Steps Land 2 of the worksheet involve validating crossing characteristic data, and
recalculating the predicted accidents or casualties if any of the data is revised, Step 3
validates the cost and effectiveness assumptions for the recommended warning device.

As a result of completing steps 1, 2 and 3, three basic inputs to the resource allocation
model tnay have changed: (1) number of predicted accidents or casualties (AC); (2)
warning device effectiveness (E); and (3) warning device cost (C). Step 4 of the worksheet
describes the procedure for determining if any input changes will affect the improvement
decision. This procedure requires the computation of the parameter (R) using the formula
below and described in part 2 of step 4:

R = Revised AC , Revised E x Previous C (1)
Previous AC PreviousE Revised C

The value of R is the ratio of the revised to previous accident or casualty
reduction/cost ratio, for the original recommended improvement action. The R value is
then compared with the appropriate decision criteria values (DC, DC2, DC3, and DCyg) as
described within part 3 of step 4 on the worksheet. The decision criteria values are
obtained from the standard output report (see Figure 5-10) of the resource allocation
model. The result of this comparison will determine if the original recommended
improvement should be revised.

The decision criteria values are computed by the standard program of the resource
allocation model for each crossing considered (see Section 5.2 for description of

programs). The formula for computing the four decision criteria are shown below:

DC; = (ACR/Cp))/(A jE 1 /C1)) (12)
DCQ = (ACR/Cp)/(A(E2-E 1)/(C2-C 1) (13)
DC3 = (ACR/Cm)/(Aj(E2/C2)) (14)
DCy = (ACR/Cp)/(Aj(E3/(C3)) (15)

54


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20


NOTICE

This document is disseminated under the sponsorship
of the Department of Transportation in the interest
of information exchange. The United States Government
assumes no liability for its contents or use thereof.

NOTICE

The United States Government does not endorse
products or manufacturers. Trade or manufacturers’
names appear herein solely because they are considered
essential to the object of this report.



P(CIAJA)= 1/(1 + 4.481 x 0.282 x 1.259 x 1.000)

= 0.386 probability of a casualty accident given an accident

The casualty accident probability is then multiplied by the predicted accidents,
computed above using equation (2), to obtain the predicted number of casualty accidents

for the sample crossing from equation (6):
CA = P(CAIA)xA
= 0.386 x 0.169

= 0.065 casualty accidents per year

The combined casualty index (CCI) is obtained from equation (8) for the sample
crossing:

CCI = (k-1) x FA+CA
where: k = fatality factor selected by user
FA = fatal accidents per year from equation (5)

CA = casualty accidents per year from equation (6)

Substituting a value of 50 for k and the above values for FA and CA, the combined
casualty index formula yields:

CCI = 49 x 0.015 + 0.065
= 0.80

5.1.2 Computer Program for Calculation of Predicted Accidents and Casualties

This section describes procedures for using the DOT accident and severity prediction
formula computer program to obtain the number of predicted accidents or casualties per
year for large numbers of crossings, and to list the crossings ranked by number of
predicted accidents or casualties. Complete information for making the computer runs is
supplied, provided the required input data are available and are in the format specified
here. Modifications can be made to the programs to accept a different format. Data in
the format specified here can be obtained from the Federal Railroad Administration,
Office of Safety Analysis.

63


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74


The above two independent predictions are combined as a weighted average using
the general accident prediction formula described in Section 3.2.4, This consists of
computing a weighted average value which is then multiplied by a normalizing constant.

3.2.2 Basic Formula

The unnormalized initial prediction of a crossing's accidents (a) is determined from
the basic accident prediction formula given in equation (1) below. The basic formula
produces a prediction on the basis of a crossing's characteristics as described in the
Inventory. The technique used for developing the basic formula involved applying
nonlinear multiple regression techniques to crossing characteristics stored in the
Inventory and to accident data contained in the FRA Railroad Accident/Incident
Reporting System (RAIRS). The 1981 through 1985 accident file and the April 1986
Inventory were used to develop the formula,

The resulting basic formula can be expressed as a series of factors which, when
multiplied together, yield the unnormalized initial predicted accidents per year (a) at a
crossing. Each factor in the formula represents a characteristic of the crossing described
in the Inventory. The general expression of the basic formula is shown below:

a=K xEIx DT x MS x MT x HP x HL (1)
where:

a = unnormalized initial accident prediction, in accidents per year at the crossing
K = formula constant

EI = factor for exposure index based on product of highway and train traffic

DT = factor for number of thru trains per day during daylight

MS = factor for maximum timetable speed

MT = factor for number of main tracks

HP = factor for highway paved (yes or no)

HL = factor for number of highway lanes


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69


TABLE 3-10. EQUATIONS FOR CROSSING CHARACTERISTIC FACTORS FOR FATAL
ACCIDENT PROBABILITY FORMULA

Fatal Accident Probability Formula: P(FA|A) = 1/(1 + KF x MS x TT x TS x UR)

CROSSING CHARACTERISTIC
FACTOR

EQUATION FOR CROSSING
CHARACTERISTIC FACTOR

Formula constant

Maximum Timetable Train Speed Factor
Thru Trains Per Day Factor

Switch Trains Per Day Factor

Urban - Rural Crossing Factor

KF = 440.9

MS = ms-0-9981

TT = (tt + 1)-0-0872
TS = (ts + 1)0-0872
UR = e0-3571ur

where:

ms = maximum timetable train speed, mph

tt = number of thru trains per day
ts = number of switch trains per day

ur: urban crossing = 1, rural crossing = 0

ur = FC10 (tens digit of functional classification). See page A-11.

27


(((DAYTHRU + 0.2) /0.2)*#0.178) *

(EXP (0.0077*MXTTSP) )*

(EXP (-0.5966*(PAVED-1) ));
ih bbbbbbe hE ELELLCLLLLC LECT TTer eee TT Tete TT Te rere
*

* FLASHING LIGHTS CALCULATION
*

BEEBE EEE EIEIO SO CSS O III G HOSS IIST IIIS TOI III
DATA IFLASH;
SET TRIM;
IF (CLASS < 5 OR CLASS > 7 ) THEN DELETE;
H= .0003351*(((AADT*TRAINS + 0.2)/0.2)#* 0.4106) #
(((DAYTHRU +0.2)/0.2)*#0,1131) *
(EXP (0.1917*MTRKS) )*

(EXP (0.1826*(TRAFLN - 1))) ;
BEEBE OCHIAI TOIT IOOIOIR ITO IIG ITOH III IOI TOTO AAI |
*

* MERGING OF TWO SETS BY CROSSINGS AND USING EFFECTIVENESS
*

JAERI EOS IOC OT TOIT ICO O ICU T A TOO III TOIT»
DATA XING;

SET IFLASH ICROSS;

BY CROSSING;

ARRAY UP UP1-UP3;

ARRAY DN DN1-DN3;

ARRAY UPDN(K) UP1-UP3 DN1-DN3;

OPTION= OPTVAL;

IF OPTION NE 1 THEN DO;

*  --~* USE EXTENDED EFFECTIVENESS VALUS *-~-
IF TRACKS > 1 AND TRAINS > 10 THEN DO;

UPl= 1-X10; UP2= 1-Xll; UP3= 1-X12; END;
ELSE IF TRACKS > 1 AND TRAINS < 11 THEN DO;

UP1= 1-X7; UP2= 1-X8;  UP3= 1-X9; END;

ELSE IF TRACKS=1 AND TRAINS > 10 THEN DO;

UPl= 1-X4; UP2= 1-X5; UP3= 1-X6; END;

ELSE IF TRACKS=1 AND TRAINS LT 11 THEN DO;

UP1= 1-X1; UP2= 1-X2; UP3= 1-X3; END;

END;

ELSE DO;

* = =--* USE STANDARD EFFECTIVENESS VALUES +*--- ;
UPl= 1-S1; UP2= 1-S2; UP3= 1-S3; END;

DO OVER UP;

DN = 1/UP;

END;

*

* ~~ =--* GENERATE INDEX VALUE TO BE USED WITH ARRAY *---
*  -+=* OF EFFECTIVENESS VALUES t---

* ;
IF CLASS = NEWCL THEN GO TO LAB1;
IF (OLDCL LT NEWCL) THEN DO;
K=2;
IF(NEWCL NE 8) THEN K= 1;
IF(OLDCL GT 4) THEN K= 3;
END;

B-5


4, RESOURCE ALLOCATION MODEL
4.1 INTRODUCTION

The resource allocation model was developed to assist state and railroad officials in
their crossing safety improvement decision process’, The procedure provides initial
recommended lists of crossing improvements for consideration. These initial
recommendations may be used by states to guide the on-site inspection of crossings by
diagnostic teams. Revised results based on information obtained by the diagnostic teams
provides a useful set of recommendations upon which state and railroad officials can

finalize crossing safety improvement plans.

The resource allocation model principally provides safety improvement
recommendations for two types of active motorist warning device upgrades; flashing
lights and automatic gates. In addition, it identifies crossings that qualify for standard
highway stop signs according to the FHWA guidelines! 4, The user of the resource
allocation model has the option of selecting either or both sets of recommendations.
Descriptions of the resource allocation model for active warning devices and stop signs
are provided below in Sections 4.2 and 4.3, respectively.

4.2 RESOURCE ALLOCATION MODEL FOR ACTIVE WARNING DEVICES
4.2.1 Overview

The resource allocation model for active warning devices provides a list of crossings
with recommended warning device improvements, The recommendations are based on
achieving the greatest accident or casualty reduction for the available budget, given the
cost and safety effectiveness of the active warning device options.

Input to the resource allocation model includes predicted accidents or casualties for
the crossings being considered, costs and effectiveness of the different safety
improvement options (e.g., flashing lights and gates), and the budget level available for
safety improvement. Accident or casualty predictions for crossings can come from any
prediction formula which computes number of accidents or casualties per year. The DOT
accident and severity prediction formulas described in the previous section were

developed for this purpose.

34


5. APPLICATION OF DOT RESOURCE ALLOCATION PROCEDURE

5.1 DOT ACCIDENT AND CASUALTY PREDICTION FORMULAS

5.1.1 Manual Calculation of Predicted Accidents and Casualties

If the number of predicted accidents or casualties is required for a few crossings, a

convenient manual procedure can be used, employing the formula tables presented in

Sections 3.2 and 3.3. Manual use of the DOT accident and casualty prediction formulas is

illustrated in the following example. Characteristics of the hypothetical crossing for

which the number of predicted accidents and casualties is to be determined are shown in

Table 5-1.

TABLE 5-1. CHARACTERISTICS OF SAMPLE CROSSING
CHARACTERISTIC VALUE
Present warning device Crossbucks
Annual! average daily highway taffic (c) 350
Total number of train movements per day (t) 15
Total number of thru trains per day (tt) 10
Total number of switch trains per day (ts) 5
Number of main tracks (mt) 2
Total number of tracks (main and other) (tk) 2
Number of thru trains per day during daylight (d) 5
Highway paved? (hp) yes
Maximum time table speed, mph (ms) 40
Number of highway lanes (h!) 2
Urban - rural location (ur) Rural
Number of years accident data, T 5
Number of accidents, N, in T years 2

59


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Substituting the factor values into the fital accident probability formula yields:

P(FAIA) = 1/(1 + KF x MS x TT x TS x UR)
= 1/(L + 440.9 x 0.025 x 0.811 x 1.169 x 1,000)
= 0.087 probability of a fatal accident given an accident

The fatal accident probability is then multiplied by the predicted accidents,
computed above using equation (2), to obtain the predicted number of fatal accidents from
equation (5) for the sample crossing:

FA =P(FAJA)xA
= 0.087 x 0.169

= 0.015 fatal accidents per year

To determine the number of casualty accidents at the sample crossing, the casualty

accident probability is first obtained using equation (4):

P(CAJA) = 1/(1 + KC x MS x TK x UR)
where: KC = formula constant
MS = factor for maximum timetable train speed
TK = factor for number of tracks
UR = factor for urban or rural crossing

The factor values for the casualty accident probability formula can be determined from

Table 3-13 using the sample crossing characteristics from Table 5-1:

KC = 4.481
MS = 0.282
TK = 1.259
UR = 1.000

Substituting the factor values into the casualty accident probability formula yields:

62


|
i

IF (OLDCL GT NEWCL) THEN DO;
K=5;
IF (OLDCL NE 8) THEN K=4;
IF(NEWCL > 4) THEN K=6;
END;
H=H*UPDN;
Fe SEN AE IESE IO IGE IEE ITO IIIS ID III III IIS
*
* CALCULATIONS FOR PREDICTED ACCIDENTS
*
© ARE HAH IU EHEC SO IOI III III III I III IIE
LAB1:
CURYEAR= 85;
TA= CURYEAR-CHANGE_Y;
IF TA < 0 THEN TA=
IF TA > 5 THEN TA = 5;
NACC= ACC1+ACC2+ACC3+ACC4+ACC5 i
TO=1./(.05+H) +
A=(H*TO + NACC)/(TA + TO);
IF CLASS LE 4 THEN A= CROSSBK*A;
ELSE IF (4 < CLASS < 8) THEN A= FLASHLK*A;
ELSE A= GATESK*A;
DROP NACC TA TO UP1-UP3 DN1-DN3 K TRAINS ;
Bee SCH UE IS SEG IERIE IIA OOE IE TOGO IIIA II IOS
*
* CALCULATIONS FOR FATAL ACCIDENTS
*
8 SHEA HE HGS III ISI IIIS ATI IO III IOI III IEF
ACCD= ACCVAL;
IF ACCD > 1 THEN DO;
THRU=DAYTHRU+NGTTHRU?;
SWITCH=DAYSWT+NGTSWT;
MS= MXTTSP**(-.9981) ?
TT= (14THRU) **(-0,0872);
TS= (1+SWITCH) **0.0872;
UR= EXP(0.3571*FC10) ;
FATPRB=1/(1. + (440. 9*MS*#TT*TS*UR) ) i
FATAL=FATPRB*A;
DROP MS TT TS FATPRB;
REO OCU E USGI IO IE IE IISA IIIT ISS
*
* CALCULATIONS FOR CASUALTY ACCIDENTS
*
© GRE S URE E E IEII SO TITORI SI TE IEE TISAI III III III ET
IF ACCD = 3 THEN DO;
MS=MXTTSP** (-0.343) 7
TRK=0.1153*TRACKS +
TRSEXP (TRK) +
URB=0,.2960*FC10;
UR=EXP (URB) ¢
CASPRB=1.0/ (1+ (4.481*MS*TK*UR) ) 7
CAS= CASPRB*A;
COMCAS= (KK- 1) *FATALt+ CAS;
DROP TRK TK UR CASPRB CAS;

B-6


i
|

INPUT DATA:
AC, , EFFECTIVENESS

c

STEP 2A

STEP 2B

a. Select Crossing
bd, If Passive Single Track,
Calculate ACR/C Ratios:

aC yF1 and ac, y Fa7Fa
Xe iXere
1 21

c. If Passive, Multiple Track,
Calculate ACR/C Ratio: AC, (E,/Co)
d. If Flashing Light,
Calculate ACR/C Ratio: AC, (E,/C,)

la, Select Crossing
b. If Passive, Calculate ACR/C
Ratio:
E
xe

c. If Flashing Light,
Calculate ACR/C Ratio:

E
AC, 3
Xe

i,
No Are YES Rank AlL Are
All Crossings Incremental ALL Crossings
Considered ACR/C Ratios Considered
? 2

NO

STEP 4

a, Select first entry.

Record warning

system, accident or casualty reduction|
and cost.

Select succeeding entries. Update
warning system decisions. Compute

cumulative total accident or casualty
reductions and costs.

FIGURE 4-1. RESOURCE ALLOCATION ALGORITHM

STOP

40



In the case where EFFECT 2/COST? is greater than EFFECT /COST], the program

calculates a ratio given by the equation:
ACRIC = AC (EFFECT2/COST 2).

This applies to all passive crossings, regardless of the number of tracks. In this case, the
installation of gates is always more cost-effective than installation of flashing lights. The
program does not calculate the incremental accident or casualty reduction/cost ratio in
this case. For convenience of storage, all accident or casualty reduction/cost ratios are
multiplied by 106; i.e., they are expressed in accidents per year per million dollars.

In addition to calculating the accident or severity reduction/cost ratio for each
crossing, RESAL.NEW also determines if a crossing is a possible candidate for standard
stop signs. For a crossing to qualify for consideration for standard stop signs, it must

meet the following criteria:

1, Total trains per day greater than 10

2. No existing standard stop signs

3. Present warning device class less than 5

4, Crossing must be single track

5. For rural area crossings, the annual average daily traffic must be less than 400

6. For urban area crossings, the annua! average daily traffic must be less than
1500

7. Crossing must be loca! highway type.

The set of crossing for which the incremental values of accident or severity
reduction/cost ratios were calculated and stored separately are now appended with all the
other crossings being analyzed and are sorted with respect to accident or severity
reduction/cost ratio in descending order. From this set, only the top few crossings which
can be upgraded within the given budget value are retained.

This new, expanded set may have some duplicate crossings. This is due to the fact
that some passive crossings which were initially upgraded to flashing lights have now
qualified to be considered for upgrade to gates, For all such crossings, the new values of

upgrade cost, accident or severity reduction/cost ratio, and accident benefit are

82


expected number of accidents and casualties at crossings based on information available
in the Inventory and crossing accident data files, The second analytical method is a
resource allocation model designed to select candidate crossings for improvement on a
cost-effective basis and recommend the type of warning device to be installed. This guide
provides complete information on how to use these two analytical methods.

This third edition differs from the second edition? in two principal ways: the
accident and severity prediction formulas have been recalibrated with recent accident
experience and the computer programs have been expanded and rewritten in the SAS
programming language. Although the new formulas are slightly better than the old, the
old formulas are still valid and quite useable. Other refinements of the DOT Procedure of
smaller magnitude have been included. A summary report on the DOT Procedure is

available which should complement the material contained in the present report3,
1.3 ORGANIZATION OF GUIDE

Chapter 2 provides a technical overview of the DOT Procedure and its two major
elements, the DOT accident and severity prediction formulas and the resource allocation

model.

Chapter 3 describes the purpose, development and characteristics of the DOT
accident and casualty prediction formulas.

Chapter 4 describes the resource allocation mode! and its data requirements.

Chapter 5 discusses procedures for use of the DOT Procedure, A sample application

is provided as a means of demonstrating its use for different situations.


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with flashing lights predominate in this category. Class 8 remains as a separate warning
device category called "gates". The resource allocation model only considers
improvements for passive and flashing light crossings, since gates are assumed to be the
most effective warning device available. Therefore, users of the model may want to
obtain a list of gate crossings for the geographical area of interest, possibly ranked by the
severity measure used in the resource allocation computation, to complement the
resource allocation results, This wil! enable the user to bring all crossings into the

analysis in some way.

Table 4-1 is a matrix showing the effectiveness and cost symbols for the three
warning device groupings used in describing the resource allocation algorithm. The matrix
reflects the possible combinations of active warning device improvements currently
considered by the model. For passive crossings, single track, two upgrade options exist;
flashing lights or gates. For passive, multiple-track crossings, the model allows only the
gate option to be considered in accordance with Federal regulations,* For flashing light
crossings, the only improvement option is gates. The mode! can be modified by extending
the basic logic to include other options; however, it would also be necessary to determine
the costs and effectiveness of any additional options that are considered.

For each combination of existing and proposed warning device, a pair of parameters
(Ej,Cj), as shown in Table 4-1, must be provided for the resource allocation algorithm,
where j = 1 for flashing lights installed at a passive crossing, j = 2 for gates installed at a
passive crossing, and j = 3 for gates installed at a crossing with flashing lights. The first
parameer 1) is the effectiveness of installing the proposed warning device at the
crossing. The second parameter (Cj) is the corresponding cost of the proposed warning
device. It has also been determined that Ej can vary according to the number of tracks
and the number of trains per day at the crossing!!, These results are given in Table 4-8.

