primary data from fir/case filestripp.iitd.ernet.in/assets/newsimage/gt-roadtrafficcrash...verka...

Role of stakeholders after crashPrimary data from FIR/Case Files

Geetam Tiwari

MoUD Chair Professor

Transportation Research and Injury Prevention Program, Department of Civil Engineering, Indian Institute of Technology, Delhi, India

Levels of data collection

Epidemiology of fatal crashes, fixing priorities

Depth of detail

Base

Intermediate

Police Data,

Traffic police

Traffic Data

Injury coding, specialists

In-depth multidisciplinary causativeCrash reconstruction

specialists

traffic management

strategies, road designs

Vehicle design,

standards, rd.furniture

design

PRIMARY LEVEL DATA

•Expected to accomplish the following functions:

1. To give perspective view of the RTC situation in terms of i. who is involved (the type of road user) ii. where (Urban or rural area, road layout),iii. when (day or night) and under what circumstances.

2. To enable trends to be examined,3. Provide a basis for comparison against which road safety

professionals may match their performance, either within or between state or national administrations.

Establishing priorities for action.

Need for the revision/modification

• Under reporting of victims specially of pedestrians, cyclists and motorcyclists etc.(Mohan etal.2005, Bhalla et al. 2016)

• Non usage of standardised recording formats at the police station level

• Doubts over some reported data due to inadequate information at the police station

• Inadequate details of impacting vehicle.

Road Accident Recording & Reporting Form 4

Comprehensive format covers

• Format for recording road accident data by the Police at accident site covers the following :.

i. A. Accident identification details

ii. B. Road related details

iii. C. Vehicles involved in accident

iv. D. Drivers details &

v. E. Persons other than drivers involved in accident.

Road Accident Recording & Reporting Form 6

New details

Simplified definitions

GPS details for location

Use of safety devises

Identification of victim & impacting vehicle

Summary of 5 regional workshops(March -June2017)

• Standardised format is not used by police stations, shortage of manpower

• Details other than FIR format should be filled by other people, police under home ministry records as per the requirements of FIR/CCTNS

• TRW must coordinate with CCTNS to improve recording &reporting.

• Some training is required at police station level for data recording

• Recording format should be filled up at the time of closing the case file.

• 30% of the details can be filled at the time of filing the FIR.

• MoRTH should provide a software combining recording and reporting format.

Section A Accident identification

Type of injury

defined

Type of collision

defined

Section B Road Details ( to identify location on the map)

Additional

information

Additional

information

Section B continued…. (physical features at location)

New

New

Column 33 , each row

for each vehicle

Vehicle 1

Vehicle 2

New

Driver of vehicle no

(col 33)

Victim vehicle vs Impacting vehicle ( col 33)

NEW

Occupant of

vehicle type to

be added)

S. No. Parameters

Total Number of FIRs data

records

Information Available in Number of FIR records

Availability (%)

Missing (%)

Remark

1 Police Report Available

306 306 100% Available/extract from each FIR's case description

2 FIR No 306 306 100% Each FIR has a year wise unique number

3 City/Town/Village Name

306 306 100% Available/extract from each FIR's case description

4 Time of Accident 306 299 98% 2% Generally mentioned in FIR

5 Date of Accident 306 306 100% Generally mentioned in FIR

6 Day of Accident 306 123 40% 60%

Days are mentioned in computerised FIRs but generally missing in Hand written FIRs. Days are filled up in "Accident Form" refer from Indian calender w.r.t. date of crashes mentioned in FIRs.

7 Holiday 306 100% Holidays are filled up in "Accident Form" from Indian calender w.r.t. dates of crashes mentioned in FIRs.

8 Hit and Run 306 306 100% Available/extract from each FIR's case description

9 Accident Severity 306 306 100% FIRs related to 279 & 304A both IPC which Accident Severity 3 only, collected from various Police Stations

10 No of Fatalities 306 306 100% Available/extract from each FIR's case description

11 Victim Information

i) Sex 306 306 100% Generally mentioned in each FIR's case description

ii) Age 306 8 3% 97% Generally not mentioned in FIR

iii) Road User Type 306 306 100% Generally mentioned in each FIR's case description

iv) Seating Position

306 306 100% Generally mentioned in each FIR's case description

v) Location of non Occupant (Pedestrian)

