San Joaquin, California, High-Speed

Rail Grade Crossing Data Acquisition

Characteristics, Methodology, and Risk

Assessment

Next Generation High-Speed

Rail Program

U.S. Department

of Transportation

Federal Railroad

Administration

Office of Research

and Development

Washington, DC 20590

Final Report

November 2006

This document is available to the

public through the National Technical

Information Service, Springfield, VA 22161

This document is also available on the FRA Web

site at www.fra.dot.gov

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1. AGENCY USE ONLY (Leave blank) 2. REPORT DATE 

November 2006 

3. REPORT TYPE & DATES COVERED 

Final report 

1999-2004 

4. TITLE AND SUBTITLE San Joaquin, California, High-Speed Rail Grade Crossing Data 

Acquisition Characteristics, Methodology, and Risk Assessment 

6. AUTHOR (S) 

John Choros, Adrian Hellman, Anya Carroll, Gary Baker, Marsha Haines,* Jon Anderson* 

5. FUNDING NUMBERS 

7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 

U.S. Department of Transportation 

Research and Innovative Technology Administration 

John A. Volpe National Transportation Systems Center 

Structures and Dynamics Division (DTS 76)/Railroad Systems Division (DTS-75)/Service Assessment 

Division (DTS 49) 

55 Broadway, Kendall Square 

Cambridge, MA 02142

8. PERFORMING 

 ORGANIZATION REPORT 

DOT-VNTSC-FRA-06-02 

9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) 

U.S. Department of Transportation 

Federal Railroad Administration 

Next Generation High-Speed Rail Program 

400 7th Street, SW 

Washington, DC 20590

10. SPONSORING OR 

MONITORING AGENCY REPORT 

NUMBER 

DOT/FRA/ORD-06/02 

11. SUPPLEMENTARY NOTES *EG&G Technical Services, Inc. 

Next Generation High-Speed Rail Program Gaithersburg, MD 20878 

12a. DISTRIBUTION/AVAILABILITY 

This document is available to the public through the National Technical Information Service, 

Springfield, VA 22161. This document is also available on the FRA Web site at www.fra.dot.gov. 

12b. DISTRIBUTION CODE 

13. ABSTRACT (Maximum 200 words) 

This report discusses data acquisition and analysis for grade crossing risk analysis at the proposed San Joaquin High-Speed Rail 

Corridor in San Joaquin, California, and documents the data acquisition and analysis methodologies used to collect and analyze 

grade crossing data and evaluate the effects of each method in the overall risk calculations for the entire corridor. This report 

describes grade crossing data acquisition techniques from existing Federal Railroad Administration (FRA) inventory data, track 

charts, site surveys and interviews, and aerial and video surveys. Costs for data acquisition and analysis associated with each 

method were documented and analyzed to determine their influences on the overall risk assessment for the corridor. Results 

using FRA’s Accident Prediction Formula indicate that all data acquisition and analysis methods are suitable for evaluating the

grade crossing risk at a given corridor. 

15. NUMBER OF PAGES 

52

14. SUBJECT TERMS 

Grade Crossings, Risk, Fast Laser Imaging-Mapping and Profiling (FLI-MAP), Geographic 

Information System (GIS), High-Speed Rail Corridors, San Joaquin, Accident Prediction 

Formula, Light Detection and Ranging 

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1 foot (ft) = 30 centimeters (cm) 1 centimeter (cm) = 0.4 inch (in) 

1 yard (yd) = 0.9 meter (m) 1 meter (m) = 3.3 feet (ft) 

1 mile (mi) = 1.6 kilometers (km) 1 meter (m) = 1.1 yards (yd) 

1 kilometer (km) = 0.6 mile (mi)

AREA (APPROXIMATE) AREA (APPROXIMATE) 

1 square inch (sq in, in2) = 6.5 square centimeters 

(cm2

) 

1 square centimeter (cm2

) = 0.16 square inch (sq in, in2) 

1 square foot (sq ft, ft2) = 0.09 square meter (m2) 1 square meter (m2) = 1.2 square yards (sq yd, 

yd2

) 

1 square yard (sq yd, yd2) = 0.8 square meter (m2) 1 square kilometer (km2) = 0.4 square mile (sq mi, mi2) 

1 square mile (sq mi, mi2) = 2.6 square kilometers 

(km2

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1 acre = 0.4 hectare (he) = 4,000 square meters (m2) 

MASS - WEIGHT (APPROXIMATE) MASS - WEIGHT (APPROXIMATE) 

1 ounce (oz) = 28 grams (gm) 1 gram (gm) = 0.036 ounce (oz) 

1 pound (lb) = 0.45 kilogram (kg) 1 kilogram (kg) = 2.2 pounds (lb) 

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pounds (lb) 

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=

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1 tablespoon (tbsp) = 15 milliliters (ml) 1 liter (l) = 2.1 pints (pt) 

1 fluid ounce (fl oz) = 30 milliliters (ml) 1 liter (l) = 1.06 quarts (qt) 

1 cup (c) = 0.24 liter (l) 1 liter (l) = 0.26 gallon (gal) 

1 pint (pt) = 0.47 liter (l) 

 1 quart (qt) = 0.96 liter (l) 

1 gallon (gal) = 3.8 liters (l) 

1 cubic foot (cu ft, ft3) = 0.03 cubic meter (m3) 1 cubic meter (m3) = 36 cubic feet (cu ft, ft3) 

1 cubic yard (cu yd, yd3) = 0.76 cubic meter (m3) 1 cubic meter (m3) = 1.3 cubic yards (cu yd, yd3)

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For more exact and or other conversion factors, see NIST Miscellaneous Publication 286, Units of Weights and 

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ii
Preface 

The objectives of this study included evaluating various data acquisition methods used to 

determine the risk at grade crossings in the San Joaquin, California, High-Speed Rail Corridor 

and recommending the most cost-effective method for use in evaluating future corridors. 

Engineers and officials responsible for evaluating grade crossing risk at a given corridor need 

an approach for assessing the quality of existing data and methods for improving it. 

This report documents data acquisition methods and data processing procedures used to verify 

and refine the existing Federal Railroad Administration (FRA) Highway-Rail Crossing 

Inventory data for the San Joaquin High-Speed Rail Corridor. This report used augmented 

data sets to refine the existing FRA data, including track charts, site visits, and aerial video 

surveying of the corridor. The effect of improved data on the overall corridor risk analysis 

was analyzed for each data acquisition method. The report documents the level of effort and 

estimated costs for each method to help officials responsible for evaluating grade crossing risk 

in a corridor determine the benefit of pursuing a method for an improved grade crossing 

inventory. 

iii
Acknowledgments 

The authors wish to thank Mr. William R. Paxton of Lakeside Associates for conducting the 

field survey of the corridor from Bakersfield to Port Chicago, California, and reporting the 

results in a clear and concise manner. In addition, thanks go to Foster-Miller and Associates 

staff for the grade crossing analysis and data collection as it relates to this report and to the 

staff of EG&G Technical Services, Inc., and CASE, LLC, who prepared this report. 

The authors would especially like to thank Mr. Robert J. McCown, retired Program Manager 

of Federal Railroad Administration’s Next Generation High-Speed Rail Program, for his 

support and funding for this project. Finally, we would like to thank California’s Department 

of Transportation and Burlington Northern Santa Fe Railway Company’s staff for their 

diligent support of the field data acquisition of this project. 

iv
Contents 

Preface........................................................................................................................................ iii 

Table of Contents........................................................................................................................ v 

Executive Summary.................................................................................................................... 1 

1. Introduction......................................................................................................................... 3 

1.1 Background................................................................................................................. 3 

1.2 Study Scope Corridor Description, Geography, and Land Use.................................. 5 

1.3 Railroad Description: Track and Operation............................................................... 8 

2. Available Data Sources for Corridor and Crossing Characteristics.................................... 9 

2.1 FRA Highway-Rail Crossing Inventory ..................................................................... 9 

2.2 Railroad Track Charts................................................................................................. 9 

2.3 State, County, and Local Transportation and Government Agencies....................... 10 

2.4 Site Visit Observations ............................................................................................. 11 

2.5 Aerial Video Survey ................................................................................................. 12 

2.6 Summary of Data Sources ........................................................................................ 13 

3. Data Structure, Organization, and Application................................................................. 15 

3.1 Use of GIS Platform.................................................................................................. 15 

3.2 Base GIS Data........................................................................................................... 15 

3.3 Methods for Clarifying Crossings Along a Corridor ................................................ 16 

4. Risk Methodology and Calculation .................................................................................. 23 

4.1 Accident Fatality Prediction Formula....................................................................... 23 

4.2 Crossing Incident History ......................................................................................... 26 

4.3 Risk Calculation........................................................................................................ 27 

5. Results: Comparison of Data Groups .............................................................................. 31 

5.1 Data Accuracy........................................................................................................... 31 

5.2 Risk Analysis Calculation Results............................................................................ 31 

5.3 Impacts of Data Groups on Risk............................................................................... 33 

5.3.1 Measured Data................................................................................................ 34 

5.3.2 Predicted Data ................................................................................................ 34 

5.3.3 Comparison of Measured and Predicted Data................................................ 35 

5.4 Compounding Risk Factors Not Evaluated............................................................... 36 

5.5 Costs Associated with Data Collection and Risk Assessment.................................. 37 

6. Conclusions and Recommendations ................................................................................. 39 

6.1 Recommendations..................................................................................................... 39 

References................................................................................................................................. 41 

Acronyms.................................................................................................................................. 43 

v
Figures 

Figure 1. High-Speed Rail Corridors in the United States................................................................4 

Figure 2. San Joaquin High-Speed Rail Corridor .............................................................................5 

Figure 3. Public Crossing with Flashing Lights, Gates, and Traffic Signals....................................6 

Figure 4. Rural Crossing Along the Study Area...............................................................................7 

Figure 5. Double and Single Track Configuration on the San Joaquin Corridor by Milepost .........8 

Figure 6. Flow Chart Indicating Progressive Development of Data Groups..................................17 

Figure 7. Severity Element Breakdown..........................................................................................25 

Figure 8. Incidents Involving Freight and Passenger Operations: 1995–1999..............................26 

Figure 9. Fatalities Involving Freight and Passenger Operations: 1995–1999..............................27 

Tables 

Table 1. Warning Device Type for Public and Private Crossings on Study Corridor Segments .....6 

Table 2. Mileage and Ownership of the San Joaquin Corridor.........................................................6 

Table 3. APF Normalizing Constants from 1992 ...........................................................................24 

Table 4. Incidents and Fatalities at Highway-Rail Crossings.........................................................26 

Table 5. Risk Results Summary by Data Group.............................................................................32 

Table 6. Recent Incidents, Fatalities, and Injuries Along the San Joaquin Corridor......................33 

Table 7. San Joaquin Data Collection and Analysis Costs .............................................................37 

vi
Executive Summary 

Upon evaluating the five different data sets used to calculate highway rail grade crossing risk 

for the San Joaquin High-Speed Rail Corridor, the Volpe National Transportation Systems 

Center (Volpe Center) found that the Federal Railroad Administration (FRA) Inventory data, 

augmented with information from track charts, provided the best correlation and was the least 

expensive to use. The five data sets analyzed were constructed from FRA inventory data, with 

missing or erroneous information supplemented by additional acquired data described in the 

report. Using this approach of sequential improvements to existing and acquired information, 

the Volpe Center developed five data sets for risk analysis. These data sets were FRA 

inventory data, FRA inventory data supplemented with certain assumptions concerning 

missing data, railroad track charts, interviews and field surveys, and aerial video surveys. 