The resource allocation model considers all crossings with either passive or flashing
light warning devices as candidates for improvements. If, for example, a single-track

37


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Rail-Highway Crossing Federal Railroad

Administration
Resource Allocation Procedure Cet ene ate
USER'S GUIDE, THIRD EDITION DOT/FRA/OS-87/10

DOT-TSC-FRA-87-1

2») August 1987
La)

US.Department
of Transportation

Federal Railroad
Administration

Federal Highway
Administration

This document is available to the U.S. public through the National Technical information Service, Springfield, Virginia 22161



0 eg amar te i

BY STATE CITY C;
IF A; ~
DSTATE= 0;
DSTATE= STATE;
PROC FORMAT;

VALUE ESTATE 1='AL' 4='AZ'
5='AR! 8="co!
='oT! 12='FL!
13='GA! 16='ID'
17='Iu! 20='Ks'
21='Ky! 24='MD'
25='MA! 28='MS'
29='MO! 32='NV!
33='NH’ 36='NY!
37='"NC! 40='OK'
41='OR' 44='RI'
45='sc! 48='TX!
49='UT! 52='VI'
53='WA' 55='"WI't 56='WY';

PICTURE PREDACC OTHER='9.999999';

DATA FINAL1;

SET DATA1C;

PROC SORT;

BY DESCENDING BENCOS;

DATA FINAL;

SET FINAL1;

RETAIN CUMCOST CUMARED 0;

CUMCOST=CUMCOST + COST;

CUMARED=CUMARED+ACCBEN;

IF EFFECT = EFl AND DCl=. THEN DC1=MINTBEN/BENCOS;

ELSE IF EFFECT = EF2 THEN DC3=MINTBEN/BENCOS;

ELSE IF EFFECT= EF3 THEN DC4= MINTBEN/BENCOS;

MACRO LABO CROSSING=XING*ID*# BENCOS=BEN/COST*RATIO
RECCAT=RECOMMD*WARNING*DEVICE
NEWCL=PRESENT*WARNING*DEVICE

TRACKS=TOTAL* TRACKS TRAINS=TOTAL*TRAINS*PER" "DAY

CUMCOST=CUMULATIVE*COST
CUMARED=CUMULATIVE*REDUCED*ACCIDENTS
STPFLG=STOP*SIGN*REQMNT
FC10=CROSSING*LOCATION*URBAN/RURAL % 7

MACRO LABB ACCIDENT=PREDICTED*ACCIDENTS*PER' ‘YEAR %7

MACRO LABF ACCIDENT=PREDICTED*FATAL' 'ACC*PER' 'YEAR %;

MACRO LABC ACCIDENT=PREDICTED*CCI* INDEX %F

MACRO LABP CROSSING=CROSSING*ID BENCOS=BEN/COST*RATIO

DSTATE= STATE COT_NAME=COUNTY CTY_NAME=CITY %;

DATA BASIC;
HARRAH RRR III III RIKKI AIR RII RII ARITA

* PRINT REPORT FOR SEVERITY MEASURE = PREDICTED ACCIDENTS
HIKARI IIR RII IIR IRR IR RII I IIIT III II IRR I TISAI IAS ITAA RIAA IIASA +

LABEL LABO LABB;
SET FINAL;
IF ACCD NE 1 THEN STOP;


PREFACE

The Department of Transportation's (DOT) rail-highway crossing accident, prediction
formula and resource allocation model, described in this report, were developed at the
Transportation Systems Center (TSC) under the sponsorship of the Federal Railroad
Administration's (FRA) Office of Safety Analysis. When used together, these procedures
provide an automated and systematic means of making preliminary cost-effective
allocations of funds for improvement options among individual crossings.

This user's guide provides complete information for application of the DOT procedures,
Preparation of this third edition was the overall responsibility of Edwin H. Farr of TSC.
Randhir Chhatwal of Bedford Research Inc., under contract to TSC, was responsible for
development and description of computer programs required for application of the

procedures.

iii


PROC PRINT SPLIT=*;
VAR CROSSING ACCIDENT BENCOS RECCAT NEWCL TRACKS TRAINS CUMCOST
CUMARED DCl DC2 DC3 DC4 STPFLG;
FORMAT NEWCL CLASS. STPFLG YES_NO.
ACCIDENT CUMARED ACCBNF.
DCl DC2 DCc3 DC4 DECIS.;
TITLE TITVAL;
TITLE2 RAIL~HIGHWAY CROSSING RESOURCE ALLOCATION RESULTS;
TITLE3 BASED ON PREDICTED ACCIDENTS PER YEAR;

DATA;

* ---%* PRINT REPORT SORTED BY CROSSING ID heme
LABEL LABO LABB LABP;

SET BASIC;

PROC SORT;

BY CROSSING;

PROC PRINT SPLIT=*;

VAR CROSSING BENCOS DSTATE COT_NAME CTY_NAME RAILROAD ROAD RRID
MILEPOST;

FORMAT DSTATE ESTATE. ACCIDENT PREDACC.;

TITLE] TITVAL;

TITLE2 PUBLIC RAIL-HIGHWAY CROSSINGS RESOURCE ALLOCATION RESULTS;
TITLE3 BASED ON PREDICTED ACCIDENTS PER YEAR (SORTED BY CROSSING IDs);

DATA;

* ---%* PRINT REPORT FOR CROSSINGS ELIGIBLE FOR STOP SIGNS *--- ;
LABEL LABO LABB ;

SET BASIC;

IF STPFLG = 0 THEN DELETE;

PROC PRINT SPLIT= *;

VAR CROSSING ACCIDENT NEWCL TRAINS AADT FC10;

FORMAT FC10O URBR.?;

TITLE1 TITVAL;

TITLE2 RAIL~HIGHWAY CROSSING RESOURCE ALLOCATION RESULTS;

TITLE3 POSSIBLE CANDIDATE CROSSINGS FOR STANDARD HIGHWAY STOP SIGNS;
TITLE4 (SEE NOTE AT THE END OF SUMMARY PAGE);

DATA FATAL;
FEI III III II RII IIT IIS I TOTTI IAI IIIA IRI III I II
* PRINT REPORT FOR SEVERITY MEASURE = FATAL ACCIDENTS
RRRRRERRRERR RRR RIK IRI IRIE III I III IIHR IRR III IIIA II IIR +
LABEL LABO LABF;
SET FINAL;
IF ACCD NE 2 THEN STOP;
PROC PRINT SPLIT=*;
VAR CROSSING ACCIDENT BENCOS RECCAT NEWCL TRACKS TRAINS CUMCOST
CUMARED DCl DC2 DC3 DC4 STPFLG;
FORMAT NEWCL CLASS. STPFLG YES_NO.
ACCIDENT CUMARED ACCBNF.
Del Dc2 DC3 Dc4 DECIS.;
TITLE TITVAL;
TITLE2 RAIL-HIGHWAY CROSSING RESOURCE ALLOCATION RESULTS;
TITLE3 BASED ON PREDICTED FATAL ACCIDENTS PER YEAR;

B-11


and casualty accident predictions. It provides a more comprehensive index of accident
severity; however, its use involves making a judgment as to the relative severity of fatal
and injury accidents.

Development of the accident severity prediction formulas involved performing
regression analyses of data on crossings which experienced accidents. The dependent
variables for the fatality and casualty regression formulas were allowed one of two values
indicating whether the accident did or did not result in a fatal or casualty accident. The
independent variables represented various characteristics of the accident crossings as
described in the inventory. Accident data for 1981 through 1985 and the April, 1986
Inventory data were used for formula development. The regression procedure used is
referred to as the "logistic discriminant method" which employs an iterative weighted
regression technique. This method is the same as that used in developing the accident
prediction formulas?.

3.3.2 Fatality and Casualty Prediction Formulas

The formulas for predicting the probabilities of fatal accidents and casualty
accidents can be expressed in terms of several factors which are combined by simple
mathematical operations in a manner similar to the basic accident prediction formula
(Section 3.2.2). Each factor in the formulas represents a characteristic of the crossing as
described in the Inventory. The probability of a fatal accident given an accident is
expressed as:

P(FAJA) = 1/(1 + KF x MS x TT x TS x UR) (3)

where: P(FAJA)

u

probability of a fatal accident, given an accident

KF = formula constant (440.9)
MS = factor for maximum timetable train speed
TT = factor for thru trains per day
TS = factor for switch trains per day
UR = factor for urban or rural crossing
25


APPENDIX A

Appendix Al contains a listing of the program ACPD.NEW, written in the SAS

language, version 82.4, which is used to calculate accident and severity predictions.
Appendix A2 shows the variable dictionary for the input SAS data set.

EROSSING RANKED}
BY PREDICTED

ACCIDENTS OR
SEVERITY

CROSSING
SORTED

% USED FOR INTERACTIVE PARAMETER INPUT
** USED FOR BATCH PROCESSING

NOTE: THE SAS PROCEDURE NEWDAT IS GIVEN IN APPENDIX A2.

Hl ' **
: InpuT MAIN.COM 1 \ pesineo report
: PARAMETERS, INTERACTIVE 1 \ PaRameTer
H FOR WYLBUR | \sertine ror
RAN ' REPORT PROCEDURE ! BATCH RUN
HIGHWAY H '
CROSSING es Sen ee 4
INVENTORY
>| FILE SAS ‘ACPD, NEW
PREP NEWDAT SAS PROCEDURE
[| procepure PROCEDURE
RAIL
HIGHWAY
‘CROSSING MATCHED NEW TEST cary
ACCIDENTS INVENTORY SA
AND ACCIDENT $ 1 Sas
DATA DATASE DATASET

BY ID
NUMBER

RUNTIME

FIGURE A-1. DATA FLOW FOR ACCIDENT OR SEVERITY PREDICTION

A-1/A-2

COUNTY
SAS
OATASET

SUMMARY

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79


3. DOT ACCIDENT AND CASUALTY PREDICTION FORMULA

3.1 INTRODUCTION

Many crossing hazard formulas have been developed in the past and used extensively
by those concerned with rail-highway crossing safety4, Examples are the New Hampshire
Formula, the Peabody-Dimmick Formula, the Mississippi Formula, and the Ohio Method.
Availability of the Inventory and national accident data by crossing were major
considerations which influenced development of the DOT accident and severity prediction
formula. The Inventory contains information on the physical and operating characteristics
of all rail-highway crossings in the United States and, thus, affords an improved basis for

rail-highway crossing accident and severity prediction.

The DOT formulas are termed "absolute" formulas since they estimate numbers of
accidents and casualties. Other formulas, such as the New Hampshire Formula, are
termed "relative" formulas since they provide an index which is associated with expected
accidents or casualties only on a relative basis, i.e., a larger index means more expected
accidents or casualties but the relationship is not linear. The distinction between absolute
and relative formulas is important when considering use of a formula to assist in
determining cost-effective allocations of improvement funds, as discussed in Section 4. If
program effectiveness is to be measured in terms of tangible benefits such as reduced
accidents, an absolute formula must be used to ensure that the benefits or alternative
actions are consistently evaluated. The use of absolute formulas, such as the DOT
formulas, is therefore recommended to support resource allocation decisions,

Both relative and absolute formulas can be used to provide rankings of crossings on
the basis of their relative hazards. A comparison of the DOT formulas with several well-
known formulas?s!7 shows the DOT formulas to have significantly improved performance

in this regard,

The formulas presented here were developed using the April 1986 Inventory and the
accidents for the years 1981, 1982, 1983, 1984, and 1985. These formulas are considered
better than those listed in the previous edition of this User's Guide2, However, the results
show that the new formulas are only slightly better and the old formulas are still


Use of average costs introduces the simplification of not accounting for the actual
variation in costs that can occur from one project to another. Average values assume, for
example, that all passive crossings upgraded to gates will cost the same. If the user can
determine more accurately the actual variation in costs for improvement options on all
crossings being considered, these costs could be used. To do so, however, will require
modification of the model program to permit cost data to be input on an individual
crossing basis, The model program currently accepts only the three cost values
(C1,C2,C3) as input.

Caution should be exercised in adjusting the costs of a few selected projects while
assigning average costs to all other projects. If this is done, decisions regarding the
adjusted crossings may be unreasonably biased by the algorithm. The effect on individual
crossing decisions of changes in a crossing's cost characteristics from the average values
can be determined manually, using a procedure described in Section 4.2.6. With this
procedure, all other decisions by the algorithm will remain constant, while it can be
determined if the decision regarding the crossing in question will change with the new

cost values,

4.2.5 Active Warning Device Effectiveness Data

Three investigations have been performed to determine the effectiveness of warning
devices in reducing accidents at rail-highway crossings. The most recent study performed
by the U.S. Department of Transportation, used information in the Inventory and the FRA
accident reporting system!!, This study compared the accident rates at crossings both
before and after warning device improvements had been made to determine their
effectiveness during the period from 1975 to 1980. A similar study, also performed for
the U.S, Department of Transportation used the same information sources for the years
1975 to 197812. A third study was performed in 1974 by the California Public Utilities
Commission!3, This study examined accident rates before and after upgrades at 1552
California crossings over the period from 1960 to 1970. The results of these three studies
are shown in Table 4-7 in terms of single "standard" effectiveness values (E;, Ez and £3)

for the three improvement options considered by the resource allocation model.

49


ID RANK;

VAR ACCIDENT CROSSING DSTATE RAILROAD ACC1 ACC2 ACC3 ACC4 ACCS

MONYEAR CLASS SWITCH DAYTHRU THRU TRACKS MTRKS MXTTSP

DPAVED TRAFLN FC1O AADT;

TITLEL TITVAL;

TITLE2 PUBLIC RAIL-HIGHWAY CROSSINGS;

TITLE3 RANKED BY PREDICTED FATAL ACCIDENTS PER YEAR;

PROC SORT DATA= FATAL;

BY CROSSING;

PROC PRINT SPLIT=*;

FORMAT DSTATE ESTATE. ACCIDENT PREDACC.;

VAR CROSSING ACCIDENT RANK DSTATE COT _NAME CTY_NAME RAILROAD
ROAD RRID MILEPOST;

TITLE4 SORTED BY CROSSING ID;

RRR RER ERR ERE IKI I RII TI IIIARIIIA IK

* REPORT PRINTING PROCEDURE FOR

* SEVERITY = COMBINED CASUALITY
FRI IRI RI RII RI TR TTR RIT IIR TIT IIT IRI TIT I IIIS

DATA CCI;
LABELI ;
SET DATA2;
IF ACCD NE 3 THEN STOP;
RETAIN RANK 0; RANK=RANK+1;
PROC PRINT SPLIT=*;
FORMAT DSTATE ESTATE. MONYEAR ICHANGE.;
FORMAT DPAVED IPAVED. ACCIDENT PREDACC.;
ID RANK;
VAR ACCIDENT CROSSING DSTATE RAILROAD ACCl ACC2 Acc3 ACC4 ACC5
MONYEAR CLASS SWITCH DAYTHRU THRU TRACKS MTRKS MXTTSP
DPAVED TRAFLN FC10 AADT;
TITLEL TITVAL;
TITLE2 PUBLIC RAIL-HIGHWAY CROSSINGS;
TITLE3 RANKED BY PREDICTED COMBINED CASUALTY INDEX (CCI);
PROC SORT DATA= CCI;
BY CROSSING;
PROC PRINT SPLIT=*;
FORMAT DSTATE ESTATE. ACCIDENT PREDACC.;
VAR CROSSING ACCIDENT RANK DSTATE COT_NAME CTY_NAME RAILROAD
ROAD RRID MILEPOST;
TITLE4 SORTED BY CROSSING ID;
FEI III RII TOTTI TOIT
* PROCEDURE FOR PRINTING SUMMARY PAGE
FTI III III TOIT TT IRR TTA IT RIA IITA I RRA
DATA SUMMRY;
MERGE TOTREC SUMACC;
FILE DD1, PRINT;
N_N = NN;
ACCD= ACCVAL;
ID1l= ID1VAL; ID2= ID2VAL;
IF IDl = 0 THEN DO;
IDi= .+ ID2= .; END;
PUT
7.1 103.6 RRO IOI II III III IIR IOI II III IR IIR II IK =
es

A-9


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: SUa4aWVYVd = «ANdNI JO AXYHHAS &
S9ARERERERRERRE RES EAAERERAREER AER AR EAD HAA ES ASSERT AAR ITE ES SE

70


The formula for predicted casualty accidents at a crossing is:

CA = P(CAJA) x A (6)
where: CA = predicted casualty accidents per year
P(CAIA) = predicted casualty accident probability from equation (4)

A predicted accidents per year from equation (2)

To illustrate characteristics of the fatal and casualty accident probability formulas,
the two functions P(FAIA) and P(CAIA) are plotted as a function of maximum timetable
train speed in Figure 3-2. The figure contains five individual plots which show how the
functions change when one of the other four factors which influence accident severity
(thru trains, switch trains, tracks and urban-rural location) is varied. The values of the
factors are shown on the individual plots.

Several observations can be made regarding the characteristics of the functions.
The probability of a fatal accident, given an accident, P(FA|A) increases as a nearly linear
function of timetable train speed. Changes in the number of thru and switch trains or the
urban-rural location of the crossings do not have a major influence on fatal accident
probability.

The probability of a casualty accident, given an accident, P(CAIA) increases as a
nonlinear function of timetable train speed. Injury accident probability generally
increases rapidly with low values of timetable train speed and then gradually assumes the
upward slope of the fatal accident probability beyond 40 mph. This is initiutively
appealing since, as accident severity increases, casualities will increasingly become
fatalities and non-fatal injuries should diminish. The number of tracks at the crossing has
a significant influence on the casualty function (casualty accident probability decreases

with the number of tracks); however, the urban-rural location has only a minor influence.

3.3.3 Combined Casualty Index Formula

The severity of crossing accidents is basically determined by two factors: injuries
and fatalities. On a casualty severity scale those accidents of lower severity wil! tend to
have more injuries while those of higher severity will tend to have more fatalities. The
frequency distribution of accident severity tends to be the opposite; i.e., injury accidents

3L


TABLE 4-2. SAMPLE CROSSINGS FOR ALGORITHM DEMONSTRATION

PREDICTED

CURRENT ACCIDENTS
WARNING PER YEAR

CROSSING DEVICE A,

X, (single track) Passive Ay = 0.3

X2 Flashing A2 = 0.2
Lights

x3 Flashing A3=0.1
Lights

TABLE 4-3, EFFECTIVENESS/COST INPUT DATA

FLASHING LIGHTS

AUTOMATIC GATES

EXISTING WARNING EQUIPMENT EQUIPMENT EQUIPMENT EQUIPMENT
DEVICE EFFECTIVENESS COST EFFECTIVENESS COST

Passive E; =0.7 Cy, =$25,000 E2= 0.9 C2 = $45,000
Flashing Lights _ _ E3 = 0.667 C3 = $35,000

43


The inflation multiplier for installation costs (MI) is determined from the average
increase in the "Materials and Supplies" index (MS) and the "Wage Rate" index (WR) trons
the year for which the latest cost information is available. The 1983 values for the MS
and WR indexes are 140 and 179, respectively. The multiplier for installation costs, MI,
for some future year beyond 1983 is therefore:

MI = (MS/140 + WR/179) (9)
2
where:

MI = inflation multiplier for installation costs
MS = materials and supplies index for the subject year
WR = wage rate index for the subject year

The inflation multiplier for maintenance costs (MM) is a weighted average of 95
percent of the installation cost multiplier MI, (determined from equation (9) above) and 5
percent of the increase in the "Fuel" index (F) from the year for which the latest cost
information is available. The 1983 value of the F index is 232. The multiplier for
maintenance costs, MM, for some future year beyond 1983 is therefore:

MM = MI x 0.95 + (F/232) x 0.05 (10)
where:

MM = inflation multiplier for maintenance costs
F = fuel index for the subject year

The cost values shown in Table 4-6 are national averages, and their use will produce
decisions by the resource allocation model useful in formulating improvement programs.
The original study to deterinine these « ost® did not reveal any significant shifts in costs
by region of the country, although some variition by railroad was observed. If other
values for the average costs of improvements are available and are thought to more
accurately reflect the application in question, these values may be substituted for those

suggested here.

aS,


Step 2A: The crossings are selected for analysis by the algorithm in the order they
appear in Table 4-2, For each crossing selected, the appropriate accident reduction/cost
ratios are calculated, corresponding to all the allowable warning device improvements
which may be made. The results of these calculations are shown in Table 4-4.

