Everything's Bigger in Texas, Including My Insurance Premiums hike:
From $380 to $530 aka 39% Price Increase!
Waiting on other shocks.
Daily I help teams with solution engineering aspect of connected vehicle data projects. (massive datasets & always some new datasets with new car models aka new technologies.) Lately in the spare time, applying some of the ML/Deep learning techniques on datasets (many are create based on observations of real datasets)To Share some thoughts on my work (main half of this blog) and the other half will be about my family and friends.
Everything's Bigger in Texas, Including My Insurance Premiums hike:
From $380 to $530 aka 39% Price Increase!
Waiting on other shocks.
In my earlier post, I
mentioned that I work as a solution architect for a vehicle data platform. In
this post, I will discuss how we use trip data information to score driver behavior,
using what we call a "smart driver" score. Like a credit score, a
driver score is an indicator of a driver's behavior, which can be used for
things like usage-based insurance.
The term "smart
driver" typically refers to an individual who can optimize their driving
behavior to improve safety, fuel efficiency, and comfort. A smart driver might
use advanced technologies, such as real-time traffic information or
driver-assist features, to make informed decisions about how to operate their
vehicle. They might also adjust their driving habits, such as reducing speed,
avoiding sudden acceleration and braking, and following a consistent pace, to
minimize fuel consumption and emissions.
Smart driver programs and
initiatives are often designed to educate drivers about the benefits of safe and
efficient driving, and to provide tools and resources to help drivers make
informed decisions on the road. Some programs may also offer incentives, such
as rewards or discounts, to encourage drivers to adopt safe and efficient
driving practices.
In the context of
connected and autonomous vehicles, the term "smart driver" may also
refer to the vehicle's advanced systems and technologies, which are designed to
optimize driving performance and provide a more comfortable and efficient
driving experience.
The term "smart
driver" is used by a variety of organizations and companies, including
automotive manufacturers, government agencies, non-profit organizations, and
technology companies.
Automotive manufacturers, such as
Tesla and General Motors, may use the term to describe the advanced
driver-assist technologies and safety features that they offer in their
vehicles.
Government agencies, such as the
National Highway Traffic Safety Administration (NHTSA) and the Environmental
Protection Agency (EPA), may use the term to describe their initiatives to
promote safe and efficient driving practices.
Non-profit organizations, such as
the National Safety Council, may use the term to describe their educational
programs that aim to promote safe and responsible driving practices.
Technology companies, such as
telematics providers and software developers, may use the term to describe
their products and services that aim to improve driving performance and provide
a more efficient and connected driving experience.
Overall, the term "smart
driver" is used to describe individuals and organizations that are focused
on optimizing the driving experience, and promoting safe, efficient, and
sustainable driving practices.
Core trip concepts, data collection,
scoring methodology
When owner is driving vehicle from point A to point B, Ignation ON/OFF cycle, smart driver system will rate your trip based on various factors such as the duration of the trip, the condition of the road, the traffic, and your driving experience. Here are some examples of factors you might consider when rating a trip:
Drive Time: How long did it take
you to complete the trip? Was it faster or slower than expected?
Driving experience: Did you enjoy driving the vehicle? Was it easy to handle, or were there any challenges with maneuvering or controlling the vehicle?
Comfort: Was the trip
comfortable? Did you experience any issues with the vehicle, such as discomfort
from the seats, or noise from the engine or road?
Road condition: Was the
road in good condition? Were there any obstacles, such as potholes or
construction, that impacted your trip?
Traffic: Was there a lot of traffic on the road? Did you experience any delays or slowdowns due to congestion?
In 1993, the global financial markets had a
positive performance overall. The Dow Jones Industrial Average (DJIA) had a
total return of 8.25% for the year, while the S&P 500 had a total return of
4.46%. The NASDAQ also had a positive performance, with a total return of
7.62%. The yield on the 10-year Treasury note also fell during this period,
which generally indicates a strong bond market.