During this study, various important risk factors could not be quantified or identified by any of 

the methods used during this analysis. One factor that could not be readily quantified was 

traffic mix between trucks and automobiles. Factors discovered by the aerial video survey, 

such as the skew of the railroad crossing to the road, crossing sight obstructions caused by 

vegetation and structures, short approach distances, and limited storage capacity of a road 

because of parallel adjacent roadways, could not be used due to the limitations of the risk 

model to incorporate this type of information into the risk analysis. 

The Volpe Center used the fatality prediction formula, derived by applying multiple nonlinear 

regression analysis to grade crossing characteristics stored in the U.S. Department of 

Transportation (DOT)/FRA Highway-Rail Grade Crossing Inventory, to calculate the risk in 

terms of fatalities per year. A Geographic Information System (GIS) platform was created to 

store grade crossing characteristics for the five data groups used and to calculate the risk 

associated with each set for the San Joaquin High-Speed Rail Corridor. Grade crossing 

characteristics and actual incidents and fatalities in the corridor included the years 1995 to 

1999. This report includes data acquisition methods, creation of the GIS platform, and the 

results of risk analyses. 

1

1. Introduction 

To conduct a detailed highway rail grade crossing study and recommend crossing closures and 

upgrades, officials need an accurate list of study corridor crossings and accurate data about those 

crossings. Officials also need an approach for assessing the quality of existing data and methods 

for improving it. 

This report documents the data collection methods and data processing procedures used to verify 

and refine the existing United States Department of Transportation (USDOT) Federal Railroad 

Administration (FRA) Highway-Rail Crossing Inventory data for the San Joaquin High-Speed 

Rail Corridor in California. This report analyzes the data collection methods used to determine 

the effect of improved data on the overall corridor risk analysis. The report documents the level 

of effort and estimated costs of methods to help determine the benefit of pursuing the method for 

an improved grade crossing inventory. 

1.1 Background 

Legislation improving safety at highway rail grade crossings has been the focus of both Congress 

and the U.S. Department of Transportation (DOT) since passage of the Highway Safety Act of 

1973. This landmark legislation initiated Federal funding of highway rail crossing 

improvements, a program that continues today. Since the enactment of the program in 1973, 

highway-rail grade crossing fatalities have declined 65 percent at public crossings—from 1,185 

in 1973 to 414 in 2000. The 1973 legislation also required that each state and railroad establish 

and maintain, on a volunteer basis, an inventory of their respective crossings. This congressional 

mandate created a joint effort of the railroad industry, State officials, and the FRA, resulting in 

the institution of the FRA Highway-Rail Crossing Inventory. This inventory, maintained by 

FRA as a database, is the only nationwide source of highway-railroad grade crossing inventory 

information. 

More recently, Congress passed legislation that encourages the development of high-speed 

passenger rail service in the United States. Section 1010 of the Intermodal Surface 

Transportation Efficiency Act (ISTEA) of 1991 established the high-speed rail corridor program 

and identified five high-speed rail corridors, including California, Chicago Hub, Mid-Atlantic, 

Northwest, and Florida. Section 1103 (c) of the Transportation Equity Act for the 21st Century 

(TEA-21), which became law in 1998, authorized a total of 11 high-speed rail corridors—five as 

specified in 1991, three as defined by TEA-21, and three additional rail corridors selected by the 

Secretary of Transportation (see Figure 1). 

3
Figure 1. High-Speed Rail Corridors in the United States 

Both pieces of legislation specifically provided funds for grade crossing hazard mitigation on the 

designated high-speed rail corridors. This program is unique in that it provides 100 percent 

Federal funds for grade crossing safety improvement projects for both public and private 

crossings. From 1993 through 2005, $69.4 million was allocated to the States from this program. 

Although this program does not require a match, the States provided more than $90.4 million in 

other Federal funds (mostly Federal Highway Administration Section 130 funds), as well as 

$63.3 million in State funds and secured $10.2 million from the railroads and others, for a total 

of $163.9 million. This grand total of $233.3 million has improved more than 320 crossings, 

constructed or supported 17 grade separations, and closed 116 crossings. 

For this reason, planning for upgraded railroad service in the targeted high-speed rail corridors 

must systematically evaluate the risk of an incident at all highway-railroad crossings, both public 

and private, on the corridor. Estimation of an incident’s risk must take into account not only the 

characteristics of the crossing and its incident history but also the planned higher train speed. To 

this end, the DOT FRA Next Generation High-Speed Rail Program sponsored a study in 1996 to 

develop a risk-based approach for assessing the implications of higher train speeds on highwayrailroad grade crossing safety and allocating limited resources to best reduce this risk. 

The initial study of New York’s Empire Corridor [1] involved a systematic approach to estimate 

relative risk and severity for 27 crossings on a segment of this designated high-speed rail 

corridor. This study adopted a methodology in which incident frequency was estimated using 

the FRA Accident Prediction Model (APM), but severity, in terms of fatalities, was based on a 

nationwide highway-railroad crossing incident database rather than historical experience for each 

particular crossing on the corridor. The reason for this more generic approach was that too few 

4
grade crossing incidents involving high-speed trains have occurred to provide significant 

statistics regarding incident severity. 

 

1.2 Study Scope Corridor Description, Geography, and Land Use 

The authors selected the portion of the California High-Speed Rail Corridor extending from Port 

Chicago to Bakersfield, shown in Figure 2, as the test case for this effort. This area was selected 

because it is owned by one railroad, thus facilitating data collection. This area provides 

passenger and freight railroad service through populated urban areas and rural communities, and 

it has industrial and agricultural zones. 

Figure 2. San Joaquin High-Speed Rail Corridor 

The San Joaquin corridor runs from Oakland to Bakersfield with a track segment running from 

Stockton to Sacramento. This report focuses on the track segment from Port Chicago to 

Bakersfield because it is owned by one railroad company, Burlington-Northern/Santa Fe 

(BNSF). Union Pacific (UP) Railroad owns the track from Port Chicago to Oakland and 

Stockton to Sacramento. 

Public and private crossings along the San Joaquin corridor have various warning device types. 

According to the baseline FRA Highway-Rail Crossing inventory data, 412 crossings exist 

between Bakersfield and Port Chicago. Of these 412 crossings, 50 are grade-separated crossings 

(29 overpass, 17 underpass, and 4 pedestrian grade crossings) and are removed from the 

inventory, leaving 362 at-grade highway-rail crossings to be analyzed in this study. Table 1 

breaks down these crossings by warning device type. The entire San Joaquin Corridor extends a 

total of 365 miles, shown in Table 2. 

5
Table 1. Warning Device Type for Public and Private Crossings on Study Corridor Segments 

Gates Flashing 

Lights 

Crossbucks Signs Signals No 

Warning 

Device 

Public 

Crossings 222 22 11 0 0 0 

Private 

Crossings 0 0 0 88 2 17 

Table 2. Mileage and Ownership of the San Joaquin Corridor 

Segment Mileage Owner 

Oakland–Port Chicago 38 UP 

Port Chicago–Stockton 43 BNSF 

Stockton–Sacramento 50 UP 

Stockton–Bakersfield 234 BNSF 

Total 365 

Figure 3 shows a typical warning device type used for a busy urban, public crossing. Figure 4

shows a rural crossing warning device type along the study area. 

Figure 3. Public Crossing with Flashing Lights, Gates, and Traffic Signals 

6
Figure 4. Rural Crossing Along the Study Area 

Some noteworthy aspects of the San Joaquin corridor exist that impacted the methods used to 

document grade crossing characteristics. Agricultural producers privately own many of the 

crossings. Data on these crossings, which experience a range of use, were difficult to obtain. 

Another characteristic is the presence of numerous steeply embanked crossings where the track 

is several feet higher than the approaching roadway. 

The average number of freight trains operating along the corridor during the day is 19 (from the 

inventory), ranging from a low of 15 to a high of 23. The average number of freight trains 

operating along the corridor at night is 14, ranging from a low of 9 to a high of 19. Between 

Bakersfield and Stockton, eight passenger trains operate during the day and two at night. 

Between Stockton and Port Chicago, five passenger trains operate during the day and three at 

night. 

Small towns, large cities, and metropolitan areas characterize the study area of this corridor. 

Fresno, the largest city, has a population of 411,600, while the city of Shafter, approximately 15 

miles northwest of metropolitan Bakersfield (population of 213,000), serves an estimated 12,000 

residents within its city limits. However, Shafter’s industrial area incorporates a workforce that 

is much larger than its community of residents; a population of 60,000 is within a 10-mile 

commute from the industrial areas. Shafter is one example of the several small communities that 

employ a large workforce of people living elsewhere. 

Vehicular traffic patterns vary substantially by season to meet the economic demands of the 

valley. East of the corridor is also home to a number of national parks and wildlife reserves, 

including Yosemite and Sequoia National Parks, which draw millions of visitors annually as part 

of a large tourist industry. Year-round industrial activity is another defining factor for the region 

surrounding the San Joaquin Corridor. Petroleum production is vital in the southern and western 

parts of the San Joaquin Valley, bringing many heavy-duty trucks to the area. The timber and 

mining industries are important to the economy of the southern Sierra Nevada and involve many 

heavy-duty trucks. 

7
1.3 Railroad Description: Track and Operation 

The BNSF corridor is 277 miles long and divided into two subdivisions, which include the 

Bakersfield Subdivision, running the length of 107.2 miles from milepost 887.7 to milepost 

994.9, and the Stockton Subdivision, running 194.1 miles from milepost 994.9 to milepost 

1189.0. Since Amtrak does not operate on the BNSF track between Port Chicago and Richmond, 

the study portion of the Stockton Subdivision covers 169.1 miles (from milepost 994.9 to 

1164.0). 