Step 3: The accident reduction/cost ratios, as calculated in step 2A, are ranked in
descending order, beginning with the largest. The warning device improvement action at
each crossing, represented by the ratios and corresponding cumulative accident reduction

and cost, are tabulated in Table 4-5.

Step 4: From the ranked list in Table 4-5, the first action selected by the algorithm
corresonds to the first ranked accident reduction/cost ratio: installation of flashing lights
at crossing X, witha cost of $25,000. The next action selected by the algorithm
corresponds to the next ranked accident reduction/cost ratio: installation of gates at
crossing X25 resulting in a cumulative cost of $60,000 for the first two projects. The
algorithm proceeds in this manner until the cumulative total cost of all improvement
actions equals the available budget (CMA X). It should be noted that the third action
selected by the algorithm does not involve an additional crossing, but revises an earlier
decision to install gates rather than flashing lights at crossing X1. This type of revision is
typical of the algorithm for normal applications, as additional funding is made available.
For the above example, if a total of $115,000 were available for improvements (CMAX =
$115,000), the algorithm would proceed through the fourth item on the list involving
crossing X3. The overall improvement actions for $115,000 would result in the
installation of gates at all three crossings.

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computed by adding the incremental values of the parameters to their earlier values
computed as upgrade to flashing lights. These crossings are assigned the new upgrade
category of gates. The new set of crossings are once again sorted by accident or severity
reduction/cost ratio in descending order.

Finally, the Resource Allocation subtask calculates the decision criteria and
generates the output in a report format. The decision criteria, DC), DC2, DC3, and DCy,
are calculated from equations (12), (13), (14), and (15), respectively, described in Section
4.2.6. If the crossing being considered is passive, single-track, the program calculates

DC] and DC2. If the crossing is passive, multiple-track, DC3 is calculated. If the
crossing has flashing lights, DCy is calculated.

The report generating procedures produce the following four reports for the selected
severity measure:

- Resource allocation report sorted by accident and severity reduction/cost ratio.
- Resource allocation report sorted by crossing ID.

- Report for crossings that qualify for standard stop signs.

~ Summary report for the run,

An example of the output from the resource allocation procedure is shown in Figures
5-10 through 5-13. The principal results of the program are given in Figure 5-10. This list
is sorted by benefit/cost ratio (fourth column from left) and the recommended new
warning device is given in the fifth colunm. Figure 5-11 gives the crossings sorted by
crossing ID and also shows other Inventory data. Figure 5-12 lists the (two) crossings that
meet the criteria for standard stop signs. These two crossings contained "YES" in the
right-most column in Figure 5-10. The input parameters to the program are given in
Figure 5-13.

83/84


TABLE 4-1. EFFECTIVENESS/COST SYMBOL MATRIX

e---~--- - - - - PROPOSED WARNING DEVICE - - - - -------~~-
FLASHING LIGHTS AUTOMATIC GATES

EXISTING WARNING EQUIPMENT EQUIPMENT EQUIPMENT EQUIPMENT
DEVICE EFFECTIVENESS COST EFFECTIVENESS COST

Passive Ey Cc} E2 C2

Flashing Lights E3 C3

passive crossing, i, is considered, it could be upgraded with either flashing lights, with an
effectiveness E}, or gates, with an effectiveness Ez. The number of predicted accidents
or casualties at crossing i is denoted as ACjs hence, the reduced accidents or casualties
per year is ACjxE, for the flashing light option and ACjxE2 for the gate option. The
corresponding costs for these two improvements are Cy and Cz. The accident or casualty
reduction/cost ratios for these improvements are AC;xE,/Cy for flashing lights and
ACjxE2/C2 for gates. The rate of increase in accident or casualty reduction versus cost
that results from changing an initial decision to instal! flashing lights with a decision to
instal! gates at crossing i, is referred to as the "incremental accident or casualty
reduction/cost rato" and is equal to ACi(E-E })/(C2-C 1). The incremental accident or
casualty reduction/cost ratio ACR/C is used by the algorithm to compare the cost-
effectiveness of a decision to further upgrade a passive crossing from flashing lights to
gates with an alternative decision to upgrade another crossing. If a passive multiple-track
crossing, i, is considered, the only improvement option allowable would be installation of
gates, with an effectiveness of E2, a cost of C2 and an accident or casualty
reduction/cost ratio of ACjxE2/C2. If crossing i was originally a flashing light crossing,
the only improvement option available would be installation of gates, with an
effectiveness of E3, a cost of C3, and an accident or casualty reduction/cost ratio of
ACjxE3/C3.

38


é
é
i

2

3.

4,

5

8.

REFERENCES

Hitz, J., ed., "Summary Statistics of the National Railroad-Highway Crossing
Inventory for Public at Grade Crossings," FRA-RPD-78-20 (Washington, D.C.:
Federal Railroad Administration, September 1978).

U.S. Department of Transportation, "Rail-Highway Crossing Resource Allocation

Procedure-User's Guide, Second Edition", Report Numbers FH WA-IP-86-11, DOT-
TSC-FHWA-85-2, July 1986.

Edwin H. Farr, "Summary of the DOT Rail-Highway Crossing Resource Allocation
Procedure-Revised", U.S. Department of Transportation, Federal Railroad
Administration, Washington, D.C. 20590, March 1987, DOT-TSC-F RA-86-2.

Federal Highway Administration, Railroad-Highway Grade Crossing Handbook
(Washington, D.C.: U.S. Department of Transportation, August 1978).

Mengert, P., Rail-Highway Crossing Hazard Prediction Research Results, FRA-RRS-
80-02 (Washington, D.C.: U.S. Department of Transportation, March 1980).

Farr, E., and J, Hitz, "Accident Severity Prediction Formula for Rail-H ighway
Crossings," FH WA-RD-83/092 (Washington, D.C.: U.S. Department of
Transportation, July 1984).

Farr, E., "Rail-Highway Crossing Resource Allocation Model," FRA-RRS-81-00!
(Washington, D.C.: U.S. Department of Transportation, April 1981).

Heisler, J., and J. Morrissey, "Rail-Highway Crossing Warning Device Life Cycle
Cost Analysis," FRA-RRS-80-003 (Washington, D.C.: U.S. Department of
Transportation, 1980).

Association of American Railroads, "Results of Maintenance Cost Study of Railroad-
Highway Grade Crossing Warning Systems, Communications Signal Division."
Annual Meeting, October 1982.


Technical Report Documentation Page

T, Report No. 2 Government Accession No 3. Recipient's catalog No.
DOT/FRA/0S-87/10

4. Title and Subtitle 5. Report Date

RAIL-HIGHWAY CROSSING RESOURCE ALLOCATION August 1987 4
PROCEDURE -USER's GUIDE, THIRD EDITION 8

6. Performing Organization Code

DTS-73

7. Author(s)

Edwin H. Farr

8. Performing Organization Report No.
DOT-TSC- FRA-87- 1

9. Performing Organization Name and Address

7 > : 10. Work Unit No. (TRAIS)
.S. Department of Transportation
Research and Special Programs Administration RR780/R7027

Transportation Systems Center 11. Contract or Grant No.
Cambridge, MA 02142

12. Sponsoring Agency Name and Address 13. Type of Report and Period Covered
U.S. Department of Transportation Final Report
Federal Railroad Administration April 1986 - January 1987
Office of Safety Analysis
Washington, DC 20590 14. Sponsoring Agency Code
RRS-21

15. Supplementary Notes

16. Abstract

The Highway Safety Acts of 1973 and 1976, the Surface Transportation Acts of
1978 and 1982 and the Surface Transportation and Uniform Relocation Assistance
Act of 1987 provide funding authorizations for individual states to improve
safety at public rail-highway crossings. Safety improvements frequently consist
of the installation of active motorist warning devices such as flashing lights
or flashing lights with gates. To assist states and railroads in determining
effective allocations of Federal funds for rail-highway crossing improvements,
the U.S. Department of Transportation has developed the DOT Rail-Highway Crossing
Resource Allocation Procedure. The procedure consists of the DOT accident and
severity Prediction formulas, which predict the number of acidents and casualties
at crossings, and the resource allocation model, which nominates crossings for
improvement on a cost-effective basis and recommends the type of warning device
to be installed. This guide provides interested users with complete informa-
tion for application of the DOT Rail-Highway Crossing Allocation Procedure.

This third edition of the guide incorporates recalibrated accident and severity
prediction formulas using recent inventory and accident experience, and a more
flexible and more complete software system.

17 Key Words 18. Distribution Statement

Rail-Highway Crossing

Grade Crossing DOCUMENTS AVAILABLE TO THE PUBLIC THROUGH

Railroad Safety Se NCIS EAS ta

Resource Allocation RERETSNE

19. Security Classif. (of this report) 20 Security C'assif (of this page) 21 No. of Pages 22. Price
UNCLASSIFIED UNCLASSIFIED 122

Form DOT F1700.7 (8-72) Reproauction of comnretes cage aucrorized



RAT= RAT3; cost= cOST3; EFFECT= EF3; RECCAT= 'GATE '; END;

ELSE DO;

RAT= RAT1; CcostT= COST1; EFFECT= EF1; RECCAT= 'FLASH'; END;

*

BENCOS= ACCIDENT*RAT*10. **6;

ACCBEN= ACCIDENT*EFFECT;

*

KEEP BENCOS ACCBEN ACCIDENT ACCD RECCAT CROSSING NEWCL TRAINS
TRACKS FC1O FC1 AADT COST1 COST2 COST3 EFl EF2 EF3 STPFLG
COST EFFECT STATE CONTY CITY RAILROAD ROAD RRID MILEPOST;

DATA INCREM;

SET RES;

* -=--%* CALCULATE INCREMENTAL BENEFIT AND COST VALUES *--~ H
IF NEWCL > 4 OR TRACKS > 1 THEN DELETE;

BENCOS= ACCIDENT* (EF2-EF1) / (COST2- COST1) *10**6;

RECCAT= 'S ‘3

ACCBEN= (EF2- EF1) *ACCIDENT;

cosT= COST2- COST1;

DATA CONC;

Oe UIE EEE EEE IEE aa E EER IOI I IITA IIIT TIO R RRA I I
* APPEND THE CROSSINGS WITH INCREMENTAL BENEFIT TO THE

* THE CROSSINGS SELECTED EARLIER AND SORT THEM BY

* ACCIDENT COST BENEFIT VALUES.

Se SUE EO IEEE IAS TCOSS RIO AITO I IOI A IIIT IIA TTI IIT II

SET RES INCREM;
PROC SORT;
BY DESCENDING BENCOS;

DATA CUMCOST;

SET CONC;

See te CALCULATE TOTAL COST AND LIMIT THE NUMBER OF *-~~
, 1ea® CROSSINGS WHICH CAN BE COVERED WITHIN BUDGET *--~
BCOST= BUDGETX ;

RETAIN TCOST 0;

TCOST= TCOST+ COST;

IF TCOST > BCOST THEN STOP?

DATA;

SET LAST;
PROC MEANS NOPRINT;

VAR BENCOS;

OUTPUT OUT=OUTMIN MIN=MINBEN;

DATA CUMCOST1 INC1;

SET CUMCOST;

DROP BCOST TCOST;

IF RECCAT = 'S * THEN OUTPUT INC1;
ELSE OUTPUT CUMCOST1;

PROC SORT DATA=CUMCOST1;

BY CROSSING;

DATA INC2;

SET INC1;


a =KxEIx DT x MS x MT x HP x HL
= 0.0006938 x 42.39 x 1.79 x 1.36 x 1.00 x 1.00 x 1.00
= 0.072

The value of (B) is determined by combining the unnormalized prediction (a) with the
crossing's accident history using Tables 3-5 through 3-9, which are developed from
equation (2a). For the sample crossing, two accidents (N) occurred over the past 5 years
(T); therefore, Table 3-9 is used. With an unnormalized accident prediction (a = 0.072)
between 0.07 and 0.08, it can be seen from Table 3-9 that the value of B will be between
0.194 and 0.206. A reasonable estimate of (B) can be determined by linear interpolation
to be B = 0.196. Thus, from equation (2b), since this is a passive crossing, the final
accident prediction (A) is:

A = 8644 x 196
= 0.169 accidents per year

To determine the number of fatal accidents at the sample crossing, the fatal!
accident probability is first obtained using equation (3):

P(FAJA) = 1/(1 + KF x MS x TT x TS x UR)
where: KF = formula constant
MS = factor for maximum timetable train speed
TT = factor for thru trains per day
TS = factor for switch trains per day
UR = factor for urban or rural crossing

The factor values for the fatal accident probability formula can be determined from Table
3-12 using the sample crossing characteristics from Table 5-1:

KF = 440.9
MS = 0.025
TT = 0.811
TS = 1.169
UR = 1.000

61


TABLE 4-6. WARNING DEVICE IMPROVEMENT COSTS, 1983

NPV NPV
IMPROVEMENT INSTALLATION MAINTENANCE LIFE CYCLE
OPTION COST COST COST
Passive to
Flashing Lights, Cy $43,800 $10,700 $54,500
Passive to
Gates, C2 $65,300 $18,700 $34,000
Flashing Lights
to Gates, C3 $58,700 $18,700 $77,400

The category of costs that are used as input to the resource allocation model
(installation, maintenance, life cycle or some combination of these) can be determined at
the discretion of the user. Installation costs reflect the immediate costs to the state and
Federal Government of completing the project. Maintenance costs are the long term
recurring costs of the project, usually to the railroads; however, some states share in
these costs. Total life cycle costs reflect the project's total cost over its useful life.

Since the costs shown in Table 4-6 have been inflating, a procedure has been
developed to produce multipliers for the installation and maintenance costs that will
increase their amounts to current dollars. The procedure uses the annual index of charge-
out prices and wage rates from the AAR!0,

47


APPENDIX Bl

LISTING OF PROGRAM RESAL.NEW i

//
//JOBCARD

//

//PROCLIB DD DSN=ZABCRUN. PROCLIB, DISP=SHR

//& EXEC SAS,REGION=900K

//DD1 DD SYSOUT=A

//FINALL DD DSN=WIP1FZU.NEWTEST, DISP=(OLD, KEEP) , UNIT=FILE,
// VOL=SER=FRASIR

//FILEB DD DSN=WIP1FZU.CITY, DISP=(OLD, KEEP) , UNIT=FILE,

// VOL=SER=FRASIR . i
//¥ILEC DD DSN=WIP1FZU. COUNTY , DISP=(OLD, KEEP) , UNIT=FILE,
7/ VOL=SER=FRASIR

//SXSIN DD *

DATA TRIM;

SET FINALL.NEWTEST;

HAKKAR RIEREIIII KIKI IR IREREERRRRREI IEEE
* THIS PROGRAM IS CALLED BY THE MAIN.COM PROCEDURE

* TO GENERATE REPORT FOR RESOURCE ALOCATION. HOWEVER
* BY SPECIFYING THE VALUES OF FOLLOWING VARIABLES, IT
* CAN BE RUN INDEPENDENTLY IN BATCH MODE.

SATEVAL = TWO DIGIT STATE CODE
COUNTVAL = THREE DIGIT COUNTY CODE i
CITYVAL = FOUR DIGIT CODE FOR CITY ae
RAILVAL = FOUR CHARACTER CODE FOR RAILROAD E
ID1VAL = SIX DIGIT NUMERIC CODE FOR THE FIRST i
CROSSING ID es
ID2VAL = SIX DIGIT NUMERIC CODE FOR THE FINAL !
CROSSING ID i
SELVAL = 1 ~FOR ACCIDENT PREDICTION
2 -FOR RESOURCE ALLOCATION
ACCVAL = 1 -PREDICTED ACCIDENTS I
2  -FATAL ACCIDENTS
3 -COMBINED CASUALTY INDEX :
OPTVAL = 1 -STANDARD EFFECTIVENESS
2  <EXTENDED EFFECTIVENESS
cl - ¢c3 = THREE VALUES OF UPGRADE costs
Sl - S3 = THREE VALUES OF STANDARD EFFECTIVENESS {
X1 - x12 = TWELVE VALUES OF EXTENDED EFFECTIVENESS
KK = FATALITY FACTOR _
BUDGETX = AVAILABLE BUDGET IN DOLLARS ie
TITVAL = A CHARACTER STRING OF THE TITLE TO BE PRINTED

AT THE TOP OF EACH PAGE IN THE REPORT Be

IF ANY OF THE VARIABLE DOES NOT HAVE ANY SPECIFIC VALUE
IT IS ASSIGNED A MISSING VALUE OF PERIOD (.)

RRRRRERARKRERERRERERIREREIIK HER RRER IRR RRR REA I IIIA RRERI ARRAY
B-3


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2



3.2.4 General Accident Prediction Formula

The general DOT accident prediction formula can be expressed as follows:

= To 17 (N (2a)
B Far a3r (5)

-8644B Passive
A = 2.8887B ‘Flashing lights (2b)
8131B Gates

where:
A = final where accident prediction, accidents per year at the crossing,

a= initial unnormalized accident prediction from basic formula (1), accidents per
year at the crossing,

N = accident history prediction, accidents per year, where N is the number of
observed accidents in T years at the crossing,

To = formula weighting factor = 1.0 / (0.05 + a).

The general DOT accident prediction formula (2a) calculates a weighted average of
a crossing's unnormalized predicted accidents from the basic formula {a) and accident
history (N/T). Values of (B), obtained from Equation (2a) for different values of the
unnormalized initial prediction (a), from (1) and different accident histories (N/T) are
tabularized in Tables 3-5 through 3-9. Each table represents results for a specific number
of years for which accident history data are available. If the number of years of accident
data, T, is a fraction, the value of B can be interpolated from the tables or determined
directly from the formula.

17


END; END;

IF ACCD= 1 THEN ACCIDENT= A;

ELSE IF ACCD= 2 THEN ACCIDENT = FATAL;

ELSE ACCIDENT= COMCAS;

DROP A COMCAS FATAL;

RHRREEIRIR REIKI RERRERRI REE RITTER IIR IIA II RIAA IITA |
*

* RESOURCE ALLOCATION PROGRAM BEGINS
*

RAERERRREREREEEE THERE ERE EEE IEEERERERERREEEREREREEEKEREEREREEE |
DATA RES;
SET XING;
CLASS= NEWCL;
* === DELETE CROSSINGS DOWNGRADED IN LAST FIVE YEARS *--- ;
IF CLASS > 7 THEN DELETE;
EFFLAG = OPTVAL; *1- STANDARD EFFECTIVENESS 2- EXTENDED EFFECT.;
COST1= CS1; | COST2= cS2; COST3= CS3;
TRAINS=NGTSWT +DAYSWT + DAYTHRU +NGTTHRU;
TRACKS = MTRKS +OTRKS;
REREREEEEEEREEREEEEREREERREREEREEREKEREEREKEREEEEREEREKERRRRKRKREE
* IDENTIFY THE CROSSINGS WHICH QUALIFY FOR THE STOP SIGNS
RRERREREREREEEREEREREREERRERERERERERREEEREEREEERRERRRERREREEEEEKER
STPFLG = 0;
IF (STOP > 0 ORCLASS > 4 OR FCl NE 9 OR TRAINS < 10 OR

TRACKS NE 1) THEN GO TO LAB3;
IF ((FC10 NE 1 AND AADT < 400) OR

(FC10 EQ 1 AND AADT < 1500)) THEN STPFLG = 1;
FXII I IIIT IIIT OT III IO TIT III ITI IAAI II ASAI IIIA.

