Dell Acqua and Russo 1

ACCIDENT PREDICTION MODELS FOR ROAD NETWORKS

Gianluca Dell Acqua
Assistant Professor, Ph.D., P.Eng.
Department of Transportation Engineering Luigi Tocchetti
University of Naples Federico II
Via Claudio 21, I-80125 Naples, Italy
Phone: +39 0817683934
Fax: +39 0817683946
E-mail: gianluca.dellacqua@unina.it

Francesca Russo (Corresponding Author)
Ph.D., P.Eng.
Department of Transportation Engineering Luigi Tocchetti
University of Naples Federico II
Via Claudio 21, I-80125 Naples, Italy
Phone: +39 0817683372
Fax: +39 0817683946
E-mail: francesca.russo2@unina.it


0BABSTRACT
This paper illustrates road safety statistical models to predict injury accidents. Since 2003 the
Department of Transportation Engineering at the University of Naples has been conducting a large
scale research programbased on the accident data collection in Southern Italy. The Italian analyzed
roadways in the Salerno Province are composed of multilane roadways for 242 kilometers and Major
and Minor two-lane rural roads for 3,101 kilometers.
Two accident prediction models were calibrated: one is associated with two-lane rural roads
and the other with multilane roadways. Explanatory variables were used including traffic flow, lane
width, vertical slope, curvature change rate, roadway segments length.
Several procedures exist in the scientific literature to predict the number of accidents per
kilometer per year, and a lot of relationships between accidents and explanatory variables exist basing
on the multiple-variable non linear regression analyses. The accident data, presented in this manuscript,
were analyzed using this procedure based on least squares method. The predicted values obtained by
calibration procedure were then compared to several models presented in the scientific literature to
analyze the residuals by using the t-test.

















Keywords: road safety analysis, calibration and validation injurious accidents prediction models


Dell Acqua and Russo 2

INTRODUCTION
Data published by the World Health Organization and the European Union demonstrated that the main
cause of death is attributed to roadway accidents which numbered 1.2 million fatalities per year round
the world.
To examine the phenomenon, and to restrict its consequences, national and international
research on road safety is being conducted to assess the relationship between vehicles, users and the
environment
Regression equations in the scientific literature we had studied attempt to predict the number
of accidents per year per kilometer and particularly significant employed variables were chosen. These
were the Average Daily Traffic (ADT) and roadway length.
The accident prediction models presented here were subsequently validated using the data set
which had not been included in the calibration phase. In fact prior to calibration, 10% of the total
number of accidents leading to injury were excluded fromthe sample data in order to validate the
procedure at the end.


LITERATURE REVIEW
The first crash prediction model for multilane roads was defined by Persaud and Dzbik (1).
The model presented relationships between the number of accidents and the traffic flow expressed as
average daily traffic (ADT) and hourly volume. The analysis was based on generalized linear models.
The results demonstrate that the accident rate increases with traffic flow.
Knuiman et al. (2) examined the effect of the mean value of roadway width for four-lane roads
on accident prediction models. A negative binominal function was used for data distribution. The results
indicated that the accident rate decreases when roadway width increases, reaching minimal crash-rate
values when the cross-section dimensions are high.
Fridstrm et al. (3) correlated the number of crashes with four variables: traffic flow, speed
limits, weather and light conditions.
Hadi et al. (4) put forward numerous crash prediction models for multilane roads and rural or
urban two-laneroads.
Persaud et al. (5) presented accident prediction models which developed different regression
equations for circular curves and tangent elements. All the analyses were then completely focused on
two-lane roads. Crash frequency was predicted by using traffic flow and roadway features.
Persaud and Lord (6) applied the generalized regression equation (GEE) to accident data from
the state of Toronto (Canada)
Some models in the scientific literature (7) can be considered as a source for other accident
regression equations. To predict crash frequency, for example, some roadway variables have been used
in the literature such as the following(8) (9):

