Safety of Highway Railroad Grade Crossings

U.S. Department of 

Transportation

Federal Railroad 

Administration

Railroad Right-of-Way Incident Analysis Research

Office of Railroad

Policy and Development

Washington, DC 20590

DOT/FRA/ORD-11/09 Final Report

April 2011
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Railroad Right-of-Way Incident Analysis Research

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6. AUTHOR(S)

Mina Chaudhary, Adrian Hellman, and Tashi Ngamdung

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

Cambridge, MA 02142

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DOT-VNTSC-FRA-09-11

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U.S. Department of Transportation

Federal Railroad Administration

Office of Railroad Policy and Development

1200 New Jersey Avenue, SE

Washington, DC 20590

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11. SUPPLEMENTARY NOTES

Safety of Highway-Railroad Grade Crossings series

Program Manager: Leonard W. Allen III

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13. ABSTRACT (Maximum 200 words)

Locations of railroad right-of-way incidents in this research were identified as hotspots. These can be defined as highway-rail 

grade crossings or locations along the railroad right-of-way where collision or trespassing risk is unacceptably high and 

intervention is justified because the potential safety benefits exceed the cost of intervention. This project categorizes the hotspots 

as grade crossing and trespass incident hotspots. Mathematical models and theories are researched to see which ones may be used 

in identifying the hotspots. For the analysis of grade crossing incident hotspots, the Transport Canada model is modified to

accommodate U.S. data and is applied to a sample of grade crossing incidents from 2003 to 2007 in the San Joaquin corridor in 

California. In analyzing trespass incident hotspots, the theory of cluster analysis, a type of spatial analysis, was researched. It 

appears that cluster analysis, used in conjunction with a geographic information system platform, would be a beneficial way of 

analyzing and predicting trespass hotspots. 

14. SUBJECT TERMS

Hotspots, grade crossing, trespass, accident prediction, severity prediction, risk, cluster analysis, 

geographic information system platform

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iii

Acknowledgments

The U.S. Department of Transportation (USDOT) Federal Railroad Administration (FRA) Office 

of Research and Development sponsored the work leading to this report. The authors would like 

to thank Sam Alibrahim P.E., Chief of the Signals, Train Control and Communications Division, 

FRA; Leonard Allen, Program Manager, Signals, Train Control and Communications Division, 

FRA; and Gary Carr, Chief of the Track Research Division, FRA for their guidance and direction 

in developing this report.

The authors also wish to thank Jim Hernandez from the California Public Utilities Commission

for his cooperation and the contribution of data that were used for this project. Appreciation is 

also extended to Mike Grizkewitsch of the FRA Office of Safety and Raquel Wright of the FRA

Office of Policy for their time, support, ideas, and exchange of data.

Anya A. Carroll, Domain Expert of Intermodal Surface Transportation Systems, and Marco 

daSilva, Highway-Rail Grade Crossing Safety and Trespass Prevention Research Program 

Manager, John A. Volpe National Transportation Systems Center, provided overall direction of 

the technical information and the report preparation, and also provided invaluable guidance and 

leadership.
iv

Contents

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

1 Introduction............................................................................................................................. 2

2 Models Researched ................................................................................................................. 4

2.1 USDOT Accident Prediction Model ................................................................................ 4

2.2 Transport Canada Model.................................................................................................. 6

2.2.1 Test USDOT Model with Canadian Data ................................................................. 6

2.2.2 Grade Crossing Accident Prediction Model ............................................................. 6

2.2.3 Grade Crossing Severity Prediction.......................................................................... 9

2.2.4 Hotspot Identification ............................................................................................. 10

2.3 University of North Carolina Pedestrian Crash Risk Model.......................................... 10

2.4 Summary ........................................................................................................................ 12

3 Grade Crossing Incident Hotspots........................................................................................ 13

3.1 Data Fields and Sources for TC Model.......................................................................... 13

3.1.1 Passive Warning Devices........................................................................................ 13

3.1.2 Flashing Lights Warning Devices........................................................................... 13

3.1.3 Gated Warning Devices.......................................................................................... 14

3.2 Application of Models on a Sample from California..................................................... 14

3.3 Results............................................................................................................................ 15

3.3.1 Crossing Incident History ....................................................................................... 15

3.3.2 Risk Calculation of the Models............................................................................... 15

3.3.3 Comparison of the Models...................................................................................... 15

3.4 Summary ........................................................................................................................ 18

4 Trespass Incident Hotspots................................................................................................... 19

4.1 Cluster Analysis ............................................................................................................. 19

4.2 Spatial Testing................................................................................................................ 19

4.3 Data Requirements......................................................................................................... 21

4.4 Collaboration with the FRA Office of Safety ................................................................ 22

4.5 Summary ........................................................................................................................ 26

5 Conclusions........................................................................................................................... 27

6 References............................................................................................................................. 29

Appendix A................................................................................................................................... 30

Appendix B................................................................................................................................... 34

Abbreviations and Acronyms ....................................................................................................... 36

Glossary of Statistical Terms........................................................................................................ 37
v

Illustrations

Figure 1. Railroad Trespass/Grade Crossing Fatalities (1990–2007)............................................ 2

Figure 2. Comparison of Observed vs. Expected Incidents for Top 50 Percent of Incidents...... 17

Figure 3. U.S. Coast Guard National Response Center Trespass Data, 2003–2007.................... 24

Figure 4. CPUC Trespass Data, 2001–2007 ................................................................................ 25
vi

Tables

Table 1. Crossing Characteristics Factors...................................................................................... 5

Table 2. Factors Tested in Accident Prediction Model Regression Analysis................................ 8

Table 3. Factors Tested in Collision Consequence Model Regression Analysis........................... 9

Table 4. Warning Device Type for Public and Private Crossings for Sample ............................ 14

Table 5. Incidents at Highway-Rail Grade Crossing for Sample, 2003–2007............................. 15

Table 6. Risk Results Summary................................................................................................... 16

Table 7. Observed vs. Estimated Frequency of Collisions.......................................................... 17

Table 8. Potential Data Requirements ......................................................................................... 22
1

Executive Summary

In the past 30 years, especially between 1993 and 2003, great strides have been made in 

improving safety at highway-rail grade crossings. Collisions at grade crossings declined 

41 percent and fatalities declined 48 percent. However, in the past 5 years, they have been 

falling at a lower rate. The number of trespass fatalities is now higher than the number of grade 

crossing fatalities. In other words, safety issues associated with both trespass and grade crossing 

incidents still need to be addressed. 

Identifying railroad infrastructure hotspots could determine which crossings in a corridor or 

community require further examination. Hotspots can be defined as highway-rail grade 

crossings or other locations along the railroad right-of-way where collision or trespassing risk is 

unacceptably high and intervention is justified because the potential safety benefits exceed the 

cost of intervention. Hotspots can be separated into grade crossing incident hotspots and trespass 

incident hotspots. 

Two models have been used in this research; the Transport Canada (TC) and U.S. Department of 

Transportation (USDOT) accident prediction model were chosen for analysis. TC model was 

used successfully in Canada and is a best-fit data model that uses negative binomial regression. 

The USDOT accident prediction model was used on the same data sample and the results of the 

two models are compared. The results show that the USDOT model predicted the number of 

accidents closest to the observed number. The TC model overestimated the number of accidents, 

which may be because of certain assumptions that were made about the data. However, for 

crossings that had an accident history, the TC model gave a more accurate prediction of 

incidents. It could theoretically be used in ranking dangerous crossings, thereby helping to 

determine grade crossing warning upgrades.
2

1. Introduction

The John A. Volpe National Transportation Systems Center (Volpe Center) provides technical 

support to the Federal Railroad Administration (FRA) on all aspects of grade crossing research. 

Significant progress has been made in the past 30 years in improving safety at highway-rail 

grade crossings. Collisions at grade crossings have declined 41 percent, and fatalities have 

declined 48 percent between 1993 and 2003. The goal is to continue the downward trend of

grade crossing incidents in spite of limited funding. Another objective is addressing the issue of 

trespass incidents. 

In recent years, there have been approximately 35 percent more trespass fatalities than grade 

crossing fatalities. For the years 2003 through 2006, the annual number of trespass fatalities 

ranged from 458 to 511, while the annual number of grade crossing fatalities ranged from 334 to 

371. Annual trespass fatalities averaged approximately 500 and grade crossing fatalities 

averaged approximately 350. Figure 1 displays the number of trespass and grade crossing 

fatalities per year for the past 17 years [1]. Grade crossing fatalities have steadily decreased over 

the years while trespass fatalities have not. However, it can be seen that grade crossing fatalities

in the past 5 years are not declining at the rate they were in previous years. Trespass and grade 

crossing incidents still need to be addressed.

Figure 1. Railroad Trespass/Grade Crossing Fatalities (1990–2007)

While trying to improve safety, the question of how to allocate limited funds for crossing 

improvements is almost always an issue for local communities or states. Identifying railroad 

infrastructure hotspots could help determine which crossings in a corridor or community require 

further examination. Hotspots can be defined as highway-rail grade crossings or locations along 

0

100

200

300

400

500

600

700

800

199019911992199319941995199619971998199920002001200220032004200520062007

Year

Fatalities

Trespass Fatalities Crossing Fatalities
3

the railroad right-of-way where collisions or trespass risk is unacceptably high and intervention 

is justified because the potential safety benefits exceed the cost of intervention. This research is 

similar to epidemiology studies of disease outbreaks in medicine. In the railroad environment, 

this includes the analysis of highway-rail grade crossing clusters exhibiting a high concentration 

of incidents.