The entire Bakersfield Subdivision is equipped with centralized traffic control (CTC). CTC is 

employed along segments where intermittent sidings or double track exist that allow the train 

dispatcher to switch a train to the other track to allow a priority train efficient movement. The 

Stockton Subdivision is equipped with CTC from milepost 994.9 to milepost 1146.4. From 

milepost 1146.4 to milepost 1163.5, the track is controlled by automatic block signaling (ABS) 

technology and track warrant control (TWC). ABS refers to a signal operated either 

automatically or manually at the entrance to a crossing. TWC refers to a section of track 

undergoing maintenance where the track foreman has received authority to slow or divert 

oncoming trains during a specific time frame. 

A total of 252.2 miles of single track along the corridor exists, while 24.1 miles of the corridor 

are double-tracked. Figure 5 depicts the San Joaquin Corridor in terms of milepost sections of 

single track and double track. 

Figure 5. Double and Single Track Configuration on the San Joaquin Corridor by Milepost 

Main Track 2: 

Milepost #: 

Main Track 1: 

887.7 892.5 

Main Track 2: 

Milepost #: 

Main Track 1: 

994.3 998.2 1087.1 1090.8 

1090.8 1128.9 1133.6 1139.3 1146.3 1164 

(Diagram not to scale) 

8
2. Available Data Sources for Corridor and Crossing Characteristics 

This chapter focuses on the wide array of databases that can be consulted to better determine 

different grade crossing characteristics in a particular railroad corridor. This chapter uses a 

variety of data sources to identify and determine crossing characteristics along the study portion 

of the San Joaquin Corridor, including the FRA Rail-Highway Grade Crossing Inventory, 

railroad track charts, State and local government agency information, site visits, and aerial video 

surveying. 

2.1 FRA Highway-Rail Crossing Inventory 

The national inventory of highway-railroad crossings was merged with incident files from the 

FRA Accident/Incident Reporting System (RAIRS) and used to analyze crash safety for the 

purpose of planning and implementing crossing improvement programs. When the inventory 

was established in the mid-1970s, railroads were responsible for making a site-specific inventory 

of each crossing and for installing a unique identifying number at each crossing. State highway 

departments provided site-specific highway information. After 1995, the inventory included 

private crossings. 

Since the inventory was established, railroads have had the responsibility for maintaining the 

crossing number marker and updating railroad-specific data when changes are made to the 

crossing. For example, if an automated warning system were installed, the railroad would 

provide this information to FRA, the custodian of the database. Similarly, highway departments 

or public utility commissions must update any change in the type of roadway or traffic volume. 

While Federal law requires participation in RAIRS, reporting inventory information is purely 

voluntary. 

The inventory database crossing identification number consists of a maximum of six numeric 

digits followed by a single alphabetical crosscheck character (e.g., 028320G). The database 

provides a baseline framework for grade crossing characteristics in high-speed rail corridors and 

is helpful in providing the location of a crossing and its associated characteristics. The FRA 

crossing identification number and milepost are the best way to track closures, relocation, or 

changes at the crossing. When an update is made, a copy of the record before the update is 

moved to the crossing history database. If a crossing is closed, its record is moved to the 

crossing history database and no longer appears in the inventory. 

2.2 Railroad Track Charts 

Railroad track charts provide infrastructure information, such as the location of crossings, 

bridges, and culverts; tracks and signals; facilities; and other miscellaneous information. Track 

charts also contain railroad operational information about the maximum train speed and the type 

of train control (CTC, TWC, and ABS). 

Track charts were useful in determining the crossing location and warning device type, the type 

of track (main, siding, or spur tracks), and the number of tracks at a crossing. The exact location 

of a grade crossing is identified by milepost in the track charts. The charts also provide 

information about FRA crossing identification, ownership status (public or private), track 

9
curvature, and the name of the roadway crossing. For this report the track charts provided by the 

railroad were considered to be the same as or more current than the FRA inventory. Charts are 

drawn to scale, thereby allowing the determination of skew road crossing relative to the main 

track. 

2.3 State, County, and Local Transportation and Government Agencies 

State transportation agencies, such as the California Transportation Department (CALTRANS) 

in this study, can provide an independent source of grade crossing location attributes that may 

supplement the FRA Highway-Rail Crossing Inventory and railroad track charts. The State DOT 

will often maintain a more current list of closed and recently separated crossings and is likely to 

be aware of low-volume private or public crossings that have been closed, with traffic diverted to 

an adjacent crossing. The State DOT usually has a railroad division that can provide additional 

railroad information. 

The exposure index factor in the grade crossing incident prediction model is dependent upon the 

number of highway vehicles each day that use the crossing. The FRA inventory contains an 

estimate of the average annual daily traffic (AADT) for all public and some private crossings. 

Because in many instances the AADT in the inventory is out of date, an effort was undertaken to 

obtain a current estimate. State, county, and city agencies responsible for the maintenance and 

construction of public roads in the State of California were contacted to obtain AADT 

information for public crossings. In addition to an estimate of AADT, each State agency was 

asked to provide the following information: 

• Year the AADT count was taken 

• Annual AADT growth factor 

• Percent mix of trucks 

• Volume of trucks carrying hazardous materials 

• Volume of buses 

In California, as in most states, different agencies have jurisdiction for each type of road. 

CALTRANS highway district offices, county public works agencies, and city traffic offices are 

responsible for State highways, county roads, and city-incorporated roads, respectively. The 

county public works agencies in California include the County Public Works, the County 

Association of Governments, and the County Resource Management Agency. 

Sometimes State agencies will have a larger grade crossing inventory than the FRA inventory. 

Often these crossings do not exist physically but are part of a deed restriction that allows a grade 

crossing to be added to a piece of property in the future. This was the case in the study portion 

of the San Joaquin Corridor where CALTRANS documents showed numerous crossings that did 

not exist physically. It is helpful for the State to track these potential future grade crossings, but 

this information is not useful in determining the current risk along the corridor. However, it 

could potentially be useful for determining a worst-case future scenario. 

Local officials maintain databases of grade crossings that have experienced fatal incidents and 

they may have more accurate incident reports than the other data sources. Because of privacy 

issues, it is often difficult to obtain incident reports from the State Department of Motor 

10
Vehicles. These reports are best obtained from the State or local highway and railroad officials. 

These reports can detail weather conditions at the time of an incident, crossing obstructions, and 

driver behavior before the incident. 

Highway officials do not use either the railroad milepost or the FRA inventory number. County 

highway officials provided maps of their geography with AADTs indicated by intersection. In 

addition to the AADT count and the year it was recorded, highway officials were asked to 

provide an AADT growth factor. This growth factor can be used to adjust the AADT counts for 

the entire corridor to a common base year. County officials provided contacts for cityincorporated roads. This report used a process similar to the one described above to obtain 

AADTs for these crossings. 

Obtaining AADT counts for private crossings was more difficult. Since they do not maintain 

private crossings, local governments generally do not record AADT counts for private roads. As 

such, public agencies must obtain permission from the property owner before taking a traffic 

count. 

2.4 Site Visit Observations 

For analyzing corridor risk, the crossings database and its attributes should be highly accurate. 

To assure the correctness of the data, some visits were conducted to a subset of the crossings 

along the study corridor. The purpose of the site visit was to confirm the physical properties of 

the crossings and to note any unusual street geometry or obstruction characteristics that make the 

crossings candidates for risk reduction measures. After reviewing the crossing listing from the 

BNSF track charts, obtaining updated AADT estimates for the majority of public crossings, and 

viewing a videotape of the corridor recorded from the head-end of a passenger train, the authors 

identified over 151 crossings as candidates for a site visit. The videotape provided identification 

of crossings that appeared to have unique road geometry. A crossing was a candidate for a site 

visit if it had the following: 

• Conflicting information among inventory, track charts, and data from State and local 

officials 

• A high number of incidents, relative to others on the corridor 

• An FRA recorded AADT of over 1,000 and the updated AADT from State highway 

agencies showed an increase of over 200 percent 

• Unique road geometry. 

The site visits included 27 private and 124 public crossings between Bakersfield and Port 

Chicago. Of the 151 crossings visited, 5 had warning devices that differed from what was 

reported in the FRA inventory, including the following: 

• Three had flashing lights and gates, while the inventory indicated signs. 

• One had only flashing lights, with the inventory reporting both lights and gates. 

• One had a wigwag, while the inventory and track charts indicated signs. 

To determine accurate grade crossing circuitry information, the BNSF Signal Department was 

contacted. The study area of the San Joaquin Corridor FRA Highway-Rail Crossing Inventory 

11
data indicates only one Constant Warning Time (CWT) device. On the other hand, the BNSF 

Signal Department reported that 59 crossings were interconnected to the CWT devices. A CWT 

device computes the train speed and activates the warning devices with the same amount of 

advanced notice to the highway user, regardless of train speed. 

The site visits revealed information that was not otherwise available. A Contra Costa county 

official stated that a DuPont refinery used two private crossings. This information was 

consistent with the BNSF track charts; however, the site visit found that no crossings existed on 

the DuPont property. The same official reported that Dow Chemical owned two crossings, one 

used by employees and the other by trucks carrying hazardous materials. The site visit found 

that these two crossings are now owned by a company that does not transport hazardous 

materials. The site visit also found that Dow Chemical trucks were using the public crossing on 

Loveridge Road. One public crossing (Werner Road) was not listed in the FRA inventory but 

was found to exist through the site visit. County officials were then contacted for an AADT, but 

they did not have the crossing on their maps. There were two instances where inventory 

numbers stenciled on signal bungalows did not match the inventory. 

The site visit data provided a great detail of information not available from the other data sources 

used in this report and may be the best method to resolve conflicting data about the crossing 

location and its characteristics. 

2.5 Aerial Video Survey 

Through a Volpe Center contract coordinated with the Army Corps of Engineers, aerial video 

surveys of the study area were conducted. The contractor, John Chance Land Surveys Inc., 

employed helicopter-based Fast Laser Imaging-Mapping and Profiling (FLI-MAP). The system 

uses Global Positioning Systems (GPS) [3] and Light Detection And Ranging (LiDAR) to 

generate a 36.5 meters (120 foot) wide corridor swath of measurement points. Approximately 

10-20 measurements per square meter were recorded, containing latitude, longitude, and 

elevation, accurate to within 10-15 centimeters. This highly detailed mesh of points, sometimes 

referred to as a Digital Elevation Model (DEM), allows visualization of surface features such as 

ballast, track, crossings, and other nearby features. DEM can be used to make many different 

kinds of measurements, some of which include track angle with respect to the road, height of 

humped crossings, and width of the ballast. In addition to the coordinate points, the FLI-MAP 

system records forward and downward looking video. Pilot/copilot comments, such as passing 

over a crossing were linked with GPS locations. 