306 100% Generally not mentioned in FIR

vi) Mode of Treatment

306 306 100%

Generally mentioned in each FIR's case description

vii) No of Days in Hospital

306 100% Generally not mentioned in FIR

12 No of vehicles 306 306 100% Generally mentioned in each FIR's case description

vi) Mode of Treatment

306 306 100%

Generally mentioned in each FIR's case description

vii) No of Days in Hospital

306 100% Generally not mentioned in FIR

No of vehicles 306 306 100% Generally mentioned in each FIR's case description

Vehicle Information

i) Type of vehicles 306 306 100% Generally mentioned in each FIR's case description

ii) Collision Type 306 293 96% 4% Available/extract from each FIR's case description

iii) Manoeuvering at Crash

306 237 77% 23% Available/extract from each FIR's case description

iv) Impacted 306 238 78% 22% Available/extract from each FIR's case description

v) Loading 306 100% Not mentioned in FIR

vi) Disposition 306 100% Not mentioned in FIR

vii) Mechanical Failure

306 100% Not mentioned in FIR

Location (urban/rural)

306 100% Generally not mentioned in FIR but extracted from FIR's case description

Collision Spot 306 304 99% 1% Generally mentioned in each FIR's case description

Road Information

i) Road Catagory 306 272 89% 11% Generally not mentioned in FIR but extracted from collision spot mentioned in FIR's case description

ii) Type of Road 306 100% Available/extract from each FIR's case description

iii) Divider 306 11 4% 96% Generally not mentioned in FIR but extracted from collision spot mentioned in FIR's case description

iv) Distance/Km stone

306 306 100% Distance generally mentioned in FIR from PS as reference

Global Position System

i) Global Position (Latitude)

306 100% Not mentioned in FIR

ii) Global Position (Longitude)

306 100% Not mentioned in FIR

18 Junction Information

i) Road 1 306 100% Generally not mentioned in FIR

ii) Road 2 306 100% Generally not mentioned in FIR

iii) Road 3 306 100% Generally not mentioned in FIR

iv) Road 4 306 100% Generally not mentioned in FIR

19 Light Condition 306 100% Generally not mentioned in FIR but extracted from time mentioned in FIR

20 Name of Police Station

306 306 100% Generally mentioned in FIR

21 Landmark 306 306 100% Generally mentioned in FIR

IIT Delhi September

18

Better recording of victim type

Victim vs Impacting vehicle

Road Crash clustering with limited information

3 road stretches with most road accidentsAdministrators perception IV

Road stretch Frequency/15

Verka Milk Plant to Bus Stand via Gurudwara Dukh Niwaran and Khanda Chowk

9-10

Bus stand to Punjabi University 7

Thapar University gate 6

Patiala HIGH RISK CORRIDORS (perceived vs real)Rural Patiala- 17 blackspots Urban Patiala- 35 blackspots

RADMS, Tamilnadu

RADMS -Tamilnadu

17-Sep-18 Tamil Nadu Government

ROAD ACCIDENT DATA MANAGEMENT SYSTEM (TN

Gov.)

Options available through Tabs

HomeAccident

ReportingAccident Analysis

Query Builder

Reports Help

17-Sep-18Tamil Nadu Government

General Query Builder

• Facilitates querying of accidents based upon accident parameters.

• Easy formation of queries –combining different conditions.

• Functionality to save queries and run saved queries.

ROAD ACCIDENT DATA MANAGEMENT SYSTEM

Kilometer Analysis

17-Sep-18 Tamil Nadu Government

ROAD ACCIDENT DATA MANAGEMENT SYSTEM

Grid Analysis

• Analysis carried out on a selected rectangular region

• Selected region divided into smaller grids of specified size

• Grid colour indicates the weightage of the grid according to the number of accidents of each severity

Grid Analysis/Cluster Analysis

Additional Analysis Options

Role of Administration

Shortcomings

Shortcomings of police data

•National level tables(NCRB) for victims are based on “road user causing the accident, therefore pedestrians and bicyclists numbers are incorrect (lower than actual numbers)

• Location is available in the case file, not marked on the map for analysis and remedial measures

IIT Delhi September

18

Data ProblemDifference between Police FIR and NCRB table

GIS

What can be done using GIS

•Map the location•Map the quantities•Map the densitiesHow was the GIS used for the project

•Digital Road Map•Projected Co-ordinate System

• Importing Excel sheet•SQL in the attribute table of four years accident

points

9/17/2018Analysis of Pedestrian fatal accidents in four years

(2006-09) in Delhi using GIS 37

Fatal Accidents in Delhi, 2006-09

9/17/2018Analysis of Pedestrian fatal accidents in four years

(2006-09) in Delhi using GIS 38

ANALYSIS AND OBSERVATIONS

9/17/2018Analysis of Pedestrian fatal accidents in four years

(2006-09) in Delhi using GIS 39

S.