( )
1.242
0.1955 0.1775 0.2716 2 0.5669 0.1208 0.0918 91 0.696
( )
1000
LN SHW MT TS PTC Y
i
ADTL
E m LEN e
- + + - -
=


(1)


where

E(m)
i
: expected accident frequencies on road section I,
ADTL: annual average daily traffic (AADT) per lane,
LEN: roadway segment length in Km,
LN: number of roadway lanes in the analyzed section i
SHW: roadway width in meters,
MT2: binomial indicator reflecting the type of center line (0 =painted, 1 =physical barrier),
TS: binomial indicator reflecting the presence of signs (0 =signs not present, 1=signs present),
PTC: binomial indicator reflecting the pattern type (0 =combined, 1 =commuter),
Y91: year 1991 (0=92, 1=91).

Martin (10), studying French interurban motorways, described the relationship between the
crash rate and volume of hourly traffic (VH), and the impact of traffic on fatal crashes.
Dell Acqua and Russo 3

Golob and Recker (11) used linear and non-linear multi-variable regression analysis to
compare the correlations between crashes and traffic flow, lighting and weather conditions. In a
subsequent study, Golob et al. (12) evaluated the effects of changes in traffic flow on road safety.
E. Hauer of the University of Toronto developed statistical regression equations to predict the
number of crashes per year in relation to geometric roadway features and traffic flow. The crash data
were analysed using binominal negative distribution (13). An innovative aspect of this study is the
introduction of an alternative instrument to measure the adequacy of accident prediction models, called
the Cu.Re method (Cumulative Residuals).
Hauer (14) subsequently calibrated other models to predict crash frequency (number of crashes
per year) on multilane urban roads by using variables listed as AADT (Annual Average Daily Traffic),
the percentage of trucks, slope, horizontal curve length, roadway width, type and width of clear zones,
danger levels of road shoulders, speed limits, points of access, and the presence and nature of parking
areas. The results demonstrated that AADT, the point of roadway access, and the speed limits were the
significant variables for predicting crash frequency. Statistical tests were developed by the same author
to test the statistical significance of the obtained results (15) (16) (17).
Caliendo et al. (18) showed a strong correlation between the number of crashes, traffic flow,
and infrastructure features. The equation-formof the accident prediction models for horizontal curves is
the following:

4
1
1.45703 0.86881 ln 0.33793 0.40863 10 L AADT
R
e l
-
- + + +


=
(2)


where

l: predicted number of severe crashes per year
L: circular curve length in kilometers
1/R: curvature radius in km
-1

AADT: annual average daily vehicle traffic per day

Tarko suggested some guidelines to examine road safety (19) (20) and to realize all the
operations needed to improve it.


DATA COLLECTION
The collected data of the number of accidents covered a period of three years from 2003 to
2005 and relate to the road network of the Province of Salerno in Southern Italy; all geometric features
and crash rates for each roadway segment were initially analyzed using a Geographic Information
System(G.I.S.).
The data were given to the Department of Transportation Engineering at the University of
Naples by the Administration of the Province of Salerno. Table 1 refers to the complete analyzed
databasewhile tables 2 and 3 refer to the database used to calibrate the models.

TABLE 1 Roadway and Injurious Features of Analyzed Network
Type of road
Type of
Analysis
Roadway segment
length
[Km]
Injurious crashes in 3
years
(2003-2005)
Divided
Roadways
Calibration 223 1,205
Validation 19 51
D.R. total 242 1,256
Undivided
Roadway
Calibration 2,651 1,131
Validation 450 510
U.R. total 3,101 1,641
TOTAL 3,343 2,897
Dell Acqua and Russo 4

The initial database is filtered by removing accident data with an ADT of less than 200
vehicles/day and records with incomplete information.
The final database comprises approximately 700 records. Table 1 shows the geometric and
harmful features of the Italian analyzed roadway.
Prior to calibrating injurious/fatal accident prediction models, 10% of these events were
randomly extracted. These injurious/fatal accidents were subsequently used to validate the regression
equations.
Tables 2 and 3 show the descriptive statistics for geometric features and I/F accidents per year
per kilometer on the Italian roads with two-lane rural and urban roads (undivided roadways) and the
multilane roadways (divided roadways) analyzed.
The broad variation in the ADT interval (minimum and maximum values) is due to the fact
that ramps are also included in the database. The study conducted here illustrates a network approach
in which it was deemed opportune not to exclude road sections that connect one functional sub-network
and to another (now including the ramps).