The survey of the corridor required 3 days, plus the initial surveys for ground station setup. The 

task required 6 different flights. The contractor provided specialized software to synchronize 

viewing of the helicopter route, laser data, pilot comments, and forward and downward video. 

The software had a user interface similar to GIS that allows the user to navigate forward, 

backward, and downward to travel along the path of the helicopter. The forward and downward 

looking videos were viewed on two separate television monitors positioned next to the computer. 

If the users identified something interesting on the videos, it was possible to pause the videos and 

make measurements using the LiDAR data. The LiDAR data and aerial video were catalogued 

on compact discs and videotapes. 

12
2.6 Summary of Data Sources 

The location of highway-rail crossings was determined by studying the FRA inventory, the 

railroad track charts; obtaining information from State DOTs, county railroad district signal 

departments, and local highway officials; and conducting site visits. Crossing characteristics 

were also determined by studying the FRA inventory and railroad track charts, obtaining 

information from the railroad district signal department, and conducting site visits. Highway 

traffic data were determined by studying the FRA inventory, obtaining information from the 

State DOT District Offices, and obtaining information from county and local highway officials. 

13

3. Data Structure, Organization, and Application 

This chapter discusses the use of a GIS platform, collection of map layers to serve as a reference 

on which to base GIS data, and the data collection methods employed to clarify the location, 

presence, and characteristics of grade crossings, including details on the different processes and 

levels of analysis used along the San Joaquin High-Speed Rail Corridor. 

3.1 Use of GIS Platform 

The authors selected a GIS platform to organize the various existing data and facilitate the 

process of verifying and adding new data for this research effort. A GIS platform provides a 

framework to place the coordinates of geographic features, such as highway-rail grade crossings, 

streets, and county boundaries, in a database that can be rendered as a map in the GIS. 

Descriptive attributes about those geographic features, such as DOT crossing identification 

numbers, street names, and county populations, can then be matched with the geographic 

features. The combination of geographic features and their attributes enable GIS to display and 

label street names or all crossings that meet specified criteria. Different geographic features are 

typically stored in what is referred to as a map layer or sometimes a map theme. If all map 

layers are stored in the same coordinate system, geographic relationships between the different 

features can be seen. For example, crossings in the crossing layer only exist where the street 

layer and rail layer intersect. In addition to simple display and query of geographic features, GIS 

platforms contain a wide variety of analytical tools for spatial analysis. This allows spatial 

analysis of sensitive locations, such as identifying all crossings within 1⁄2 mile of a school. 

3.2 Base GIS Data 

The first step in using a GIS platform for a particular study is to collect map layers that will serve 

as a reference. These components can then be used to construct additional study specific layers, 

such as a highway-rail crossing layer. 

The first layers to be established are typically the geographic boundaries of states and counties in 

the study area. These map layers can be easily obtained from many sources, including the DOT 

Bureau of Transportation Statistics (BTS). 

Roadways are a required base layer for a highway-rail crossing study. TIGER/Line data, 

developed by the U.S. Census Bureau, is one of the most complete and consistent sources for a 

road layer. The state road layers vary dramatically from state to state. In many cases, states may 

have geographically accurate road layers, but often smaller streets are missing or do not have 

street name attributes. It may be worthwhile to have all available street layers in GIS for crossreference. 

Rail-related base layers must also be prepared. A national railroad track layer can be obtained 

from BTS. Once a railroad track layer is established, it can be used as a subset to establish a 

study corridor layer. Passenger and freight station layers may also be useful. While the Amtrak 

station layer is publicly available, the freight station layer may not be. A good substitution for 

the freight stations may be the Geographic Names Information System (GNIS) from the U.S. 

Geological Survey (USGS), which can be used to find places referenced on track charts. GNIS 

15
contains information for almost 2 million physical and cultural geographic features in the United 

States. 

Additional image-based layers may be incorporated into GIS. These layers are also 

geographically referenced so that, if the map projections are the same, they will be 

geographically coincident with the above-mentioned layers. Image layers are unlike the abovementioned layers, known as vector layers, because they only serve as backdrops, thereby 

precluding typical geographical analysis and attribute queries. 

One of the most useful image-based layers is the Digital Raster Graphics (DRGs) from the 

USGS.1 The DRGs are scanned USGS topographical maps that have been geographically 

referenced for use in a GIS platform. DRG contains street names, building footprints, railroad 

tracks, city names, and other information that can be useful in locating highway-railroad grade 

crossings. Since the incorporation of DRG made viewing data in the GIS much more intuitive, 

they should be considered a requirement. DRGs were also very helpful in determining land use 

near crossings and the population in the area served by the crossings; both of which were used to 

estimate the AADT for private crossings. 

Digital orthophotos are aerial or satellite images where each layer has been corrected and 

geographically referenced. Digital orthophotos that cover about a 40-mile portion of the corridor 

were obtained. While integration of digital orthophotos may not be critical to the study, they can 

also help to make GIS display more intuitive. If the digital orthophotos are at a large enough 

scale (e.g., 1:5,000), they may prove very useful in establishing the existence and grade of 

crossings. Private roads that may not exist in the roads layer may also be visible. The date the 

aerial photo was taken should also be considered since new roads can be added and crossings 

eliminated or grade separated. 

Once all of the base map layers are integrated into the GIS database, it is easier to resolve 

crossing location discrepancies by being able to quickly locate referenced streets, mileposts, and 

nearby crossings. 

3.3 Methods for Clarifying Crossings Along a Corridor 

Numerous ways and methods exist to enhance the accuracy of highway-rail grade crossing data 

along a high-speed rail corridor. This study focuses on methods of collecting data to clarify the 

location, presence, and characteristics of grade crossings. This section details the different 

processes and levels of analysis used along the San Joaquin High-Speed Rail Corridor. The 

method, shown in Figure 6, begins with the data contained in the FRA Highway-Rail Crossing 

Inventory, proceeds through extrapolating characteristics for crossings with missing data, 

analysis of track charts, interviews with transportation officials and a site survey, and concludes 

with an aerial video survey of the corridor. This section summarizes preliminary screening 

results for the number of grade crossings assessed. Section 4, Risk Methodology and 

Calculation, further details the results. 

 

 

1

 See the following Web page for further information about DRGs: http://topomaps.usgs.gov/drg. 

16
Data Group 1 

FRA Inventory 

Data Group 2 

Extrapolation

Data Group 3 

Track Charts

Data Group 4 

Reconciliation 

Interviews and 

Site Survey

Data Group 5 

Aerial Video Survey 

Figure 6. Flow Chart Indicating Progressive Development of Data Groups 

Referred to in This Study 

Data Group 1–FRA Baseline Highway–Railroad Crossing Inventory 

The FRA inventory includes the basic source of crossing data. Crossings within the study area 

were selected from the inventory based on the railroad division, subdivision, branch, and 

milepost indicators. The report includes a selection of a total of 412 crossings, including gradeseparated crossings. They were placed in the GIS platform at the intersection of the study 

corridor and the street found in the crossing record. Some crossings could not be located 

because the street names could not be found in the GIS layer or were missing from the inventory; 

this was especially true of private crossings. A milepost layer was created for the study corridor 

based on the milepost values of the crossings that were located. The crossings that were still 

missing were then positioned in the crossing layer based on the milepost value in the inventory. 

This method was very useful when trying to resolve street name changes, database 

inconsistencies, and the location of private crossings. 

After the 50 grade-separated crossings were removed from Data Group 1, 362 at-grade crossings 

remained of a total of 412 crossings identified by the FRA inventory. 

Data Group 2–Inferential Extrapolation 

Data Group 2 is an analytical data set consisting of the same crossings in Data Group 1 with 

certain assumptions made about types of missing data. Where missing, linear attributes, such as 

train speeds and the number of tracks, were inferred from adjacent crossings so that all crossings 

would have a risk value as described in Section 4. 

17
Data Group 3–Track Charts 

Data Group 3 consists of crossing characteristics based on railroad track charts obtained from 

BNSF. The track charts were reviewed and relevant information was extracted and used to 

populate a new table. The following data items for most crossings were obtained from the track 

charts: 

• FRA grade crossing identification number 

• Street name 

• Type (public, private, or pedestrian) 

• Position (at grade, underpass, or overpass) 

• Milepost 

• Warning device type 

• Number of tracks 

• Maximum passenger train speed 

• Maximum freight train speed 

The compilation of the data from track charts requires diligence. Many other types of data 

depicted on the track chart exists and it is advisable to review it to highlight all crossings first. 

Good quality control can be maintained if one person reads the track chart while another person 

enters the information in the database. By adding a crossing column to the database, the order of 

the crossings as found on the track chart is maintained, and it is easier to resolve problems when 

crossing mileposts are incorrect on the track chart, or a crossing is assigned an incorrect FRA 

crossing identification number. It is not uncommon that some of the data may be missing, such 

as the grade crossing identification number or the warning device type. 

Reading a track chart can be a complex undertaking. The following example presents a typical 

track chart notation. The track chart reference “.63 pub gr xing, F/G, 28361L,” indicates FRA 

identification number 028361L. This is a public, at-grade crossing at milepost 887.63. The top 

of the track chart indicates the mile increments. The crossing has flashing lights and gates as a 

warning device type. The number of tracks can be visually taken off of the track chart. The 

freight and passenger speeds are usually displayed parallel to the track, for example, “ÅP79 F55 

| P20 F20Å,” indicating where the speed changes. This reads as “maximum Passenger train 

speed 79 mph, maximum freight train speed 55 mph, changing maximum passenger and freight 

train speeds to 20 mph.” Most track charts have a legend and abbreviations. 

Data Group 3’s track chart data set contained three FRA grade crossing identification numbers 

that were duplicated. Two crossings showed a duplicate crossing number but with different 

alphabetical crosscheck characters. The crosscheck characters review revealed the incorrect 

crossing. Nine crossings did not have an FRA crossing identification number. At this level, 

resolving these discrepancies was not attempted.

