Innovative Tools and Techniques in Identifying Highway Safety ... · • Swapnil Samant • Sushant...

TxDOT Research Project 0-6912

Innovative Tools and Techniques in Identifying Highway Safety Improvement

Projects

Ioannis (Yianni) Tsapakis & Bahar Dadashova

October 11, 2017

1

Project TeamTxDOT

• Darrin Jensen (PM)

• Darren McDaniel - TRF

• Jacob Chau - Waco

• America Garza - Corpus Christi

• Brad Tiemann - Tyler

• Jeffery Vinklarek - Yoakum

• Rebecca Wells - Atlanta

• Kelli Williams - Odessa

Non-TxDOT

Ismael Soto

TTI

• Ioannis Tsapakis (PI)

• Karen Dixon (Co-PI)

• Bahar Dadashova

• Srinivas Geedipally

• William Holik

• Jerry Le

• Jing Li

• Swapnil Samant

• Sushant Sharma

20-6912 - Innovative Tools and Techniques in

Identifying Highway Safety Improvement Projects

Topics

• Research Questions

• General Framework

– Network Screening for Segments

– Network Screening for Intersections

– Crach Analysis and ViSualization (CAVS) Products

– Project Prioritization Tool

• Recommendations



Research Questions

How can TxDOT:

• Allocate HSIP funds in the most cost-effective manner?

• Create a level playing field for all districts participating in the HSIP?

• Improve and streamline existing HSIP processes?



General Framework

5

Network Screening

Diagnosis

Countermeasure Selection

Economic Appraisal

Project Prioritization

Evaluation

0-6912 - Innovative Tools and Techniques in Identifying Highway Safety Improvement Projects

6

Network Screening Diagnosis Select Countermeasures Economic Appraisal Prioritize Projects Safety Effectiveness Evaluation

Establish Focus

· Crashes occurred on on-system mainlanes

· Reduce number and severity of fatal and incapacitating injury crashes

Identify Network and Establish Reference Populations

· On-system main lane segments

· Group roadway segments by HPMS functional class

Screen and Evaluate Results

· Calculate performance measure(s) for each site

· Create table and map that show the results of network screening

· Rank sites based on performance measure(s)

Select Performance Measures

· Given the data that are currently available at TxDOT, consider the following

performance measures:

o Average crash frequency

o Crash rate

o Critical rate

o Excess predicted average crash frequency using method of moments

o Excess expected average crash frequency using SPFs

o Probability of specific crash types exceeding threshold proportion

o Excess proportions of specific crash types

Select Screening Method

· Sliding window method (preferred)

· Simple ranking method (simple, but not as reliable as sliding window

method)

Review CAVS Data

· Review crash locations using GIS tools

· Descriptive statistics of crash conditions (e.g., counts of crashes by type, severity, roadway or environmental conditions)

Assess Supporting Documentation

· Obtain and review documented information that provides addition

perspective to the CAVS data review. This information may include:

o Current traffic volumes for all travel modes

o As-built construction plans

o Relevant design criteria and pertinent guidelines

o Inventory of field conditions

o Relevant photo or video logs

o Maintenance logs

o Recent traffic operations or transportation studies

o Land use mapping and traffic access control characteristics

o Historic patterns of adverse weather

o Known land use plans for the area

o Records of public comments on transportation issues

o Roadway improvement plans in the site vicinity

o Anecdotal information about travel through the site

Assess Field Conditions

· Validate safety concerns identified from the review of crash data and

relevant documentation

· Travel through the site from all possible directions and modes

· Consider the following factors:

o Roadway and roadside characteristics

o Traffic conditions

o Traveler behavior

o Roadway consistency

o Land uses

o Weather conditions

o Evidence of problems (e.g., broken glass, skid marks, damaged signs)