TABLE 2 Descriptive Statistics of the Geometric and Injurious Features of rural and urban roads

Length
[Km]
ADT
Average Daily Traffic in vehicles/day
Roadway
width
[m]
Injurious events
per year per Km
Average 4.60 3,300.15 7.69 0.12
Standard error 0.18 148.11 0.07 0.01
Median 3.25 1,789.75 7.00 0.00
Mode / 1,174.00 7.00 0.00
Standard deviation 4.36 3,554.75 1.75 0.28
Sample variation 18.98 12,636,264.81 3.05 0.08
Kurtosis 22.19 3.10 2.62 15.20
Asymmetry 3.20 1.78 1.38 3.49
Interval 50.02 18,855.50 11.83 2.13
Minimum 0.08 201.50 4.50 0.00
Maximum 50.11 19,057.00 16.30 2.13
Sum 2,651.21 1,900,889.20 4,427.47 71.36
Total 576.00 576.00 576.00 576.00

TABLE 3 Descriptive statistics of the geometric and injurious features of multilane roads

Length
[Km]
ADT
Average Daily Traffic in vehicles/day
Roadway
width
[m]
Injurious events
per year per Km
Average 3.82 18,904.78 7.44 1.69
Standard error 0.42 1,957.54 0.38 0.36
Median 2.44 12,696.88 7.25 0.00
Mode / 41,675.00 11.25 0.00
Standard deviation 3.33 15,413.69 2.96 2.82
Sample variation 11.07 237,581,939.93 8.74 7.94
Kurtosis 1.82 -1.19 -0.72 12.20
Asymmetry 1.57 0.59 0.30 3.00
Interval 14.38 48,259.33 11.50 16.47
Minimum 0.52 1,247.67 3.50 0.00
Maximum 14.90 49,507.00 15.00 16.47
Sum 236.78 1,172,096.44 461.00 104.78
Total 62.00 62.00 62.00 62.00


Dell Acqua and Russo 5


CALIBRATION OF I/F ACCIDENT PREDICTION MODELS
The I/F crash roadway prediction models were calibrated using statistical software (Statistica
Statsoft).
This systempermitted the analysis, in an attempt to analyze regression, of the degree of
correlation and some statistical parameters explaining the statistical significance of the employed
variables.

Calibrating the Accident Model for Multilane Roadways
The descriptive statistics for the database employed to calibrate fatal crash prediction models
for multilane roadways are briefly summarized in Table 4; they cover 223 kilometers of Italian
roadways analyzed within the Salerno Province network.
The Gauss-Newton method based on the Taylor series was used to estimatethe coefficients of
employed variables. All the parameters included in the model are significant with a 95% confidence
level.

TABLE 4 Descriptive Statistics of Variables Employed to Calibrate the Prediction Model
Variable Average m Standard Deviation s Minimum Maximum
ADT [vehicles/day] 17,372.24 15,746.20 166 49,507
Roadway Segment length [Km] 3.65 3.38 0.06 14.90
Injurious accidents per year [crashes/year] 6.58 11.17 0.00 56.00

The best specification of this ordinary-least-square model (OLS) was developed using 1,205
injurious crashes; the equation-formis the following:

0.5761
1
0.4087
1000
A D T
y L u

=


(3)

where

y
1
: number of fatal crashes per year observed on roadway segment length L
u

ADT: average daily traffic in vehicles/day observed in three years
L
u
: length of the analyzed roadway segment
The adjusted coefficient of determination (r) of the model is equal to 67.3%.
Using Hauer s procedure, a diagramof residuals was plotted based on the ADT values as shown in
Figure 1. The residual is the value of the difference measured between the predicted value of fatal
accidents using the model and the real value of the number of accidents surveyed on the sameroadway
segment. In the diagram, the residual values were placed on the y-axis, while the ADT/1,000 values are
reported in the x-axis corresponding to the same roadway segments. The traffic variable was plotted on
the diagramfromthe lowest to the highest value.