No. ROADNAME

PEDESTRIAN

FATAL ACCIDENTS

PEDESTRIAN

FATAL ACCIDENTS’

Rate per year

1 RING ROAD 363 2.22

2 MEHRAULI BADARPUR RD 89 1.58

3 GRANT TRUNK ROAD 141 1.50

4 SHIVAJI MARG (najafgarh road) 77 1.28

5 OUTER RING ROAD 193 1.03

6 NH-8 76 0.90

7 ROHTAK ROAD 76 0.83

8 AURBINDO MARG 22 0.65

• Analyzed Pedestrian Fatal Accidents, 2006-09

Top eight roads of Delhi in decreasing order of pedestrian fatal accidents, 2006

• Analyzed Rate of pedestrian fatal accidents per year over the

arterial roads

Contd.

• Pedestrian fatalities by buses in four years = 353

• Minor Roads = 35%

• Arterial Roads = 22%

• Sub-Arterial Roads = 15%

• Collector Roads = 11%

Rest 17% were not captured in 20m buffer on both sides of centerline of roads

Analysis of fatalities at arterial roads done by GIS shows that –

• 14% of Arterial roads’ accidents were at arterial to arterial road intersections

• 10% of Arterial roads’ accidents were at arterial to sub-arterial road intersections

• Rest 76% at mid-blocks of arterial roads

Buffer radii chosen at arterial intersections was 150m and at arterial-sub arterial intersections was 15m

9/17/2018Analysis of Pedestrian fatal accidents in four years

(2006-09) in Delhi using GIS 40

Contd.

9/17/2018Analysis of Pedestrian fatal accidents in four years

(2006-09) in Delhi using GIS 41

Critical Road sections for pedestrian accidents over Ring Road,

Delhi,2006-09

LEGEND

Contd.

9/17/2018Analysis of Pedestrian fatal accidents in four years

(2006-09) in Delhi using GIS 42

Density map for pedestrian accidents in Delhi,

2006-09

LOWHIGH

Striking Vehicle – Bus

• 22% were on arterial roads

• 15% were on sub-arterial roads

• 11% were on collector roads

• 35% were on minor roads

• 17% was not captured in the given buffer distance

Monday, 17 September 2018 TRIPP, IIT Delhi 43

Striking Vehicle – Unknown

• 28% were on arterial roads

• 13% were on sub-arterial roads

• 4% were on collector roads

• 37% were on minor roads

• 17% was not captured in the given buffer distance

Monday, 17 September 2018 TRIPP, IIT Delhi 44

Kernel Density Map

Monday, 17 September 2018 TRIPP, IIT Delhi 45

Fatalities around Sample Stops

Monday, 17 September 2018 TRIPP, IIT Delhi 46

• Moran’s Index = 0.119

• Expected Index = –0.003

• z-Score = 0.587

• p-value = 0.557

• Pattern does not appear to be significantly different than random

Conclusions

• Fatalities around bus stops do not display any significant clustering

• Proportions of striking vehicles and victims around bus stops is not significantly different from all of Delhi

• Hence, bus stops are not particularly more unsafe than other areas in the city

• Lack of correlation between safety score in audit and high-crash locations of bus stops indicates revision of PTA audit toolkit required

Monday, 17 September 2018 TRIPP, IIT Delhi 47

Future Directions

•FIRs should be freely available (Haryana and Punjab FIRs on web)

•Specialist groups in research and academic institutes for secondary level analysis( evaluation of design standards, construction guidelines,etc)

•Specialists groups in Academic institutes in collaboration with automotive industry for vehicle standards

Korea Road Authority System

Spatial Analysis System and Statistical Analysis System

Data received at state level, cleaned by state

analysts

Possible data sources

Data Sources Possible biases

1 Police

FIR/casefiles

Injury crashes are under reported

2 Hospital data Crash details are not recorded, depends on

the specialty of the hospital

3 Special

Agencies(conc

essionaires)

BOT operators or toll operators maintain

record of crashes causing damage to the

road furniture along with other crashes

4 Insurance Depends on what injuries and damage to

the vehicles are compensated

5 Research teams Misses out fatal pedestrian and bicycle

crashes where victims are removed quickly

Table 5: Data sources and possible biases

Recommendations

Short term

• The recording format should be filled by the investigating officer. A copy of the completed form can be attached with the case file and also sent to DCRB.

• An excel data base can be created for generating the required reports at the police station.

REcommendations

Long Term

• Data of fatal crashes may be maintained separately with more complete information with the help of RTO, PWD, NHAI.

• Fatal crash data quality should be checked at a regional or state level and saved in a central data base. This may require TRW, MoRTH appointed state level officers.

• IITS can assist TRW-MoRTH in coordinating the checking, and cleaning of the fatal crash data and periodic training of police officers for improving the recording and reporting of crash data.