In this document, hotspots are broken down into two categories, grade crossing and trespass 

incidents. The research and analysis varies for each category. For grade crossing incident 

hotspots, the TC model was chosen for analysis. It was used successfully in Canada and is a 

best-fit data model that uses negative binomial regression. It identifies hotspots and hotspot 

clusters based on user-defined thresholds for frequency and consequences. The data 

requirements necessary to use this model in the United States have been researched and will be 

demonstrated on a data sample from California. The USDOT accident prediction model will 

also be used on this data sample and the results of the two models are compared.

For trespass incident hotspots, cluster analysis was deemed a useful theory that could be used in 

developing a model that could predict trespass incident hotspots. Cluster analysis uses negative 

binomial regression and can be normalized for exposure. Currently, trespass data is being 

examined as raw data. It would be beneficial to be able to identify potential hotspots. While 

researching trespass incident hotspots, the Volpe Center began working with the CPUC to 

compare the quality of CPUC trespass and grade crossing incident data with the National 

Response Center, operated by the U.S. Coast Guard (USCG). This is being performed so the 

National Response Center may be used as an alternative data source. During the course of this 

project, the Volpe Center also worked with FRA’s Office of Safety to supply trespass data from 

the National Response Center so it can be mapped on a Geographic Information System (GIS) 

platform. If mapped on a GIS platform, there is great potential for cluster analysis of trespass 

incidents. The data can be stratified to conduct demographic studies. For example, male 

trespass incidents could be examined versus female. There is a great deal of potential for future 

research in this area.
4

2. Models Researched

The idea of using the TC model for grade crossing incident hotspot research and using cluster 

analysis for trespass hotspot research came after a comprehensive literature review was 

performed. The pertinent models examined are listed below.

2.1 USDOT Accident Prediction Model 

According to Hauer and Persaud, the safety of a specific grade crossing is a direct function of the 

expected number of accidents and the resulting severity over some unit of time [2]. In this 

context, Hauer and Persaud define expected as what would be the average, in the long run, if it 

was possible for all relevant conditions to remain unchanged. Also, accident frequency is not 

synonymous with safety, but is instead an indirect measure. In general, two categories of 

variables, causal factors and accident history, are the best descriptors of grade crossing safety. 

Again referencing Hauer, the USDOT rail-highway crossing accident prediction formula, first 

published in 1981, was the first accident prediction model to linearly combine both causal factors 

and accident history.

The first attempt in the development of the USDOT model was to use linear regression analysis. 

Although the results were interesting, they were less than satisfactory as compared with previous 

accident prediction models. The salient findings of Mengert are given below [3]:

Variables associated with train and vehicle movements (traffic moment) total approximately 

90 percent of the predictive force of the regression model.

Linear regressions with more than eight variables produces inferior results due to issues 

related to co-linearity, including misdirected signs of variables.

Linear regression techniques are not necessarily the best for producing accurate accident 

prediction models. The regression techniques studied did not appear to provide any benefit 

over pre-existing modeling techniques.

The key finding in this research was that the grade crossing accident probability curve is 

nonlinear, resembling the hyperbolic tangent function, tanh(x) =

1

1

2

2

x

x

e

e

. This assertion formed 

the basis for the derivation of the USDOT accident prediction formula. To this end, Mengert’s 

approach was to develop an accident prediction model for each warning device class (i.e. 

crossbucks, flashing lights, and gates with flashing lights). The first step of this process was to 

build a regression model using only variables associated with the traffic moment.

1

 The next step 

was to express the model as a third degree polynomial. Finally, variables unassociated with the 

traffic moment, such as the number of main tracks and number of highway lanes, were integrated 

within the model to produce the accident prediction model or the ―comprehensive‖ model. The 

most general form of the model, consisting of only traffic moment related variables, is expressed 

in Equation 2-1 on the next page.

 

1 Traffic moment refers to the product of the average daily rail and average vehicle traffic at a highway-rail crossing.
5

(Eq. 2-1)

Where

ai = the regression coefficients

T = train movements

C = vehicle movements

After some manipulation, the more familiar form of the equation, as published by Farr, is arrived 

at [4].

a K EI DT MS MT HP HL 

(Eq. 2-2)

Where

a = un-normalized 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

DT = number of through trains per day during daylight hours 

MS = maximum railroad timetable speed

MT = number of main tracks

HP = highway paved factor

HL = number of highway lanes

After normalizing for accident history, this is expressed as

( N / T ),T /( . a ) 

T T

T

( a )

T T

T

B o

o o

o

1 0 05

(Eq. 2-3)

Where

To is derived from the basic un-normalized accident prediction formula, a

N/T = the accident history of the grade crossing, expressed as ratio of the number of 

accidents in T years

N = the number of accidents recorded for a crossing in T years

T = the number of years, usually 5, but can be any number

To obtain the normalized value, the predicted value, B, is multiplied by the appropriate 

normalizing constant depending on the type of warning device used (see Table 1 below).

Table 1. Crossing Characteristics Factors

Warning Device Groups Normalizing Constants

Passive 0.6768

Flashing Lights 0.4605

Gates 0.6039

 *Source: 2007 Accident Prediction Formula Normalizing Constants

 http://safetydata.fra.dot.gov

[log ( 1)] log ( 1) log ( 1)

log ( 1) log ( 1) [log ( 1)]

10 10 5

2

10 4

2

10 310 210 0 1

a C a T C

a a T a C a T
6

2.2 Transport Canada Model

2.2.1 Test USDOT Model with Canadian Data

The goal of the research, funded by TC and performed by the University of Waterloo, was to 

develop a risk-based methodology for identifying grade crossing hotspots in Canada [5]. The 

initial focus of this research was to test the applicability of the USDOT accident frequency and 

severity prediction formulas with Canadian grade crossing data. Grade crossings in the TC 

database were classified by warning device type (passive, lights, gates), train speed, and levels of 

traffic exposure. Next, the Chi-Square Goodness-of-Fit Test was used to determine if the 

differences between the predicted and observed collision values were statistically significant. 

For the 1,724 reported grade crossing collisions from 1993 to 2001, a poor goodness-of-fit was 

observed, with the USDOT accident prediction formula overestimating the total by 349 

collisions or approximately 20 percent.

Similarly, the differences between the predicted and observed values using the USDOT severity 

models were found to be statistically significant. As with the accident prediction model, one of 

the main disadvantages of the USDOT severity prediction model is the tendency to overestimate 

the fatality and casualty calculations. TC found grade crossing collisions involving fatalities and 

injuries to be a small subset of the total crossing collisions. As such, they chose to develop a 

combined fatality and injury severity model with an overall collision severity score based on the 

number of fatalities, injuries, and property damage cost.

2.2.2 Grade Crossing Accident Prediction Model

Historically, linear and nonlinear regression analysis techniques have been the preferred means 

for modeling the probability of highway collisions and highway-rail grade crossing collisions. 

However, in the past 20 years, research has focused on the exponential family of discrete-time 

stochastic distributions. These distributions are used to model naturally occurring random 

processes with the following characteristics:

There are n independent repeated trials

The probability of success, p, is constant from trial to trial 

Each trial results in an outcome that may be classified as a success or failure

Each outcome, is described by a random variable

Grade crossing accidents, which are essentially random events, can be described by the 

exponential family of distributions. This is especially true for discrete time intervals, such as 

1 year or 5 years, in which the characteristics of a grade crossing are relatively constant. Two 

discrete-time distributions, the Poisson and negative binomial, are employed specifically to 

describe the distribution of random variables, such as grade crossing collisions, over a given time 

interval. As such, Poisson and negative binomial regression analyses have been employed 

extensively for accident frequency modeling of grade crossings. 
7

The Poisson probability function is expressed as:

P(n; λt) = 

!

( )

n

n

t

it

e

(Eq. 2-4)

Where

P(n; λt) = the probability of n accidents occurring at a grade crossing per unit time t

t = the time interval being studied

 = average number of collisions per unit time

 n = the number of collisions in the time interval t

Now, the expected number of collisions, in time interval t, can be expressed as 

( ) 

i

i Xi

E n e

(Eq. 2-5)

Where 

E(n)

= the expected number of collisions at a grade crossing per unit time t

i

= a vector of unknown regression coefficients that can be estimated by 

 standard maximum likelihood methods.

Xi = grade crossing geometric, spatial/land use, and other relevant attributes 

 that impact accident frequency

A key requirement of the Poisson probability distribution is that the mean and variance are equal 

in value. This means that the closer they are to each other, the better the goodness-of-fit between 

the regression model and the observed data. However, under real world conditions, the variance 

is frequently larger than the mean. This results in a condition known as overdispersion, in which 

the model coefficients are not accurate.

The negative binomial probability distribution, which in its limiting form, converges to the 

Poisson distribution, can be used to relax the constraint of the mean and variance being equal. 

Under this condition, Equation 2-5 can be rewritten as 

( ) 

i

i

i Xi

E n e

(Eq. 2-6)

Where 

i e

is called a Gamma-distributed error term (Lee, Nam, and Park, 2005) [6].