* SELECT EFFECTIVENESS VALUES
HREKRRRRRERRRRERRRR RIK KIRK IR RII TRI RII III RTT IIIT ITA RTI III IIE
LAB3:

IF EFFLAG = 1 THEN DO;

EFl= $1; EF2= S2; EF3= S83;
GO TO LAB4;
END;
IF TRACKS > 1 AND TRAINS > 10 THEN DO;
EF1l= X10; EF2= X11; EF3= X12; END;
IF TRACKS > 1 AND TRAINS < 11 THEN DO;
EF1l= X7; EF2= X8; EF3= X9; END;
IF TRACKS = 1 AND TRAINS > 10 THEN DO;
EFl= X4; EF2= X57 EF3= X6; END;
IF TRACKS = 1 AND TRAINS < 11 THEN DO;
EFl= X1; EF2= X2; EF3= X3; END;

PIRI III III III III RR TTI III IOI III II III ISI IK.
* COMPUTE BENEFIT RATIOS ACCIDENT AND COST BENEFITS

BREE RERIKERERII AIRS IIS IIR IIIS IIIT TIARAS IISA RII AIA III IIS
LAB4:

RAT1 = EF1/COST1;
RAT2 = EF2/COST2;
RAT3 = EF3/COST3;
*

IF NEWCL < 5 AND TRACKS > 1 THEN DO;
RAT= RAT2; COST= COST2; EFFECT= EF2; RECCAT= 'GATE '; END;
ELSE IF NEWCL > 4 THEN DO ;

B-7



29

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STEP 4:

DETERMINE IF RECOMMENDED WARNING DEVICE IS REVISED IF AC, E OR C HAS CHANGED.

Instructions for Determining If Recommended Warning Device Should Be Revised

1. Obtain Decision Criteria Values From Resource Allocation Model Output:

DC, = DCg = DC3 = Dey =

Revised AC Revised E

x Previous C
Previous AC Previous E

Revised C

2. Calculate: R =

3. Compare R with Appropriate Decision Criteria as Shown Below:
3a. Existing Passive Crossing 3b. Existing Passive Crossing 3e. Existing Flashing Light Crossing
(Classes 1, 2, 3, 4) (Classes 1, 2, 3, 4) (Classes 5, 6, 7)

Single Track Multiple Tracks

Comparison Decision Comparison Decision Comparison Decision

Deg R Gates Dez << R Gates DCy< R Gates

Dc; S R< Deg Flashing Lights R<DCz No Installation R<DCy No Installation
R< DC; No Installation

4. Revised Recommended Warning Device Installation:*

STEP 5:

DETERMINE OTHER CROSSING CHARACTERISTICS THAT MAY INFLUENCE WARNING DEVICE INSTALLATION DECISIONS.

Multiple tracks where one train/locomotive may obscure vision of another train?

Percent trucks
Passenger train operations over crossing?
High speed trains with limited sight distance?#*

Combination of High Speeds and moderately high volumes of highway
and rail traffic?#*

Either, or any combination of, high vehicular traffic volumes, high

|

numbers of train movements, substantial numbers of school buses or trucks

carrying hazardous materials, unusually restricted sight distance
or continuing accident occurrences?#*

"The cost and effectiveness values for the revised warning device are assumed to change by an amount
proportional to the change in these values for the initial recommended warning device as determined in Step 3.

"*Gates with flashing lights are the only recommended warning device per 23CFR 646.214(b)(3) (4).

FIGURE 4-2.

53

FIELD VERIFICATION WORKSHEET (Cont.)


&y
ofl

PUT @36
END;

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MULTI';
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PUT @36 '
END;

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7/836 IEA EOI TEI ICSI IO IOI IOI IIIS IS IOI IIIA + ary

'

' STANDARD EFF. VALUES

' EXTENDED EFF. VALUES

SINGLE

PASSIVE TO FLASHING :
PASSIVE TO GATES 3
FLASHING TO GATES 3

UPGRADE COSTS-
PASSIVE TO FLASHING
PASSIVE TO GATES
FLASHING TO GATES

AVAILABLE BUDGET

TRACK

' y2 @74 Y8 @84 Y5 @95 Al;
" y3 @74 ¥9 @84 Y6 @95 A2;

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FOO III TOO IIA III TITAS AIA RS

' TOTAL NUMBER OF CROSSINGS ANALYZED:

NUM_CRO;

ALL CANDIDATE CROSSINGS FOR STANDARD HIGHWAY STOP f1Gte’
ARE SINGLE TRACK, LOCAL CROSSINGS. REFER TO P.

’
1
' 8B-9 OF THE MANUAL OF UNIFORM TRAFFIC CONTROL DEVICE!
' FOR FACTORS TO BE CONSIDERED PRIOR TO MAKING STOP ':

'

SIGN INSTALLATION DECISIONS. ';
' INVENTORY DATE: APRIL 1986';

TITLE TITVAL;

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71


Referring to Tables 3-5 through 3-9, the value of (B) is determined from the
intersection of the appropriate column and row for the values of the initial prediction (a)
and the observed number of accidents (N). Thus, if a = 0.05 and N = 4, for T = 5 (Table 3-
9), the value of (B) is 0.300.

The normalizing constants used in formula (2b) are reset periodically so that the sum
of the predicted accidents (from 2a) in each group (passive, flashing lights, gates) for the
top 20 percent most hazardous crossings exactly equals the number of accidents which
occurred in a recent period for the top 20 percent of that group. Simply stated, the
normalizing constant is the ratio of the actual number of accidents to the predicted
number of accidents, In theory, these constants could be calculated for subsets of
crossings (e.g., for individual States) so that final predictions (A) would reflect the recent
experience of that subset. The efficacy of such fine tuning has not been tested by the

DOT.

An investigation of the general DOT accident prediction formula and the tables will

show the following interrelationship of A, B, a, and N/T:

I, The value of (B) will be a weighted average of a and N/T, i.e., it will lie
between the values of a and N/T.

2. Ifa-= N/T, then the final prediction (A) will equal a normalizing constant times
(a) or N/T.

3. If no accident history is available, T = 0, then the final prediction {A) will equal
a normalizing constant times the initial value (a) from the basic formula,

It is expected that the basic formula (1) and the accident history formula (2a) will
not change significantly in the near future. However, the normalizing constants used in
{2b) could change slightly from year-to-year as accident experience and Inventory changes
are applied. The normalizing constants will be recalculated periodically and will be
published annually in FRA's Rail-Highway Crossing Accident/Incident and Inventory
Bulletin starting with Bulletin No, 10 to be published in 1988 for Calendar Year 1987.

23


* REPORT GENERATION PROGRAM BEGINS

* DESCRIPTION OF COUNTY AND CITY IS ADDED TO EACH
* RECORD TO BE INCLUDED IN THE RANK LISTING
RRR RIE ERE AREER RAEI
DATA DATA1A;

SET XING;

RENAME CONTY=COUNTY_C CITY=CITY Cc;

PROC SORT;

BY STATE COUNTY_C ;

DATA DATA1B;

MERGE DATA1A(IN=A) FILEC.COUNTY ;

BY STATE COUNTY_C ;

IF A;

PROC SORT;

BY STATE CITY_C;

DATA DATA1C;

MERGE DATA1B(IN=A) FILEB.CITY;

BY STATE CITY C;

IF A;

DSTATE= 0; DPAVED= 0;

DSTATE= STATE; DPAVED= PAVED;
KRHRRERERERERERERERIER ERATE RR IKIEHEENIEKREAR AKI K
* COMBINING CHANGE MONTH & YEAR ATTRIBUTES
HHRKHKRRRRER IKI IK IIE ER BERRI IRR EERE RRR EERIIR »
MONYEAR= 100*CHANGE_Y +CHANGE_M;

DROP STATE;

PROC SORT;

BY DESCENDING ACCIDENT;

DATA DATA2;

SET DATA1C;

IF N_ > NN THEN STOP;

PROC MEANS NOPRINT;

VAR ACCIDENT;

OUTPUT OUT=SUMACC SUM=TOTACC;

MACRO LABELI

LABEL DSTATE=ST DPAVED= HWY*PVD

CROSSING=XING*ID*# RAILROAD=RR
ACCl==->*__ *81 ACC2=NUM*___*82

?

ACC3=0F* *83 ACC4=ACC* *84
ACC5=<--* #85 MONYEAR=DATE*OF*CHG
CLASS=WD*CL SWITCH=TOTL*SWIT*TRNS

DAYTHRU=DAY *THRU*TRNS
THRU=TOTL*THRU*TRNS
TRACKS=TOTL*TRKS MTRKS=MAIN*TRKS

MXTTSP=TTBL*SPD TRAFLN=HWY*LNS
FC10=URBN*RURL ACCIDENT=PRED*ACCDS
COT_NAME=COUNTY CTY_NAME=CITY SF

RARER RRR RRR RARE IKKE IRI IKI KIKI R IARI RRR RAR IR IRI RII 7

* DEFINE FORMATS TO BE USED FOR PRINTING VALUES ?

FOI TIT I IIIT IIIT III RTI IIIA

PROC FORMAT;

VALUE ESTATE 1='AL' 2='AK! ‘ 4='AZ'
='AR! 6='"CAt ' =tCO!
='cr! 10='DE' ll='De' 12='FL!

A-7


2, DOT RAIL-HIGHWAY CROSSING RESOURCE ALLOCATION
PROCEDURE - OVERVIEW
There are currently approximately 197,000 public at-grade rail-highway crossings in
the United States. At an average cost of over $55,000 per installation, there are
insufficient funds available to install automatic warning systems at each of these
crossings. The DOT Procedure was designed to assist in determining how limited safety
improvement funds should be allocated to specific crossings and warning device

improvements to achieve the greatest reduction in accidents and casualties.

Figure 2-1 illustrates the basic functions of the DOT Procedure. Inventory
information and the accident histories of the individual crossings being considered are
used by the DOT accident prediction formula to provide a list of crossings ranked by the
estimated number of accidents or casualties that will occur at each crossing. State
crossing programs commonly use such rankings, produced by various formulas, as a basis
for determining safety improvements; i.e., crossings are improved in the order of their
predicted accident levels, with the crossing having the highest accident rate treated first,
and so forth, However, if the program objective is to achieve maximum accident
reduction for a given total cost, this procedure must be extended to consider the different
warning device options which are available for each crossing and their differing costs and
effectiveness for reducing accidents. For example, installing a flashing light at the
crossing with the tenth highest accident rate might yield a higher accident reduction/cost
ratio than installing an automatic gate at the most hazardous crossing. Consequently, the
resource allocation model uses the predicted accidents or casualties at each crossing
together with information on the safety effectiveness and costs of alternative warning
device improvements and the funding level available to determine the most cost-effective
set of improvement decisions; i.e., decisions on which crossings to improve and the types
of warning devices to install at those crossings to result in the greatest accident or
casualty reduction given the available funding.

The DOT Procedure does not dictate final decisions for crossing improvements, but
does recommend programs to aid in making informed decisions. As an analytical
procedure, its recommendations are dependent on accurate input data and assumptions,
Errors in the Inventory and inaccuracies in assumptions regarding warning device cost and
effectiveness are normal and may cause inappropriate recommendations. To ensure


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LIST OF TABLES

Table Page
3-1 EQUATIONS FOR CROSSING CHARACTERISTIC FACTORS 12

3-2 FACTOR VALUES FOR CROSSINGS WITH PASSIVE WARNING DEVICES 14

3-3 FACTOR VALUES FOR CROSSINGS WITH FLASHING LIGHT WARNING

DEVICES 15
3-4 FACTOR VALUES FOR CROSSINGS WITH GATE WARNING DEVICES 16
3-5 VALUES OF B, GIVEN THE INITIAL PREDICTION AND ACCIDENT

HISTORY, 1 YEAR OF ACCIDENT DATA (T=!) 18
3-6 VALUES OF B, GIVEN THE INITIAL PREDICTION AND ACCIDENT

HISTORY, 2 YEARS OF ACCIDENT DATA (T=2) 19
3-7 VALUES OF B, GIVEN THE INITIAL PREDICTION AND ACCIDENT

HISTORY, 3 YEARS OF ACCIDENT DATA (T=3) 20
3-8 VALUES OF B, GIVEN THE INITIAL PREDICTION AND ACCIDENT

HISTORY, 4 YEARS OF ACCIDENT DATA (T=4) 21
3-9 VALUES OF B, GIVEN THE INITIAL PREDICTION AND ACCIDENT

HISTORY, 5 YEARS OF ACCIDENT DATA (T=5) 22
3-10 EQUATIONS FOR CROSSING CHARACTERISTIC FACTORS FOR

FATAL ACCIDENT PROBABILITY FORMULA 27
3-11 EQUATIONS FOR CROSSING CHARACTERISTIC FACTORS FOR

CASUALTY ACCIDENT PROBABILITY FORMULA 28
3-12. FACTOR VALUES FOR FATAL ACCIDENT PROBABILITY FORMULA 29

3-13. FACTOR VALUES FOR CASUALTY ACCIDENT PROBABILITY FORMULA = 30

4-1 EFFECTIVENESS/COST BY SYMBOL MATRIX 38
4-2 SAMPLE CROSSINGS FOR ALGORITHM DEMONSTRATION 43
4-3 EFFECTIVENESS/COST INPUT DATA 43
4-4 STEP 2: CALCULATION OF ACCIDENT REDUCTION/COST RATIOS 45
4-5 STEP 3: RANKING OF ACCIDENT REDUCTION/COST RATIOS 46

4-6 WARNING DEVICE IMPROVEMENT COSTS, 1983 47

viii


TITLE2 PUBLIC RAIL-HIGHWAY CROSSINGS RESOURCE ALLOCATION RESULTS;
TITLE3 BASED ON PREDICTED COMBINED CASUALTY INDEX (SORTED BY CROSSING
IDs);

DATA;

* =-+-* PRINT REPORT FOR CROSSINGS ELIGIBLE FOR STOP SIGNS *--- ;
LABEL LABO LABC;

SET CCI;

IF STPFLG = 0 THEN DELETE;

PROC PRINT SPLIT= *;

VAR CROSSING ACCIDENT NEWCL TRAINS AADT FC10;

FORMAT FC10 URBR.;

TITLE1 TITVAL;

TITLE2 RAIL~HIGHWAY CROSSING RESOURCE ALLOCATION RESULTS;

TITLE3 POSSIBLE CANDIDATE CROSSINGS FOR STANDARD HIGHWAY STOP SIGNS;
TITLE4 (SEE NOTE AT THE END OF SUMMARY PAGE);

DATA SUMMRY?;

* = =---* PRINT SUMMMARY FOR INPUT PARAMETERS AND THE RUN *--- ;
SET TOTREC;

FILE DD1, PRINT;

ACCD= ACCVAL;

OPTION= OPTVAL;

Yl= X1; Y2= X2; Y3= X3; Y4= X4; Y5S= X5i Y6= X67

Y7= X7; Y8=X8; Y9=X9; Al= X11; A2= X12; AO= X10;
IDl= IDIVAL; ID2= ID2VAL;

IF IDl= 0 THEN DO;

IDl= .; ID2= .7 END;

COST1= CS1; COST2= CS2; COST3= CS3; BCOST= BUDGETX;

PUT LL [BEN RRR RRR IRI IIIT RR IR IR RII TRIAS RII RII IA III IAI IRE IR
kN
;

PUT Q@36 '* SUMMARY OF INPUT PARAMETERS
Ve

PUT @36 '* FOR RESOURCE ALLOCATION PROCEDURE
RY

PUT @36

VHRR IR IIIS IKEA RITE RRR ERE y

PUT /@36 ' TITLE : TITVAL ';

PUT @36 4 ' STATE : STATEVAL ';

PuT @36 ' COUNTY : COUNTVAL ';

PuT @36 0 0' CITY : CITYVAL ';

PUT @36 ' RAILROAD : RAILVAL ';

PUT @36 ' CROSSING ID Hy “3

PUT @36 = -BOTTOM OF RANGE : ' IDI;

PUT e360 -TOP OF RANGE 3: ' ID2;

PUT @36 ‘' SEVERITY TYPE : ACCVAL (1) PREDICTED

ACCIDENTS ';

Pur @36 =

PUT e36 6!

IF ACCD = 3 THEN

PUT @36 ' FATALITY FACTOR
PuT @36 ' EFFECTIVENESS
Pur @36 ' CHOICE

Pur e366

IF OPTION = 1 THEN DO;

(2) FATAL ACCIDENT! ;
(3) COMBIN. CASUALTY INDEX’

KK ';
' OPTION ' (1) STANDARD ‘3
(2) EXTENDED ';

;



d. Accident Reduction to Cost Ratio

Compute accident or severity reduction to cost ratio and, using this as the

key value, sort the set in descending order.
e. Resource Allocation
Execute the resource allocation data step, and allocate the new class to all

candidate crossings. Compute benefit/cost ratio, cumulative cost, cumulative

accident benefit, and decision criteria.

f. Report Writing
Execute one of the three report writing procedures to print a report,
depending on the selected severity measure. Each procedure prints the

following report in three or four parts:

1. List of crossings and associated data items sorted by accident reduction to
cost ratio. (See Figure 5-10).

2. Set of crossings as listed above along with subset parameters sorted by
crossing IDs. (See Figure 5-11).

3. List of crossings eligible for stop signs. (See Figure 5-12).
4. Summary Report (See Figure 5-13).

g- Summary Printing

Execute the summary data step which prints the input data as well as
number of crossings analyzed. (See Figure 5-13).

@

The calculation of the accident or severity reduction/cost ratio for each crossing
depends on the crossing's current warning device and the number of tracks at the crossing.
If the crossing already has gates (warning device class 8), it is deleted from consideration.