Once Data Group 3 was completed, it was compared to Data Group 1 or raw FRA inventory data 

set. The most notable difference between the two was that Data Group 1 included 362 at-grade 

crossings and 50 grade-separated crossings, while Data Group 3 track chart data set comprised 

only 335 at-grade crossings. Approximately 75 percent of the additional 27 crossings in Data 

18
Group 1 were private at-grade crossings. Reading and analyzing railroad track charts is fairly 

labor intensive. However, this type of analysis is a worthwhile low-cost approach compared 

with the benefits it provides. 

Data Group 4–Data Reconciliation, Interviews, and Site Survey 

Development of Data Group 4 involved reconciliation of data from previous steps, interviews 

with State and local officials, and site surveys. 

First, Data Group 1 inventory was merged with Data Group 3 track chart data to resolve as many 

discrepancies as possible. The Data Group 2 set was only an inferential extension of Data Group 

1 and was used for risk assessment purposes and not merged into this data set. The process of 

merging data sets 1 and 3 involved the creation of a unique list of all grade crossing 

identification numbers found in the two different data groups. A data field was created in the 

GIS database indicating whether the crossing appeared in Data Group 1 inventory only, Data 

Group 3 only, or both. Separate fields containing inventory and track chart values for mileposts 

and street names were created. Crossings that only existed in the track charts were then added to 

the GIS grade crossings map layer that was constructed for Data Group 1. Mileposts from the 

two different sources were compared to identify significant discrepancies. A difference of less 

than 1/10 of a mile was common, such as 944.68 versus 944.65. Using the GIS milepost map 

layer, it was usually apparent which milepost value was correct. Determining the correct 

milepost values for crossings that differ by a small distance does not usually impact the outcome 

of a risk analysis. However, larger milepost discrepancies are frequently indicators of problems 

resulting from changed street names or miscoded FRA grade crossing identification numbers. In 

6 out of 7 cases where the mileposts differed by more than 2/10 of a mile, the track chart 

information was correct. 

The preliminary Data Group 4 set and GIS map layers indicated many crossings with conflicting 

data that could only be resolved by a call to State and local officials and/or a site visit. Data 

obtained by this method were then used to further refine Data Group 4 and the associated GIS 

grade crossing map layer. This resulted in an increased confidence that inconsistencies in public 

crossings were resolved. 

State and local officials are not as familiar with private crossings as they are with public 

crossings. Site visits to private crossings are more difficult to conduct. Therefore, an alternative 

method was developed to provide an estimate of AADT on private crossings. The method 

assumed that vehicular traffic at the crossing was a function of the land use in the abutting area. 

Topographical maps for the corridor were viewed, and the land surrounding each private 

crossing was used to categorize the crossings into one of three land use categories. Based on 

discussions with local traffic engineers in California, an AADT was developed for each land use 

category. A key factor in estimating the AADT for a private crossing is whether or not alternate 

access exists to the site served by the private crossing. If no alternate access exists, then more 

traffic will exist, as well as a higher AADT. A minimum AADT of 5 was assigned to all private 

crossings that provide access to drainage ditches, open spaces, or waterways, or are one of the 

possible access routes to a low volume site. A second AADT category of 20 consisted of 

crossings providing primary access to the following: 

19
• Two or fewer homes 

• Open space near a developed area 

• Commercial uses with other means of access 

• An agricultural transfer station with no more than one siding 

Other business and industrial sites with no other means of access, primary access to more than 

two homes, and transfer stations with more than one siding were assigned an AADT of 50. 

Questions existed concerning the presence of private crossings in Data Group 1 but not in Data 

Group 3 track charts. At this stage, private crossings were assumed to exist but having 

sufficiently low AADTs to be depicted on the track charts. This explains why Data Groups 1 

and 4 contain almost the same number of crossings. Both contain many more crossings than 

Data Group 3; thus Data Group 3 is a subset of Data Group 1. The Data Group 4 level data set 

contained 363 at-grade crossings on the study corridor. 

Data Group 5–Integration of Data from Aerial Video Survey 

The aerial survey data in Data Group 5 was used to verify and enhance Data Group 4. The 

videos obtained during the aerial video survey were the primary FLI-MAP input to Data Group 

5. This particular study did not use the detailed LiDAR obtained by the system. 

To create this data set, crossing locations from Data Group 4 were imported into the FLI-MAP 

software application. The FLI-MAP forward and downward looking videos were then reviewed 

for the entire corridor. This process was very labor intensive. As such, it was necessary to stop 

the video and review every crossing wherever a Data Group 4 crossing appeared, a pilot 

comment was indicated, or there appeared to be a crossing that was not previously noted. If a 

crossing was in the GIS database, then it had to be verified. 

Once the presence and identification number of a crossing were verified, the attributes from the 

aerial video survey were extracted. These included the following: 

• Rail skew angle with respect to the road 

• Number of tracks 

• Stopping and approaching sight distance 

• Vehicle storage space 

• Presence of a vertically elevated crossing 

• Whether or not the road is paved 

• Number of vehicle traffic lanes 

• Elevation above sea level 

• FLI-MAP based latitude and longitude 

• Miscellaneous crossing comments 

While some attributes were easily characterized from the aerial video survey, other attributes, 

such as warning device type milepost, street name, or crossing identification could not be 

obtained. An educated guess about the warning device type could sometimes be made by its 

20
shadow, but this was not verifiable and not recommended. Some attributes were also difficult to 

collect because of crossings obscured by shadows from trees or nearby structures. 

Results of the aerial video surveying method showed that the majority of private crossings did 

not exist. Resolving the presence of private crossings with State and local officials was difficult 

since they do not maintain records about private crossings. Confirming private crossings by site 

visit was exacerbated, as they were difficult to locate, with access to private land not always 

permitted. 

The aerial surveying process was very expensive and time consuming yet useful for verifying 

existing attributes, such as the number of lanes, number of tracks, and crossing surface type. The 

aerial survey also provided some additional attributes, such as traffic queue lengths or existence 

of median barriers, although these items were not incorporated in the risk calculation. The most 

useful aspect of the aerial video survey was the resolution of questions about the presence and 

grade of certain crossings. As such, Data Group 5 contained 319 at-grade crossings.

21

4. Risk Methodology and Calculation 

The standard US DOT Accident Prediction Formula (APF) predicts the probable number of 

incidents at a crossing and can be run using data found in the FRA rail crossing inventory or with 

user supplied data [4]. In 2000, FRA, in conjunction with the Volpe Center, modified the US 

DOT APF to calculate the severity of accidents at highway-rail grade crossings. The modified 

APF was applied to the Empire Corridor [1] in New York State between Peekskill and 

Rensselaer to test the new severity component. One new facet of that study involved using a 

new vehicle mix to account for high truck volumes. This study applied the modified APF 

methodology to the San Joaquin Valley High-Speed Rail Corridor in central California. 

The modified APF risk assessment model is intended as an analytical methodology to provide 

guidance in allocating the funds available for upgrading warning device levels, which maximizes 

risk reduction. The relative scaling of risk among a group of candidate crossings may be all that 

is needed. Therefore, an absolute value that tracks the historical account is not necessary, 

provided that the model does not exhibit a bias of over or underpredicting one or several 

crossings along the study corridor. Without the historical adjustment, the model prediction 

should not be expected to equal the actual experience. 

4.1 Accident Fatality Prediction Formula 

Risk is defined as the product of the probability of an event occurring and the severity of that 

event. This report defines probability as the predicted number of accidents at a grade crossing 

per year. Severity is defined as the number of fatalities (on the train and in highway vehicles) per 

incident. Fatalities were chosen as an essential measure of safety because injury counts 

introduce ambiguity. As such, this report defined the risk metric as predicted fatalities per year. 

The probability of an accident at a particular crossing is a function of the physical and traffic 

characteristics of the crossing itself. To predict accident frequency, this study used the standard 

US DOT model [4]. 

This model APF was derived by applying nonlinear, multiple regression techniques to crossing 

characteristics stored in the US DOT/FRA Highway-Rail Grade Crossing Inventory and to 

accident data contained in the FRA Railroad Accident/Incident Reporting System. This 

approach yielded a formula that predicted the probable number of accidents at a given crossing 

based on the data found in the inventory. The equation is dominated by the exposure index term 

that combines the average daily traffic count and the number of trains. This equation is the 

following: 

a = K * EI * MT * DT * HP * MS * HL 

 

 where 

a = unnormalized accident prediction (accidents/year at the crossing) 

K = constant for initialization of factor values at 1.00 

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

MT = number of main tracks 

23
DT = number of through trains per day during daylight hours 

HP = highway paved factor 

MS = maximum railroad timetable speed 

HL = number of highway lanes 

To obtain the normalized value, the predicted value was multiplied by the appropriate 

normalizing constant [5] depending on the type of warning device used (see Table 3). 

Table 3. APF Normalizing Constants from 1992 

Warning Device Groups Normalizing Constants 

Passive 0.8239 

Flashing Lights 0.6935 

Gates 0.6714 

The normalizing constants were again updated in 1998, but since this analysis encompassed the 

years 1995 through 1999, the 1992 normalizing constants were used as a conservative estimate. 

Vehicle type mix at the crossings was another factor in determining the probability of a fatal 

incident. The AADT data for most of the crossings was provided by California highway officials 

in different jurisdictions. 

Several notes exist regarding the accident frequency portion of the standard US DOT formula. 

First, the APF should be weighted with the actual accident history at each crossing to get a more 

accurate prediction estimate. However, in determining the effect of increasing speeds at a given 

crossing, the history was no longer applicable, so the unweighted prediction was used. Second, 

obtaining an estimate for the accident rate at an improved crossing applies an effectiveness rate 

to the baseline prediction. Because of the planned increase in train speeds, this method was not 

used. Instead, the accident rate was predicted from the above equations. 

Although the standard US DOT model includes a severity portion, this report does not use it for 

several reasons. First, the model does not differentiate between freight trains and passenger 

trains (which have a much greater severity potential). Secondly, the output is the likelihood of a 

fatal accident, not a prediction of the numbers of fatalities, which was needed for this risk 

analysis. Third, the model was not designed for higher speed crashes. Since the focus of this 

study is to characterize the risk at such crossings, an independent severity model was needed. 

Accident probability, the first element of risk, was calculated as described above. Therefore, a 

methodology was needed to express severity as a function of train speed. 

In the modified APF model developed by the Volpe Center [1], the severity of a grade crossing 

accident was dependent on a number of factors, including accident type, type of highway 

vehicle, type of train, and train speed. The Volpe Center developed the severity model by 

statistically breaking down accidents into categories with distinct crash mechanics. Each of 

these categories was then examined using historical data, statistics, and crashworthiness analyses 

to predict severity, an approach illustrated in Figure 7. 