The approach taken by TC was to construct a model using Poisson regression analysis, a 

technique that is widely employed in collision prediction modeling. For the three types of grade 

crossing warning devices—(i) signs, (ii) signs and flashing lights, and (iii) signs, flashing lights, 

and gates—a separate regression analysis model was developed. Regression analysis was used 

on Canadian grade crossing collision data from 1993 to 1996 to develop the models, and data 

from 1997 to 2000 was used to validate them. Of the nine factors in Table 2 that were tested in 
8

the model, a total of five were found to be statistically significant, depending on the model. 

Many of these factors are reflected in the USDOT accident prediction formula as well.

The expressions for each warning device type are shown below.

Table 2. Factors Tested in Accident Prediction Model Regression Analysis

E (mS ) = e

[-5.66 + 0.0128 * TSPD + 0.3791 * ln (EXPO)] (Eq. 2-7)

E (mF ) = e

[-9.1620 + 0.0112 * TSPD + 0.0151 * SW + 0.6103 * ln (EXPO)] (Eq. 2-8)

E (mG ) = e

[-7.2304 + 0.0118 * RSPD + 0.1912 * TN + 0.3526 * ln (EXPO)] (Eq. 2-9)

Where 

TSPD = Maximum train speed in miles per hour (mph)

RSPD = Road speed in kilometers per hour (km/h)

EXPO = Crossing exposure factor, AADT × Number of daily trains

SW = Road surface width in feet 

TN = Number of railroad tracks in both directions

In Equation 2-7, the expression for passive crossings, only train speed and exposure were found 

to be statistically significant. The expression for flashing lights in Equation 2-8 includes an extra 

statistically significant variable, road surface width. As in the USDOT model, train speed is not 

a statistically significant variable at gated grade crossings but road speed is, as shown by 

Equation 2-9. 

As described in the TC report, the Poisson model is constrained such that the mean number of 

collisions is equal to the variance. However, frequently the collision frequency variance exceeds 

the mean, indicating a lack of explanation in the underlying Poisson model. This is known as 

Poisson overdispersion and could result in significant prediction error. The negative binomial 

regression technique reduces the overdispersion effect by relaxing the Poisson model assumption 

of the mean equaling the variance. Empirical Bayesian analysis, which calibrates the Poisson 

model with historical collision data, is another approach to overcoming the overdispersion 

phenomena and has been used in hotspot modeling.

In light of the overdispersion concern, TC tested the three Poisson models using the Scaled 

Deviance and Pearson χ

2

tests, where values close to 1.0 indicate overdispersion is low. 

Although a small amount of overdispersion was measured for each model, it was not considered 

significant. Moreover, the Chi-Square Goodness-of-Fit Test showed good correspondence 

Warning device type Road surface width

Train vehicle speed Traffic exposure (average annual daily traffic

(AADT) × number of daily trains)

Road vehicle speed Road class (arterial/other)

Number of tracks Road pavement condition (paved/unpaved)

Track angle
9

between the modeled and the measured values. The Empirical Bayesian technique was used to 

calibrate the Poisson expressions with 4 years of crossing collision data (1997–2000). However, 

since grade crossing collisions are low probability events, accident history calibration did not 

provide appreciable improvement over the stand-alone Poisson regression modeling for the 4-

year time period. As such, TC selected the stand-alone Poisson model for grade crossing 

collision.

2.2.3 Grade Crossing Severity Prediction

As noted previously, TC developed an overall consequence score that is a weighted sum of 

severity resulting from fatalities, injuries, and property damage, as shown in Equation 2-3. This 

technique not only facilitates grade crossing hotspot identification but also (i) encompasses all 

grade crossing collision data, not just collisions for grade crossings with fatalities and injuries, 

and (ii) addresses co-linearity between fatalities and injuries, which, if not dealt with, results in 

counterintuitive model variables. It works for all crossings because once a collision has 

occurred; the consequences are independent of the warning device. TC defined severity in terms 

of the consequence score below.

CSi = 44 x NFi + 1 x NIi + 1 x PDi (Eq. 2-10)

Where

NFi = Number of fatalities

NIi = Number of injuries

PDi = Property damage

The first attempt at fitting the consequence score involved Poisson regression analysis on 

Canadian grade crossing accident data for 1997–2001. The 826 reported accidents were 

randomly divided into two sets of 413, one for regression modeling and the other for model 

validation. Of the eight factors in Table 3 that were tested in the model, the four independent 

variables in Equation 2-11 were found to be statistically significant. However, the Pearson χ

2

and scaled deviance values showed significant overdispersion, an indication of significant 

prediction error. When TC used negative binomial regression analysis, considerable 

improvement in overdispersion was observed, as indicated by the Pearson χ

2

and scaled deviance 

values being close to 1.0. The resulting model is shown below.

Table 3. Factors Tested in Collision Consequence Model Regression Analysis

E (Cq /C) = e (0.3426 * PI - 0.2262 * TN + 0.0069 * TA + 0.0250 * TSPD) (Eq. 2-11)

Where

E (Cq/C) = Expected consequence/collision

PI = Number of persons involved

Train speed Road surface width

Road speed AADT

Number of tracks Number of trains per day

Track Angle Number of road vehicle occupants
10

TN = Number of railway tracks both directions

TA = Track angle

TSPD = Maximum train speed in mph

2.2.4 Hotspot Identification

The final piece of the TC research constituted development of a system to differentiate between 

hotspot and non-hotspot grade crossings with respect to a specified threshold value. The key was 

to construct a combined risk index that inherently encompassed both the expected frequency and 

consequence of a collision. This metric, expressed in terms of risk per year, results from the 

product of the expected frequency and the collision consequence score in Equation 2-10. By 

defining the risk index in this manner, crossings that are in one of the following two categories, 

1) low expected collision frequency and high collision consequence score, and 2) high expected 

collision frequency and low collision consequence score, could potentially yield a low combined 

risk index. Since resources for risk mitigation treatments are finite, this approach provides a 

mechanism to ―filter out‖ those grade crossings with a low risk index and focus in on the ones 

that have a high risk index.

Intuitively, the threshold value is a user-defined number and is implicitly dependent on a variety 

of subjective factors that may be specific to the grade crossings and treatments being evaluated. 

However, the guiding principle is to determine the level of investment that produces the largest 

decrease in risk for the smallest capital expenditure. One way to find this balance is by 

performing a cost-benefit study of the proposed grade crossing treatments. An alternative 

approach involves defining a low threshold value such that the majority of crossings being 

evaluated satisfy the hotspot criteria. If the hotspot crossings are considered collectively, it is 

possible to calculate a total risk index by summing the risk index of all the individual crossings. 

As such, the benefit associated with a specific treatment can be ascertained from the resulting 

decrease in the total risk index.

2.3 University of North Carolina Pedestrian Crash Risk Model

Schneider developed an approach to pedestrian accident risk using Poisson and negative

binomial regression analysis [7]. The general problem posed by this research, as well as the 

methodology and results show significant parallels to the FRA grade crossing and railroad rightof-way trespass hotspot program. As such, this work has great promise and presents the 

opportunity to leverage the results from another transportation mode as a foundation. 

In his research, Schneider divided the University of North Carolina (UNC) at Chapel Hill 

campus roadway network into 38 intersections and 56 segments. In this approach, intersections 

are the network nodes and the segments are the network links equivalent to lengths of roadway 

between intersections. Each of the 94 total intersections or segments was then classified by 

pedestrian crash-risk, exposure, roadway, and land use attributes. For each segment/intersection 

of the UNC campus roadway network, Schneider used traffic and pedestrian volume maps, field 

observations, and geographic information systems (GIS) measurements to populate a pedestrian 

risk database. This database consisted of three exposure, five roadway, and seven land use 

attributes. Additionally, police reports of car accidents on the UNC campus for the 5-year period 

from October 1994 to September 1999 were used to develop a representation of pedestrian 
11

accident history. During that timeframe, 127 pedestrian accidents were recorded in Chapel Hill, 

of which 57 occurred on the UNC campus, including one fatality in 1999.

Concurrently, Schneider administered a survey to a randomly selected group of UNC students, 

faculty, and employees, including pedestrians and drivers, to gather data on the locations of 

perceived pedestrian and driver risk. They were also asked if they perceived a higher risk for 

being involved in accidents at night versus during the day and whether they had been involved in 

a vehicle-pedestrian or pedestrian-vehicle ―near miss‖ during the past month. In addition to the 

survey, the respondents were requested to fill out two maps. For the first map, they were asked 

to identify the three locations that they perceived to have the highest risk of pedestrian crashes 

during daylight. Also, if they perceived that the risk was different at night, they were requested 

to identify them on the second map. 

Using the data from the police recorded accidents and perceived risky locations, Schneider 

developed a series of pedestrian risk models based on Poisson and negative binomial regression 

analysis. The results were paired by observed risk and perceived risk and are shown below. 