FIGURE 1 Diagram ADT-cumulated quadratic residuals.

0
50
100
150
200
0 10 20 30 40 50
S
q
u
a
r
e
d

I
/
F

c
r
a
s
h
e
s

p
e
r

y
e
a
r
ADT/1,000 [vehicles/day]
Dell Acqua and Russo 6

Good predictions of accident models subsists up to an ADT value of 10,000 vehicles per day.
Around this value, the residuals actually start to be significantly noticeable. Cumulated quadratic
residuals corresponding to the ADT values greater than 10,000 vehicles a day show a vertical jump
more correctly known as an outlier (22). The presence of an outlier indicates an observation very
different froma sample data distribution and it appears when the real crash rate is very dissimilar to the
predictive value using regression equations. In this case, it is necessary to carry out more investigations
to decide whether to use these observations or not.
Observed residuals have a minimumvalue of zero and a maximumvalue of 6.50 injurious
crashes per year per kilometer. The average value is 0.48 injurious crashes per year per kilometer, while
the standard deviation is 1.66 injurious crashes per year per kilometer.

Calibrating the Accident Model for rural and urban Roadways
The descriptive statistics of some variables employed to calibrate injurious crash prediction
models on these roadway types are briefly summarized in Table 5.
The database consists of 576 records and covers a total of 2,651 kilometers of Italian roads
analyzed within the Salerno Province network.

TABLE 5 Descriptive Statistics of Employed Variables in the Prediction Model
Variable Average m Standard Deviation s Minimum Maximum
TGM [vehicles/day] 3,300.15 3,554.75 202 19,057
Section length [Km] 4.60 4.36 0.08 50.11
Injurious accidents per year [crashes/year] 0.65 2.03 0.00 25.00
V [Km/h] 58.54 10.04 30.00 83.30
Roadway width [m] 7.69 1.75 4.50 16

All the parameters included in the model are significant with a 95% confidence level. The best
specification of the ordinary-least-square model (OLS) was produced using 1,131 injurious crashes. The
equation-formis the following:

( )
0.6444
11.7399 0.1739 3.5583 3.7087 0.2514
1
1000
V C P C T L a A D T
y L u e
- + + - -
=


(4)

where

y
1
: the number of fatal crashes per year observed on roadway segment length L
u

ADT: average daily traffic in vehicles/day observed in three years
L
u
: length of the analyzed roadway segment
V: mean value for speed in free flow conditions on a selected roadway segment in Km/h
CP: slope coefficient equal to 0.8 for low slopes, 0.9 for high slopes and 1 for very high slopes
CT: tortuosity coefficient of 0.8 for low tortuosity, 0.9 for high tortuosity and 1 for very high
tortuosity
L
a
: roadway width in meters
The adjusted coefficient of determination (r) of the model is equal to 68.0%.
Observed residuals have a minimumvalue of zero and a maximumvalue of 1.89 injurious
crashes per year per kilometer; the average value is 0.03 injurious crashes per year per kilometer, while
the standard deviation is equal to 0.26 injurious crashes per year per kilometer.
Figure 2 is a CuRe (Cumulative Residuals) diagram. Figure 2 shows how the model is
statistically significant until the ADT value is equal to 5,000 vehicles per day. In the diagram, the
residual values were placed on the y-axis, and the x-axis gives the ADT values, which correspond to the
same roadway segments. The traffic variable was plotted on the diagramfromthe smallest to the
highest value.

Dell Acqua and Russo 7


FIGURE 2 CuRe diagram .


VALIDATION OF ACCIDENT PREDICTION MODELS
This section presents the validation procedure for crash prediction models for roads with rural
and urban roadways and for multilane roadways. This method evaluates the accuracy of two injurious
accident prediction equations by analyzing the differences in observed and predicted values. The data
used to validate the two regression equations have not been included in the calibration phase of some
models. 10% of the total observed accident data was initially extracted fromthe entire database for
subsequent use in the validation procedure.
60 roads with undivided roadway segments were used in this phase, giving a total length of
220 Kilometers and a total of 58 injurious crashes recorded in the three years from2003 to 2005.
7 roads with divided roadway segments were then used in this phase for a total length of 18
Kmand a total of 11 injurious crashes over the samethree years.
The descriptive statistics of the features observed on roads with undivided roadway and roads
with divided roadways which were used to validate the two prediction injurious accident models are
shown in Table 6.