Identify Safety Concerns

· Compile information to identify any specific crash patterns that could be

addressed by one or multiple countermeasures

Identify Contributing Factors

· Consider human, vehicle, and roadway contributing factors before, during, and after the crash. Possible contributing factors associated with different manners of collision and types of crashes on roadway segments include, but are not limited to:

o Vehicle rollover

- roadside design

- inadequate shoulder width

- excessive speed

- pavement design

o Fixed object

- obstruction in or near roadway

- inadequate lighting

- inadequate pavement markings

- inadequate signs, delineators, guardrail

o Nighttime

- poor visibility or lighting

- poor sign visibility

- inadequate channelization or delineation

- excessive speed

o Wet pavement

- pavement design

- inadequate pavement markings

- inadequate maintenance

o Opposite-direction sideswipe or head-on

- inadequate roadway geometry

- inadequate shoulders

- excessive speed

o Run-off-the-road

- inadequate lane width

- slippery pavement

- inadequate median width

o Bridges

- alignment

- narrow roadway

- visibility

Select Potential Countermeasures

· Review CAVS data and identify possible contributing factors

· Identify countermeasures that may address the contributing factors

· Conduct cost-benefit analysis, if possible, to select preferred treatments

Quantify Crash Reduction

· Calculate monetary benefits:

o Estimate change in crashes by severity

o Convert change in crash frequency to annual monetary value

o Convert monetary value to a present value

· Calculate costs:

o Calculate construction and other implementation costs

o Convert costs to present value

· Economic evaluation methods for individual sites:

o Net present value method

o Benefit-cost ratio (BCR) method

o Cost-effectiveness analysis (effectiveness is measured by the difference between predicted crash frequency and observed crash frequency)

Identify Economically Justified Countermeasures

· Identify one or more candidate countermeasures for possible implementation at each site (the countermeasures must be economically justified based on economic appraisal results)

· Return to the step of Select Countermeasures if considered countermeasures are not economically justified

Evaluation Types

· Safety effectiveness evaluation may include:

o Evaluating a single project at a specific site to determine the effectiveness of the project

o Evaluating a group of similar projects to determine the effectiveness of those projects

o Evaluating a group of similar projects to quantify a CMF for a countermeasure

o Assessing the overall effectiveness of specific types of projects or countermeasures in comparison to their costs

Evaluate Non-Monetary Factors

· Non-monetary considerations include:

o Public demand

o Public perception and acceptance of safety improvement projects

o Meeting established and community-endorsed policies to improve mobility or accessibility along a corridor

o Air quality, noise, and other environmental considerations

o Road user needs

o Providing a context sensitive solution that is consistent with a community’s vision and environment

Prioritize Projects

· Prioritization methods include:

o Incremental benefit-cost analysis ranking

o Ranking by economic effectiveness measures

o Optimization methods (including basic optimization methods and multi-objective resource allocation method)

Safety Evaluation Methods

· Evaluation methods include:

o Observational before/after evaluation studies

o Observational before/after evaluation studies using SPFs – the Empirical Bayes Method

o Observational before/after evaluation study using the comparison-group method

o Observational before/after evaluation studies to evaluate shifts in collision crash type proportions

o Observational cross-sectional studies

o Experimental before/after evaluation studies

Esta

blish F

ocu

s

Network Screening for On-System Main-Lane Segments

Iden

tify

Netw

ork

and

Esta

blish R

efe

ren

ce P

op

ula

tion

s

Import TxDOT Road-Highway

Inventory Network (RHiNo) data into

ArcGIS

Filter for on-system main lane segments and create a feature

class

The RHiNo attribute “REC” is used to differentiate various segment types.

Add a lane-width attribute to the feature

class

Calculate the lane width

Lane width is calculated as SUR_W (surface width) divided by NUM_LANES (number of lanes).

Dissolve main lane segments based on selected attributes

The attributes include district, county, highway name, functional class, AADT, number of lanes, lane width, shoulder width and use (both inside and outside), and median width.