E(mpo) = e

[-13.5 + 0.625β1

+ 0.672β2+0.381β3-0.935β4+0.0940β5-0.159β6+0.0810β7-0.000841β8+0.000996β9+0.000783β10] (Eq. 2-12)

E(mnbo) = e

[-13.5 + 0.625β1

+ 0.667β2+0.383β3-0.932β4+0.101β5-0.158β6+0.0806β7-0.000822β8+0.000973β9+0.000764β10] (Eq. 2-13)

E(mpp) = e

[-19.9 + 1.05β1

+ 1.25β2+1.05β3-0.797β4-1.01β5+0.00750β6+0.090β7+0.000218β8-0.000225β9-0.000636β10] (Eq. 2-14)

E(mnbp) = e

[-14.4 + 0.867β1

+ 0.871β2+0.796β3-0.246β4-0.697β5+0.0159β6+0.0643β7+0.000316β8-0.000348β9-0.000644β10] (Eq. 2-15)

Where 

E(mpo) = the Poisson regression model for police recorded accidents

E(mnbo) = the negative binomial regression model for police recorded accidents

E(mpp) = the Poisson regression model for perceived accident risk

E(mnbp) = the negative binomial regression model for perceived accident risk

β1 = natural logarithm of a segment/intersection in feet

β2 = natural logarithm of the estimated daily pedestrian volume

β3 = natural logarithm of the estimated daily vehicle volume

β4 = 0 if the location is a segment and 1 if the location is within 50 feet of an 

intersection

β5 = 0 if there is no sidewalk, 1 if there is a sidewalk on one side of the street, or 

2 if there are complete sidewalks on both sides of the street

β6 = the number of bus stops per 1,000 linear feet

β7 = the number of marked crosswalks per 1,000 linear feet

β8 = the distance the nearest campus library
12

β9 = the distance to the nearest of over 30 academic buildings

β10 = the distance to the nearest sports stadium

For the observed data, the Poisson model (Eq. 2-12) performed slightly better than the negative 

binomial regression model for two reasons. First, the significance levels of the parameter 

coefficient estimates were better. Second, there was no statistical difference between the mean 

and variance of the police reported incident risk model, indicating a low level of dispersion and a 

better fit for the Poisson model. Conversely, for perceived risk, the binomial regression model of 

Equation 2-15 returned a better goodness-of-fit than the Poisson model, which exhibited a 

statistically significant level of dispersion.

2.4 Summary

Two grade crossing accident risk prediction models were described in this section. In addition, a 

pedestrian crash risk model for highway-highway intersections and highway segments was 

presented. The first of the grade crossing accident risk models, the USDOT accident prediction 

formula, is mathematically rigorous and widely accepted within the grade crossing community. 

However, the applicability of this model for hotspot identification has not yet been validated. 

The TC risk prediction model, which is the foundation for this report, employs the more modern 

techniques of Poisson and negative binomial regression analysis. Although it has not been in 

service for as many years as the USDOT accident model, it has undergone extensive testing 

using Canadian grade crossing data. The methodology used by TC to identify grade crossing

hotspots should apply to the U.S. grade crossing inventory as well and would be the subject of a 

future research endeavor.

The approach employed in the UNC research effort was to describe the entire campus roadway 

network in terms of intersections and road segments. The network accident frequency is then 

expressed using Poisson and binomial distributions. This methodology provides a framework 

with the potential for duplication in the modeling of grade crossing and trespass hotspot 

locations. The parallels between the modeling of roadway network and railroad right-of-way 

hotspots should be explored, so as to leverage the benefits of previously documented research.
13

3. Grade Crossing Incident Hotspots

FRA has had a memorandum of understanding with TC in the past. The TC model has been 

quite successful in Canada and the question was how it would work in the U.S. In comparison to 

the USDOT accident prediction model, the TC model appears to be simpler and easier to modify. 

To compare the two models, a sample of grade crossing incident data from U.S. data also needed 

to be in the same format as the TC model. This data formatting is described below.

3.1 Data Fields and Sources for TC Model

This subsection focuses on the data field requirements that are needed for the TC Model. Like 

the USDOT Model, the TC Model consists of three separate regression expressions, one for each 

of the three warning devices (Type S for Passive, F for Flashing Lights, and G for Gates). These 

three regression expressions are detailed below.

3.1.1 Passive Warning Devices

The accident prediction formula for crossings with passive warning devices is:

E (mS ) = e[-5.66 + 0.0128 * TSPD + 0.3791*ln (EXPO)] (Eq. 3-1)

Where: TSPD = maximum train speed (mph)

EXPO = cross product of AADT and number of trains daily

The three data elements required to run the above expression are easily accessible from the FRA 

National Highway-Rail Crossing Inventory (Crossing Inventory) database. The maximum train 

speed (TSPD) is located in column AI, titled ―MAXTTSPD,‖ and is under the correct variable 

miles per hour. For EXPO, the AADT is located in column CD, titled ―AADT,‖ and the year the 

AADT collected is available in column DO, titled ―AADTYEAR.‖ The number of daily trains is 

located in column EE, titled ―TOTALTRN.‖

3.1.2 Flashing Lights Warning Devices

The accident prediction formula for crossings with flashing light warning devices is:

E (mF ) = e[-9.1620 + 0.0112 * TSPD + 0.0151 * SW + 0.6103 * ln (EXPO)] (Eq. 3-2)

Where: TSPD = maximum train speed (mph)

SW = surface width (feet (ft))

EXPO = cross product of AADT and number of trains daily

The locations of the TSPD and EXPO variables are mentioned in the above paragraph. The 

surface width (SW) is not available in the FRA Crossing Inventory database, but the database 

provides the number of traffic lanes crossing the railroad tracks. The number of traffic lanes 

crossing the railroad tracks is located in column BY, titled ―TRAFICLN.‖ The assumption was 

made that the average width of a traffic lane would be 12 feet; therefore, the number of traffic 
14

lanes crossing the railroad tracks was multiplied by 12 feet to get the surface width of each 

crossing.

3.1.3 Gated Warning Devices

The accident prediction formula for crossings with gated warning devices is:

E (mG ) = e[-7.2304 +0.0118 * RSPD + 0.1912 * TN + 0.3526 * ln (EXPO)] (Eq. 3-3)

Where: RSPD = road speed (kilometers per hour (km/h))

TN = number of railway tracks (both directions)

EXPO = cross product of AADT and number of trains daily

The locations of the EXPO variables are mentioned in the above paragraphs. The number of 

railroad tracks (TN) is not easily accessible from the FRA Crossing Inventory. They are 

organized into main track, other track, and SEPIND. SEPIND is the number of tracks another 

railroad operates. To obtain the number of railroad tracks (TN), the sum of the following fields 

is taken: number of main track, other track and SEPIND. The RSPD variable is located in 

column EQ, titled ―HWYSPEED.‖ The value is in miles per hour, but the accident prediction 

formula for crossings with gated warning devices in the TC model calls for this value in km/h. 

To convert to kilometers per hour, the mph value is multiplied by 1.609344. (If the RSPD of the 

crossings has a value of 0 mph, an assumption of 25 mph or 40.23 km/h was made.)

3.2 Application of Models on a Sample from California

The crossings on the San Joaquin high-speed rail corridor that run from Port Chicago to 

Bakersfield were used to test the TC accident prediction model and to determine whether it is 

more reliable or accurate than the USDOT accident prediction model. The San Joaquin corridor 

was chosen over other corridors (namely the three corridors that the Volpe Center has researched 

in the past: North Carolina sealed corridor, Chicago-St. Louis high-speed corridor, and San 

Joaquin corridor) because it encompasses crossings with a greater variety of warning devices 

present. Also, other methods were used to validate crossing inventory data along this corridor in 

the previous Volpe study.

The portion of the San Joaquin corridor used in this study consists of 229 public at-grade and 36 

private at-grade crossings with the total of 265 at-grade crossings to be analyzed in this report. 

These crossings are broken down by warning device type in Table 4.

Table 4. Warning Device Type for Public and Private Crossings for Sample

Gates Flashing Lights Passive Total

Public 

Crossings 220 6 3 229

Private 

Crossings 1 1 34 36
15

3.3 Results

3.3.1 Crossing Incident History

Five-year incident data from 2003 to 2007 was compiled from the FRA RAIRS database for the 

265 at-grade crossings on the San Joaquin corridor to validate and compare the USDOT accident 

prediction model and the TC accident prediction model. There were a total of 77 incidents for 

the 5-year period with an average of 15.4 incidents per year along the corridor. Of the 265 atgrade crossings along the corridor, only 56 crossings had at least one incident during the 5-year 

period. Table 5 below summarizes the incidents at those crossings by year.

Table 5. Incidents at Highway-Rail Grade Crossing for Sample, 2003–2007

Public Crossing Private Crossing Total

2003 13 1 14

2004 20 1 21

2005 10 0 10

2006 15 1 16

2007 15 1 16

Total 73 4 77

3.3.2 Risk Calculation of the Models

USDOT Accident Prediction Model

The USDOT accident prediction model was applied to the 265 highway-rail grade crossings to 

calculate the expected number of incidents at each crossing. The expected number of accidents 

was generated by retrieving data fields from the FRA Crossing Inventory database. Of the 265 

crossings, 34 crossings lacked required data to calculate the expected number of accidents. On 

average, there were 14.22 predicted accidents per year for the 231 crossings that were reviewed 

along the corridor.

TC Model

The appropriate expressions were applied to the 265 at-grade crossings located along the corridor 

to calculate the expected number of incidents. The majority of variables required to calculate the 

expected number of incidents was obtained from the FRA Crossing Inventory database. 