TABLE 6 Descriptive Statistics of Employed Variables to Validate Models
Variable m s Min Max Total
ROADS WITH DIVIDED ROADWAYS:
ADT[vehicles/day] 10,156.10 7,965.12 161,300 23,499.50 71,092.67
Section L [Km] 2.64 2.55 0.53 7.99 18.49
Injurious accidents per year 0.52 1.39 0.00 3.67 3.67
ROADS WITH UNDIVIDED ROADWAYS:
ADT 3,038.11 3,120.35 230.25 14,972.00 194,439.15
Section L [Km] 3.54 3.23 0.12 16.79 226.56
Injurious accidents per year 0.30 0.82 0.00 5.00 19.33
V [Km/h] 59.09 9.39 30.00 74.30 -
Tortuosity Coefficient [-] 0.86 0.07 0.80 1.00 -
Slope Coefficient [-] 0.94 0.06 0.80 1.00 -

The validation procedure estimates the following synthetic statistical parameters:
Residual (D
i
) =value estimated fromthe difference between predicted fatal crash values UY
i
U and
the observed fatal crash values Y
i
: D
i
=UYU -Y
i
.
MAD (Mean Absolute Deviation) =constant value equal to the sum of the absolute values of
D
i
divided by the total number (n) of observations

n D MAD
n
i
i
/
1

=
=
(5)

MSE (Mean Squared Error) =constant value equal to the sumof D
i
squared divided by n
Dell Acqua and Russo 8

n D MSE
n
i
i
/
1
2

=
=
(6)

I =constant value equal to the square root of MSE divided by the mean of the predictive
injurious crashes
2
( )
0 . 1
i i
i
Y Y
n
I
Y
n
-
=

(7)
The predictive accident model for roads with a divided roadway has a MAD value of 3.55
injurious crashes per year per kilometer and an MSE value of 17.93 squared injurious crashes per year
per kilometer.
The predictive accident model for roads with undivided roadways presents a MAD of 0.48
injurious crashes per year per kilometer and an MSE of 0.72 squared injurious crashes per year per
kilometer.
Thus, it can be concluded that two accident prediction models are statistically significant
because the residual values are in a limited range around the mean. This was confirmed by a low value
for MAD, MSE and I indicators. In particular, I reflects a good prediction when lower than 0.1. This
holds for both models.
The accident prediction models for roads with undivided roadways were compared with
Tarko s crash prediction model (19). Two models were applied to the sample data used to validate the
regression equations. This procedure was carried out to verify that the residuals given by the two
models may be judged statistically in different ways. A total of 64 fatal accidents were randomly
extracted fromthe database used for the validation procedure because this is the sample size used by
Tarko. The mean value of the residuals obtained fromthe accident prediction model reached fromthe
Italian regressions was 0.50 injurious crashes per year per kilometer, while according to Tarko s model
the result should have been 0.72. A t-test was then conducted to verify whether the mean value of
residuals obtained from the application of our model is statistically different fromthe mean value of
residuals for Tarko. For the Std. Dev. value of our model equal to 0.55 injurious crashes per year and
for the Std. Dev. value of Tarko s model equal to 0.58 injurious crashes per year the two models
produce similar results at the significant level of 3% (13).


