestion

Predictive Capabilities & Analytics

• traffic congestion

• fatigue analytics

MVS Portal

• single view of most important data

• more immediate impact on safety

Road Conditions

• amber / red status

• quicker notification

2018 Tengizchevroil


Roadmap

4

2017Phase 1 – 3Data Lake

Data Pump

June-July 2017Fatigue

ManagementProof of concept

2018 Ph4 W1BI Reports (5)

Events HeatmapsOvertaking

RCDB

2020 Ph4 W3Predictive Analytics

High Level Deliverables

2018 – 2019 Ph4 W2BI Reports (15)

Fleet ManagementNear RT VisualizationAccess Control and

Notifications

2018 Tengizchevroil


Architecture

5

Query and update ETL Metadata (offsets, etc)

Stream data For NRT Analysis

...

Spark Direct Stream job per entity

...

SQL

Database

HDInsight

Spark

Key Vault

Query secrets

Event Hubs

Any data-science

toolkit

SQL Data

Warehouse (Data

Marts)

Microstrategy

Any geo-spatial

BI or mapsDetected overtaking events stream

HDInsight

Spark

Spark Direct Stream job forNRT events

Spark Direct Stream job per entity

...

HDInsight

Hive

...

Data is stored inORC/Tx Hive tables

...

Data Lake Store

HDInsight

Hive + LLAP

HDInsight

SparkEvent Hubs

event-overtaking

RedisStream to hot cache required for NRT Analysis data

Hot cache for NRT Analytics

HDInsight

Spark

HDInsight

Spark

Data

Sources

BI & Reporting

Ad-Hoc

Data

Lake

NRT Complex

Event Processing

NRT Machine Learning

Advanced Batch Analytics

Machine Learning

Batch Reports

SQL, JDBC, ODBC

Raw Area Prepared Area

Dedicated Hub for

ArcGIS GeoEvent Server

Legend:Telematics data flow

Internal infrastructure data flow

Detected violations data flow

External and ad-hoc data flow

Log Analytics

Log Analytics, Health Dashboards, Alerting

fleet

weather

road status

Notification

MVS Data Lake

External

Vendor

Data

2018 Tengizchevroil


BI Architecture

6

AzureData Lake Store

ETLDevelopment(VIRTUAL MACHINE)

Data MartDevelopment

(AZURE SQL DATA WAREHOUSE)

BI AnalyticsDevelopment(VIRTUAL MACHINE)

PO

LYB

ASE

Data MartQA


PO

LYB

ASE

BI AnalyticsUAT

(VIRTUAL MACHINE)

Data MartProduction


PO

LYB

ASE

BI AnalyticsProduction

DATA MART

LANDING

DATA MART

LANDING

DATA MART

LANDING

ETLQA & Production(VIRTUAL MACHINE)

Component Description

Data Lake / Warehouse PaaS (Azure Data Lake Storage)

ETL IaaS (Azure Virtual Server, SSIS, Visual Studio in Dev only)

Data Mart PaaS (Azure SQL Data Warehouse)

BI Analytics IaaS (Azure Virtual Server, MicroStrategy)

ETL Packages Polybase (Warehouse to Landing), SSIS (Landing to Data Mart)

2018 Tengizchevroil


Data Complexity

— Users

• 6,000 Vehicles (Will grow to 12k)

• 13,000 Active Drivers (Will grow to 20k)

• 140+ Contracting companies

• 500 Supervisors and Superintendents

— Data

• Constant ingestion of data from vendor

• > 115 M miles drives by vehicles in the last year

• 5.3M records added each day

• 3B records reflecting 3 years of historical data

• Data Mart is currently loaded 2x a day from lake

7

2018 Tengizchevroil


Driver Fatigue, Distraction Detection and Intervention POC

— Guardian by Seeing Machines Ltd:

• Artificial Intelligence face tracking

system

— Proof of Concept Deployment Project

(June – July 2017):

• 60 days trial

• Representative sample of 50 vehicles

— Proof of Concept Results:

• Reduction in fatigue events – 96%

• Reduction in distraction events – 93%

— Next Steps:

• Scaled deployment to 450 vehicles in

2018, potentially further deployment to

1,050 vehicles in 2019

8

2018 Tengizchevroil


BI Reports Wave 1

9

2018 Tengizchevroil


BI Reports Wave 1

10

2018 Tengizchevroil


BI Reports Wave 1

11

2018 Tengizchevroil


BI Reports Wave 1

12

2018 Tengizchevroil


BI Reports Wave 1

13

2018 Tengizchevroil


BI Reports Wave 1: Event Heat Map Live Demo

14

2018 Tengizchevroil


BI Reports Wave 2: Supervisor and Superintendent Dashboards

15

2018 Tengizchevroil


BI Reports Wave 2: Supervisor and Superintendent Dashboards

16

2018 Tengizchevroil


Overtaking Algorithm - Opportunity

17

Overtaking - act of one vehicle going past another slow moving vehicle, traveling in the same directions, on a road

- unsafe overtaking is dangerous- reported 5 to 10 unsafe overtakings per day in TCO

2018 Tengizchevroil


Overtaking Algorithm - Solution

18

Layer Tools

Input - Vendor Telemetry Data

- GPS Point Positions Data

- Queue in the Azure Event Hub

Model - Spark Job

- Spark Java API

- Point of Interest and Vehicle Order Change

- 3rd Order Hermite Interpolation

Output - Output to the Azure Event Hub

- Stream consumed by 2 application

1. Overtaking Verification Application

2. Overtaking Persisted Jobs

2018 Tengizchevroil


Overtaking Algorithm - Result

19

- 6000 suspected overtaking events per day

- 1500+ suspected unsafe events per day

- Validated – 70% positive violations

- First ever algorithm implemented in our fleet management system

- Next Steps:

- Discussion with business and stakeholders to figure out how we

will change drivers behaviors

- As accuracy and frequency of data improve the ways to increase

accuracy will be explored

2018 Tengizchevroil


Questions and Answers

20

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