However, some of the variables were not easily accessible so certain assumptions were made. 

Section 3.1 Data Fields and Sources for TC Model above described those assumptions. Of the 

265 at-grade crossings, five crossings lacked required data to calculate the expected number of 

accidents. For the 260 crossings that were used in the calculation, on average there were 26.07

predicted accidents per year. 

3.3.3 Comparison of the Models

The USDOT accident prediction model and the TC model were compared against observed 

accidents to determine the reliability and accuracy of each model. Three different methods were 

used for this comparison. First, overall raw incident data was examined. This was the observed 

average number of incidents for 265 grade crossings, taken from the RAIRS database over 

5 years, 2003–2007. Next, for each individual crossing, incident data for the same years, 
16

2003–2007, was reviewed. And last, the Chi-Square Goodness-of-Fit Test was used to compare 

the accuracy of the models. 

First, the raw accident data was examined to determine which model predicted the number of 

incidents closest to the observed number that is in the FRA Railroad Accident Incident Reporting 

System – Highway-Rail Grade Crossing (RAIRS) database for the period 2003–2007. To 

compare the predicted number of incidents from the USDOT model and the TC model, the same 

number of crossings needed to be used. For this analysis, it was 229 crossings; the original 265 

crossings could not be used because of missing data that would be required to calculate the risk. 

The USDOT model predicted 14.22 incidents per year; the TC model predicted 23.01 incidents 

per year. The average number of incidents per year for the entire 265 crossings on the corridor, 

based on the observed data, was 15.40. However, when only the same 229 crossings used in the 

models were included, the average number of annual incidents decreased to 14.80. The results 

are summarized in Table 6 below.

Based on this comparison, the TC Model was found to overestimate the average number of 

incidents per year along the San Joaquin corridor. Some of the assumptions that were made in 

the calculations for the TC model (see Section 3.1 Data Fields and Sources for TC Model) may 

have led to this overestimation. Also, the observed data values showed that the 36 crossings with 

the missing data fields contained a small amount of risk, as indicated by the smaller risk value 

for the 229 crossing data set. Other than bringing the USDOT model into closer alignment with 

the observed data, this did not significantly impact the inferences drawn from this analysis.

Table 6. Risk Results Summary

Model Number of 

Crossings 

Analyzed

Average Annual 

Incidents

Observed 265 15.40*

Observed 229 14.80*

Expected (TC 

Model) 229 23.01

Expected (USDOT 

Model) 229 14.22

*Average of actual incidents over the 5 years

Next, individual crossings were examined to determine how well the models predicted incidents 

for each crossing compared with the observed number of incidents. The top 50 percent of 

observed incidents at each crossing was plotted with expected number of incidents from each 

model. As can be seen from Figure 2 on the next page, the predicted number of incidents from 

the TC model is a closer fit to the observed number of incidents than that of the USDOT model. 

For crossings that had an incident history, the TC model gave a more accurate prediction of 

incidents. However, both models employ historical national data to predict accidents at specific 

crossings. Although they are accurate on the national level, resolution may be influenced by 

local variations when examining a relatively small number of grade crossings or a specific 

corridor.

1TS86ZRM
17

Figure 2. Comparison of Observed vs. Expected Incidents for Top 50 Percent of Incidents

Last, the χ

2 was used to determine which model was a better fit to the observed data. To 

calculate the χ

2

, each model was evaluated by comparing the observed frequency of collisions at 

grade crossings versus estimated frequency. Initially, the number of collisions was divided into 

six bins, but since each bin should have at least five crossings with collisions greater than zero 

for expected frequency, the bins were combined and reduced to two bins [8]. The observed 

frequency is the number of crossings with the average collision per year for the 5-year time 

period from 2003 to 2007 for that bin. The estimated frequency is the number of crossings with 

collisions that were calculated using the TC or USDOT model. Table 7 presents the observed 

and estimated collision frequencies and their chi-square value.

Table 7. Observed vs. Estimated Frequency of Collisions

Number of 

Collisions (y)

Observed 

Frequency of 

Crossings with y

Estimated Frequency of Crossings 

with y Collisions

χ

2

for TC 

Model

χ

2

for U.S.

Model

USDOT 

Model TC Model

0–0.1 175 198 147 2.672 5.333

>0.1 54 31 82 17.065 9.561

Total 229 229 229 19.736 14.894

The χ

2 was calculated using the following expression:

Expected

Observed Expected X

2

2

( )

 (Eq. 3-4)

A low chi-square value (not statistically significant) suggests a good match between observed 

and expected results. For either model to be a good fit, the calculated χ

2

value should be less 

than the critical value at a 5 percent level. Using the critical value for a chi-square distribution 

Comparision of Observed v/s Expected

0

0.2

0.4

0.6

0.8

1

1.2

028380R028395F028755B028381X028349E

028379W

028454F028705X028409L028527N028517H028582N028673U

028512Y

028324J

029645U

028386G

028627T028325R

028403V

028708T028452S

Crossing ID

# of Accident per Year

Observed US DOT Expected TC Expected
18

table with v = 1 degree of freedom, the critical value was found to be χ

2

0.05 = 3.841. Since both 

of the models’ χ

2

values are greater than 3.841, there is insufficient information to determine 

which model is a better fit to the observed collisions. For the 0–0.1 category, the TC model 

performed better than the USDOT model. But for y > 0.1, the USDOT model performed better 

than the TC model. 

Overall, both models yielded poor χ

2

results. This could be because grade crossing collisions are 

rare events, and as such, large sample sizes are needed to achieve a reasonable population 

distribution to obtain more accurate results. 

3.4 Summary

Based on the above test cases, the TC model performed better on crossings with some accident 

history. It is also easier to use, in terms of manipulation, and risk calculation, than the USDOT 

model. The TC model may be a better model in assessing risk to rank dangerous crossings so 

that in a cost benefit analysis, the appropriate warning upgrades could be selected. However, this 

finding is based on using the San Joaquin corridor as a sample and a bigger sample would be 

needed to support this result. The USDOT accident prediction model still performed better when 

all crossings in the sample were analyzed.
19

4. Trespass Incident Hotspots

4.1 Cluster Analysis

Cluster analysis is a way of grouping similar objects into respective categories. The goal is to 

organize observed data into meaningful structures. Cluster analysis is primarily used when there 

is no theory to explain the facts. It can be used to discover data structures without providing 

explanations. Cluster analysis is an exploratory data analysis tool that sorts different objects into 

groups such that the degree of association between two objects is maximal if they belong to the 

same group and minimal otherwise.

In the railroad environment, cluster analysis could be used to identify hotspots. The concept is 

similar to epidemiology studies of disease outbreaks in medicine. If trespass incidents were 

plotted on rail lines and grade crossings, it would be easier to see where there was an 

unacceptable concentration or cluster of incidents. Furthermore, demographic studies could be 

performed. For example, compare the hotspots for men versus women; hotspots for teenagers; or 

urban versus rural. This information could be useful for communities in targeting safety efforts 

to certain social groups.

4.2 Spatial Testing

Schneider used four different quantitative techniques to evaluate the spatial distribution of 

police-reported pedestrian crash locations on the UNC Chapel Hill campus [7]. The first two 

techniques fall under the category of intra-distributional spatial testing, meaning that tests are 

performed on each spatial distribution separately. The latter two, known as inter-distributional 

tests, are designed to examine the relationship between points in a specific distribution. 

The first of the intra-distributional tests, Ripley’s K-statistic, is used to measure clustering by 

comparing the number of points within a radius, ds, to the expected number for a spatially 

random distribution. If the sum of the number of points within ds around each point is greater 

than expected for a random pattern with the same radius, then the points have a tendency to be 

clustered. The K-statistic is expressed as

( ) ( ) 2

p

p q

i iq

s I di i

N

A

K d

(Eq. 4-1)

Where

A = the total study area in square feet

N = the incident sample size

ip = the location of the specific incident under study

iq = nearby incidents of the same type

I(dipiq)

= the number of events, iq, within distance ds of each event ip, summed for 

all events, ip. 

From the above formula, the physical meaning of the K-statistic is not readily apparent. To 

overcome this challenge, Schneider illustrates a transformation of the K-statistic into what is 

known as an L-function, as shown below.
20

 

( )

( )

s

s

s d

K d

L d

(Eq. 4-2)

In this form, values of L(ds) greater than zero show a propensity for clustering, whereas values 

less than zero demonstrate randomness for the radius of distance ds.

The other intra-distributional test, nearest neighbor analysis, is a tool for discerning locations 

within a spatial distribution that are nearer to each other than what would be found in a random 

distribution. This phenomenon, known as clustering, occurs when the mean random distance 

between the points is less than the minimum random distance calculated from the standard error 

of a random distribution. This is illustrated in Equation 4-3, below.

 

0.26136 0.5

2

min

A

N

t

N

A

D

(Eq. 4-3)

Where

A = the total study area in square feet

N = the incident sample size

t = the probability level in the Student’s t-distribution

A

N

2

0.26136

= the standard error distance of a random distribution

Inter-distributional testing is used to determine the relationship between points in a distribution. 