76



RENAME BENCOS=IBEN RECCAT=IREC ACCBEN=IABEN COST=ICOST;
KEEP CROSSING BENCOS RECCAT ACCBEN COST;

PROC SORT;

BY CROSSING;

DATA REP1;
MERGE CUMCOST1(IN=A) INC2;
BY CROSSING;
IF A;
PROC SORT;
BY DESCENDING BENCOS;
DATA REP2;
* <---%* COMPUTE DECISION CRITERIA VALUES hem
MERGE REP1 OUTMIN;
RETAIN MINTBEN 0;
IF MINBEN = . THEN MINBEN= 0;
MINTBEN= MINTBEN+ MINBEN;
IF EFFECT = EFl THEN
DC2=MINTBEN/ (ACCIDENT* (EF2-EF1) / (COST2-COST1) #10. **6) 7
IF IREC= 'S ' THEN DO;
DC1=MINTBEN/BENCOS ;
BENCOS=ACCIDENT*EF2/COST2*10**6;
ACCBEN=ACCBEN+IABEN;
cost =COST + ICOST;
RECCAT= 'GATE ';
END;
KEEP CROSSING ACCBEN BENCOS RECCAT NEWCL TRACKS TRAINS DC2
DC1 ACCIDENT COST MINTBEN EFFECT EFl EF2 EF3 STPFLG
STATE AADT FC10 ACCD CONTY CITY RAILROAD ROAD RRID MILEPOST;
PROC FORMAT;
VALUE CLASS 1-4='PASS '
5-7='FLASH';
VALUE YES_NO 0=NO
1=YES;
VALUE URBR 0=RURAL
1=URBAN;
PICTURE ACCBNF OTHER='9.999999';
PICTURE DECIS OTHER='9.999';

DATA DATA1A;

* e--%* ADD STATE, CITY AND COUNTY DESCRIPTION TO EACH *#---
* <---* RECORD IN THE SELECTED SET team
SET REP2;

RENAME CONTY=COUNTY_C CITY=CITY_C;

PROC SORT;

BY STATE COUNTY_C ;

DATA DATA1B;

MERGE DATAIA(IN=A) FILEC.COUNTY ;

BY STATE COUNTY_C ;

IF A;

PROC SORT;

BY STATE CITY_C;

DATA DATA1C;

MERGE DATALB(IN=A) FILEB.CITY;

B-9



RRERIKRARRRERERE REE EIR IIR IERIE ERE IRAE RARER
*

* BASIC FLASHING LIGHTS CALCULATION
*
FEI III III IIS IO IIIT I IIIS IS ISIS
DATA IFLASH;
SET TRIM;
IF (CLASS < 5 OR CLASS > 7 ) THEN DELETE;
H= .0003351*(((AADT*TRAINS + 0.2)/0.2)** 0.4106) *
(((DAYTHRU +0.2)/0.2)**0.1131)*
(EXP (0.1917*MTRKS) ) *

(EXP (0.1826*(TRAFLN - 1))) ;
KERIKERI ERIE IKKE ERE ERR ERIKA KIER
*

* BASIC GATES CALCULATIONS
*

FOI OIC ITI III III IIIT II IIR II
DATA IGATE;
SET TRIM;
IF (CLASS NE 8) THEN DELETE;
H= .0005745*(((AADT*TRAINS + 0.2)/0.2)** 0.2942) *
(((DAYTHRU +0.2)/0.2) **0.1781)*
(EXP (0,1512*MTRKS) )*
(EXP (0.1420*(TRAFLN - 1))) ;

FEIT III TIT IOI AI TI III II IIIT IS. |
*

* MERGING OF THREE SETS BY CROSSINGS AND USING EFFECTIVENESS
*

FOI IO III III IO TI II III IIT
DATA XING;

SET IGATE IFLASH ICROSS;

BY CROSSING;

ARRAY UP UP1-UP3;

ARRAY DN DN1-DN3;

ARRAY UPDN(K) UP1-UP3 DN1-DN3;

UP1= 1-S1; UP2= 1-S2; UP3= 1-S3;
DO OVER UP;

DN = 1/UP;

END;

*

*

IF CLASS = NEWCL THEN GO TO LAB;
IF (OLDCL LY NEWCL) THEN DO;
K=2;
IF(NEWCL NE 8) THEN K= 1;
IF(OLDCL GT 4) THEN K= 3;
END;
IF (OLDCL GT NEWCL) THEN DO;
K=5;
IF (OLDCL NE 8) THEN K=4;
IF(NEWCL > 4) THEN K=6;
END;
=H*UPDN ;
HARKER ERE REIKI IIR III IIIA TIKIT TERRE IK

A-5


where ACR/Cyp equals the minimum accident or casualty reduction/cost ratio
corresponding to the last (lowest) improvement action selected by the resource allocation
model. These decision criteria represent the amount by which the accident or casualty
reduction/cost ratio for a particular improvement action can be changed and still be
selected by the model. The improvement actions corresponding to the decision criteria
(DC), DC2, DC3 and DCy) are, respectively, single-track passive to flashing lights, single-
track passive to gates, multiple-track passive to gates, and flashing lights to gates.
Comparing the R value to the decision criteria is equivalent to determining if the actual
change in accident or casualty reduction/cost ratio due to revised data is still within the
limits permitting selection of the same improvement action.

To demonstrate use of the revision procedure, the following hypothetical example is
provided. A single-track passive crossing was selected by the resource allocation model
for upgrading to gates. This crossing is listed as the second crossing (ID# 636R) on the
sample standard output report of the resource allocation model shown in Figure 5-10. The
crossing was inspected by a diagnostic team, and it was found that some of the data from
the Inventory used in calculating the predicted accidents were incorrect. In addition, the
assumed values for the installation costs and effectiveness of gates at the crossing were
deemed inappropriate. Using the new data, a revised prediction of accidents was
calculated according to the tabularized procedure described in Section 5.1.1. The
previous and revised accident prediction, cost, and effectiveness parameters for the
crossing are listed below:

Previous Revised
Predicted Accidents, A 0.19 0.26
Warning Device Effectiveness, E 0.90 0.87
Warning Device Cost, C $65,300 $115,000

55


TABLE 4-8. EXTENDED SET OF EFFECTIVENESS VALUES FOR
WARNING DEVICE IMPROVEMENTS

WARNING SINGLE SINGLE MULTIPLE MULTIPLE
DEVICE TRACK TRACK TRACK TRACK
IMPROVEMENT NUMBER

OPTION OF TRAINS/DAY: $10 >10 $10 >10
Passive to

Flashing Lights, E1 0.75 0.61 0.65 0.57
Passive to

Gates, Eg 0.90 0.80 0.86 0.78

Flashing Lights
to Gates, E3 0.89 0.69 0.65 0.63

4.2.6 Field Verification and Revision of Resource Allocation Results

Crossings selected for improvements by the resource allocation model should be
inspected by a diagnostic team to determine the accuracy of input data and the
reasonableness of the recommended improvement. The inspection may show that data
from the Inventory are not correct, resu!ting in an inaccurate predicted accident or
casualty rate. Also, the assumed warning device effectiveness and cost may be found
inappropriate for the particular crossing. In addition, the diagnostic team should make
note of hazardous conditions at crossings, such as limited sight distance or hazardous
materials traffic, that are not included in the resource allocation model but should be
considered before making a final decision. A manual procedure has been developed to
evaluate the impact of changes in crossing data on the improvement decision made by the
resource allocation model. This procedure can be performed without rerunning the model
and is incorporated in a worksheet, shown in Figure 4-2. The worksheet guides the
diagnostic team through the on-site evaluation procedure using a five-step set of
instructions.

51


are found in the table and multiplied together. The factor values for the three warning
device categories (passive, flashing lights and gates) are found in Tables 3-2, 3-3 and 3-4,
respectively. Detailed procedures for use of the tables and computer automation of the
accident prediction formula are presented in Section 5.1.

An inspection of the factor value tables shows that exposure index (EI), based on the
product of annual average daily highway traffic (c) and average daily train traffic (t), has
the strongest relationship to predicted accidents. All other factors can be seen as having
a weaker relationship to predicted accidents.

3.2.3 Accident History

The second independent prediction of a crossing's accident rate is derived from the
crossing's accident history. This information is obtained from the FRA RAIRS file which
contains records of all accidents that occurred at crossings. The required measure of
accident history is the ratio N/T, where N is the number of accidents which occurred at a

crossing over a period of T years.

Use of accident history, along with the unnormalized prediction obtained from the
basic formula, improves the overall prediction. This improvement comes about because
accident history serves as a surrogate for other characteristics which affect crossing
hazards but are not included in the Inventory; e.g., sight distance, or the timing of
highway and train traffic. The most accurate predictions, in theory, will result from the
use of all the available accident history, assuming crossing characteristics remained
constant. However, the extent of improvement is minimal if data for more than 5 years
are used, It is therefore recommended that only data for the most recent 5 years of
accident history be used. This ensures good performance from both the accident
prediction formula and use of the most relevant data. Accident history information more
than 5 years old may be misleading because of changes that occur to crossing
characteristics over time. If it is known that a significant change has occurred toa
crossing during the most recent 5 years, such as a warning device upgrade, only the
accident data since the change should be used.

13


flashing lights. The accident or casualty reduction is ACjxE3 at a cost of C3. The total
accident or casualty reduction at each step is the sum of the previous accident or casualty

reductions and the total cost is the sum of the previous costs.

In addition to determining the total accident or casualty reduction (total benefit)
and cost at each step, the algorithm also determines the particular warning systems which
are to be installed at particular crossings. Since the crossings which were affected are
known, the actual accidents or casualties, location, and all other information in the
Inventory for those crossings are also known. Thus, the output of the program could
include any of this information and any computations based on this information. Several
types of output are shown in Section 5.2

Step 5: The cumulative total cost at each step, proceeding down the list of accident
or casualty reduction/cost ratios, is compared with the available budget specified as input
to the algorithm. When the total cost equals or exceeds the budget, the program ends.
Otherwise, the sequential procedure described in step 4 continues.

4.2.3. Demonstration of Model Algorithm

To demonstrate operation of the algorithm, an example which considers the three
crossings described in Table 4-2 follows. For this example predicted accidents, Aj, rather
than predicted casualties will be used as the measure of crossing hazard. The predicted
accidents per year and current warning device information for the crossings together with
assumed warning device cost and effectiveness parameters, presented in Table 4-3,
constitute the input for the algorithm. The algorithm proceeds through the following
steps which were described in the previous section and in Figure 4-4.

Step 1: The effectiveness/cost ratio for flashing lights (E1/C ) is greater than that
for gates (E2/C2); hence, the algorithm follows step 2A. This implies that the most
effective first action which can be taken at a passive crossing is the installation of
flashing lights.

42


TABLE 4-5. STEP 3: RANKING OF ACCIDENT REDUCTION/COST RATIOS

EjAj DEjAi rcj
ACCIDENT WARNING CUMULATIVE
REDUCTION/ DEVICE ACCIDENTS ACCIDENTS
COST IMPROVEMENT REDUCED REDUCED CUMULATIVE
RANK RATIO ACTION PER YEAR PER YEAR COSTS
I 8.4 x 10-6 Install Flashing 0.21 0.21 $25,000
Lights at
Crossing X1
2 3.8 x 10°6 Install Gates at 0.13 0.34 $25,000
Crossing X2
3 3.0 x 1076 Install Gates at 0.06 0.40 $80,000
Crossing X]
4 1.9 x 10-6 Install Gates at 0.07 0.47 $115,000

Crossing X3

4.2.4 Active Warning Device Cost Data

As described above, the resource allocation model requires data on the costs of the
warning device improvement options. A study has been performed to determine average
national values of these costs’. The costs determined include the initial installation cost
(including procurement) and the net present value (NPV) maintenance costs over the life
of the equipment which are added together to yield the total life cycle cost. These costs
were originally determined in 1977 dollars. An additional study was performed by the
Association of American Railroads (AAR) in 1982 to determine the annual maintenance
costs of warning devices?. The AAR study results for maintenance costs were combined
with the earlier study results for installation costs and updated to 1983 dollars using the
procedure outlined below! !, These 1983 warning device costs are presented in Table 4-6

46


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75


useable for ranking crossings according to their expected number of accidents per year.
In addition, the new formulas are a refinement and simplification of the old formulas.

The functions of the DOT accident and severity prediction formulas are described in
Figure 3-1. The formulas provide a means of calculating the expected annual number of
accidents and casualties at a crossing on the basis of the crossing's characteristics
described in the Inventory and the crossing's historical accident experience described in
the FRA Railroad Accident/Incident Reporting System (RAIRS). The accident and
severity predictions are produced by the DOT formulas in two steps. Predicted accidents
are obtained in the first step using a set of formulas described in Section 3.2. The
resulting accident predictions are expressed as the expected number of accidents per year
at a crossing. If desired, predicted accident severity is then obtained in the second step
using another set of formulas as described in Section 3.3. The severity calculations
depend on the use of predicted accident results from the first step. The severity
predictions for a crossing are expressed in three ways: (1) expected number of fatal
accidents per year, (2) expected number of casualty accidents per year, and (3) total
combined casualty index (a weighted combination of fatal and injury accidents per year).

3.2 DESCRIPTION OF FORMULAS FOR ACCIDENT PREDICTION
3.2.1 Overview

Accident predictions are produced by combining two independent predictions of a
crossing's accidents to produce a more accurate resultant prediction. The two
independent predictions are obtained from the following sources:

1. A formula described in Section 3.2.2 provides an unnormalized initial
prediction of accidents on the basis of a crossing's characteristics as described in
the Inventory. This formula, termed the "basic formula", is used in a manner similar
to other common formulas such as the Peabody-Dimmick formula.

2. A second prediction is provided by the actual observed accident history at a
crossing as described in Section 3.2.3. This prediction assumes that future accidents
per year are approximated by the average historical accident rate. It is referred to

as a crossing's "accident history".


TABLE 4-7. STANDARD SET OF EFFECTIVENESS VALUES POR WARNING
DEVICE IMPROVEMENTS

WARNING DEVICE 2nd DOT 1st DOT CALIFORNIA
IMPROVEMENT STUDY, 1975 STUDY, 1975 STUDY, 1960
OPTION to 1980 DATA to 1978 DATA to 1970 DATA
Passive to Flashing Lights, E, 0.70 0.65 0.64
Passive to Gates, E2 0.83 0.84 0.88
Flashing Lights to Gates, E3 0.69 0.64 0.66

The effectiveness values resulting from the three studies are similar but differences
exist. These differences are probably a reflection of variations in crossing characteristics
over time and regions of the country. The question arises as to which set of values to use
for the resource allocation model. As with the cost data, any set of values which the user
feels accurately reflect the situation being evaluated may be used. Without other
information to the contrary, the effectiveness values from the latest DOT study are
recommended, since they were most recently developed, and they used the largest data

base of national scope.

The latest DOT study on warning device effectiveness determined that several
crossing chracteristics, out of many investigated, had a significant influence on warning
device effectiveness. Specifically, it was found that the effectiveness of warning device
upgrades was less for crossings with multiple tracks and crossings with greater than 10
trains per day. These results were used to develop an "extended" set of effectiveness
value shown in Table 4-8. At the option of the user, the resource allocation model has the
capability to use either the extended set of values or the reduced set of standard values
shown in Table 4-7. Unless otherwise specified by the user, the resource allocation model
uses the extended set of values since their use results in improved performance of the
model.

50


TABLE OF CONTENTS

Section

L

2.

3.

4,

Introduction

1.1 Purpose
1.2 Background
1.3 Organization of Guide

DOT Rail-Highway Crossing Resource Allocation Procedure - Overview
DOT Accident and Casualty Prediction Formula

3.1 Introduction
3.2 Description of Formulas for Accident Prediction

3.2.1. Overview

3.2.2. Basic Formula

3.2.3. Accident History

3.2.4 General Accident Prediction Formula

3.3 Description of Formulas for Accident Severity

3.3.1 Overview
3.3.2 Fatality and Casualty Prediction Formulas
3.3.3 Combined Casualty Index Formula

Resource Allocation Model

4.1 Introduction
4.2 Resource Allocation Model for Active Warning Devices

2.1 Overview

2.2 Description of Model Algorithm

2.3 Demonstration of Model Algorithm

-2.4 Active Warning Device Cost Data

2.5 Active Warning Device Effectiveness Data

2.6 Field Verification and Revision of Resource Allocation Results

4,3, Resource Allocation Model for Standard Highway Stop Signs
Application of DOT Resource Allocation Procedure
5.1 DOT Accident and Casualty Prediction Formula

; «lL Manual Calculation of Predicted Accidents and Casuaities

1
«1.2 Computer Program for Calculation of Predicted Accidents
and Causualties

10
13
17
24
24
25
34

34
34

34
36
42
46
49
51
56
59
59
59

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22


The basic accident prediction formula computes the initial predicted accident rate
for each crossing on the basis of the crossing's current warning device class. If, during the
last 5 years, a change in warning device took place, the formula computes the basic
predicted accidents on the basis of the previous warning device class and then makes an
adjustment to the predicted accidents using the appropriate effectiveness factor (see
Tables 4-7 and 4-8) to account for the influence of the warning device change. For
individual crossings, this procedure more accurately determines the short term (less than
5 years) change in the crossing's accident rate than use of the basic formula for the new
warning device. For example, if a passive crossing was upgraded to gates in the last 5
years, the passive (Crossbucks) formula would be used and the result would be multiplied
by the effectiveness factor for gates (1.0 - the effectiveness of the upgrade to gates) to
obtain the initial predicted accidents for the crossing with gates. Similarly, the predicted

id


be the same as that by accident or casualty prediction; and () since the number of
crossings that are realistic candidates for stop signs tre wo few and their costs are so low,
stop sign installation decisions will be made primarily on fu tor other than their

accidents or casualty reduction/cost ratios relative to a tive warning, device projects.

The stop sign candidate report can be produced either with or without the report of
active warning device recommendations, If the resource allocation procedure is used to
produce both reports, it is possible that the samme crossing ¢ ould appear on both lists; ies,
a crossing that is a possible candidate for stop signs may also be a candidate for an active
warning device. To provide a means of integrating this information, the report on active
warning device recommendations will indicate, at the option of the user, if a crossing is

also a candidate for stop signs.

58


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tt
ts
tk
ur

THRU TRAINS
SWITCH TRAINS
TRACKS

URBAN (1), RURAL (0)

PROBABILITY
nN
qT

FIGURE 3-2.

PROBABILITY

tt=10 ts=1
tk=1 ur=1
eich
even
50

100

100

PROBABILITY

PROBABILITY

tk=l
or
wed
2b
Se
a
0 50 100
ms
100
ttl ts=10
s tk=l ur=t
ptcnls
2
ptenihy
Og 50 Too
ms

TYPICAL PLOTS OF PROBABILITY OF FATAL ACCIDENTS P(FAIA) AND

PROBABILITY OF CASUALTY ACCIDENTS P(CAIA) AS A FUNCTION OF
TIMETABLE TRAIN SPEED ms.

32


RAIL-HIGHWAY CROSSING _HESOU
VERIFICATION WON

LLOCATION rrocepun
T

This worksheet provides a format and instructions for uae in field evaluation of cromaings to determine if
initial recommendations for warning device installations from the Resource Allweattus Frovedure should be
revised. Steps 1 through 5, described below, should be followed in making the deteraination In Steps 1 and 3,
the intial information (left column) is obtained from office inventory data prior te the fleld inspection. In
‘Step 4, the decision criteria values are obtained from the Resource Albacatton Mutal printout,

STEP 1; VALIDATE DATA USED IN CALCULATING PREDICTED ACCIDENTS.

CROSSING INITIAL REVISED
om

CHARACTERISTICS

TNEORMATION

Crossing Number

Location

Existing Warning Device

Total Trains Per Day (t) ee ne
Annual Average Daily Highway Traffic (c)
Total Switch Trains Per Day (ts)

Day Thru Trains (4)

Total Thru Trains Per Day (tt)

Number Of Main Tracks (mt)

Total Number Of Tracks (tk)

Is Highway Paved? (hp)

Maximum Timetable Speed, mph (ms)
Highway Type (nt)

Number Of Highway Lanes (hl)

Urban-Rural Location (ur)

Number Of Years Of Accident History (T)

|

Number Of Accidents In T Years (N)

Predicted Accident Or Casulaty Rate (AC)

STEP 2: CALCULATE REVISED ACCIDENT OR CASUALTY PREDICTION FROM DOT FORMULA IF ANY DATA IN STEP 1 HAS
BEEN REVISED.

Revised Predicted Accidents or Casulaties (AC) =

STEP 3: VALIDATE COST AND EFFECTIVENESS DATA FOR RECOMMENDED WARNING DEVICE.

INITIAL REVISED
INFORMATION INFQRMATION

Assumed Effectivness Of Recommended Warning Device (E)
Assumed Cost Of Recommended Warning Device (C)

Recommended Warning Device Installation

|

FIGURE 4-2, FIELD VERIFICATION WORKSHEET
52


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18


//
7/3OBCARD

APPENDIX Al

LISTING OF PROGRAM ACPD.NEW

//
//PROCLIB DD DSN=ZABCRUN. PROCLIB, DISP=SHR
//& BXEC SAS ,REGION=900K
7/DD1 DD SYSOUT=A
//FINALL DD DSN=WIP1FZU.NEWTEST, DISP=(OLD, KEEP) , UNIT=FILE,
7/ VOL=SER=FRASIR
//FILEB DD DSN=WIP1FZU.CITY, DISP=(OLD, KEEP) , UNIT=FILE,
7/ VOL=SER=FRASIR
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// VOL=SER=FRASIR
//S¥SIN DD *
DATA TRIM;
SET FINALL.NEWTEST;
REKEKKREKEKREREEEKEIR KEE IRERIE RR IRIEREEREREK RE REREEREKEE
THIS PROGRAM IS CALLED BY THE MAIN.COM PROCEDURE
TO GENERATE REPORT FOR ACCIDENT PREDICTION. HOWEVER
BY SPECIFYING THE VALUES OF FOLLOWING VARIABLES, IT
INDEPENDENTLY IN BATCH MODE.