The first part of Figure 7 is the top-level tree, where accidents were broken into the two main 

branches, Train Striking Highway Vehicle and Highway Vehicle Striking Train. This analysis 

24
focused on high-speed rail operations and was designed to set an upper bound for risk. As such, 

it was assumed that all trains involved in the predicted accidents were passenger trains and that 

the trains traveled at the maximum allowable track speed. 

The middle section of Figure 7 shows the breakdown of the highway vehicle into train crash 

mode. Since automobiles, trucks, and truck-trailers account for 99.1 percent of the grade 

crossing accidents involving passenger trains (excluding pedestrians and unspecified vehicles), 

only those three categories were considered in this analysis. As can be seen from the figure, in 

each case, harm to the train and the highway vehicle was generated from the impact itself. If the 

crash caused a derailment, additional risk may accrue. The final section of Figure 7, the tree 

structure for train into highway vehicle, is based on similar logic. 

Train Into

Highway Vehicle

Highway Vehicle

Into Train

Grade Crossing Accident

Additional Train

Severity

Derailment No Derailment

Train Severity

Highway Vehicle Severity

Auto

Additional Train

Severity

Derailment No Derailment

Train Severity

Highway Vehicle Severity

Truck

Additional Train

Severity

Derailment No Derailment

Train Severity

Highway Vehicle Severity

Truck-Trailer

Highway Vehicle into Train

Additional Train

Severity

Derailment No Derailment

Train Severity

Highway Vehicle Severity

Auto

Additional Train

Severity

Derailment No Derailment

Train Severity

Highway Vehicle Severity

Truck

Additional Train

Severity

Derailment No Derailment

Train Severity

Highway Vehicle Severity

Truck-Trailer

Train into Highway Vehicle

Figure 7. Severity Element Breakdown [1] 

25
4.2 Crossing Incident History 

The data in this report were compiled from the US DOT FRA Highway Rail Accident/Incident 

Identification database for identified crossings to obtain the number of incidents, injuries, and 

fatalities at each crossing from 1995 to 1999. As previously described, the composition of each 

data group varies by the number of grade crossings and crossing characteristics, including 

incidents and fatalities. Therefore, each data group represents a slightly different depiction of the 

San Joaquin Corridor risk. Table 4 presents a summary of incidents and fatalities at highway-rail 

crossings by train operation type from Data Group 1. 

Table 4. Incidents and Fatalities at Highway-Rail Crossings 

Along the Study Corridor for Data Group 1 from 1995 to 1999 

Freight Operations Passenger Operations 

Year Incidents Fatalities Incidents Fatalities 

1995 18 3 9 1 

1996 15 1 2 0 

1997 9 3 10 3 

1998 17 7 7 4 

1999 14 6 12 4 

Total 73 20 40 12 

There were 113 incidents associated with all train traffic in the corridor, resulting in 32 fatalities, 

all in highway vehicles. Passenger trains were involved in 40 incidents along the study portion 

of the San Joaquin Corridor, resulting in 12 fatalities. Freight trains were involved in 73 

incidents in the corridor, resulting in 20 fatalities. The baseline condition passenger train data of 

fatal incidents indicates a trend of an increasing number of fatalities as the late 1990s came to a 

close. Figure 8 presents the data involving freight and passenger train incidents between 1995 

and 1999, while Figure 9 shows the fatalities for the same time period. 

0

5

10

15

20

1995 1996 1997 1998 1999

Number of Incidents

Freight

Operations

Passenger

Operations

Figure 8. Incidents Involving Freight and Passenger Operations: 1995–1999 

26
0

2

4

6

8

1995 1996 1997 1998 1999

Number of Fatalities

Freight

Operations

Passenger

Operations

Figure 9. Fatalities Involving Freight and Passenger Operations: 1995–1999 

Since accident data is tabulated by grade crossing identification number, the lack of one in the 

US DOT FRA Highway-Rail Accident inventory can result in overlooked accidents. Detailed 

examination of the records that did not have grade crossing identification numbers insured that 

all accidents associated with the study corridor were included. 

4.3 Risk Calculation 

Since the list of crossings in each data group and the attributes about the individual crossings 

were constantly being refined, Volpe Center researchers wrote a series of programs to calculate 

individual crossing risks and the total corridor risk for each of the different data groups. In so 

doing, the probability, severity, and risk calculations and study corridor summaries could be 

recalculated easily for any or all of the different data groups. 

The organization of the data into many tables, all referenced by grade crossing identification, 

made the GIS application highly flexible. Data tables, such as the Highway-Rail Crossing 

Inventory or Highway-Rail Accident/Incident data, were maintained for the entire State of 

California. Data from different sources, such as the site visits and the AADT information 

obtained from State and local officials, were maintained in separate tables. Five additional 

tables, one for each data group set, were also maintained. These tables include the list of 

crossings for the data group along with attributes collected during compilation of that data group 

(e.g., the track chart warning devices in Data Group 3 or the aerial survey that identified a 

number of highway lanes in Data Group 5). 

For risk to be calculated, Volpe Center researchers obtained many variables from a wide range of 

sources. Since each data group had different associated data and the higher data groups could 

fall back on previous data when missing, each data group risk calculation varied. 

27
Data Group 1 

Risk calculations for the crossings listed in Data Group 1 were based on data found in the FRA 

inventory. Since it was not possible to calculate risk for crossings that lacked the required data, 

Volpe Center researchers performed a risk assessment for only 255 of the 362 at-grade crossings. 

Data Group 2 

The Data Group 2 risk calculation used the same set of crossings as Data Group 1, with certain 

assumptions made for missing data. For example, crossings with no AADT values were 

assumed to have an AADT of 20, and crossings lacking pavement data were assumed to be 

paved if they were public and not paved if they were private. In addition, missing values of FRA 

inventory data, track chart data for train speeds, number of tracks, or trains per day were 

programmatically inferred from the nearest crossing with a valid value. Using these 

assumptions, Volpe Center researchers calculated a risk for all 362 at-grade crossings. 

Data Group 3 

The risk calculation for Data Group 3 was based on the list of crossings compiled from the track 

charts. The number of tracks and train speeds were attained from the track charts. If the warning 

device type could not be determined from the track charts, Volpe Center researchers used the 

values from the FRA Highway-Rail Crossing Inventory. The inputs used for AADT, trains per 

day, paved, and number of highway lanes were obtained from the FRA inventory just as they 

were for Data Group 2. For the 335 at-grade crossings in this group, a risk value could not be 

calculated for 9 of the crossings because of missing data. 

Data Group 4 

Data Group 4 represents an integration of Data Groups 1 and 3, with additional data obtained 

from the site surveys, conversations with State and local officials, and the more sophisticated 

topographic map-based AADT estimates for private crossings. The risk calculation was more 

complicated at this stage since the required attributes were acquired from many tables. In 

addition, any missing attribute data was obtained from previous data groups, where possible. 

Risk could not be calculated for 5 of the 363 at-grade crossings at this level because of missing 

data. 

Data Group 5 

Data Group 5 incorporated data obtained from the aerial surveys. The program to calculate risk 

for this data group contains the most sophisticated logic because when data is missing it can be 

sought from any of the previous levels. Attributes, such as AADT and train speed that were not 

obtainable from the aerial video survey, were obtained from data in previous levels. In this data 

group, risk could not be calculated for 8 of the 319 at-grade crossings. 

Volpe Center researchers developed risk tables for each data group. The data group risk 

calculation table contains the following: 

• Probability, severity, and risk estimates for each crossing 

• Data value used in the calculation 

• Source of the value 

28
By maintaining the data value and source information for each crossing, it was possible to 

determine how much each source contributed to each attribute. For example, in Data Group 4, 

32.5 percent of the data on warning devices came from the track charts, 40.7 percent came from 

site visits, and 25.6 percent came from the inventory. 

Once the risk calculations were performed, an historical accident summary table for each of the 

five groups was generated by summing the incidents and fatalities for all the crossings in each 

data group. A study corridor summary was then generated that compared the real versus 

predicted incidents and fatalities for each data group. 

The data organization and risk calculation were independent of the GIS platform and could have 

been generated using a standard relational database. However, programming the risk 

calculations as part of a GIS platform added a mapping capability. GIS was very useful in 

locating crossings and resolving discrepancies in order to construct accurate lists of crossings for 

use in the risk calculation. The GIS platform was also used to provide a much more intuitive 

display of crossing attributes and risk calculation results. Although not pursued in this study, 

this flexible method of risk calculation would allow for various scenarios to be tested, such as the 

addition or removal of crossings or modification of crossing attributes (e.g., AADTs). 

29

5. Results: Comparison of Data Groups 

Volpe Center researchers analyzed and compared the methods described in Section 4 to 

determine the overall risk effect by method on the corridor. Data collection methods, 

documented in Section 4 along with the level of effort and estimated costs, were used to 

determine the optimal methodology for performing a corridor risk analysis. 

5.1 Data Accuracy 

No single source of grade crossing data exists that is suitable to use as is to perform a risk 

assessment. Group 1, FRA Highway-Rail Crossing Inventory, represented the best data source 

to use as a baseline for further clarification although Data Groups 1 and 2 included the same 

number of crossings. Data Group 2 employed certain assumptions to populate data base gaps 

that are required for risk calculation. For example, this may involve assumptions about assigning 

a train speed from the nearest adjacent crossing. In-house staff can perform data collection and 

analysis of the first two data groups. The data accuracy of the combined Data Groups 1 and 2 

data set was valid for most crossings but did have gaps in warning device type and AADT. 

The Data Group 3 data set, railroad track charts, was useful at improving the location of a 

crossing and the warning device type. For Data Group 3, more time was required to validate the 

track charts with FRA inventory. The difficulty in creating a complete and accurate grade 

crossing inventory for the corridor was in deriving the most recently completed list and location 

of crossings. For most applications, the first three data groups provide an accurate description of 

the corridor crossings. 

Another disadvantage of Data Group 4, Interviews and Site Assessment, relates to the scope of 

the analysis and the crossings analyzed. The number of crossings with discrepancies should be 

defined before the Data Group 4 data collection process begins because it would be costly to 

interview local officials in every town for every crossing. 

The benefit of interviews with local transportation officials is that AADT and traffic volumes can 

be obtained. Depending on the scope of crossings involved, interviews can be very time 

consuming without necessarily adding a significant amount of information that could increase 

the robustness of the risk assessment. 

Site visits for crossings with a high incident history, unique characteristics, unknown location, 

and warning devices are the best way to ascertain conflicts between different data groups. It is 

preferable to have a plan of which crossings to cover, the type and format of data for collection, 

and a camera. Photographing the crossing is vital because it can remove some of the 

subjectiveness of the site visit and description of crossing characteristics. A digital camera is 

recommended because the photos can be integrated into the GIS (or any other) application for 

easy storage and retrieval. 