The first test, the widely known chi-square test, can be employed to ascertain if the actual and 

expected incident locations have a similar spatial distribution. This involves dividing the 

railroad right-of-way into grade crossings and the segments between them and classifying them 

according to the number of reported incidents. Here, a segment is defined as the distance 

between two grade crossings. This can be expressed as:

n

i O

O - O

e

r e

i

i i

1

 

2

2 ( )

(Eq. 4-4)

Where

i = the number of reported incidents on a segment (0, 1, 2, 3...)

Oi = the number of grade crossings/segments with 0, 1, 2, 3, etc., incidents

Oie

= the expected number grade crossings/segments with 0, 1, 2, 3, etc., incidents
21

The second Inter-distributional testing technique is the G-function, which is a measure of the 

fraction of a specific incident within any given distance of another specific incident type. This 

function is shown in Equation 4-5 below:

 

#( )

( )

^

n

w w

G w

i

(Eq. 4-5)

Where

# = ―number of incidents‖

wi = the distance from a reported incident, r1, to the nearest reported incident, r2

w = a radius around all reported incidents in the study area

n = the total number of incidents in the study area

By plotting the cumulative probability distribution (CDF) of 

( )

^

G w

against x, the distance 

between two nearby events, it is possible to determine if the two events are clustered. If the CDF 

increases at a high rate for low values of x and then levels off, then the two points are considered 

to be clustered. Likewise, if the distribution function is flat for low values of x and then 

accelerates, the events are not clustered and therefore random.

4.3 Data Requirements

This research seeks to determine if areas of high trespass activity are indeed clustered into 

hotspots or if they are random. This requires resolving the exact geographical coordinates of 

trespass incidents on a segment of the railroad right of way, as well as the length and endpoint 

locations of the segment. FRA does not maintain the location of trespass incidents at a 

resolution less than the county level. Other sources, such as the USCG National Response 

Center and State and local governments like the California Public Utilities Commission (CPUC) 

do maintain this information. However, the key is obtaining and verifying the quality of the 

data. The next step after spatial analysis is to develop a regression analysis model of the 

expected pedestrian risk locations. Theoretically, the results of one analysis should validate the 

other and a test of this nature would be a good indicator of the legitimacy of the regression 

analysis modeling.

Two potential sources of geographical coordinates of segments are U.S. Class I railroads, which 

maintain detailed track charts of their entire infrastructure, and, as above, State and local 

government agencies. Also, the FRA Office of Safety is in the process of developing a highly 

detailed map of the entire U.S. railroad network in GIS format. 

Table 8 illustrates a potential list of data requirements for regression modeling development. 

This list is by no means final, but it represents the types of variables that could be used in the 

regression modeling. As indicated, some of the data is available from the FRA Rail Accident 

and Incident Reporting System (RAIRS) database. However, many are not and will have to be 

obtained from multiple disparate sources, including the ones discussed previously in this section. 

For the spatial/land use category, potential data sources include the U.S. Census Bureau, which 

maintains detailed databases that can be exported.
22

Table 8. Potential Data Requirements

Dependent Exposure Rail ROW Spatial/Land Use

Number of Trespass 

Incidents/Segment Segment length

Number of main 

tracks (RAIRS)

Distance from 

crossing, bridge, 

highway, etc.

Estimated 

trespassers/day

Number of switch 

tracks (RAIRS)

Distance from park, 

beach, etc.

Estimated trains/day

Maximum 

Timetable speed 

(RAIRS

Urban or Rural 

Location (population 

density)

Mix of freight/ 

passenger trains 

(RAIRS)

Mainline or 

Switchyard 

(RAIRS)

Industrial or 

Residential

Number of switch 

trains/day (RAIRS)

4.4 Collaboration with the FRA Office of Safety

In March 2008, the Volpe Center established ties with FRA’s Office of Safety, which is 

independently investigating approaches that target trespass hotspot locations for enforcement 

and/or education. The intent is to effectively coordinate and share research by leveraging the 

strengths of both organizations and in the process ensure that duplication of effort is minimized. 

This effort involves two tasks, as described below.

1. The objective of this task was twofold. The first was to verify the benefits offered by 

current GIS software tools by mapping USCG National Response Center generated data 

onto the FRA GIS rail network described in Section 4.3. The second was to compare the 

quality of the two databases, identify areas of disagreement, and seek a resolution.

By means of the USCG National Response Center online database, the Volpe Center 

created a database of trespass incidents for California for the years 2003–2007. The 

USCG National Response Center database contains many of the same data fields as the 

FRA RAIRS. Although the USCG database is incomplete, it does contain some data 

fields not found in RAIRS, such as latitude/longitude, railroad subdivision name, 

milepost number, and sometimes the nearest station. This information is critical to 

geolocating the exact position of a trespass incident. In the future, it is anticipated that 

the latitude and longitude data will be added to the RAIRS database. 

For the years 2003–2007, the Volpe Center built a database of trespass incidents in 

California from USCG National Response Center master database. The California data 

contains 477 trespass incidents, as compared with the 686 trespass incidents found in 

RAIRS for the same years. After receiving guidance from FRA, the Volpe Center 

reworked the USCG National Response Center data structure into a format that was 

compliant with the FRA GIS rail network, and transmitted it to the FRA Office of Safety 

in May 2008. In September 2008, FRA transmitted the California trespass data back to 
23

the Volpe Center mapped onto the FRA GIS rail network. This map is shown in 

Figure 3.

2. The second task involved comparing the quality of CPUC trespass incident data 

against USCG National Response Center data between 2001 and 2007. In July 2008, the 

FRA Office of Safety transmitted the CPUC trespass to the Volpe Center. This map is 

shown in Figure 4.
24

Figure 3. USCG National Response Center Trespass Data, 2003–2007
25

Figure 4. CPUC Trespass Data, 2001–2007
26

4.5 Summary

Spatial analysis testing of pedestrian-vehicle accidents in the highway domain has yielded 

beneficial results and shows promise for the identification of railroad trespass hotspots. This 

approach will improve the ability of researchers to discern hotspot clusters from random events. 

As with much of this research, the quality of the railroad trespass incident data will have 

significant impact on the validity of the test results. This holds true for the regression model 

development as well. As this is new research, the complete set of hotspot data requirements and 

potential data sources have not been thoroughly identified. The Volpe Center has begun to 

explore how this information can be gathered from other sources, including the U.S. Census 

Bureau, the USCG National Response Center, and the CPUC. To this end, the Volpe Center is 

collaborating with FRA’s Office of Safety to rationalize the information in these sources.
27

5. Conclusions

Preliminary results of testing the TC accident prediction model with U.S. grade crossing 

inventory and accident history data have been positive. In the sample used, the San Joaquin

corridor, the TC model gave a more accurate accident prediction for crossings with accident 

history. But looking at all crossings, including those with no accident history, the USDOT 

model gave a more accurate accident forecast than the TC model. However, it should be noted 

that the TC model may not have performed well when looking at all crossings because of some

of the assumptions that were made in this project’s adaptation of the TC model to U.S. data. 

Some of the data that was used in Canada for the TC model is not available in the U.S. crossing 

inventory database. These assumptions may have led to overestimation.

Further analysis is still required to assess the portability of the TC model in its present form to 

U.S. data. Theoretically, there should be little distinction between U.S. and Canadian data since 

the railroads and highways in both countries are constructed using North American design 

standards and practices. However, differences in the distribution of grade crossing inventory and 

accident history data could yield disparate accident prediction and consequence models for each 

country. Ideally, the TC model approach should be duplicated with U.S. grade crossing 

inventory data and accident history to develop U.S. specific models. As mentioned earlier, the 

TC model makes it easier to manipulate, run, and calculate the risks than the USDOT grade 

crossing model. This could make it more desirable for states or communities looking to 

customize the model to fit their environments. It could be used in assessing risk to rank 

dangerous crossings. Then, in a cost benefit analysis, the appropriate warning upgrades could be 

selected. 

Currently, FRA is mapping trespass incidents from raw data. Although this is valuable, a 

prediction model would have even more benefits. The development of a trespass hotspot 

prediction model would allow researchers to evaluate the impact of various treatment options 

prior to actually implementing them. Also, if the hotspots could be mapped on a GIS platform, 

more possibilities for trespass incident predictions could exist. More detailed demographic 

studies could also be performed.

However, the randomness of many trespass incidents may preclude the feasibility of constructing 

a reliable prediction model. Spatial analysis, which has shown promise in the evaluation of 

vehicle collisions with pedestrians, is a viable alternative approach and is worth pursuing.

As with most research, finding good data sources can be difficult, but they are the key to a good 

analysis. In its current state, the FRA trespass incident database does not provide the necessary 

location resolution to perform spatial analysis. This information may reside in other databases, 

including the USCG National Response Center and those maintained by individual State and 

local agencies. It is important to reconcile the structural and reporting differences among 

disparate databases, as well as validate the quality of the data sources. This is imperative to 

obtaining a good sample size. 
28

Another lesson learned is that by looking at research literature for different modes, it is possible 

to come up with potential tools that can be applied in the railroad environment. The theory of 

cluster analysis was found by looking at work involving the study of pedestrian incidents 

involving cars on a university campus. The UNC research broke the campus roadway network 

into intersections and road segments, but the same theory could be applied to trespass incidents 

in the railroad infrastructure. 
29

6. References

[1] da Silva, M. (2008). Railroad Infrastructure Trespass Detection Performance Guidelines. 