Project dissolved feature class

Projection coordinate system: NAD_1983_2011_Texas_Centric_Mapping_System_Lambert

Import three years of CRIS crash data (Excel format) into

ArcGIS

Select target crashes:

· Fatal and incapacitating crashes

· On-system crashes

· Main/proper lane crashes

· Crashes with valid coordinates

· Crashes with valid highway name

· Crash_Severity = ‘FATAL’ or ‘INCAPACITATING INJURY’

· On_System_Flag = ‘Yes’

· Road_Part = ‘MAIN/PROPER LANE’

· Crash_Latitude <> 0 AND Crash_Longitude <> 0

· HWY <> Null

Delete fields not needed

Export displayed crashes as feature

class

Display selected crashes on ArcMap

Geographic coordinate system:GCS_WGS_1984

Project crash feature class

Projection coordinate system: NAD_1983_2011_Texas_Centric_Mapping_System_Lambert

Use the functional classification from the TxDOT Roadway Safety Design Workbook to reclassify RHiNo segments in order to apply SPFs

Merge adjacent segments with similar characteristics

for each group

Find adjacent segments for each segment

Identify ‘similar’ adjacent segments

Update attribute values for identified ‘similar’ adjacent segments

Merge ‘similar’ adjacent segments

Find two nearest segments for each

crash

Both segments’ highway names do not match with

the highway name of the crash

Select the segment that is closer to the

crash

Only one segment’shighway name matches with

the highway name of the crash

Both segments’highway namesmatch with thehighway name

of the crash

Project crash to corresponding

segment

Extract DFO for projected crash from

RHiNo

Use ArcGIS tool ‘Generate Near Table’

Use the ArcGIS tool ‘Locate Features Along Routes’

Sele

ct P

erfo

rman

ce M

easu

res

Review availability of other data and

functions at TxDOT

For example, SPFs calibrated for Texas roads

Use multiple performance measures to improve the level of confidence in the results. Performance measures that currently can be used at TxDOT are:

· Average crash frequency

· Crash rate

· Critical rate

· Excess predicted average crash frequency using method of moments

· Excess expected average crash frequency using SPFs

· Probability of specific crash types exceeding threshold proportion

· Excess proportions of specific crash types

Apply available data and functions

App

ly S

cre

enin

g M

eth

od

Generate a feature class of points along each segment at 0.1

mile interval

Assign number to each generated point

Numbering starts at 1 for each segment. Both start and end points are numbered.

Assign window group number(s) to each

generated point

· Window size is 0.3 miles

· Window moves at 0.1 mile increment

· For segments <= 0.3 miles, only end points are labeled as Window Group 1

· For segments > 0.3 but <= 0.6 miles, multiple points are labeled as Window Group 1, or Window Group 2 depending on the location of point

· For segments > 0.6 miles, multiple points are labeled as Window Group 1, or Window Group 2, or Window Group 3 depending on the location of point

Split the point feature class into three feature

classes by window group

Split segments at points from each

window group respectively

Apply the ArcGIS tool ‘Generate Points Along Lines’

Assign window ID to newly created windows (sub-

segments)

Window ID = Segment ID + “_” + Window Group Number + “_” + FID

Apply the ArcGIS tool ‘Split Line at Point’

Scre

en

an

d E

val

uate

Result

s

Apply sliding window method to roadway segments of a specific functional class

Map crashes on segments

Calculate performance measures for each

window

Rank windows based on one or multiple

performance measures

· Sites that repeatedly appear at the higher end of the list could become the focus of more detailed site investigations

· Sites that repeatedly appear at the low end of the list could be ruled out for needing further investigation

· Differences in the rankings due to various performance measures will become most evident at sites that are ranked in the middle of the list

Criteria for determining ‘similar’ segments:

· Functional classification: two adjacent segments belong to the same roadway functional class

· Highway name: two adjacent segments have the same highway name

· Number of lanes: two adjacent segments have the same number of lanes

· ADT: the difference in ADT values between two adjacent segments is less than or equal to a certain percent, which varies by the magnitude of the ADT

· Median width: the difference between two adjacent segments is less than or equal to 0.5 ft.

· Inside shoulder width: the difference between two adjacent segments is less than or equal to 0.5 ft.

· Outside shoulder width: the difference between two adjacent segments is less than or equal to 0.5 ft.