*

+e

CAN BE RUN

SATEVAL
COUNTVAL
CITYVAL
RAILVAL
ID1VAL

ID2VAL
SELVAL

ACCVAL

OPTVAL

cl - C3
Sl - S3
Xl - X12
KK

NN
BUDGETX
TITVAL

"

NPWNEPNOH

i

TWO DIGIT STATE CODE

THREE DIGIT COUNTY CODE

FOUR DIGIT CODE FOR CITY

FOUR CHARACTER CODE FOR RAILROAD

SIX DIGIT NUMERIC CODE FOR THE FIRST
CROSSING ID

SIX DIGIT NUMERIC CODE FOR THE FINAL
CROSSING ID

-FOR ACCIDENT PREDICTION

~FOR RESOURCE ALLOCATION

~PREDICTED ACCIDENTS

-FATAL ACCIDENTS

-~COMBINED CASUALTY INDEX

-STANDARD EFFECTIVENESS

“EXTENDED EFFECTIVENESS

THREE VALUES OF UPGRADE COSTS

THREE VALUES OF STANDARD EFFECTIVENESS
TWELVE VALUES OF EXTENDED EFFECTIVENESS
FATALITY FACTOR

NUMBER OF RANKED CROSSINGS TO BE PRINTED
AVAILABLE BUDGET IN DOLLARS

A CHARACTER STRING OF THE TITLE TO BE PRINTED
AT THE TOP OF EACH PAGE IN THE REPORT

IF ANY OF THE VARIABLE DOES NOT HAVE ANY SPECIFIC VALUE
IT IS ASSIGNED A MISSING VALUE OF PERIOD (.)

A-3


The resource allocation algorithm systematically computes the accident or casualty
teduction/cost ratios, including incrementals, of all allowable improvement options for all
crossings under consideration. The individual accident or casualty reduction/cost ratios
are then sorted and selected by the algorithm so that the associated improvements result
in the maximum accident or casualty reduction obtainable for the available budget. The
total cost of the improvements is the sum of the individual project cost (Cj, C2 and C3).
The total accident or casualty reduction is the sum of the individual accident or casualty
reductions of the form ACjxE}j.

A flow diagram describing the logic of the resource allocation algorithm is shown in
Figure 4-1. The input to this program consists of the set of crossings for which the model
is to apply, the accidents or casualties predicted per year for these crossings, the warning
device parameters (effectiveness, C}, C2, C3) and the available budget (CMAX). It should
be noted that several values of E can be used to account for different crossing situations.
Multiple effectiveness values for each type of upgrade, currently available for the
algorithm, are discussed in more detail in Section 4.2.5.

The algorithm, described in Figure 4-1, proceeds according to the following steps in
computing optimal resource allocations.

Step 1: The reasonable assumption is made for the algorithm that E2>E, and
C2>C|. This assumes that gates are more effective at passive crossings than flashing
lights and that gates cost more. However, the effectiveness/cost ratio for flashing lights
(E /C}) could be greater or less than that for gates (E2/C2). If E1/CPE2/C2, the
algorithm computes incremental accident or casualty reduction/cost ratios for al!
allowable improvements at each crossing according to the procedure outlined in step 2A
below. Step 2A is based on the assumption that flashing lights have a greater
effectiveness/cost ratio than gates. If the opposite is true--that gates have an
effectiveness/cost ratio equal to or greater than flashing lights (E1/C gE 2/C2)-- then step
2B is followed for computing the improvement accident or casualty reduction/cost ratios.
Step 2B assumes that gates will always be installed at passive crossings.

Step 2A: Two accident or casualty reduction/cost ratios are calculated for each

single-track passive crossing, ACjxE /C, and the incremental ratio ACjx(E2-E ))/(C2-C}),
where ACj is the number of accidents or casualties predicted per year for the crossing.

39


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80


The use of the stop signs at railroad-highway grade crossings shall be limited to
those grade crossings selected after need is established by a detailed traffic
engineering study. Such crossings should have all of the following characteristics:

1. Highway should be secondary in character with low traffic counts.
2. Train traffic should be substantial.

3. Line of sight to an approaching train is restricted by physical features such that
approaching traffic is required to reduce speed to 10 miles per hour or less in
order to stop safely.

4. At the stop bar, there must be sufficient sight distance down the track to
afford ample time for a vehicle to cross the track before the arrival of the
train,

The engineering study may determine other compelling reasons for the need to
install a stop sign. However, this should only be an interim measure until active
traffic contro! devices can be installed. Stop signs shal! not be used on primary
through highways or at grade crossings with active traffic control devices.

Whenever a stop sign is installed at a grade crossing, a stop ahead sign shal! be
installed in advance of the stop sign,

The resource allocation model provides, at the option of the user, a list of crossings
that are possible candidates for standard highway stop signs. This list is produced by
selecting from the passive crossings under consideration those with less than 400 average
daily traffic (ADT) counts for rural roads and less than 1500 ADT counts for urban roads,
greater than 10 trains per day and single tracks, The crossings on the list are ranked by
the accident or casualty prediction measure selected by the user. Unlike the resource
allocation model results for active warning devices, the stop sign list is not ranked by
accident or casualty reduction/cost ratios. The reason for this is two fold: (1) based on
presently available information it is assumed that al! stop sign upgrades have the same
cost and effectiveness; hence, a ranking by accident or casualty reduction/cost ratio

would

57


If the crossing has flashing lights or other active devices (warning device classes 5, 6
and 7), an accident or severity reduction/cost ratio (ACR/C) for upgrading to gates is
calculated according to the equation:

ACRIC = AC (EFFECT 3/COST3)

where AC is either the number of predicted accidents, the number of fatal accidents, or
the combined casualty index for the crossing from the accident and severity prediction
formulas, and COST3 and EFFECT3 are the cost and effectiveness of the upgrade, as
discussed in Section 4. It is important to note here that, if the user has chosen to
implement standard effectiveness values throughout the resource allocation model,
EFFECT] simply represents the single effectiveness value for a crossing upgrade.
However, if extended effectiveness values are in use, EFFECT; can have one of four
values depending on the crossing's number of trains and tracks (see Section 4.2.5 on
extended effectiveness values).

If the crossing is passive (warning device classes !-4) but has multiple tracks, an
accident or severity reduction/cost ratio for upgrading to gates is calculated according to

equation:

ACR/C = AC (EFFECT 2/COST 2)
This forces gates to be installed at multiple track passive crossings in accordance with
Federal guidelines. If the crossing is passive but has only one track, an accident or

casualty reduction/cost ratio is calculated for upgrading to flashing lights according to
the equation:

ACRIC = AC (EFFECT }/COST})

The incremental accident or severity reduction/cost ratio equation for installing a gate at
the passive crossing is shown below and is calculated in the Resource Allocation Subtask:

ACR/C = AC (EFFECT2-EFFECT 1)/(COST 2-COST })

81


The probability of a casualty accident, given an accident, is expressed as:

P(CAIA) = 1/(1 + KC x MS x TK x UR) (4)

where: P(CAIA)

probability of a casualty accident, given an accident

KC = formula constant (4.481)

MS = factor for maximum timetable train speed
TK = factor for number of tracks

UR = factor for urban or rural crossing

The equations for calculating values of the crossing characteristic factors are listed
in Table 3-10 for the fatal accident probability formula and Table 3-11 for the casualty
accident probability formula. To simplify use of the formulas, the values of the crossing
characteristic factors have been tabulated for typical! values of crossing characteristics.
These values are to be found in Tables 3-12 and 3-13 for the fatal accident and
casualtyaccident probability formulas, respectively. An inspection of the factor value
tables shows the relative influence of the various factors on accident severity. In the
case of fatal accident severity (Table 3-12) maximum timetable train speed has factor
values which range over two orders of magnitude while the other factor values range over
less than one order of magnitude. Maximum timetable train speed, therefore, has a much
stronger influence on fatal accident severity than the number of trains or the urban-rural
location of the crossing. For casualty accident severity (Table 3-13) the number of tracks
has a slightly greater influence on severity than maximum timetable train speed. The
urban-rural location of the crossing has the least influence on casualty accident severity.

To obtain predicted numbers of fatal and casualty accidents the fatal and casualty
accident probabilities, from equations (3) and (4) are multiplied by predicted accidents
from equation (2), Hence, the formula for predicted fatal accidents at a crossing ist

FA = P(FAIA)x A (5)
where: FA = predicted fatal accidents per year

P(FAJA) = predicted fatal accident probability from equation (3)

A = predicted accidents per year from equation (2)

26


1, INTRODUCTION

1.1 PURPOSE

This is the third edition of a document intended to provide interested persons with
complete information on applying the DOT Rail-Highway Crossing Resource Allocation
Procedure. The material is presented in non-technical terms with references given to the
relevant technical reports.

1.2 BACKGROUND

The Highway Safety Acts of 1973 and 1976 and the Surface Transportation
Assistance Acts of 1978 and 1982 and the Surface Transportation and Uniform Relocation
Assistance Act of 1987 provide funding authorizations for individual states to improve
safety at public rail-highway crossings, Safety improvements frequently consist of the
installation of motorist warning devices such as flashing lights or flashing lights with
gates. In support of these safety efforts, several projects have been undertaken by the
U.S. Department of Transportation (DOT) to assist states and railroads in determining
effective use of Federal funds for rail-highway crossing safety improvement. One of
these projects has developed the DOT Rail-Highway Crossing Resource Allocation
Procedure to assist state and railroad program managers in identifying candidate crossings
for improvement. This procedure, referred to hereafter as the DOT Procedure,
recommends crossing safety improvements that yield the greatest accident reduction
benefits based on consideration of predicted accidents and casualties at crossings, the
cost and effectiveness of warning device options, and the budget limit,

Two analytical methods have been developed as part of the DOT Procedure. Their
development followed completion of a joint U.S, DOT-AAR (Association of American
Railroads) National Rail-Highway Crossing Inventory (hereafter referred to as the
Inventory), which numbered and collected inventory information for al! public and private
crossings in the United States!*. The first analytical method included in the DOT
Procedure is the DOT Accident and Severity Prediction Formula, which computes the

*References begin on page 84,


These two ratios correspond to the two actions available for single-track passive
crossings, either to install flashing lights or a revised decision to install gates. For
multiple-track passive crossings, only the accident or casualty reduction/cost ratio for
installation of gates is calculated (ACjxE2/C2), to conform with Federal regulations. For
each crossing equipped with flashing lights, the algorithm computes ACjxE3/C3,
corresponding to an upgrading from flashing lights to gates. The accident or casualty

reduction/cost ratio is represented in units of accidents or casualties prevented per year
per dollar.

Step 2B: The algorithm computes the accident or casualty reduction/cost ratio
ACjxE2/C2 for passive crossings and the ratio ACjxE3/C3 for crossings with flashing
lights. These accident or casualty reduction/cost ratios are associated with installing only
gates at crossings. For this case, these actions are always optimal relative to the
alternative of installing flashing lights, since the accident or casualty reduction/cost ratio
and the absolute cost of gates are greater than for flashing lights.

Step 3: Regardless of whether step 2A or 2B is followed, all of the accident or
casualty reduction/cost ratios calculated by the algorithm are ranked with the largest
first. The list of accident or casualty reduction/cost ratios represents a sequence of
optimal decisions starting with the top of the list.

Step 4: This step consists of a series of iterations, where the algorithm progresses
down the list of ranked accident or casualty reduction/cost ratios. This process is
equivalent to making the optimum decision of achieving the maximum accident or
casualty reduction/cost ratio at any given step on the list is calculated as ACjxE1/C}, a
decision is made to install flashing lights at a passive crossing, with an accident or
casualty reduction of ACjxE} and cost of Cj. If the accident or casualty reduction/cost
ratio is ACjx(E2-E 1)/(C2-C}), a previous decision to install flashing lights is changed to
install gates at a passive crossing. The incremental accident or casualty reduction of
changing the previous decision is ACjx(E2-E1), and the incremental cost is C2-Cj. If the
accident or casualty reduction/cost ratio is ACjxE2/C2, then a decision is made to install
gates at a passive crossing without prior consideration of flashing lights. The accident or
casualty reduction is ACjxE2 at a cost of C2. If the accident or casualty reduction/cost
tatio is AC;xE3/C3, then a decision is made to install gates at a crossing which had

41


LIST OF TABLES (Cont.)

Table « Page
4-7 STANDARD SET OF EFFECTIVENESS VALUES FOR WARNING DEVICE
IMPROVEMENTS 50
4-8 EXTENDED SET OF EFFECTIVENESS VALUES FOR WARNING
DEVICE IMPROVEMENTS 51
5-1 CHARACTERISTICS OF SAMPLE CROSSING 59

ix/x


Cost data for the warning device options may include total! life cycle costs (the sum
of procurement, installation, and maintenance), or the costs associated with only a
particular phase of a project. These costs are needed for the following categories of
active warning device improvements currently considered by the model: flashing lights
for a previously passive crossing, gates for a previously passive crossing, and gates for a
crossing previously equipped with flashing lights. Cost data on warning device
improvements which can be used for the resource allocation model are presented in
Section 4.2.4,

Warning device effectiveness required by the resource allocation mode! is a number
between 0 and | which determines the fraction by which accidents are expected to be
reduced by installation of a warning device. Effectiveness is a relative measure involving
both existing and proposed warning devices at a crossing to be upgraded. If automatic
gates have an effectiveness of 0.83, when installed at a crossing with a passive warning
device, the accident rate at the crossing will be reduced by 83 percent. Automatic gates
installed at a crossing with flashing lights would have a lower effectiveness, An
improvement which completely eliminates accidents, such as grade separations or
closures, would have an effectiveness of 1.0; i.e., it is 100 percent effective. Values of
effectiveness for different active warning device improvement combinations are
presented in Section 4.2.5.

The budget level for crossing improvements, used as input to the resource allocation
model, should include the total multi-year funding available, even though it may exceed a
single year's budget. The reason for this is that the resource allocation mode! will
produce a different and possibly conflicting set of decisions depending upon the budget
level used. If, for example, the first-year budget of a 2-year program is used, a specific
set of decisions will result from the model. Use of the model again for the next year's
budget, incorporating the crossing improvements made the previous year, will result in a
new set of decisions. Some of the new decisions may involve further improvements to
crossings just upgraded the previous year, resulting in an inefficient program. The best
approach would have been to use the total 2-year budget for the first application of the

model, and then fund the improvement decisions over a 2-year period.

The resource allocation model is intended to assist state and railroad planners in

formulating decisions on crossing improvements. There are a number of applications

35


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TABLE OF CONTENTS (Cont.)

Section
5.2 Computer Program for Resource Allocation Model
APPENDIX A
APPENDIX B
REFERENCES

vi

A-I
Bel

R-1


DATA;
LABEL LABO LABB LABP}
* ---* PRINT REPORT SORTED BY CROSSING IDS tenn
SET FATAL;
PROC SORT;
BY CROSSING;
PROC PRINT SPLIT=*;
VAR CROSSING BENCOS DSTATE COT_NAME CTY NAME RAILROAD ROAD RRID
MILEPOST;
FORMAT DSTATE ESTATE. ACCIDENT PREDACC.}
TITLE] TITVAL;
TITLE2 PUBLIC RAIL-HIGHWAY CROSSINGS RESOURCE ALLOCATION RESULTS:
TITLE3 BASED ON PREDICTED FATAL ACCIDENTS PER YEAR (SORTED BY CROSSIN
IDs);
DATA;
* --<* PRINT REPORT FOR CROSSINGS ELIGIBLE FOR STOP SIGNS *=<~- }
LABEL LABO LABF;
SET FATAL;
IF STPFLG = 0 THEN DELETE;
PROC PRINT SPLIT= *;
VAR CROSSING ACCIDENT NEWCL TRAINS AADT FC10;
FORMAT FC1O URBR.?;
TITLEL TITVAL;
TITLE2 RAIL-HIGHWAY CROSSING RESOURCE ALLOCATION RESULTS}
TITLE3 POSSIBLE CANDIDATE CROSSINGS FOR STANDARD HIGHWAY STOP SIGNS:
TITLE4 (SEE NOTE AT THE END OF SUMMARY PAGE);
DATA CCI;
PeNewemmrrrrrrrrrrrrrrrrrrrrrrorr oro r lS tht Dk
* PRINT REPORT FOR SEVERITY MEASURE= COMBINED CASUALTY INDEX
errmnrrmrrrrerrrrrrrrrrrrrrtr ot oro titi LLL
LABEL LABO LABC;
SET FINAL;
IF ACCD NE 3 THEN STOP;
PROC PRINT SPLIT=*;
VAR CROSSING ACCIDENT BENCOS RECCAT NEWCL TRACKS TRAINS CUMCOST
CUMARED DCl DC2 DC3 DC4 STPFLG;
FORMAT NEWCL CLASS. STPFLG YES_NO.
ACCIDENT CUMARED ACCBNF.
pC1l pDCc2 DC3 DC4 DECIS.;
TITLE TITVAL:
TITLE2 RAIL-HIGHWAY CROSSING RESOURCE ALLOCATION RESULTS;
TITLE3 BASED ON COMBINED CASUALTY INDEX (CCI);

DATA;

*% << PRINT THE REPORT SORTED BY CROSSING IDS tenn j
LABEL LABO LABC LABP;

SET CCI;

PROC SORT;

BY CROSSING;

PROC PRINT SPLIT=*;

VAR CROSSING BENCOS DSTATE COT_NAME CTY_NAME RAILROAD ROAD RRID
MILEPOST?

FORMAT DSTATE ESTATE. ACCIDENT PREDACC. ;

TITLEL TITVAL;

B-12


A SAS computer procedure called ACPD.NEW is written to generate accident and

severity prediction listings. The program listing for ACPD.NEW is contained in Appendix

A-1. The program executes a number of data steps which accomplish the following

subtasks:

a. Data Subsetting
From the data set comprising all the grade crossings, select the set of crossings
for which accident prediction is to be made and ranked.

b. Accident and Severity Prediction
Compute basic predicted accidents (H) for every selected crossing based on its
warning device type. Using the appropriate severity prediction formula,
compute the predicted accidents or fatal accidents or combined casuality index.

c. Report Printing
Execute the specific report generating procedure depending on the severity
measure selected earlier. This procedure prints the following reports:
(1) Listing of grade crossings sorted by rank,
(2) Listing of grade crossings sorted by crossing IDs.

d. Summary Printing

Execute the summary data step which prints the input data as well as run time

summary.

The Accident and Severity Prediction subtask is divided into three sections. The

first sec

tion, calculates the basic number of predicted accidents (H) for a crossing. The

program uses one of three different equations to make this calculation. The equation used

is dependent on the warning device classification of the current crossing. For warning

device classes 1-4 the Crossbucks (passive device) equation is used, classes 5-7 the

Flashing Lights equation is used, and for class 8 the Gates equation is used.

64


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3.3. DESCRIPTION OF FORMULAS FOR ACCIDENT SEVERITY PREDICTION
3.3.1 Overview

The effort to develop accident severity prediction formulas was motiviated by the
recognition that rail-highway crossing accidents are not equally severe. In recent years
about 67 percent of crossing accidents resulted in no casualties while all fatalities
resulted from only 6.6 percent of all accidents. Clearly, crossings that exhibit a tendency
toward more severe accidents, should be given priority for safety improvements. A
formula which can help in identifying these crossings will improve the safety benefits
obtained from crossing improvements. The severity prediction formulas described here
represent the results of an effort to achieve that objective6,

Two casualty prediction formulas have been developed; a fatal accident prediction
formula and a casualty accident prediction formula. When used with the accident
prediction formulas, described in Section 3.2, these two formulas provide two measures of
accident severity; predicted fatal accidents and predicted casualty accidents, A fatal
accident is defined as an accident which results in at least one fatality independent of
injuries or property damage. A casualty accident is an accident which results in at least
one fatality or at least one injury independent of property damage.

