Driver profile caused accident

Profile of Drivers who caused accidents

Prepared byRituparna Sarkar

Outline

• Project Objective• Data Source And Variables• Research Question• Data Preprocessing • Method of Analysis• Results• Recommendations

Objective

• Evaluate the profile of drivers causing different types of accidents.• Detect anomalies present in the data.• Detect missing values and outliers.• Bin the ages of drivers and cause of accidents.• Profile of drivers for type of accident.

Data- Lists of variables

COMP_CODE Company CodeCLAIM_NO Claim no#CLAIM_YEAR Claim yearVEHICLE_TYPE Vehicle typeREG_NO Registrations NumberREG_DATE Registration DateVECHICLE_MAKE Vechicle ManufacturerCAPACITY CapacitySUM_ASSURED Sum assuredAGE_DRIVER Age of driverSEX_DRIVER Gender of driverSELF_PAID_OTHER Relation of the driver with the vehiclesLICENCE_ISSUE_YEAR When license was issued to the driverCAUSE_ACCIDENT Major cause for the accidentPLACE_ACCIDENT Where the accident took placeLOSS_NATURE Nature of loss due to accidentESTIMATED_LOSS Estimated loss due to accidentPLACE_OF_REPAIR Where was the vehicle taken for repairOS_PROVISION OS_PROVISIONDATE_SURVEY When the survey was conductedPARTS Cost of parts for repairLABOUR Cost of labour for repairDEPRECIATION_AMT The amount by which the value of the car will depreciate.ASSESSED_LOSS The monetary loss assessed by the companyPAID_AMT The amount paid by the companySALVAGE_AMT The amount by which an asset depreciates each periodDATE_PAYMENT When was payment made by the companyMODE_SETTLEMENT Mode of settlement of paid amountFEE_EXPENSE Fee paid for insurance processingTIME_ACCIDENT Time of accidentDATE_ACCIDENT Date of accident

Sample Data

Research Questions-

Data are explored with focus on following questions

• City or highway -where are most accidents happening ?• Is it better if the owner drives their own car?• Does all state show a similar condition?• What causes of accident are majorly significant?• Which “cause of accident” results into higher loss?• Are vehicle makers producing low quality product?• Does driver’s license issuing year a good indicator of the

risk of accident?

Methodology

• Started with data organization and time frame of study• Exploratory analysis and descriptive statistics.• Profile Development• Evaluation of results Interpretation• Recommendations from the analysis

Pre-processing• Illegal values• Number of illegal values are found in variables like REG_NO,

VEHICLE_MAKE, CAPACITY, PARTS, LABOUR, DEPRECIATION_AMT, ASSESSED_LOSS, PAID_AMT, SALVAGE_AMT.

• Data columns (PARTS LABOUR DEPRECIATION_AMT ASSESSED_LOSS PAID_AMT SALVAGE_AMT ) had un-natural high values. On close examination, it turned out to be date value stored as number. It is suspected that these dates values are part of PAYMENT_DATE variable but may have shifted when the files was being converted into different formats.

• State code has been extracted from REG_NO and illegal values were rectified using the standard code table available on Wikipedia

• VEHICLE_MAKE was cleaned and reduced to a considerable number of levels.• Missing values• Variables with few missing values PARTS, LABOUR were imputed using mean

values of the type of vehicle.• Variables like SALVAGE_AMT and FEE_EXPENCE has high percentage of

missing values, so these variables were not considered for analysis.

Continue..

• Outliers• Detected in variables dealing with money like SUM_ASSURED,

ESTIMATED_LOSS.• Age has been binned into groups (Less than 25, 26-35, 36-45, 46-

55, 56 and above)• VEHICLE_TYPE was reduced by one level by merging

MOPEDS/SCOOTERS and SCOOTERS into 1 category.• All date type variables have been broken into year, quarter and

month.• Anomalies also detected in data.• Records are present where claim year is before accident year.

Time Frame of study

• As shown above, more than 80% of the sampled data has been surveyed in 2000 and 2001.