Report No. DOT/FRA/ORD-11/01. Washington, DC: Federal Railroad Administration.

[2] Hauer, E., and Persaud, B. N. (1987). How to Estimate the Safety of Rail-Highway Grade 

Crossings and the Safety Effects of Warning Devices. Transportation Research Record 1114, pp. 

131 – 140. National Research Council, Washington, DC.

[3] Mengert, P. (1979). Rail-Highway Crossing Hazard Prediction Results. Washington DC: 

Federal Railroad Administration, U.S. Department of Transportation. 

[4] Farr, E. H. (1987). Summary of the DOT Rail-Highway Crossing Resource Allocation 

Procedure-Revised. Washington DC: Federal Railroad Administration, U.S. Department of 

Transportation.

[5] Saccomanno, F., Fu, L., Ren, C., and Miranda, L. (2003). Identifying Highway-Railway 

Grade Crossing Black Spots: Phase 1. Montreal, Canada: Transport Canada Development 

Centre.

[6] Lee, J., Nam, D., and Park, D. (2005). Analyzing the Relationship between Grade Crossing 

Elements and Accidents. Journal of the Eastern Asia Society for Transportation Studies. 

Volume 6, pp.3658-3668.

[7] Schneider, R.J. (2001). Development of a Proactive Approach to Pedestrian Safety 

Planning. Chapel Hill, NC: University of North Carolina.

[8] Walpole, R., and Myers, R. (1989). Probability and Statistics for Engineers and Scientists, 

Fourth Edition. New York, New York: Macmillan Publishing Company.
30

Appendix A

Observed versus Estimated Accident
31

CROSSING

Observed 

Average 

per Year 

(5 Yrs)

PRED 

ACCID/YR

TC 

Model 

Results

028380R 1 0.03402 0.09363

028395F 0.6 0.05483 0.11952

028755B 0.6 0.07027 0.10437

028381X 0.6 0.07444 0.1354

028349E 0.4 0.02591 0.03823

028379W 0.4 0.02608 0.03182

028454F 0.4 0.06117 0.10802

028705X 0.4 0.06423 0.0913

028409L 0.4 0.06686 0.09932

028527N 0.4 0.06809 0.10348

028517H 0.4 0.08295 0.13111

028582N 0.4 0.09711 0.15679

028673U 0.4 0.14032 0.17011

028512Y 0.4 0.14682 0.22388

028324J 0.2 0.01412 0.01539

029645U 0.2 0.02153 0.08345

028386G 0.2 0.02968 0.06656

028627T 0.2 0.03098 0.05724

028325R 0.2 0.03132 0.04842

028403V 0.2 0.03501 0.05482

028708T 0.2 0.03619 0.04712

028452S 0.2 0.0384 0.05107

028310B 0.2 0.03889 0.07174

028459P 0.2 0.04018 0.05392

029697L 0.2 0.04084 0.07283

028623R 0.2 0.04412 0.06091

028687C 0.2 0.04835 0.06668

028591M 0.2 0.04919 0.0694

028343N 0.2 0.05121 0.08647

028456U 0.2 0.0539 0.07668

028556Y 0.2 0.05865 0.08568

028397U 0.2 0.06558 0.09699

028767V 0.2 0.06757 0.14222

028707L 0.2 0.06762 0.09967

029604P 0.2 0.06953 0.11095

028478U 0.2 0.0803 0.12363

028688J 0.2 0.08093 0.14968

029608S 0.2 0.08366 0.09854

028739S 0.2 0.08477 0.13068

028732U 0.2 0.08527 0.1316

028583V 0.2 0.08559 0.13476

028780J 0.2 0.08572 0.15892

028752F 0.2 0.0865 0.13389

028682T 0.2 0.09019 0.14076

028410F 0.2 0.10544 0.12199

028585J 0.2 0.10863 0.17933

028528V 0.2 0.1116 0.18708

029654T 0.2 0.11321 0.1416

028706E 0.2 0.11946 0.14026

CROSSING

Observed 

Average 

per Year 

(5 Yrs)

PRED 

ACCID/YR

TC 

Model 

Results

028781R 0.2 0.13212 0.18989

028580A 0.2 0.14331 0.17785

028427J 0.2 0.16047 0.20179

028573P 0.2 0.19654 0.18478

029598N 0.2 0.19804 0.25511

028328L 0 0.01591 0.01776

028787G 0 0.02117 0.02456

029638J 0 0.0213 0.02661

028632P 0 0.02218 0.02671

028633W 0 0.02244 0.02708

028789V 0 0.02304 0.02719

028645R 0 0.02317 0.02814

028326X 0 0.02331 0.02808

029637C 0 0.02441 0.03132

028635K 0 0.02454 0.03015

029770G 0 0.02462 0.03165

028327E 0 0.02554 0.03132

028383L 0 0.02554 0.03132

028709A 0 0.0266 0.03258

028728E 0 0.02716 0.0396

028631H 0 0.02723 0.03416

028660T 0 0.02738 0.03373

028308A 0 0.0278 0.03467

028440X 0 0.0278 0.03467

028322V 0 0.0278 0.04197

028385A 0 0.0278 0.04197

028453Y 0 0.0278 0.04197

028401G 0 0.02795 0.04185

028791W 0 0.02798 0.04154

028753M 0 0.02809 0.03478

028674B 0 0.0282 0.03494

028671F 0 0.0282 0.04231

028670Y 0 0.0282 0.05122

028716K 0 0.02847 0.03534

028316S 0 0.02859 0.03585

028643C 0 0.02974 0.03796

028321N 0 0.03003 0.03803

028348X 0 0.03011 0.0378

028384T 0 0.03101 0.04785

028438W 0 0.03132 0.04

028455M 0 0.03132 0.04

028441E 0 0.03132 0.04842

028638F 0 0.03137 0.07182

028309G 0 0.03187 0.05335

028344V 0 0.03325 0.04257
32

028639M 0 0.03366 0.05332

028637Y 0 0.03373 0.06472

028408E 0 0.03377 0.0525

028595P 0 0.03411 0.05417

CROSSING

Observed 

Average per 

Year (5 

Yrs)

PRED 

ACCID/YR

TC 

Model 

Results

028431Y 0 0.0344 0.04478

028784L 0 0.03511 0.04503

028461R 0 0.03524 0.04607

028782X 0 0.03577 0.04604

028434U 0 0.0364 0.04789

028457B 0 0.0364 0.04789

028433M 0 0.03667 0.04832

029685S 0 0.03709 0.07582

029768F 0 0.03787 0.05303

028717S 0 0.03795 0.04989

028783E 0 0.03834 0.05003

028786A 0 0.03838 0.0501

028317Y 0 0.0384 0.06183

029693J 0 0.03847 0.06544

029639R 0 0.03887 0.05541

028788N 0 0.03889 0.0509

028315K 0 0.04035 0.05419

028329T 0 0.04068 0.05473

028640G 0 0.04084 0.09452

028734H 0 0.04086 0.0545

028715D 0 0.04102 0.06628

028306L 0 0.04148 0.05602

029573T 0 0.04153 0.06667

028662G 0 0.04175 0.05592

028437P 0 0.04179 0.08285

028721G 0 0.04233 0.05685

029698T 0 0.0428 0.06221

028628A 0 0.04315 0.07181

028458H 0 0.04327 0.05892

028598K 0 0.04366 0.06015

028618U 0 0.04366 0.06015

028607G 0 0.04366 0.08817

028775M 0 0.04396 0.05796

028730F 0 0.04396 0.05949

029677A 0 0.04454 0.07799

028393S 0 0.04463 0.06115

028446N 0 0.04463 0.06115

028442L 0 0.04463 0.07403

029641S 0 0.04469 0.06617

028406R 0 0.04545 0.09076

028445G 0 0.04709 0.06521

028334P 0 0.04766 0.08009

028439D 0 0.04821 0.06707

028449J 0 0.04821 0.06707

028341A 0 0.04835 0.11833

028714W 0 0.04879 0.06741

028300V 0 0.05028 0.08541

028570U 0 0.05035 0.08639

028650M 0 0.05048 0.10493

CROSSING

Observed 

Average per 

Year (5 

Yrs)

PRED 

ACCID/YR

TC 

Model 

Results

028726R 0 0.05059 0.10318

028736W 0 0.05135 0.10504

029614V 0 0.05136 0.07717

029616J 0 0.05136 0.07717

028606A 0 0.05211 0.09002

028367C 0 0.05211 0.08839

028785T 0 0.05257 0.07305

028330M 0 0.05262 0.07449

028656D 0 0.0531 0.07606

028689R 0 0.05312 0.07464

028624X 0 0.05354 0.07681

028647E 0 0.05391 0.11351

028626L 0 0.05456 0.11517

028733B 0 0.05463 0.07719

028302J 0 0.05472 0.2977

029651X 0 0.05553 0.08475

028422A 0 0.05583 0.07922

029607K 0 0.05589 0.08541

029603H 0 0.05589 0.15157

028462X 0 0.05595 0.08017

028430S 0 0.05643 0.08105

028779P 0 0.05701 0.11801

028400A 0 0.05706 0.09846

028686V 0 0.05815 0.08318

028710U 0 0.05858 0.08392

028675H 0 0.05899 0.08462

028392K 0 0.05912 0.18401

028778H 0 0.0592 0.08281

028744N 0 0.05934 0.18495

029613N 0 0.05968 0.0924

028619B 0 0.0602 0.0884

028394Y 0 0.06042 0.13807

028729L 0 0.06129 0.0886

029647H 0 0.06215 0.097

029650R 0 0.06223 0.09715

028416W 0 0.06256 0.09079

028432F 0 0.06299 0.11083

028323C 0 0.06369 0.09365

029660W 0 0.06398 0.12037

028719F 0 0.06536 0.09569

028724C 0 0.06557 0.09605

028773Y 0 0.0657 0.09383

029643F 0 0.06677 0.183

029649W 0 0.0668 0.12803
33

029599V 0 0.06721 0.11887

028748R 0 0.06779 0.09998

028601R 0 0.07076 0.10728

028376B 0 0.07097 0.10467

028725J 0 0.07186 0.15714

CROSSING

Observed 

Average 

per Year 

(5 Yrs)