The severity prediction formulas are designed to be used with the general accident
prediction formula (2) to produce the estimates of fatal and casualty accidents per year at
crossings. The severity prediction formulas used without the accident prediction formula
produce estimates of the probability of a fatal or casualty accident given that an accident
occurred, For example, the fatal accident prediction formula estimates the probability of
a fatal accident given that an accident occurred at a crossing; |.e., fatal accidents per
accident. When this estimate is multiplied by the crossing's estimated accidents from the
accident prediction formula (2) the result is predicted fatal accidents per year at the
crossing. As an example, if a crossing has a predicted accident rate of 0.5 accidents per
year and a predicted fatal accident probability of 0.2 fatal accidents per accident, the
result will be a predicted fatal accident rate of .2 x .5 or 0.1 fatal accidents per year,

In addition to predicted fatal and casualty accidents per year, a third measure of
accident severity can be obtained from use of both severity prediction formulas. This
measure, referred to as the combined casualty index (CCI), is a weighted sum of the fatal

24


APPENDIX A2

LISTING OF NEWDAT PROCEDURE

//
//JOBCARD

//PROCLIB DD DSN=ZABCRUN. PROCLIB, DISP=SHR

//& EXEC SAS,REGION=900K

//FILEB DD DSN=WIP1FZU.NEWTEST , DISP=(NEW, KEEP) , UNIT=FILE,

// VOL=SER=FRASIR, SPACE=(TRK, (150,10) , RLSE)

//FILEA DD DSN=WTP1FZZ.SEV. INDEX, DISP=SHR, UNIT=MSS

//SXSIN DD *

JRA OIG CI GIGI OOO IIIT IOI OTE OSI SOT I I ek
* THIS PROCEDURE IS USED TO CREATE SAS DATASET NEWTEST FROM

* MATCHED ACCIDENT AND INVENTORY DATA. THE DATA SET IS USED BY

* BOTH ACPD.NEW AND RESAL.NEW PROGRAMS.

BEI EEC CARO E ESO SOCIO OTT IOSI OEE TOT IOUT TOU TIO IIE |
DATA FILEB.NEWTEST;

INFILE FILEA;

INPUT

CROSSING $ 1-7 STATE $ 8-9
INTID 1-6

CONTY $ 10-12 CITY $ 13-16
RAILROAD $ 17-20 CHANGE_Y 21-22
CHANGE M 23-24 ROAD $ 25-41
RRID $ 42-51 MILEPOST $§ 52-57
OLDCL 58 NEWCL 59
STOP 60 NGTSWT 64-65
NGTTHRU 66-67 TRAINS 61-63
DAYSWT 68-69 DAYTHRU 70-71
MXTTSP 72-74 MTRKS 78-75
OTRKS 76-77 PASS TRN $ 78-78
PAVED $ 79-79 TRAFLN 80-80
FC1O 81-81 FCl 82-82
AADT 83-88 PCTTRUK $ 89-90
CHANGE_D 91-92

Acco 93-94 aAccl 95-96
AcCc2 97-98 aAcc3 99-100
ACC4 101-102 acc5 103-104;
* The parameters ACCO thru ACCS are taken from accident

history file. If the change year of the warning device
is within the period of six years, the accidents prior to
warning device change are set to zero.

All other parameters are taken from the Rail Highway
Crossing Inventory file.


IGG
errTrre rer rT TT TT Trrrrrrerrerrerrre rT See ee eee TTT ETE Teh 2 2
MACRO CROSSBK .8644°%; (94/7
MACRO FLASHLK .8887 3%) 0 gu
MACRO GATESK .8134 %; “cou!
ISTATE= STATEVAL; .
ICOUNTY= COUNTVAL?
ICITY= CITYVAL;
IRAIL= 'RAILVAL';
ID1= ID1VAL;
ID2= ID2VAL;
IF ISTATE NE . THEN DO ;
IF STATE NE ISTATE THEN DELETE; END;
IF ICOUNTY NE . THEN DO;
IF CONTY NE ICOUNTY THEN DELETE; END;
IF ICITY NE . THEN DO;
IF CITY NE ICITY THEN DELETE; END;
IF IRAIL NE '.' THEN DO;
IF RAILROAD NE IRAIL THEN DELETE; END;
IF (ID1 > 0 ) THEN DO;
IF INTID < IDl THEN DELETE; END;
IF (ID2 > 0 ) THEN DO;
IF INTID > ID2 THEN DELETE; END;

CLASS=NEWCL;

IF CHANGE Y > 80 THEN CLASS= OLDCL;

IF OLDCL > 8 THEN CLASS = NEWCL;

HK KKK KEKE REREEEKRKEKREREREEEERER REE REE ERERRK KKK
* GET TOTAL NUMBER OF CROSSINGS BEING ANALYZED

HRA RIE KEITH RIE REE EE EKER IERIE REREREERERERRRERRE KKK
TRACKS=MTRKS+OTRKS ;

DROP ISTATE ICOUNTY ICITY IRAIL IDl ID2 INTID FCl;

PROC MEANS NOPRINT;

OUTPUT OUT=TOTREC N= NUM_CRO;
HARRIE RARER REE REE KKK
*

* COMPUTE H VALUE FOR DIFFERENT CLASS TYPE

*

RIKI IRIE REE RK
HRRKKKIKEREREE REE EHREEE RRR ARERERERERRREERRRERRERRRKKKK RK KK
*

* BASIC CALCULATION FOR PASSIVE

*

KKK KKK KEKE KIKI TKR ERIK IK REAR IKEA EEK IER REE KEEKERRR KKK
DATA ICROSS;

SET TRIM;

*

* DELETE ALL NON PASSIVE CROSSINGS;

* :

IF (CLASS > 4) THEN DELETE;

H= .0006938*(((AADT*TRAINS +0.2)/.2)**.37)*
(((DAYTHRU + 0.2)/0.2)**0.178) *
(EXP (0.0077*MXTTSP) ) *
(EXP (-0.5966*(PAVED-1) ));

i

A-t

i
y
|


accuracy of the input data, they should be validated by a diagnostic team as part of their
normal duties in making field evaluations of recommended improvements. While in the
field, the diagnostic team should also make note of other considerations that may impact
final improvement decisions but are not included in the DOT Procedure. These
considerations should include highway congestion, school bus and hazardous materials
traffic, restricted sight distance, visual clutter, and other unusually hazardous, costly or
mitigating characteristics of individual crossings. A procedure for performing this
evaluation is described in Section 4.2.6. Results of the DOT Procedure, findings of the
diagnostic team, inclusion of any state warrants, and the judgement of state and local
officials should all be considered before final improvement decisions are made.

The primary role of the DOT Procedure is to assist states and railroads in developing
crossing safety improvement programs. The first stage in developing these programs is
usually to prepare a list of candidate crossings for safety improvements. To assist in
preparing this list, the DOT accident prediction formula can be used to rank crossings by
predicted accidents or casualties to identify hazardous crossings potentially needing
safety improvements. The resource allocation model can then be used to evaluate
alternative programs for improving these crossings. For example, the impacts on program
benefits of changes in key program parameters such as budget limits, warning device
installation strategies (e.g. flashing lights only, gates only) and warning device cost and
effectiveness assumptions can be determined. Analysis of these results could help in
deciding upon budget levels for crossing improvements and in determining the
effectiveness of implementing state warrants specifying installation strategies. Once key
program parameters have been decided upon, the DOT Procedure will provide an initial
recommended program, based on cost-effectiveness considerations, for review by the
state. The DOT Procedure is also useful for railroads in providing recommended uniform
improvement programs over their entire rail system that passes through several states.

Initial results of the DOT Procedure provide useful guidance to diagnostic teams by
specifying crossings with recommended improvements that should be field inspected and
data that must be checked for accuracy. Using the field verification procedure described
in Section 4.2.6, diagnostic teams can determine revised cost-effective improvement
decisions for particular crossings where original data were found incorrect. The revised
results obtained by the diagnostic team then form a useful basis upon which state and
local officials can finalize crossing improvement programs.

5/6


Yor

pur O3q te SUMMARY OF INPUT PARAMETERS
ety

puT - @360 Ot * FOR ACCIDENT AND SEVERITY PREDICTION
ay
PUT @36
RES SESSESESE SLES LSS ESET EEE EEE EEE ESET EE TET ES ETE TESST TEES ST oe
PUT /@36 ! TITLE TITVAL ';
PUT @36 ' STATE STATEVAL ';
PUT @36 ! COUNTY COUNTVAL ';
PUT @36 '! CITY CITYVAL ';
PUT @36 ' RAILROAD RAILVAL ';
PUT @36 ' CROSSING ID ‘5
PUT @36 ' BOTTOM OF RANGE: ' ID1;
Pur 46@36 = TOP OF RANGE ' ID2;
PUT @36 ' RECORDS TO BE "3
IF N_N < 195000 THEN
PUT @36 ' PRINTED : NN ';
ELSE
PUT @36 ' PRINTED : ALL ';
PUT e@36 ' SEVERITY TYPE : ACCVAL (1) PREDICTED

ACCIDENTS ';
PUT @36 =!

Q ‘ (2) FATAL ACCIDENT! ;
PUT 36

(3) COMBIN. CASUALITY

INDEX';

IF ACCD = 3 THEN

PUT @36 =! FATALITY FACTOR : KK ';

PUT

1103.6 RII III III TOIT RI IOI IRIN:

PUT //@36 ' SUM OF PREDICTED ACCIDENTS st
TOTACC;

PUT /@36 ' TOTAL NUMBER OF CROSSINGS ANALYZED :
NUM_CRO;

es /@36 ' INVENTORY DATE: APRIL 1986';

LL ABE NRRRIR RRR IR ERK RRR IKE RRR IRI REIT RIK RRR RII RIK IRR IRI IIR ERIN
TITLEL TITVAL;


ce
g)
ge

qt

MACRO CROSSBK .86%4 %; . %
MACRO FLASHLK .8887 %; :
MACRO GATESK .8131 %;
ISTATE= STATEVAL;
ICOUNTY= COUNTVAL;

IcITY= CITYVAL;

IRAIL= 'RAILVAL'?}

ID1= IDLVAL:

ID2= ID2VAL;

IF ISTATE NE . THEN DO ;
IF STATE NE ISTATE THEN DELETE; END;
IF ICOUNTY NE . THEN DO;

IF CONTY NE ICOUNTY THEN DELETE; END;
IF ICITY NE . THEN DO;

IF CITY NE ICITY THEN DELETE; END;
IF IRAIL NE '.' THEN DO;

IF (RAILROAD NE IRAIL ) THEN DELETE; END?
IF (IDl > 0 ) THEN DO;

IF INTID < ID1 THEN DELETE; END;

IF (ID2 > 0 ) THEN DO;

IF INTID > ID2 THEN DELETE; END;
*

*
CLASS=NEWCL?
IF CHANGE_Y > 80 THEN CLASS= OLDCL;

IF OLDCL > 8 THEN CLASS = NEWCL;
Pepeerewwee@rrrrrrrrrrrr rT rrr Trtririrr ror iE iti ht hs hoe

* DELETE ALL RECORDS FOR GATES IF RESOUCE ALLOCATION
* IS TO BE PERFORMED AND GET THE TOTAL NUMBER OF
* CROSSINGS BEING ANALYZED.

FOI IORI II IIR TTI IAI ITI I TTI RIAA IAAI IARI IRI |
SELECT= SELVAL;

IF (SELECT = 2 AND NEWCL = 8 ) THEN DELETE;
TRACKS=MTRKS+OTRKS ;

DROP ISTATE ICOUNTY ICITY IRAIL ID1 ID2 INTID ;
PROC MEANS NOPRINT?;

OUTPUT OUT=TOTREC N= NUM_CRO;

errr rererrrrrnrrrrrrrrrrrrirorero irri rt EPIL t thd
*

* COMPUTE H VALUE FOR DIFFERENT CLASS TYPE
*

eewerrererrrrrrrrrrerrrrrrrrrrirrirrrr rr Tt tei tt,
eereerrnrrrrrrrrrrrrrrrrrrrriori irre rt Sate D lll
*

* CALCULATION FOR PASSIVE

*

Peererererrrrrrrrrrrrrrrrrirrrrrir rr rr te SE ESSE lle
DATA ICROSS;

SET TRIM?

*

* DELETE ALL NON PASSIVE CROSSINGS;
* :

IF (CLASS > 4) THEN DELETE;
H= .0006938*(((AADT*TRAINS +0.2)/.2)**.37)*

;

B-4


First, the basic formula (1) is used to determine the unnormalized prediction (a):
a=K x EI x DT x MS x MT x HP x HL
where:

a = unnormalized initial accident prediction

K = constant

EI = factor for exposure (product of highway and train traffic)
DT = factor for number of thru trains per day during daylight
MS = factor for maximum timetable speed

MT = factor for number of main tracks

HP = factor for highway paved (yes or no)

HL = factor for number of highway lanes

The basic formula factor values (K, El, DT, MS, MT, HP, and HL) can be determined
from Table 3-2 for passive crossings, using the crossing's characteristics listed in Table
5-1:

K = 0.0006938
El = exposure index factor value for the product of 350 average daily highway
vehicle and 15 total train movements per day (c x t = 5250) = 42.39

DT = 1.79
MS = 1.36
MT = 1.00
HP = 1.00
HL = 1.00

Substituting the factor values into the basic formula yields:

60


Examples of the output of ACPD.NEW program are shown in Figures 5-1 through
5-9. This output represents three separate calculations. Figures 5-1, 5-2, and 5-3 are for
predicted accidents, Figures 5-4, 5-5, and 5-6 are for predicted fatal accidents. Figures
5-7, 5-8 and 5-9 are for combined casualty index. The first part of each set of the three
outputs define the parameters of the crossings listed. The second part is the ranking by

predicted accidents computed on the basis of the desired severity measure. All parameter
values used in the computation of predicted accidents and severity prediction are included
in the output. The third part presents the list of crossings sorted by crossing ID. This
thad part enables users to find a crossing on the ranked list (second part) when only the
crossing ID is known.

%2 COMPUTER PROGRAM FOR RESOURCE ALLOCATION MODEL

This section describes the computer program for the resource allocation model
discussed in Section 4. The model is run by a SAS computer procedure called
RESAL.NEW. The program listing for RESAL.NEW is contained in Appendix B. The
Program executes a number of data steps to accomplish the following subtasks:

a. Data Subsetting
From the dataset comprising all of the rail-highway crossings, select the
set of non-gate crossings for which accident prediction is made and for which

the available budget is to be allocated.

b. Accident and Severity Prediction

Compute basic predicted accidents (H) for every selected crossing using
the passive or flashing lights formula. Using the appropriate severity formula,
compute the predicted accidents or fatal accidents or combined casualty index.

c. Identify the Crossings for Stop Signs

Check eligibility of each crossing for stop signs. If it meets the criteria,
assign the "yes" attribute to the crossing,

66


Three sets of equations are used to determine the value of each factor, one for each

of the following three categories of warning devices:
1. Passive, including the following warning device classes:

Class | - No signs or signals
Class 2 - Other signs

Class 3 - Stop signs

Class 4 - Crossbucks

2. Flashing lights, including the following warning device classes:

Class 5 - Special, e.g., flagman
Class 6 - Highway signals, wig-wags or bells
Class 7 - Flashing lights

3. Gates, including the following warning device class:
Class 8 - Automatic gates with flashing lights

The crossing characteristic factors for the three warning device categories are
shown in Table 3-1. Each set of factor equations should be used only for crossings with
the warning device classes for which it was designed. For example, if it is desired to
estimate the unnormalized number of accidents at a crossing with crossbucks, then the
passive set of equations should be used. If it is desired to estimate the unnormalized
number of accidents at a crossing recently upgraded from one warning device category to
another, use the formulas for the prior category and apply the effectiveness factor for the
upgrade. See Section 5.1.2 for a more detailed discussion.

The numerical! value of each factor is related to the degree of correlation that a
specific crossing characteristic was found to have with crossing accident rates. For those
cases in Table 3-1 where the value of the factor is indicated as a constant 1.0, it was
found that the characteristic did not have a significant relationship to crossing accidents.

The structure of the basic formula makes it possible to construct look-up tables of
numerical values for the crossing characteristic factors. To evaluate the basic formula at
a particular crossing whose Inventory characteristics are known, the values of the factors

1


LIST OF PARAMETERS USED IN NEWDAT PROCEDURE

VARIABLE VARIABLE DESCRIPTION

CROSSING CROSSING ID NUMBER

INTID INTEGER VALUE OF CROSSING ID

STATE LOCATION STATE CODE

CONTY LOCATION COUNTY CODE

cITY LOCATION CITY CODE

RAILROAD RAILROAD CODE

CHANGE_Y YEAR OF LAST WARNING DEVICE CHANGE
CHANGE_M MONTH OF LAST WARNING DEVICE CHANGE
ROAD ROAD OR STREET NAME

RRID RAILROAD DESIGNATION

MILEPOST MILEPOST AT CROSSING

OLDCL OLD CLASSIFICATION OF THE CROSSING
NEWCL NEW CLASSIFICATION OF THE CROSSING
STOP STOP SIGNS

TRAINS NUMBER OF TOTAL TRAINS

NGTSWT NUMBER OF NIGHT SWITCH TRAINS
NGTTHRU NUMBER OF NIGHT THRU TRAINS

DAYSWT NUMBER OF DAY SWITCH TRAINS

DAYTHRU NUMBER OF DAY THRU TRAINS

MXTTSP MAXIMUM TIME TABLE SPEED

MTRKS MAIN TRACKS

OTRKS OTHER TRACKS

PASS_TRN NUMBER OF PASSANGER TRAINS

PAVED IS HIGHWAY PAYED ?

TRAFLN TRAFFIC LANES

FC1O TENS DIGIT OF FUNCTIONAL CLASSIFICATION
Fel UNITS DIGIT OF FUNCTIONAL CLASSIFICATION
AADT ANNUAL AVERAGE DAILY TRAFFIC
PCTTRUK PERCENTAGE TRUCKS

CHANGE_D DAY OF LAST WARNING DEVICE CHANGE
Acco, Accl "SIX FIELDS OF NUMBER OF ACCIDENTS
aAcc2, AcCc3 IN LAST SIX YEARS. EACH FIELD HAS
Acc4, Accs TWO POSITIONS. THE MOST RECENT YEAR

Is acc5."