In terms of international markets, the Nikkei 225 in Japan had a
total return of -0.31%, while the FTSE 100 in the United Kingdom had a total
return of 3.57%. The DAX in Germany had a total return of 9.58%.
The U.S. economy continued to expand during this period, with
the Gross Domestic Product (GDP) growing by 2.9% in 1993, and the unemployment
rate falling to 6.9%. The inflation rate was also relatively low, at 3.0%.
It was worth mentioning that the interest rates were relatively
high at that time, with the Federal funds rate at 3.25% by the end of 1993.
Overall, the financial markets in 1993 were characterized by
steady growth, low inflation, and a strong bond market.
I am working
in vehicle data management system design, development, and support.
While I can not specify
fine details, (i.e. data used by specific auto insurance providers), data is
being collected heavily and used by many.
1.
Risk assessment: By
collecting and analyzing data about a vehicle's usage, insurance companies can
better understand the risks associated with insuring that vehicle. For example,
data about a vehicle's speed, acceleration, and location can be used to assess
the likelihood of an accident occurring.
2.
Premium calculation:
Based on the risk assessment, insurance companies can calculate the premium for
insuring a vehicle, taking into account the specific risks associated with that
vehicle.
3.
Fraud detection: By
collecting data from onboard systems and other sources, insurance companies can
detect instances of fraud, such as staged accidents or false claims.
4.
Claims management: In
the event of a claim, insurance companies can use the data collected by the
vehicle data management system to verify the details of the claim and assess
the damage to the vehicle.
5.
Usage-based insurance:
Insurance companies can offer usage-based insurance plans, which allow drivers
to pay premiums based on the actual usage of their vehicles. By collecting and
analyzing data about the usage of vehicles, insurance companies can determine
the appropriate premium for each driver.
Here are some popular vehicle data signals that are commonly collected and analyzed by vehicle data management systems.
1.
Speed: The speed at
which the vehicle is traveling can provide insights into the driving behavior
of the driver and the likelihood of an accident occurring.
2.
Acceleration: The rate
at which a vehicle accelerates can provide insight into the driving style of
the driver and the likelihood of an accident occurring.
3.
Location: By tracking
the location of a vehicle, insurance companies can determine the risk
associated with insuring that vehicle, based on factors such as the road
conditions, traffic density, and likelihood of accidents.
4.
Engine data: Engine
data, such as RPM and fuel consumption, can provide insights into the health
and performance of a vehicle, which can impact the likelihood of an accident
and the cost of repairs.
5.
Brake data: By tracking
the usage of a vehicle's brakes, insurance companies can assess the driving
behavior of the driver and the likelihood of an accident occurring.
6.
Steering data: Steering
data, such as the angle of the steering wheel and the force applied to the
steering wheel, can provide insight into the driving style of the driver and
the likelihood of an accident occurring.
7.
Fuel data: Fuel data,
such as the amount of fuel consumed and the fuel efficiency of a vehicle, can
provide insights into the driving behavior of the driver and the likelihood of
an accident occurring.
I used to be very conservative but sometime at the end of april, started expermenting with options & littlebit of crypto. end result is the following. Need to see the rest of the years. good thing is option income via calls is good i.e. 2 to 3%. For the rest of the year, i will continue the same. Still some capatial needs to be allocated. Hopefully put it work after Sept quarter/correction.
I didn’t like the selection
of funds that HAS provider offered me in HAS account.
So, I end up picking pimco commodity
fund based on its past dividend returns.
See the result of my portfolio. All my others account in RED, this is solid
green. Amazing run plus great dividends. (May be at the end of Q3, will reduce
the position size… I have a feeling, a great downturn ahead of us)
Business and systems architects
got good vision on a critical enterprise data product. After some key decisions,
everyone worked hard and initial few years later, a good product was deployed/operational.
Some wants to make/take it too
great & at the same time, someone in the leadership thought, it is his baby
and want to survive on it for rest of his life. so, he hired incompetent
leaders to manage people.