• Also most of the accidents has taken place in 2000 and 2001.• So moving forward, we will consider only significant years for further

analysis, as the sample data for other years are very less and might not be able to provide correct picture of those years.

• Also, as surveys were conducted during 2000 & 2001. Other year’s data (+/- 2 years) will be more prone to human errors based on respondents memory.

• The analysis of years of claims and payments also show a similar patter and are concentrated around 2000 and 2001.

• Analysis of the vehicle registration year shows that the data is concentrated between 1998-2001.

• It is also indicative that most vehicles were almost new when they meet accident, which also might be because the drivers were novice.

Hence forward, filter would be applied on Survey Year to consider only 1998 - 2002

Exploratory Analysis • More accidents happen

within city limits compared to highways.

• Females are part of lesser number of accidents compared to males.

• Only male drivers are involved in highway accidents.

The above insights may be surfacing because1) In terms of driving, Indian females are not

seen behind the wheels frequently.2) People don’t frequent highways until very

confident with driving skill. They generally hire a driver.

3) When on highways they are very alert but in city limits their level of alertness is generally lower.

Locational details

• Most number of accidents1) Maharashtra 2) Andhra Pradesh 3) Gujarat

• States of AP, MP, Gujarat & Maharashtra have similar ratios for “Place of accidents”. (around 35% on Highways and rest within city)

• States of Karnataka and UP shows higher percentage of accidents within city limits (90% & 75% respectively)

• Rest of the states show only 1 kind of accidents Within City Limits – Delhi, RajasthanHighway - Bihar, Chhattisgarh.

• Almost 48% of the accidents occur when the owner drives their vehicle. This may also be because most of the car owners are urban citizens and within city limits they would generally drive themselves.

• Least accidents happen when the car is driven by not the owner or a paid driver. Here the person driving can be the owners relative or an unpaid driver.

• Driver Error/Negligence (26%), Collision (16%), Traffic Congestion (14%) are the main reasons for accidents

• A large part of the causes of accident is uncategorized and grouped together as “Others” (32%).

• Over speeding, Mechanical breakdown contribute to a very small portion of the cause of accident.

Exploratory Analysis

Comparison between City and Highway accidents

• Higher volume of city accidents take place compared to Highway.

• Also the total of sum assured is higher for City limit, hence proving that the Insurance sector has better business opportunity in city rather than highway.

• Also the registration number of the vehicles gives the idea of the location of accidents but this logic is mostly valid for city limit accidents. As vehicles travelling on highways tend go across state and hence the assumption would be less accurate.

• Also, accidents on highways tend to take longer to be recorded and also due to lack of services on highway may not always go through official processes.

Hence the analysis of accidents within city limits would be more beneficial and profiling would produce more accurate results

Analyzing the city accidents

67% of the accidents happened within city limits.

60% of the accidents happen with cars followed by 30% happening with mopeds/scooters/motorcycles.No Taxi accidents have happened within city limits

Most of the cars and 2-wheelers involved have been registered between 1999-2001.

98% of the drivers are males with average age of 35 and range of 18 – 62 years of age.

56%of the time the owner was driving the vehicle themselves. This jumps to near 60% for private vehicles like cars, mopeds/scooters and motorcycles, while around 85% of the time public vehicles (Bus, Taxi, 3 Wheeler) are driven by Paid drivers.

• But only 3.25% of the vehicles from these mentioned manufacturers have meet accident because of mechanical breakdown, which clears the quality issue and thus the high numbers above are indicative of there market dominance.

• 55% of the cars involved is accident belong to Maruti followed by 12% made by Tata Motors.

• 55% of the Motorcycles involved are made by Hero Honda followed by 21% by Bajaj and 10% from Yamaha

• Moped/Scooters are mostly made by Bajaj (26%), Kinetic (26%), LML (20%) or TVS (15%)

• High number of accidents are indicative of 2 things

1. The quality of cars manufactured are low

2. The manufacturer is the market leader and sales volumes are very high

• Maharashtra (MH) leads for most Cars, Motorcycle and CGV accidents.

• Madhya Pradesh (MP) leads for most bus accidents, while Andhra Pradesh (AP) for 3-wheelers.