5.2 Risk Analysis Calculation Results 

Table 5 shows the risk assessment results by data group. In Data Group 1, Baseline FRA 

Highway-Rail Crossing Inventory, a total of 362 crossings were analyzed, and 107 crossings did 

31
not have a risk factor calculated because of gaps in the data. For this data group, 22.60 average 

annual recorded incidents occurred in the San Joaquin Corridor between 1995 and 1999, 

resulting in 6.40 fatalities on an average annual basis. The output of APF predicted 18.89 

incidents during the time frame from 1995 to 1999, with a total predicted risk of 5.44 fatalities 

per year. The difference between the historical and predicted values may be due to the lack of 

risk assessment performed for 107 crossings. 

The Data Group 2 data set has 362 crossings like Data Group 1. Unlike Data Group 1, the Data 

Group 2 data set was completely populated using the inferential extrapolation technique, so a 

risk value could be calculated for all of the crossings. The average annual incidents remained the 

same as in Data Group 1 at 22.60. However, the addition of 107 crossings into the risk 

calculation yielded an increase in the predicted annual values to 22.97 incidents and 7.26 

fatalities. 

Table 5. Risk Results Summary by Data Group 

Data Group 

Number 

of 

Crossings 

Risk Not 

Calculated1

Average 

Annual 

Incidents 

1995-1999 

Average 

Annual 

Predicted 

Incidents2

Average Annual 

Fatalities 

1995-1999 

Total Predicted

Risk3

Data Group 1 362 107 22.60 18.89 6.40 5.44 

Data Group 2 362 0 22.60 22.97 6.40 7.26 

Data Group 3 335 9 22.20 20.73 6.40 6.75 

Data Group 4 363 5 21.60 28.76 6.40 9.54 

Data Group 5 319 8 20.80 27.09 6.00 8.92 

1. Unable to calculate risk due to lack of complete data attributes. 

2. Based on the DOT Basic Formula. 

3. Based on the Empire Corridor Study Formulations.

The Data Group 3 results, based on the analysis of the BNSF track charts, resulted in nine grade 

crossings that lacked sufficient data attributes to calculate risk. This data set, however, consisted 

of only 335 grade crossings. The average annual recorded incidents declined slightly to 22.20, 

and the average annual recorded fatalities for the time period remained at 6.40. The smaller data 

set size also resulted in a commensurate decrease in the predicted number of annual incidents to 

20.73, and the predicted fatalities in the corridor to 6.75 per year. 

Data Group 4 included information from site visits and calls to State and local transportation 

officials to obtain more accurate AADT measurements. This yielded a data set of 363 grade 

crossings, with 5 crossings lacking the required data attributes necessary for a risk calculation. 

The average recorded annual incidents were 21.60 and 6.40, respectively. The site visit and 

interview data resulted in the average annual predicted incidents of 28.76 and 9.54 average 

annual predicted fatalities, the highest of the data groups. This may be indicative of some 

inconsistencies in the site visit data and the interviews with State and local government officials, 

especially concerning private crossing demographics and usage. 

Data Group 5, consisting of the FLI-MAP survey, encompassed 319 crossings with 8 crossings 

missing the data for a risk assessment calculation. The average annual recorded incidents 

decreased to 20.80, and the average annual recorded fatalities decreased to 6.00 because one of 

the fatal incidents occurred at a crossing that lacked sufficient information to perform a risk 

32
calculation. Like Data Group 4, the predicted annual values of 27.09 incidents and 8.92 

predicted fatalities were well above the measured values. Again, this may be attributed to 

inconsistencies in the human knowledge obtained from the site visits and interviews that were 

used in the risk prediction calculation. 

Table 6 presents data as information for the two most recent years, 2000 and 2001. One incident 

resulting in 7 of the 14 passenger train fatalities occurred when an Amtrak train collided with a 

minivan carrying 7 agricultural workers in 2001. 

Table 6. Recent Incidents, Fatalities, and Injuries along the San Joaquin Corridor 

Freight Train Related Passenger Train Related 

Year Incidents Fatalities Injuries Incidents Fatalities Injuries 

2000 19 1 3 11 5 3 

2001 14 2 1 13 14 3 

5.3 Impacts of Data Groups on Risk 

Two factors that were inherent to every data group had a major impact on the results shown in 

Table 5. First, each data group consisted of a unique set of grade crossings in terms of the 

number of crossings and the characteristics of the crossings. Second, except for Data Group 2, 

each data group had a subset of crossings that did not contain the required inputs for calculating 

risk. Both of these factors impacted the results for the following: 

• Average annual incidents 

• Average annual predicted incidents 

• Average annual fatalities 

• Total predicted risk 

The number of grade crossings in each data group includes the following, with the number of 

crossings without a risk calculation in parentheses: 

• Data Group 1–362 (107)–FRA inventory 

• Data Group 2–362 (0)–Extrapolated FRA inventory 

• Data Group 3–335 (9)–Railroad track charts 

• Data Group 4–363 (5)–1 and 3 integrated with site surveys 

• Data Group 5–319 (8)–Aerial survey 

Several key points can be derived from this list. The first is the high number of crossings in Data 

Group 1 that did not have a risk calculation. The second is that Data Groups 1, 2, and 4 are 

extremely close in terms of size. As explained previously, Data Group 2 strongly resembles 

Data Group 1, with grade crossing information extrapolated where possible, to estimate the risk 

for the 107 crossings that were omitted in Data Group 1. Both data groups may contain 

crossings that no longer exist. 

33
5.3.1 Measured Data 

From Table 5 the observations included the following: 

Fatality Trends 

• Data Groups 1-4 yielded 6.40 average annual fatalities. In terms of this metric, they 

depict the San Joaquin Corridor in the same manner. 

• Data Group 5, resulting from the FLI-MAP survey, produced 6.0 average annual 

fatalities. The 319 grade crossings in this data group represented the most accurate 

characterization of the corridor at the time this study was performed. Since Data Groups 

1-4 contained many crossings that were actually closed between 1995 and 1999, to 

maintain consistency the authors treated these crossings as open for the entire study 

period. However, if an incident or fatality had occurred at one of these crossings, it 

would not have been captured in Data Group 5. 

Incident Trends 

• Data Groups 1 and 2 contain the identical average annual incident values of 22.60. This 

shows that, in terms of measured incidents, both data groups characterize the San Joaquin 

Corridor in the same manner. 

• For Data Group 3, a slight reduction occurred in the measured average annual incidents 

from 22.60 to 22.20, with respect to Data Groups 1 and 2. This difference is a direct 

result of the fact that the railroad track charts contain 27 fewer crossings than the FRA 

inventory. If an incident had occurred at one of these crossings, then it would not have 

been captured in Data Group 3. 

• Although Data Group 4 represents a corridor with 363 grade crossings, the most of any 

data group, it showed a decrease in average annual measured incidents to 21.60. Since 

Data Group 4 is the result of the integration of Data Groups 1 and 3, it represents a 

refinement of both the FRA inventory and the railroad track charts. If an incident had 

occurred at a crossing indicated as open in either Data Groups 1 or 3, but found to be 

closed during the reconciliation process, then it would not have been included in Data 

Group 4. 

• Data Group 5 showed the fewest number of average incidents, 20.80 of all the data 

groups. As described previously, this is indicative of Data Group 5 containing the most 

current data, whereas the other data groups contain data regarding crossings closed 

during the evaluation period. 

5.3.2 Predicted Data 

In reference to Table 5, the following observations included: 

• Data Group 1 had the lowest values, 18.89 and 5.44, for average annual predicted 

incidents and total predicted risk, respectively. This seems reasonable since a risk 

calculation could be performed for only 255 of the 362 grade crossings in the data group. 

• The Data Group 2 risk calculations included as inputs the inferentially extrapolated 107 

grade crossings in Data Group 1 that had missing data. This permitted risk modeling on 

the entire 362 crossings, thereby yielding the increased predicted annual incident and 

total risk values of 22.97 and 7.26, as compared to Data Group 1. 

34
• Data Group 3, railroad track charts, contained 27 fewer grade crossings (mostly private 

from track chart information) than the FRA inventory and did not include risk inputs 

from 9 crossings in its data set. This could possibly have been the cause of the slight 

decrease in the predicted annual incident and total risk values from Data Group 2. 

• Data Group 4 had the highest values of average annual predicted incidents and total 

predicted risk, 28.76 and 9.54, respectively. This seems reasonable given that Data 

Group 4: (1) represents a reconciliation of the FRA inventory and the railroad track 

charts and (2) contains more accurate data from local and State government officials 

regarding warning devices, AADT volumes, and growth factors, as well as the mix of 

trucks, buses, and commercial vehicles. Both of these items contributed to higher risk 

calculation values. 

• Data Group 5, resulting from the FLI-MAP survey, depicted the San Joaquin Corridor 

with 319 grade crossings. This represented the most accurate estimation of the number of 

grade crossings on the corridor at the time this analysis was performed. Data Groups 1-4 

contained many crossings that were actually closed or nonexistent but had not been 

updated in the FRA inventory or the railroad track charts. However, as with Data Group 

4, Data Group 5 contains more accurate data from local and State government officials 

regarding warning devices, AADT volumes, and growth factors, as well as the mix of 

trucks, buses, and commercial vehicles. These factors resulted in Data Group 5 having 

the second highest predicted annual incidents and total risk values of 27.09 and 8.92, 

respectively. 

5.3.3 Comparison of Measured and Predicted Data 

For simplicity, the five Data Groups can be separated into two sets, 1-3 and 4-5. The first set 

uses the FRA inventory as the source for AADT volumes and does not even account for the 

effect of AADT growth factors from truck, bus, and commercial vehicle mix on risk assessment. 

The second set uses data from local and State government officials as the source for AADT 

volumes, and growth factors, as well as the mix of trucks, buses, and commercial vehicles. The 

lack of AADT growth factors in the FRA inventory reflects the lower predicted annual incidents 

and total predicted risk in Data Groups 1-3. Conversely, the incorporation of the AADT growth 

factors in risk calculation translated into a high prediction for annual incidents in Data Groups 4-

5. 