This led to constantly delivered contributors
are ignored but new commers are promoted. This news alone devastated the key
guys. When they realized, they simply moved on within weeks. Product is still evolving, and this happened. Now
a good product became a mediocre product team and quality issues started (this
is SAD & yet real story of a company which dreams BIG yet makes bad decisions.)
A people leader needs
to inspire, ( worst case stay neutral) else great turns in to shit quickly.
More later....
input json:
{
"shipping_address": {
"street_address": "1600 Pen Avenue NW",
"city": "Washington",
"state": "DC",
"type": "business",
"additionalProperties": {
"test": "one",
"test1": "two"
}
}
}
spark code;
# File location and type
file_location = "/FileStore/tables/mock_example-1.json"
file_type = "json"
# CSV options
infer_schema = "false"
first_row_is_header = "false"
delimiter = ","
# The applied options are for CSV files. For other file types, these will be ignored.
df = spark.read.format(file_type) \
.option("inferSchema", infer_schema) \
.option("header", first_row_is_header) \
.option("sep", delimiter) \
.load(file_location)
display(df)
Since Spark 2.3, the queries from raw JSON/CSV files are disallowed when the referenced columns only include the internal corrupt record column
Solution: usuually spark expects one json message per line..
In general we use Notepad etc, to format JSON examples. ( just to validate the strucrue of the documents)... if U are saving formatted JSON, then spark will fail with the above error.
notepad JSON plugin offers compressJSON option too. so compress/save it. It works fine
Based on 50K vehicles, average distance driven/kWh consumed.
In Box plot form
Kwh consumption (data includes some outliers)
After outliers cleanup
Everyday, I work on connected car data projects.
Lately few people repeatedly keep asking what it means. So I put brief Q&A.
Something I am sharing. (small slice but hoping this is useful)
What is connected Car?
A car with have access to the Internet and
communicate with traditional automotive components, such as the engine and
electronics, as well as the smart devices of a driver. All via telematics* system.
What type of car data
are talking and how it is useful to a driver or vehicle owner or auto maker or 3ed
party?
the most common use of car data is to
improve the driving experience by collect the data about driver behavior events
i.e., from ignition on to ignition off. This data improves following experiences
for the driver.
·
Finding
fuel location/battery charging station as needed
·
Local
business searches and promotions
·
Journey
route weather/traffic updates
·
Real-time
data communications about any emergency situations (flat tire etc.), crash etc
·
Location
sharing, fast theft response
·
Insurance
discounts based on good driving behaviors/usage-based discounts
For vehicle manufactures
a) Data helps to measure the performance/reliability of the vehicles. Data helps to pinpoint about unforeseen issue(s) with new & old vehicles. Data helps voluntary recall vehicles for specific issues.
b)
Data helps to catch fraudulent
warranty clams/odo tampering issues
c)
Various service
offering such as oil changes, end of brake pad changes etc.
d)
Offering customer services
for example geo fence boundaries for family of drivers. With Teen drivers.
For 3ed party companies:
Vehicle location data
helps in forecasting about live traffic conditions
3ed parity insurance
providers to offers discounts based on driving behaviors, usage.
Automatic pothole information improves the road conditions
Near real time weather data to forecast real time weather
Usually Telematic* systems
are integrated with Satellite navigation systems and onboard computers and back office systems. Not only data collections, back office controls and refreshes the software inside cars too via over the air updates.
Following are final statistics. ( ~15 years of journey.)
Nowadays, this year, I am unable to find anytime.
Following is public profile.
No
|
Fund name
|
Annual dividends
|
1
|
VEMAX
|
4
|
2
|
VGENX
|
4
|
3
|
VGHCX
|
10
|
4
|
VGSLX
|
6
|
5
|
VFIAX
|
6
|
6
|
VUSTX
|
4
|
BEP
|
BIP
|
AMZA
|
REML
|
DX
|
CHI
|
GUT
|
ARCC
|
CII
|
EXG
|
APLE
|
BGR
|
RRC
|
PCI
|
AM
|
VEON
|
BGCP
|
AWP
|
CHMI
|
AMLP
|
GDX
|