• Gujarat is place with maximum Moped/Scooter accidents and also the place where most collisions happen.

• Accidents caused due to AOG Perils, Over speeding, Mechanical Breakdown mostly happen in Maharashtra.

• Andhra Pradesh (AP) leads for most accidents cased by Driver error/ Negligence. It is also the only state where every type of vehicle accidents have been registered.

• Karnataka (KA) leads for most accidents caused with Parked Vehicles.

• Most drivers causing accidents were issued license within last 5-6 years.

• Motorcycles are the major contributors for accidents due to negligence.

• The average age of drivers of motor cycle is way below 35, and hence can be a major reason why most motor cycle accidents are caused due to driver error/negligence.

• Recent licenses have been issued to only 3-wheeler drivers.

• But driver negligence is one of the major cause of accidents across all public vehicles

• For 3-wheelers, over speeding is also a prominent factor

• Other than 3-wheeler drivers rest drivers are close or above the average age of 35.

• The number of mechanical break-downs may be very less but the average estimated loss is the highest.

• A similar case is seen with AOG Perils.

Results InterpretationProfile for City accidents

Vehicle Type

Driver type Age Group of Drivers causing accidents

% of the License issued in the last 10 years

Top 3 Cause of accident Nature of loss and average estimated monetary value

Bus (1%)

Paid (100%) 26-35 years (100%) 0% No prominent reason Damage (INR 1,20,000.00)

Cars (59%)

Self (58%) 26-35 years (~ 36%) 75% Traffic Congestion (33%)Others (33%)Driver Error/Negligence (23%)

Damage (INR 24,000.00)

36-45 years (~ 34%) 50% Others (45%)Driver Error/Negligence (25%) Collision (20%)


Paid (18%) 26-35 years (~58%) 55% Driver Error/Negligence (55%)Others (28%)


Others (22%) 26-35 years (~44%) 70% Others(40%)Traffic Congestion (20%)

Damage (INR 18,000.00)Theft-Partial (INR 17000.00)

36-45years (~30%) 57% Driver Error/Negligence (43%)Collision (29%)

Damage (INR 11,000.00)Theft-Partial (INR 23000.00)

Vehicle Type




CGV (6%)

Paid (100%) 26-35 years (30%) 60% No prominent reasons Damage (INR 1,99,000.00)

36-45 years (40%) 50% No prominent reasons Damage (INR 1,68,000.00)

Moped/Scooter (13%)

Self (64%) Less than 25 years (35%)

100% Collision (40%)Others (40%)

Damage (INR 2562.00)

46-55 years (28%) 50% Driver Error/Negligence (75%)Traffic Congestion (25%)


26-35 years (21%) 100% Traffic Congestion (65%)Others (33%)


Others (32%) 36-45 years (57%) 50% Collision (50%)Driver Error/Negligence (25%)Others (25%)


26-35years (29%) 100% Others (50%)Traffic Congestion (50%)


Vehicle Type




Motorcycle (20%)

Self (64%) Less than 25 years (43%)

100% Driver Error/Negligence (56%)Others (22%)


26-35 years (48%) 100% Driver Error/Negligence (30%)Traffic Congestion (30%)Collision (20%)


Others (33%) Less than 25 years (55%)

100% Driver Error/Negligence (83%)Collision (17%)


Three Wheelers (2%)

Self (68%) 26-35 years (100%) 50% Driver Error/Negligence (50%)Over-speeding (50%)


Main Message

• 26-35 years is the most common age group. • Young drivers are more riskier.• Driver Error/Negligence is the most common cause of

accidents for private vehicles.• Public vehicles don’t have any prominent reasons for

accident.• Average Estimated loss for private vehicles is 10 times

that of public vehicles

Recommendation

• Person’s relationship with the vehicle should be considered.• The year of license issued should be a major factor.• Analysis results can be used for better design of insurance

terms and premium.• Collect more data on driver’s mental physical condition for a

better picture• Addition of medical conditions would also add more clarity.• Weather condition data like rain, haze etc. can also be

additional help.

Driver profile caused accident

Data & Analytics

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