+Aep aed uyeay auo veya score

16

BL°L9 OO0OLE - LooodE
g27£9 Q0000F - 1000E2
nz"es ooo0gz - Loo0gt
£0" nS 0000g! - L000EL
L705 QO00£! - LoooLt
6noln 000011 - 10006
Shhh 00006 ~ 1000L
ne Lh 0000L - 10009
€a°6E 00009 - 1000S
SSLE 00005 - L000n
soqors sauet Aemy3TY = TH Lene 0000h ~- L000
yozoej paads atqezamTy unmtxem = SH 6h°2E 0000f - L00S2
soyoes paaed AemySty = dH 19*0€ oo0se - L000z
goqoRJ sutes, ySnosyy Aep = 1d hh gz o000z ~- L00St
Joye] syxOeI] UTeM = IW 9Ls2 00051 ~- Looot
4oqoey xaput aunsodxe - 19 6E £2 oo00t - Log
adn ‘Aep sad sutesy so saqunu oy3 Aq pattdyytnm *ydy ‘ep ued sotoTyaa AemysTY JO yaqunU = wdu X uu ane 0008 = - 1009
queqsuos eTnmios = ¥ 19°61 0009 - 100h
thle 000n - L00£
0z°9t O00€ = - 10S2
ort 06 SS'St 00S2  - L002
00°8 se LS*ht 0002 ~ t09L
oot og get 00gt =~ Loft
00°t Sh LLr2t oof = - ‘1008
00"L OL a9°2 O9-th ggrtt oooL =~ LOL
00°1 39 1g'2 On-LE 6L'0t 00L == :109
00°L 09 LE‘2 of-1z ez"oL 009 - 10S
oot SS 9172 oz-Lt 69°6 00S == LOW
oo't 0S tore ol 00°6 00h > LOE
00°L Sh B6"t 6 Ste oof ~ 10%
Lee 6 00't On Ort 8 Stk 002 ~ tat
OL*z 8 0071 SE 6ert L fer9 02k - 18
nE*2 L a) o0"t of neh 9 66°S og 7 1S
£0°2 9 gz § oot se 6L"L S 9Lth os ~ te
cans S fet 4 0071 02 2uh 4 Sith of 712
StL 4 Astt = € ort st noth £ LSE oz - at
zert € Vl Ce SEL 2 00°L at orb 2 86° on “9
SLL 2 gk ok oo"L S Berk t gee S -t
ort L cork (894) oot 0 00°! ° 0071 0 oot #0 ShLS000°0
8 seuey au paard In syoedy SH paads id suTedy 1a ade Kudu ¥
AesusTH Aemy3tH UTeW aTqeqaery ysnoayy feq
wnayxey

THX dH XIN X SHX IG X 19 x ¥ = & FVINNHOS DISVa JO WHOS TVESNID

SADIARG ONINUVA ALVD HLM SONISSOUD YOd SAN TVA MOLOVA “h-€ AIGVL



g-01 X 6°T =

000'se sqy3ty
10 = 0/aVv —— —— Burysey gy €x
299°0
g-OT X 8°€ =
o00's€ s1ysty
ZOPDMOV Suryse| a ex
299°0,
g-O1 X O'€ = g-OI X #°8 =
oe 000‘¢z - 000‘s# 000°¢z yoras aTBuls
EO = D/ADV €"0 = O/AODV aatssed tx
L'°0- 6°0 £0
£9 Tg-%5 I5
— |iv = o/aov fy = o/aov — }iv = o/aoV
€q Iq-2q} 1q
DNISSOUD SDNISSOUO AAISSVd 2D NISSOUD aOIAaG DNISSOUD
LHDIT LV SALVD OL AAISSVd LV DNINUVA
ONIHSV14 LV SLHDIT DNIHSV 14 SLHDI1 DNIHSY13 ANawaND
SALVD TIVLSNI ONITIVISNI WOUS TIVISNI

NOISIOdC ASIARY

wececccmeamcnne| SNOLLdO LNAW AOU dW nnn

SOILVU 1SOD/NOILONGaY LNAGIOOV dO NOILVINOTVS +2 dalS “## ATGVL

45



ad”

bpm be bee foam ue

WIRY Dee DAE eM ae MD

ahem MAT fhe IMT! a ot) A Le
foe THO hom My! Nye U NES beet Ny!
13—'NH' fanthat She NM! tG~'Ny!
s7— INC! 1h= ND! 30” OH! 40~'OR!
41='OR!' 42='"PA' 43='PR'  44='RI!
45='sct 46='SD' 47='TN' 48='TX'
49='UT' 50='VT! 51='"VA' 52='vVI'

53='WA' -54='WV'  55='WI' 56='WY';
VALUE IPAVED 1= YES

2= NO;
PICTURE PREDACC OTHER='9.999999';
PICTURE ICHANGE 0-801 '

OTHER='99-99' ;

RRR ERIE ERR EIR REE EERE REE REE
* REPORT PRINTING PROCEDURE FOR *

* SEVERITY TYPE = PREDICTED ACCIDENTS *
FO III IIR III III III IITA RI III +

DATA BASIC;

LABELI ;

SET DATA2; ;

IF ACCD NE 1 THEN STOP; pat (Nee

RETAIN RANK 0; RANK = RANK +1; a

PROC PRINT SPLIT=*;

FORMAT DSTATE ESTATE. MONYEAR ICHANGE.;

FORMAT DPAVED IPAVED. ACCIDENT PREDACC.;

ID RANK;

VAR ACCIDENT CROSSING DSTATE RAILROAD ACC1 ACC2 ACC3 ACC4 ACCS

MONYEAR CLASS SWITCH DAYTHRU THRU TRACKS MTRKS MXTTSP

DPAVED TRAFLN FC10 AADT;

TITLEL TITVAL;

TITLE2 PUBLIC RAIL-HIGHWAY CROSSINGS;

TITLE3 RANKED BY PREDICTED ACCIDENTS PER YEAR;

PROC SORT DATA= BASIC;

BY CROSSING;

PROC PRINT SPLIT=*;

FORMAT DSTATE ESTATE. ACCIDENT PREDACC. ;

VAR CROSSING ACCIDENT RANK DSTATE COT_NAME CTY_NAME RAILROAD
ROAD RRID MILEPOST;

TITLE4 SORTED BY CROSSING ID;

KEKKEKREEEEEEEERE REE REREEKEERRERERKARAKKAKKR RRR RAKE KK KEE

* REPORT PRINTING PROCEDURE FOR

* SEVERITY = FATAL ACCIDENTS

RREKRKRKEEERERE RRR ERE RE REE REE REE RR ERE EERE REE RRR RREEEE |

DATA FATAL;

LABELI ;

SET DATA2;

IF ACCD NE 2 THEN STOP;

RETAIN RANK 0; RANK=RANK+1;

PROC PRINT SPLIT=*;

FORMAT DSTATE ESTATE. MONYEAR ICHANGE. ;

FORMAT DPAVED IPAVED. ACCIDENT PREDACC. ;

A-&


TABLE 3-11. EQUATIONS FOR CROSSING CHARACTERISTIC FACTORS FOR CASUALTY
ACCIDENT PROBABILITY FORMULA

Casualty Accident Probability Formula: P(CAIA) = 1/(1 + KC x MS x TK x UR)

CROSSING CHARACTERISTIC EQUATION FOR CROSSING
FACTOR CHARACTERISTIC FACTOR
Formula Constant KC = 4.481

Maximum Timetable Train Speed Factor MS = ms-0.343

Number of Tracks Factor TK = e0-1153tk

Urban - Rural Crossing Factor UR = e0-296ur

where:

ms = maximum timetable train speed, mph

tk = total number of tracks at crossing

ur: urban crossing = I, rural crossing = 0

ur = FC1O (tens digit of functional classification). See page A-11.

28


souet AemySty Jo saqonu = Ty
ydu ‘paeds atqeqamty wnwyxem = su
0°2 = ou pue gr, = eaf zpaaed Aemusty = dy

4uattAep Buyunp Aep vad suyesy ynouyy Jo vequnu

7

syoRuy UTeW JO yaqunu = qu

Aep sad cutesy Jo uequnu

*

ep sad satotuen AeMYBTY Jo yaqunu = 0

(etayozm ‘0? ot qwzigi'o? or igliro(2*0/(2°0 + P)) | znezro(2"0/(2"0 * 9 X )) | SHLSO00"O S3LV9
is)
SLHDIT 7
(a-Tuygzet 0? ol m161°0? orl tern so(270/(270 * P)) | goin o(2"07(2"0 + 4X >)) | LSEEOOO"O | ONTHSVIG
ont (444) 9965" 0°? ot su)200°0? aut *o(2"0/(2"0 * P)) Le“o(2°0/(2"0 + 9 x 2)) | BE69000"0 | SAISSVd
TH dH ih SH id. 1g >)
HOLOVS HOLOVS WOLOVA [xoLova aagds YOLIVa HOLOVS AINVLSNOD | AYODSLVO
‘SaNv'1 GgAVd ‘SMOWEL WIAVLANIL SNIVEL XSQNT vinnuod | ONISSOYD
AVAHOIH AWMHOIH NIVK WOWIXVR HONOUHL kVG ‘BuNSOdXS
SHOLOVA DILSINSLOVUVHD ONISSOUD

HX dH X INX SH X Id x Ig XH = © FYTNWHOA OIS¥A 40 WHOd TWY3NED

SUYOLOVA OILSTYALOVUYVHD ONISSOUD YOd SNOLLWNOE ‘I-€ ATAVL



Using the above data, the R value is calculated using equation (11) (also shown on
the worksheet, step 4, part 2):

R = (.26/.19) (.87/.90) (65,300/115,000)
= 0.751

The decision criteria for this crossing, obtained from the standard output report of the
resource allocation model, Figure 5-10, are:

DC; = 0.318

DC2 = 0.780

DC3 = not computed since the crossing is single track
DCy = not computed since the crossing is passive

Comparing R with the decision criteria values, as described in step 4, part 3a of the
worksheet, shows that R is greater than DC], but less than DC2. This means that the
original decision to install gates at this crossing should be revised to install flashing lights
as the most cost-effective decision if the new data for the crossing are assumed correct.

4.3, RESOURCE ALLOCATION MODEL FOR STANDARD HIGHWAY STOP SIGNS

The most recent DOT study on warning device effectiveness!! determined that
standard highway stop signs may be effective in reducing crossing accidents. The average
level of effectiveness for upgrades to standard highway stop signs from other passive
devices was found to be 0.35 (95 percent confidence interval, 0.16 to 0.54). This level of
effectiveness coupled with their low cost ($400 installation or $800 total 30-year life
cycle cost, including "stop ahead" signs, for a two-stop sign installation) make standard
highway stop signs worthy of consideration for certain crossing situations!!, The FHWA
has established the following guidelines for the selection of candidate crossings for stop
signs#,14;

56


10.

13.

14,

15.

16.

17,

Association of American Railroads, Economics and F inance Department, "Indexes of
Railroad Material Prices and Wage Rates-Railroads of Class I" (Washington, D.C.,
Published Quarterly).

Farr, E., and J. Hitz, "Effectiveness of Motorist Warning Devices at Rail-Highway
Crossings," FH WA-RD-85-015 (Washington, D.C.: U.S. Department of
Transportation, July 1984).

Morrissey, J., "The Effectiveness of Flashing Lights and F lashing Lights with Gates
in Reducing Accident Frequency at Public Rail-Highway Crossings," FRA-RRS-80-
005 (Washington, D.C.: U.S. Department of Transportation, March 1980).

California Public Utilities Commission, "The Effectiveness of Automatic Protection
in Reducing Accident Frequency and Severity at Public Grade Crossings in

California," (San Francisco: State of California, June 30, 1974).

Manual on Uniform Traffic Control Devices, Sec. 8B-9, U.S. DOT, FHWA, 1983.

National Highway Traffic Safety Administration, "The Economic Cost to Society o!
Motor Vehicle Accidents," U.S. Department of Transportation, Washington, D.C.,
1983,

Miller, T. et. al., "Alternative Approaches to Accident Cost Concepts: State of tlc
Art," FH WA/RD-83/079, (Washington, D.C.: U.S. Department of Transportation,
January 1984),

Faghri, A. and M.J. Demetsky, "Evaluation of Methods for Predicting Rail-Hiyliw.is
Crossing Hazards," FH WA/VA-86/32 (Virginia Highway and Transportation Pew |
Council, Charlottesville, VA, March 1986).

1200 copies


Figure
2-1
3-1

3-2

4-1

5-3

5-4

LIST OF ILLUSTRATIONS

RAIL-HIGHWAY CROSSING RESOURCE ALLOCATION PROCEDURE

DOT RAIL-HIGHWAY CROSSING ACCIDENT AND SEVERITY
PREDICTION FORMULAS

TYPICAL PLOTS OF PROBABILITY OF FATAL ACCIDENTS
P(FAJA) AND PROBABILITY OF CASUALTY ACCIDENTS
P(CA| A) AS A FUNCTION OF TIMETABLE TRAIN SPEED ms
RESOURCE ALLOCATION ALGORITHM

FIELD VERIFICATION WORKSHEET

EXAMPLE OF INPUT PARAMETERS FOR PREDICTED ACCIDENTS
PER YEAR

EXAMPLE OF RANKED LIST OF CROSSINGS FOR PREDICTED
ACCIDENTS PER YEAR

EXAMPLE OF CROSSINGS SORTED BY ID FOR PREDICTED
ACCIDENTS PER YEAR

EXAMPLE OF INPUT PARAMETERS FOR PREDICTED FATAL
ACCIDENTS PER YEAR

EXAMPLE OF RANKED LIST OF CROSSINGS FOR PREDICTED
FATAL ACCIDENTS PER YEAR

EXAMPLE OF CROSSINGS SORTED BY ID FOR PREDICTED
FATAL ACCIDENTS PER YEAR

EXAMPLE OF INPUT PARAMETERS FOR COMBINED CASUALTY
INDEX

EXAMPLE OF RANKED LIST OF CROSSINGS FOR COMBINED
CASUALTY INDEX

EXAMPLE OF CROSSINGS SORTED BY ID FOR COMBINED
CASUALTY INDEX

EXAMPLE OF INPUT PARAMETERS FOR RESOURCE ALLOCATION
REPORT PROGRAM

EXAMPLE OF CROSSINGS SELECTED FOR UPGRADE
EXAMPLE OF SELECTED CROSSINGS SORTED BY ID

EXAMPLE OF CANDIDATE CROSSINGS FOR STOP SIGNS

vii

Page

32
40
52

67

68

69

70

7

72

73

74

75

77
78
79
80


/67

*

* CALCULATIONS FOR PREDICTED ACCIDENTS
*

FEISS GIOTTO I ITO RI II IAT +
LAB1:

CURYEAR= 85;

TA= CURYEAR-CHANGE_Y;

IF TA < 0 THEN TA= 0;

IF TA > 5 THEN TA = 5;

NACC= ACC1+ACC2+ACC3+ACC4+ACC5;
THRU=DAYTHRU+NGTTHRU ;
SWITCH=DAYSWT+NGTSWT;

TO=1./(.05+H) ;

A=(H*TO + NACC)/(TA + TO);

CLASS = NEWCL;

IF CLASS LE 4 THEN A= CROSSBK*A;

ELSE IF (4 < CLASS < 8) THEN A= FLASHLK*A;
ELSE A= GATESK*A;

DROP NACC TA TO UP1-UP3 DN1-DN3 K TRAINS ;

HERR KIRK IK REE RR IHKI RIKKI RI RARER RIKI KIER IR ERA
*

* CALCULATIONS FOR FATAL ACCIDENTS
*

FERGUS III III TOI III II IIIT
ACCD= ACCVAL;

IF ACCD > 1 THEN DO;

MS= MXTTSP**(-,9981) ;

TT= (1+THRU) **(-0.0872) ;

TS= (1+SWITCH) **0.0872;

UR= EXP(0.3571*FC10) ;

FATPRB=1/(1. + (440,9*MS*TT*TS*UR) ) ;

FATAL=FATPRB4A;

DROP MS TT TS FATPRB;
RRR RRR RETR
*

* CALCULATIONS FOR CASUALTY ACCIDENTS

*

RRRRRRRRERERIR IKI RIKKI RRR RRR ERITREA REIKI ARASH ERI
IF ACCD = 3 THEN DO;

MS=MXTTSP** (-0,343) ;

TRK=0.1153*TRACKS;

TK=EXP (TRK) +

URB=0.2960*FC1O;

UR=EXP (URB) ;

CASPRB=1.0/(1+(4.481*MS*TK*UR) ) ;

CAS= CASPRB*A;

COMCAS= (KK- 1)*FATAL+ CAS;

DROP TRK TK UR CASPRB CAS;

END; END;

IF ACCD= 1 THEN ACCIDENT= A;

ELSE IF ACCD= 2 THEN ACCIDENT = FATAL;

ELSE ACCIDENT= COMCAS;

DROP A COMCAS FATAL;

HHH HA HIRI IRI IR IRIN III IARI RR RII III III IIIS Ss

?

A-6


i
t

tend to be more frequent than fatal accidents. Thus, a comprehensive indicator of total
accident casualty impacts should take into account both the number and nature {ice.,
injuries versus fatalities) of accident casualties. Using this approach, a crossing that has,
for example, many injury accidents can be considered on the same scale as one with few
fatal accidents. The combined casualty index (CCI) formula was developed to achieve this
objective,

The CCI formula is a weighted sum of the predicted fatal accidents per year (FA)
and the predicted injury accidents per year (IA). It is expressed as:

CCl=kxFA+IA (7)

This formula can be considered an "equivalent injury" accident function. It converts fatal
accidents to equivalent injury accidents using the fatality factor k and adds this value to
the number of injury accidents. The units for CCI could be "equivalent injury accidents

per year",

The user of the CCI formula must specify a value for the constant k. This value
indicates the relative impact of fatal versus injury accidents. The user is best qualified to
determine the basis upon which an appropriate value of k is to be selected. A number of
studies have been performed that are relevant to this topic!5,!6, Based on results of
accident costs!6 a value of 50 for k may be reasonable for users who are unsure as to

which value to use.
Making the substitution IA = CA - FA, equation (7) becomes:

CCl = kx FA+CA-~-FA
=(k-1)xFA+CA (3)

33


APPENDIX B

This Appendix contains a listing of the program RESAL.NEW, written in the SAS

language, version 82.4, which is used to calculate resource allocation results.

* RESOURCE

ALLOCATION
INPUT MAIN.COM DESIRED REPORT BASED ON PRED.

H
1
i
PARAMETERS, INTERACTIVE | \ PARAMETER ‘ACC.
FOR WYLBUR 1 \ serting For
H

REPORT PROCEDURE BATCH RUN

\

RAIL
HIGHWAY
CROSSING

INVENTORY

lolalalalatetetatatatatatatatatateteteietaietaiatay ldaeiatanael 4 RESOURCE

ALLOCATION.

—»| SORTED BY
CROSSING ID

FILE SAS RESAL. NEW

PREP NEWOAT SAS PROCEDURE
PROCEDURE PROCEDURE

\

vi

RESOURCE
ALLOCATION
CROSSINGS FOR
STOP SIGN

Rall
HIGHWAY
CROSSING

ACCIDENTS

\

MATCHED
Inventory NEW TEST city

AND ACCIDENT! Sas Sas
OATA DATASET DATASET RUNTIME

SUMMARY

\

couNTY
SAS
OaTaseT

* USED FOR INTERACTIVE PARAMETER INPUT
** USED FOR BATCH PROCESSING

FIGURE B-1. DATA FLOW FOR RESOURCE ALLOCATION

NOTE: THE SAS PROCEDURE NEWDAT IS GIVEN IN APPENDIX A2.

B-1/B-2


where the mode! can be useful in this role. In its primary application, the model could use
the state listing of crossings, ranked by predicted accidents or casualties, to produce a list
of suggested improvement projects. The project list indicates which crossings are to be
upgraded and the type of upgrade to be performed. The state can then use this suggested
program as a basis to select crossings for on-site inspections by diagnostic teams. The
diagnostic teams can validate original data used by the model, revise the suggested
program if data has changed and obtain additional information on potential crossing
hazards for consideration prior to finalizing program plans. A procedure for accomplishing
this evaluation process is described in Section 4.2.6.

The resource allocation model can also be used to assess the sensitivity of
improvement decisions to variations in the input parameters of warning device cost and
effectiveness and predicted crossing accidents. If, for a given crossing or set of crossings,
these parameters are known to be different than originally assumed, the new values can be
substituted into the mode! and new results obtained. The effect of the new parameters
can be assessed by a comparison of new improvement decisions with those resulting from
the previous assumptions. This type of application is useful in evaluating the impacts of
known or proposed changes in crossing characteristics, such as increases in train or

highway traffic on certain routes, or closures of specific crossings.

The resource allocation model is also useful for evaluating the impacts of
alternative program strategies. The mode! can be easily modified to incorporate
constraints imposed on certain improvement actions by state warrants or guidelines. An
example of such a constraint would be a gates-only policy at crossings with train speeds
exceeding certain values. Variations in program budgeting such as inclusion versus
exclusion of warning device maintenance costs and single-year versus multi-year funding

limits, can also be evaluated with the resource allocation model.

4.2.2 Description of Model Algorithm

Three categories of warning device classes are considered by the resource allocation
algorithm, and are the same categories evaluated by the accident prediction formulas.
Warning device classes | through 4 are grouped together and called "passive" warning
devices, meaning that they are not train-activated devices. Classes 5, 6, and 7 are
grouped together and called "flashing lights," since public crossings which are equipped

36