PRED 

ACCID/YR

TC 

Model 

Results

028735P 0 0.07294 0.15999

028464L 0 0.07353 0.16305

028337K 0 0.07459 0.1429

028391D 0 0.07477 0.11836

028578Y 0 0.07648 0.11777

028423G 0 0.08076 0.12456

028390W 0 0.08191 0.13203

028620V 0 0.08207 0.12816

029606D 0 0.08754 0.10404

028429X 0 0.09028 0.14235

029578C 0 0.09733 0.15437

028746C 0 0.09968 0.15869

028704R 0 0.09985 0.13698

029617R 0 0.09999 0.12202

028584C 0 0.10182 0.16595

028428R 0 0.10873 0.17788

028424N 0 0.10873 0.17789

029732X 0 0.11365 0.20645

029773C 0 0.12163 0.40776

028743G 0 0.12237 0.24813

028273B 0 0.13336 0.16165

028551P 0 0.13796 0.16992

028569A 0 0.13917 0.17171

028553D 0 0.13967 0.14546

028574W 0 0.14544 0.18103

028558M 0 0.14735 0.18387

028425V 0 0.14792 0.18302

028672M 0 0.15188 0.22646

028577S 0 0.15251 0.19162

028669E 0 0.15686 0.23538

028549N 0 0.17131 0.22248

028539H 0 0.20046 0.32518

028667R 0 0.25551 1.14501

029766S 0.4 #VALUE! 0.11346

028745V 0.2 #VALUE! 0.12724

029709D 0 #VALUE! 0.01702

028653H 0 #VALUE! 0.04472

029115E 0 #VALUE! 0.0597

029656G 0 #VALUE! 0.0597

028594H 0 #VALUE! 0.07268

028596W 0 #VALUE! 0.07268

028597D 0 #VALUE! 0.07268

028605T 0 #VALUE! 0.07268

028629G 0 #VALUE! 0.07268

028636S 0 #VALUE! 0.07268

028655W 0 #VALUE! 0.07268

028718Y 0 #VALUE! 0.07523

028737D 0 #VALUE! 0.07523

028738K 0 #VALUE! 0.07523

CROSSING

Observed 

Average 

per Year 

(5 Yrs)

PRED 

ACCID/YR

TC 

Model 

Results

028364G 0 #VALUE! 0.07584

028795Y 0 #VALUE! 0.07645

028774F 0 #VALUE! 0.07881

029122P 0 #VALUE! 0.11219

029096C 0 #VALUE! 0.11346

029743K 0 #VALUE! 0.11346

029767Y 0 #VALUE! 0.11346

028622J 0 #VALUE! 0.12293

028652B 0 #VALUE! 0.12293

028345C 0 #VALUE! 0.12618

028740L 0 #VALUE! 0.12724

028772S 0 #VALUE! 0.13329

028776U 0 #VALUE! 0.13329

028370K 0 #VALUE! 0.18156

029678G 0 #VALUE! 0.27162

029717V 0 #VALUE! #NUM!

029718C 0 #VALUE! #NUM!

029719J 0 #VALUE! #NUM!

029755E 0 0.00232 #NUM!

028372Y 0 0.00247 #NUM!
34

Appendix B

Chi-Square Goodness-of-Fit
35

Number 

of 

Collisions

Observed 

Collisions 

Expected 

Collisions 

from TC 

Model

Expected 

Collisions 

from U.S.

Model

χ

2

for 

TC 

Model

χ

2

for 

U.S.

Model

0 - 0.1 175 198 147 2.672 5.333

0.1 - 0.2 0 29 70 29.000 70.000

0.2 - 0.3 40 2 9 722.000 106.778

0.3 - 0.4 0 0 1 - -

0.4 - 0.5 10 0 1 - -

> 0.5 4 0 1 - -

Total 229 229 229 753.672 182.111

Number of 

Collisions

Observed 

Collisions 

Expected 

Collisions 

from TC 

Model

Expected 

Collisions 

from U.S.

Model

χ

2

for 

TC 

Model

χ

2

for 

U.S.

Model

0 - 0.1 175 198 147 2.672 5.333

0.1 - 0.3 40 31 79 2.613 29.165

>0.3 14 0 3 - -

Total 229 229 229 5.285 34.498

Number of 

Collisions

Observed 

Collisions 

Expected 

Collisions 

from TC 

Model

Expected 

Collisions 

from U.S.

Model

χ

2

for 

TC 

Model

χ

2

for 

U.S.

Model

0 - 0.1 175 198 147 2.672 5.333

>0.1 54 31 82 17.065 9.561

Total 229 229 229 19.736 14.894
36

Abbreviations and Acronyms

AADT average annual daily traffic

CPUC California Public Utilities Commission

FRA Federal Railroad Administration

GIS geographic information system

km/h kilometers per hour

mph miles per hour

RSPD road speed

TC Transport Canada

UNC University of North Carolina

USCG U.S. Coast Guard

USDOT U.S. Department of Transportation

Volpe Center John A. Volpe National Transportation Systems Center
37

Glossary of Statistical Terms

Bayes’ Theorem: relates the conditional and marginal probabilities of two random events. For 

example, the probability of Event A occurring, given that Event B has occurred.

Chi-squared test: a statistical hypothesis test where the test statistic has a chi-square 

distribution when the null hypothesis is true.

Colinearity: indicates that a set of points are on a single straight line.

Cluster analysis: the classification of objects into different groups.

Dispersion: the variability or spread in a variable or probability distribution. For example, if all 

the data points in the sample are identical, then the dispersion would be zero. If the data points 

differ greatly from one another, then there would be overdispersion.

Empirical Bayes method: a method that uses empirical data to evaluate the conditional 

probability distributions that arise from Bayes’ theorem.

G-function: an inter-distributional spatial test that is a measure of the fraction of a specific 

incident within any given distance of another specific incident type.

Goodness-of-fit: measures how well a statistical model fits the observations by the discrepancy 

between observed values and the values expected under the model.

Inter-distributional spatial testing: tests are designed to examine the relationship between 

points in a specific distribution. 

Intra-distributional spatial testing: tests are performed on each spatial distribution separately.

Linear regression: a form of regression analysis where the relationship between one or more 

independent variables and a dependent variable is modeled by a least squares function, the linear 

regression equation. This equation is a linear combination of one or more model parameters, 

which are the regression coefficients. For example, a linear regression equation with one 

independent variable represents a straight line.

Mean: the expected value of a random variable.

Nearest neighbor analysis: an intra-distributional spatial test that looks for locations within a 

spatial distribution that are nearer to each other than what would be found in a random 

distribution.

Negative binomial probability distribution: a discrete probability distribution. For example, 

it can be used if one wants to know how many times a coin will be tossed to get k number of 

heads.
38

Negative binomial regression: an extension of the Poisson regression model that allows the 

variance of the process to differ from the mean.

Nonlinear regression: a form of regression analysis where observational data are modeled by a 

function which is a nonlinear combination of the model parameters and depends on one or more 

independent variables. Exponential and logarithmic functions are examples of nonlinear 

regression.

Normalize: to make something more normal; to remove statistical error in repeated measured 

data

Poisson probability distribution: a discrete probability distribution that expresses the 

probability of a number of events occurring in a fixed period of time if these events occur with a 

known average rate and independently of the time since the last event. For example, an event 

could be modeled as a Poisson distribution would be how many customers a cashier rings up in 

an hour.

Poisson regression: assumes the response variable Y has a Poisson distribution and the 

logarithm of its expected value can be modeled by a linear combination of unknown parameters. 

For example, it can be used when the outcome variable is comprised of counts, often rare events.

Regression analysis: techniques for the modeling and analysis of numerical data consisting of 

values of a dependent variable and of one or more independent variables. The dependent 

variable in the regression equation is modeled as a function of the independent variables, 

constants, and a random variable. For example, regression can be used for forecasting data in a 

time-series.

Ripley’s K-function: an intra-distributional spatial test that compares the pattern of the data to 

that produced by a homogeneous Poisson point process, where cases are considered events.

Statistically significant: if a result is unlikely to have occurred by chance.

Spatial analysis: methods to study entities using their topological, geometric, or geographic 

properties.

Variance: a measure of statistical dispersion; averaging the squared distance of its possible 

values from the mean.