· Lane width: the difference between two adjacent segments is less than or equal to 0.5 ft.

· Inside/outside shoulder use: both adjacent segments allow curb parking (either diagonal or parallel parking) on inside/outside shoulder or both do not allow shoulder parking

R2

R3

R4

R5

√

√

√

√

R6

R7

√

√

U2

U3

U4

U5

√

√

√

√

U6

U7

√

√

Functionalclass

Highway name

±40%

±40%

±50%

±50%

-

-

±20%

±30%

±40%

±50%

±50%

-

Number of lanes

ADT

-

±0.5 ft.

Lane widthMedian width

Inside shoulder

width

Outside shoulder

width

R1

U1

Inside shoulder

use

√

√

√

√

√

√

√

√

√

√

√

√

√ ±20%√

√ ±30% -√

-

-

-

-

-

-

-

-

-

-

-

±0.5 ft.

±0.5 ft.

±0.5 ft.

±0.5 ft.

-

-

-

-

-

-

-

-

-

-

±0.5 ft.

±0.5 ft.

±0.5 ft.

±0.5 ft.

-

-

-

-

-

-

-

-

-

-

-

±0.5 ft.

-

-

-

-

-

-

-

-

-

-

-

-

-

-

-

√

√

-

-

-

-

√

√

√

√

-

Outside shoulder

use

-

-

-

√

√

-

-

-

-

√

√

√

√

-

· For the attributes ‘functional class’, ‘highway name’, and ‘number of lanes’ retain the original values

· For the attributes ‘ADT’, ‘median width’, ‘inside shoulder width’, ‘outside shoulder width’, and ‘lane width’ update attribute values for both segments with a length-weighted average value

· For the attributes ‘inside shoulder use’ and ‘outside shoulder use’ indicate whether diagonal or parallel parking is available

Use the ArcGIS tool ‘Dissolve’ to merge segments

Create segment groups based on HPMS functional

classification

Dissolve segments in each group

based on selected attributes

Combine all groups of segments into one feature class

Sort segments based on functional classification and

highway name

Assign new ID to each segment

Disaggregate the feature class of all segments into separate feature classes

based on functional classification

Select performance measures

B

A

C

D E

F

G

Enter project data in Excel

Calculate benefits B(i) of each

project i

Calculate the difference in

benefits between two projects

Filter for projects with SII>1

Sort projects in ascending order by construction

cost

Calculate the difference in costs

between two projects

Compute incremental

benefit cost ratio (IBCR) between

two projects

Start with the first two projects in the list

All projects have been ranked and removed

from the initial list

IBCR>1

For example:

IBCR = (B(2)-B(1)) / (C(2)-C(1))

B(i) = SII(i) x C(i)

i = 1, 2, …, n

SII(i) = Safety improvement index of project i

C(i)= Construction cost of project i

IBCR<1

Same project costs

Consider the project selected in the last pairing as the best economic

investment

Remove the project selected in

the last pairing from the initial list

More projects in the initial list to

be compared

End of the list (all projects have been compared)

Repeat calculations for the remaining unranked projects in the initial list

Consider project with higher cost and

compare it with next project in the list

Consider project with lower cost and

compare it with next project in the list

Consider project with higher benefits and compare it with next

project in the list

Rank the project based on the order in which it was included with the other projects that were previously determined to be the best economic investment.

Include it with other projects

determined to be the best economic

investment

Extract crash data from CRIS for the last three years

Extract preventable crash criteria for each work code (WC)

Determine applicable WCs for each crash

location

Filter for specific crash types

Apply data quality control criteria

Filter for:

· Fatal (K) and incapacitating (A) injury crashes

· On-system crashes

· Mainlane crashes

Select crashes with valid:

· Highway name

· Geographical coordinates (latitude and longitude)

Source: TxDOT HSIP Work Code Manual

Import data table in ArcGIS

Map crashes on RHiNo using geographical coordinates

Import RHiNo in ArcGIS

Geographic coordinate system:GCS_WGS_1984

Create shapefile containing

mapped crashes

Filter crash data by single WC or combination of

WCs

Crate feature class for each

selected WC or combination of

WCs

Create unique symbol for each

feature class

Convert each feature class into

a kml layer

More kml layers need to be created.