RESULTS
The two injurious crash prediction models were calibrated by using the sample data observed
on the roadway network of the Province of Salerno in Southern Italy.
The database used to calibrate the prediction model on the multilane roadway covered 223
kilometers where 1,205 injurious crashes occurred from2003 to 2005.
The Gauss-Newton method based on the ordinary-least-square model (OLS) was developed to
performa final regression equation to predict the injurious accidents per year per kilometer.
All the parameters included in the model are significant to a 95% level of confidence, and the
adjusted coefficient of determination (r) of the model is 67.3%. The variables used in this prediction
model are the ADT value (average daily traffic in vehicles/day observed over three years) and the L
u

value (length of the analyzed roadway segment).
The database used to calibrate the prediction model on the roads with an undivided roadway
(two-lane rural roads and urban roads) covered 2,651 roadway kilometers, where 1,131 injurious
crashes took place from2003 to 2005.
All the parameters included in the model are significant to a 95% level of confidence, and the
adjusted coefficient of determination (r)is equal to 68.0%. The model predicts the number of fatal
crashes per year per kilometer and depends on the ADT value (average daily traffic in vehicles per day
observed over three years), the L
u
value (length of the analyzed roadway segment), the L
a
value
(roadway width), the V value (mean speed value in free flow conditions on a selected roadway
segment), and the slope and tortuosity coefficient.
Dell Acqua and Russo 9

Two accident prediction models were then validated using an accident database which had not
been used to calibrate the prediction models. These two accident prediction models are statistically
significant because the residual values are in a limited range around the mean.
Figure 3 shows a diagramof the accident prediction models calibrated for the Roads with
undivided roadways once some values have been established. In the case of Figure 3, the L
a
value and V
value have been established. Respecting all the general features of the roadway shown in Table 5 and
the maximumvalue of injurious crashes per year per Km observed on the roadways analyzed as shown
in Table 2, the calibrated accident prediction model on roads with undivided roadways can be used.
Other ranges of values for some used variables are to be respected; in particular the V value between 40
Km/h and 65 Km/h, and L
a
value must fluctuate between 8.5mand 9.5m; for a V value between 65
Km/h and 80 Km/h, the L
a
value must fluctuate between 9.5mand 10.5m.
Assigning maximumvalue to speed V, in order to avoid an overestimated predicted value thus
conferring a pragmatic sense to the results, the L
a
value must belong to a specific range. There is an
empirical rule following this formula:
0.686 40.07 La V = -

40.07
0.686
La
V
+
=
(8)

According to this empirical procedure, once the minimumvalue of the highway width is
established, the maximumspeed value to insert in the model is obtained.



FIGURE 3 Abacus of accident prediction models on roads with undivided roadways where
La=10.50 m and V=70 Km/h.


CONCLUSIONS
The proposed objective was to identify the relationship between the existing causality events
among the geometric and functional characteristics of the examined network (in the Province of
Salerno) and the number of recorded injurious crashes. Once the data was gathered, a database was
created in order to process the data.
This project referred only to injurious crashes; a similar procedure could be used for total crash
rates or only for injuries or deaths. The proposed models can be used for accident analysis on the road
network and they can be arranged next to the detailed models (e.g. crashes at intersections) also through
ad hoc investigations of specific sites. The suitability of the models to the data can be measured using
many coefficients, but the adjusted coefficient of determination r has been used in this manuscript-
The functional formof the models was designed to reproduce the data; other functional forms could
reproduce the data and an automated stepwise procedure could be implemented in this application.
The accident prediction models here presented should be improved: more information will be
needed and other variables will be analyzed for this purpose while others will require further perfection.
The results obtained could be said to be satisfactory. The two prediction accident models here
presented could contribute to the planning phase preceding the design of intervention. In particular, this
pertains to the programming of road improvement operations (also laid out in regulations), which make
Dell Acqua and Russo 10

it possible to target the spending of public funds by governing administrations or directing
infrastructures, depending on the number of crashes predicted by the model. Especially as it concerns
the evaluation and programming of road safety improvement operations, which are to be adopted in the
provincial road networks, concentrating on those areas of the infrastructural network that are deemed
critical froma safety point of view.
In particular it is therefore possible to identify whether:
1. varying the traffic makes it possible to identify priority interventions.
2. to improve a road section through significant interventions.
3. to evaluate the investment related to a project that includes the insertion of a new branch in the
network.

1BACKNOWLEDGEMENTS
The authors would like to thank the Province of Salerno

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