Lec 32, Ch5, pp.131-153: Highway Safety Improvement Program (objectives)

Learn the components of FHWA’s Highway Safety Improvement Program

Know typical data collection and data maintenance methods (through reading)

Learn typical accident data analysis methods and a method to identify locations with accident rates higher than average locations

Learn a method to identify hazardous locations and elements

“Evaluate crash data, redesign and reconstruct the highway system where the potential for high crash rates exists”

What we discuss in class today… (emphasis on yellow-color topics)

Current accident statistics Components of HSIP by FHWA Accident rates typically used Methods of summarizing accident data A method to identify high-accident-rate

locations: expected value analysis A method to identify hazardous locations

and elements

Wrong jump!POW!

Almost 29% of the fatal accidents involved speeding in 2000 (about 12,350 lost their lives).

Component of the Highway Safety Improvement Program (HSIP) by FHWA

Need estimates of the effectiveness of safety design features

Accident data collection and record systems

One of the most basic functions of traffic engineering is keeping track of the physical inventory.

Example: AIMS (Accident Info Mgmt System) by JMW EngineeringAccident spot map

Collision diagram

Types of statistical displaysThe purpose of the display dictates the type of display – temporal, spatial, accident type, etc.

Methods of summarizing accident data

Occurrence

Involvement Severity

Types of accidents

Numbers of accidents

Time period

Categories of vehicles

Categories of drivers

Environmental conditions

No. of deaths

No. of injuries

No. of POD

Types of statistics

Typical accident rates used“Bases” are needed to compare the occurrence of accidents at different sites.

Population based:

Area population

No. of registered vehicles

No. of licensed drivers

Highway mileage

Exposure based:

VMT

VHT

Severity index:

No. of deaths/accident

Typical basic accident rates:

general accident rates describing total accident occurrence

fatality rates describing accident severity

involvement rates describing the types of vehicles and drivers involved in accidents

Sample accident rates in pages 139-140 (rates are usually annual values)

Rate per million of entering vehicles (for intersections):

Rate per 100 million vehicle miles (for highway segments):

V

AREMV

000,000,1

VMT

ARMVM

000,000,100

(See Example 5-1 and 5-2)

(Thousands)

Determining high/low-accident locations: Expected value analysis (p.140)

H0: Accident rate at the location under consideration in the group is equal to the average rate of the group.

H1: Accident rate at the location under consideration in the group is not equal to the average rate of the group (In another words, we are trying to find whether the site under study is “unusual” or not. We are not specifically proving it is “over-represented” or not.)

ZSxEV

Locations with a higher accident rate than this value would normally be selected for specific study.

Note this method is used only to compare sites with similar characteristics.

z = 1.96 for the 95% confidence level

“Over-represented”“Under-represented”

Not over-represented or under-represented

%5.2 %5.2

zsx zsx x

Example 5-3 (modified): An intersection with 14 rear-end, 10 LT, and 2 right-angle collisions for 3 consecutive years (p.141)

Check about rear-end collisions

34.1046.496.15.140.705.0 toEV Rear-end collisions are over-represented at the study site at 95% confidence level, since 14 > 10.34.

Check about LT collisions

92.1288.096.107.390.605.0 toEV

LT collisions are not over-represented or under-represented at the study site at 95% confidence level, since 0.88<10 < 12.92.

Control site Rear-end

LT collisions

Right-angle

1 8 11 4

2 5 12 5

3 7 4 3

4 8 5 6

5 6 8 7

6 8 3 8

7 9 4 4

8 10 9 5

9 6 7 6

10 7 6 7

Mean 7.40 6.90 5.5

SD 1.5 3.07 1.58

Check about right-angle collisions 65.104.296.158.15.505.0 toEV Right-angle collisions are under-represented at the study site at 95% confidence level, since 2 < 2.4.

Identifying hazardous locations and elements Crashes happen randomly and are “rare events.” You cannot identify hazardous locations simply on the basis of the number of crashes.

A technique known as the critical crash rate factor method is one method used to identify hazardous locations. This method incorporates the traffic volume to determine if the crash rate at a particular location is significantly higher than the average for the type of facility.

We compute first the statewide CR value for similar types of roadway, which works as a “threshold.”

TB

AVRTF

TBAVRCR

5.0

CR = critical crash rate, per 100 million VMT or per million entering vehicles

AVR = average crash rate for the facility type (in terms of PDO crashes; a multiplicative factor is used to convert the impact of death/injury accidents to PDO accidents)

TF = test factor, Z score (remember this needs to be a one-way analysis because we try to find a “significantly higher” or “hazardous” location) for a given confidence level,

TB = traffic base, 100 million VMT or million entering vehicles

Identifying hazardous locations and elements (cont)

Once the critical crash rate is computed, compute segment crash history in terms of PDO equivalents.

Then compute Crash Ratio as defined below:

Crash Ratio =Segment crash history

Statewide crash history

If the resulting accident ratio is greater than 1.0, then a safety problem is likely to exist.

(Review Example 5-4)