All kml layers have been created.

KML layers

CRIS Database

Network Screening Crash Analysis and ViSualization (CAVS)

Project Prioritization

Level 2 Diagram


Network Screening for Segments

7

A

B

AB = 1.0 mile

0.2 miles

0.1 miles


Performance Measures

8

HSM Performance Measure

1. Crash Frequency

2. Crash Rate

3. Equivalent Property Damage Only (EPDO) Average Crash Frequency

4. Relative Severity Index (RSI)

5. Critical Rate

6. Excess Predicted Average Crash Frequency Using Method of Moments

7. Level of Service of Safety (LOSS)

8. Excess Predicted Average Crash Frequency Using Safety Performance Functions (SPFs)

9. Probability of Specific Crash Types Exceeding Threshold Proportion

10. Excess Proportion of Specific Crash Types

11. Expected Average Crash Frequency with Empirical Bayes (EB) Adjustment

12. EPDO Average Crash Frequency with EB Adjustment

13. Excess Expected Average Crash Frequency with EB Adjustment


Network Screening ArcMap Toolbox



Network Screening Results



Provide to all Districts in 2018 &

2019 HSIP

Intersection Data Collection


13

Northern San Antonio

Intersection Data



Intersection Data

CRIS

2013-2015

Crash Numbers

Crash Severity

Crash Location

RHiNo

2013-2015

ADT

Lane Width

Shoulder Width

Manually Collected

Traffic Control & # Legs

# Through, Left and Right Turn Lanes

Right-turn Channelization



Evaluation: KABC Crashes

*Bold numbers are the intersection IDs



Summary: Performance MeasuresPerformance Measure Accounts

for RTM

Bias

Accounts for

Traffic

Volume

Accounts

for Data

Variance

Uses

Roadway

Design

Elements

Establishes

Threshold for

Similar Sites

Applicability Requires

Reference

Population based

Computation

Developed for Specific

Crash Type

Crash Frequency No No No No No Simple No No but can be applied

Crash Rate No Yes, but can

be biased

towards low

traffic

volume

No No No Simple No No but can be applied

Critical Rate No Yes Yes No Yes Moderate Yes No but can be applied

Excess PACF Using

Method of Moments

No Yes Yes No Uses average

crash frequency

per reference

population

Difficult Yes No but can be applied

Excess PACF Using

Safety Performance

Functions (SPFs)

No Yes No Yes Uses predicted

average crash

frequency

Difficult (if

the variables

used for

CMFs are

missing)

No but requires

CMFs which are

RP based

Yes

Probability of Specific

Crash Types Exceeding

Threshold Proportion

Not

Affected

No Yes No Yes Difficult Yes Yes, but could be biased

towards the sites with

unusually high crash

frequency

Network Screening Results


17

Very high risk (Adjusted Weighted Ranking <= 10%)

Very low risk (Adjusted Weighted Ranking >= 90%)

¯

Loop 410

CAVS



CAVS

19Implementation Project 5-6912

CAVS


Master Plans


Project Prioritization – IBCR



Benefits

23

• # projects submitted to HSIP by 57%

• Time to identify projects by 15-50%

• Project prioritization method complements SII


Recommendations

24

• Implement network screening & CAVS (5-6912)

• Evaluate projects and countermeasures (0-6961)

• Develop intersection inventory

• Develop new SPFs


Thank You!!!



Ioannis Tsapakis

[email protected]

(210) 321-1217

Karen Dixon

[email protected]

(979) 845-9906

mailto:[email protected]

mailto:[email protected]

Innovative Tools and Techniques in Identifying Highway Safety ... · • Swapnil Samant • Sushant...

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Transcript of Innovative Tools and Techniques in Identifying Highway Safety ... · • Swapnil Samant • Sushant...