Data Group 2 results provided the most correlated actual to predicted incident values. At this 

grouping level, the actual number of incidents was 22.60, and the predicted number of incidents 

between 1995 and 1999 was 22.96. Likewise, Data Group 3 results provided the highest 

relationship between fatalities and predicted risk, 6.40 and 6.75, respectively. Since Data Group 

5 contains the highest fidelity data set, it was initially expected to yield a predicted corridor risk 

closest to the actual data. Some possible explanations for these outcomes include the following 

[4]: 

1) The APF in the DOT model employs historical national data to predict accidents and 

fatalities at specific crossings. Although the model is accurate on the national level, its 

resolution may be influenced by local variations when examining a relatively small 

number of grade crossings or a specific corridor such as San Joaquin. As such, the APF 

35
model is dependent upon accurate input data and assumptions. Discrepancies in the FRA 

inventory resulting from incorrect data collection or entry and inaccuracies in 

assumptions regarding warning device effectiveness may result in incorrect 

accident/fatality prediction values. 

2) Use of accident history, in conjunction with the unnormalized prediction obtained from 

the basic formula, increases the overall robustness of the accident prediction model. 

Prevailing research has shown that accident history can be used as a substitute for other 

characteristics that affect crossing risk but are not included in the FRA inventory (e.g., 

sight distance or skewness), as was the case with the San Joaquin data. However, 

quantifying this theory is difficult and has not yet been proven. 

3) Since fatal accidents at grade crossing are such rare events, the data collected for the 

years 1995-1999 may be too small a sample size to accurately capture the grade crossing 

risk for the San Joaquin Corridor. However, analysis has shown that little improvement 

is realized in accident and fatality prediction for more than 5 years of cumulative data. 

Employing accident history from the most recent 5 years of accident history ensures good 

performance from both the accident prediction formula and use of the most relevant data. 

Accident history older than 5 years may have inherent inaccuracies resulting from 

changes in grade crossing properties over time. 

5.4 Compounding Risk Factors Not Evaluated 

Numerous compounding variables are not currently included in the risk assessment calculation, 

one of which is the traffic mix. While trucks pose a greater risk to train occupants than 

passenger cars, it is very difficult to obtain an accurate vehicle mix for any given crossing 

without intensive contact with State DOT and local officials. Measuring local truck volume at 

specific grade crossings is useful if the data can be readily obtained. A collision between a 

heavy truck and a locomotive can cause greater damage, injuries, or loss of life due to the higher 

probability of derailment. When the local truck volume mix is not available, the truck constants 

found in the FRA model may be applicable. Although the model is accurate on the national 

level, its resolution may be influenced by local variations when examining a relatively small 

number of grade crossings. 

In conducting a site distance risk assessment, it is preferable to rely on empirical evidence and 

quantifiable data. The sight lines and distances are not incorporated within the current risk 

assessment models; it may be helpful to track incidents and accidents at crossings where the 

view is largely obstructed. 

In addition to structures, other obstructions, such as sidings, bridges, and vegetation, may also 

complicate the site line to a crossing. Little can often be done to reduce the visual blockage from 

bridges and structures. 

Humped crossings are especially risky if trucks and tractor-trailers with low clearance, such as 

construction equipment carriers, traverse the crossing. The elevation of some humped crossings 

can be so great that the undercarriage of a truck can strike the crossing, causing the truck to get 

36
stuck. While humped crossing guidelines exist, many nonconforming crossings still exist that 

pose special risks especially at unregulated, private crossings. It is not easy to classify railroad 

crossings by the approach grade to include in a risk assessment model. Therefore, humped 

crossings are the best candidates for closure, relocation, grade separation, or highway approach 

improvements to eliminate the hump. 

Congestion and recurring queues are compounding variables that are difficult to capture in the 

risk assessment model. Some crossings exist where the roadway approach is a short distance 

from an arterial or highway. In this instance, when gates close for an approaching train, the 

motor vehicle traffic queue can back up to the main arterial or highway, posing a significant risk 

to vehicles in the queue to being struck from behind by a vehicle on the main highway traveling 

at high speeds. Traffic queues such as this can compel motorists to drive over the grade 

crossing, despite the warning of an approaching train. Better integration between nearby traffic 

signals and crossing warning devices or installation of traffic signals integrated with the track 

signal can solve this problem. 

5.5 Costs Associated with Data Collection and Risk Assessment 

The research for this study was primarily conducted in 1999. Table 7 shows costs associated 

with various data collection methods and analyses of the project. The table does not include 

costs of the analysis for this report, adjustments to the data, and preparation of this report. The 

most expensive data collection method for this study was the aerial video survey. This was 

slightly higher than all other costs combined. The least expensive item was the site visits, which 

included 1 week in the field and travel expenses, and consisted of inspecting 151 out of the 412 

crossings in the original inventory. Assuming 50 percent of the integration costs, a realistic 

figure for the site visits is $25,200. Assuming the FRA data inventory and track charts analyses 

were each around $37,100, the site visit cost is approximately 32 percent lower. The costs for 

track chart analysis, site visits, and aerial surveying are incrementally added to the base cost for 

FRA data retrieval. 

Table 7. San Joaquin Data Collection and Analysis Costs1

 

Data Collection Method Cost Percent 

of Total Cost

FRA Inventory Data Collection $37,100 10.2%

Track Chart Data Collection $37,100 10.2%

Site Visits and Interviews $25,200 6.9%

FLI-MAP $199,000 54.6%

GIS Development, Data Analysis, and Data Integration $65,900 18.1%

Total $364,300 

1

Source: Volpe Labor Accounting and Contract Records 

The project costs escalated with the incorporation of Data Group 4 and Data Group 5 data. For 

these two, outside consulting resources with special expertise were needed to augment in-house 

37
staff efforts (it could be argued that conversations with State and local officials and site visits 

could be done in house; obviously the aerial surveying had to be contracted out). 

38
6. Conclusions and Recommendations 

This report evaluates the benefits (if any) of using improved data collection techniques for 

improving the accuracy, location, and attributes of highway-rail crossings along a portion of the 

San Joaquin High-Speed Rail Corridor in central California. 

Once all of the data groups were assembled, a risk assessment was performed on each one. The 

risk, in terms of annual fatalities, calculated using the FRA Highway-Rail Crossing Inventory 

data, is slightly underestimated by 15 percent from the actual annual corridor fatalities. For the 

remaining Data Groups, 2 to 5, the risk is overestimated from 5.5 percent to 49 percent. Perhaps 

the best correlation between the risk analysis model results and the recorded fatalities on the 

corridor was obtained by using the FRA Highway-Rail Crossing Inventory data set augmented 

by the railroad track charts (Data Group 3). This is a good indication that using the FRA Data 

Group 2 Highway-Rail Crossing Inventory data set and the railroad track charts provides the best 

possible risk analysis for a corridor. Care should be taken when making a decision about which 

data group is the most appropriate as the methods used in this study may not translate to other 

railroad corridors. 

Various important risk factors (site distance, skewness, vertical clearance) could be quantified or 

identified by the methods used in this analysis but could not be incorporated into the current risk 

models available. In some cases, these variables could not be included in the analysis. One 

example is the mix of trucks and automobiles. Some factors discovered by the aerial video 

survey, such as the skew of the railroad crossing to the road, crossing sight obstructions caused 

by vegetation and structures, short approach distances and limited storage capacity of a road 

because of parallel adjacent roadways, could not be used due to limitations in the existing risk 

models. Further studies are required to modify the risk analysis models to include these factors 

in any future analysis. 

It was found that the FRA inventory data is the least expensive option for a first order 

approximation of the risk involved in a given corridor. This can give an indication as to the most 

critical crossings that require mitigation. It is also useful to determine the number of fatalities 

and whether further data is required for a refined analysis. 

6.1 Recommendations 

• Based on the corridor risk assessment calculations for each method, the most economical 

approach is to use FRA inventory data augmented with information obtained from track 

charts. These two data sets, when combined and analyzed, yielded the closest correlation 

in annual fatalities, as compared to what actually occurred in the corridor during the 

period covered in this analysis. 

• Site visits, if required, should be conducted only at specific locations after an initial 

evaluation has been performed using the first three methods discussed in this report. 

Aerial and video surveying was expensive while the results obtained yielded the largest 

difference between actual and predicted risk. 

39
• The DOT model should be updated to incorporate information obtained by the aerial 

video survey, such as skewness of the road to the track, site clearance, and adjacent 

traffic access. 

• When building the GIS database and platform, all types of crossings should be included 

at all levels. Initially each group was built using only at-grade crossings, but it was then 

found that sometimes one source would show that a particular crossing was at-grade, 

while another source showed it was grade separated. It is much easier to resolve 

discrepancies when all crossings are included in all groups. 

40
References 

1. Mironer, M., M. Coltman, and R. McCown. “Assessment of Risks for High-Speed Rail 

Grade Crossings on the Empire Corridor.” Federal Railroad Administration, U.S. 

Department of Transportation, Research and Special Projects Administration, John A. 

Volpe National Transportation Systems Center. Cambridge, MA. August 2000. 

2. “Highway-Rail Crossing Inventory Instructions and Procedures Manual.” Federal Railroad 

Administration, Washington, DC. December 1996. 

3. “Understanding GIS: The Arc/Info Method.” Environmental Systems Research, Inc. 

Redlands, CA. 1991. 

4. “Rail-Highway Crossing Resource Allocation Procedure.” Federal Railroad 

Administration, Office of Safety Analysis. DOT-TSC-FRA-87-1. Washington, DC. 

August 1987. 

5. “Highway-Rail Crossing Accident/Incident and Inventory Bulletin.” Federal Railroad 

Administration, Washington, DC. 1994. 

41

Acronyms 

AADT Average Annual Daily Traffic 

APF Accident Prediction Formula 

APM Accident Prediction Model 

ABS automatic block signaling 

BTS Bureau of Transportation Statistics 

BNSF Burlington-Northern/Santa Fe Railroad Company 

CALTRANS California Transportation Department 

CTC Centralized Traffic Control 

CWT Constant Warning Time 

DEM Digital Elevation Model 

DOT Department of Transportation 

DRG Digital Raster Graphics 

FLI-MAP Fast Laser Imaging-Mapping and Profiling 

FRA Federal Railroad Administration 

GIS Geographic Information System 

GNIS Geographic Name Information System 

GPS Global Positioning System 

ISTEA Intermodal Transportation Efficiency Act of 1991 

LiDAR Light Detection and Ranging 

MP Milepost 

TEA-21 Transportation Equity Act for the 21st Century, 1998 

TIGER Topologically Integrated Geographic Encoding and Referencing 

UP Union Pacific Railroad Company 

USGS U.S. Geological Survey 

TWC track warrant control 

VNTSC Volpe National Transportation Systems Center 

RITA Research and Innovative Technology Administration 

43
44