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Transportation Research Procedia 20 ( 2017 ) 288 – 294
2352-1465 © 2017 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the organizing committee of the 12th International Conference “Organization and Traffic Safety
Management in large cities”
doi: 10.1016/j.trpro.2017.01.025
Available online at www.sciencedirect.com
ScienceDirect
12th International Conference "Organization and Traffic Safety Management in Large Cities",
SPbOTSIC-2016, 28-30 September 2016, St. Petersburg, Russia
Evaluation of Functional Efficiency of Automated Traffic
Enforcement Systems
Mukhtar Kerimov 2a, Ravil Safiullin 2b, Alexey Marusin 2c
*
, Alexander Marusin 1d
1Yuri Gagarin State Technical University of Saratov, 77 Politehnicheskaja str.,Saratov, 410054, Russia
2 Saint Petersburg State University of Architecture and Civil Engineering, 4 2nd Krasnoarmeyskaya str., Saint Petersburg, 190005, Russia
Abstract
Traffic safety is a characteristic feature of road transport systems. Road traffic safety is regarded as a difficult challenge which
requires a system approach to the management of the road traffic system and its functional features like variability of the structure
of the street and road network and its technical condition, complexity of the hierarchical structure of road transport systems and
technologies exploited in them. The research resulted in a model of functioning of automated traffic enforcement facilities and
identified the factors which affect the effective functioning of automated traffic enforcement facilities which were used to develop
the dependencies regarding the number of accidents to evaluate the effectiveness of traffic enforcement cameras.
© 2016 The Authors. Published by Elsevier B.V.
Peer-review under responsibility of the organizing committee of the 12th International Conference "Organization and Traffic
Safety Management in large cities".
Keywords: Automated traffic enforcement system, traffic safety, mathematic model, correlation and regression analysis, functional efficiency.
1. Introduction
Reduction of traffic accidents in the RF road transport system is one of the main social priorities.
* Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 .
E-mail address: martan-rs@yandex.ru a, safravi@mail.ru b, 89312555919@mail.ru c*, 89271333424@mail.ru d
© 2017 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Peer-review under responsibility of the organizing committee of the 12th International Conference “Organization and Traffic Safety
Management in large cities”
289
Mukhtar Kerimov et al. / Transportation Research Procedia 20 ( 2017 ) 288 – 294
The number of traffic accidents and the amount of the injured can be decreased by exploiting advanced IT and
communications technologies and traffic management facilities [Government of the Russian Federation (2013)].
Here modern methods and technologies of traffic enforcement systems are becoming more urgent [Kerimov and
Safiullin (2016)].
Therefore, this research is intended to study design solutions which can ensure efficient functioning of the
automated traffic enforcement system.
The automated traffic enforcement system is regarded as a system with numerous parameters which can be
represented in "input-output" terms as follows (Figure 1) [Kerimov et al. (2015)].
А
X
B
E
e1
en-1
en
e2
bn
bn-
b2
b1
xn
xn-1
x2
x1
qn
qn-1
q2
q1
Q
Fig. 1. Functional model of automated traffic enforcement system.
A vector function of controlled parameters
X
operates at the input of the system. This group of factors includes
geometrical characteristics of the sections of a street and road network and specific features of traffic flows [Kerimov
et al. (2015)]. Another combination of inputs represented by a vector function
E
includes factors related to the
performance characteristics of the traffic enforcement facilities and driving etiquette. The vector function of
uncontrolled parameters
B
can be interpreted as a normal mode interference of stochastic nature [Kerimov et al.
(2015)]. These parameters include traffic conditions, technical characteristics of vehicles, etc. The output process can
be defined by a multi-dimensional vector
Q
which indicates the functional quality of facility. Here the functional
quality means the capacity of a traffic enforcement system to perform its intended functions at a given level for a
certain period of time.
290 Mukhtar Kerimov et al. / Transportation Research Procedia 20 ( 2017 ) 288 – 294
The cause and effect relation between the specified groups of parameters can be modeled by the following ratio:
Q = A[X, B, E],
ǡ ǡ א
where А is a system operator;
V is a given set of acceptable states of vectors Х, В and Е;
is an universal quantifier.
Traffic safety level (Рtsl) is defined by an optimal combination of functions implemented by the traffic enforcement
system and their actual values for a given period of time. An optimization model of functioning of the traffic
enforcement system has been developed to evaluate this level. The qualitative and quantitative composition of factors
which are used by the model primarily depends on research objectives [Chernyaev et al. (2015)]. Below is the
dependency of the traffic safety level on different indicators
்ܲௌ ൌܲ൫ܳ
ǡܳ
௦ǡܪ
ǡܪ
௦ǡܷ൯Ǥ (1)
The model has the following symbols:
U – management function (actions taken by the automated traffic enforcement center);
Hf – quality indicator of functioning of the traffic enforcement system (according to data processing for a given
period of time). This indicator is used by the automated traffic enforcement center;
Hs – indicator of the management function of the traffic enforcement system (is also used by the automated traffic
enforcement center when executing an administrative penalty);
Qs – indicator of execution of administrative penalty;
Qf – indicator of functional efficiency of data processing of the automated traffic enforcement system (at the level
of automated traffic enforcement center).
These are the main factors for the automated traffic enforcement system but they are not the only ones. Their
qualitative and quantitative composition depends primarily on research objectives.
The optimization model has numerous criteria. There are different methods of vector optimization for solving this
kind of problems. Application of these methods requires identification of individual indicators as limitations.
Stochastic nature of assessment indicators of the traffic safety system requires a stochastic problem with one
criterion. Solving this problem will require an assumption that the number of possible actions and the number of
assessments of the traffic safety level for each action and/or proposal are limited. This condition is compliant with the
actual state of things because stochastic events which are associated with traffic safety are used for evaluation.
The impact of different factors (management, technical, technological, economic and social) on the traffic safety
was assessed in the comprehensive researches which covered the period from 2009 to 2015 in separate regions of the
Russian Federation, namely: Moscow, Moscow region, Voronezh region, Saratov region, the Republic of Tatarstan,
Saint Petersburg and Leningrad region [Minnikhanov (2009)].
The following indicators which affect the traffic safety were considered: "the number of orders on administrative
offences"; "amount of penalties paid"; "number of traffic enforcement cameras"; "population density in a region";
"transport density in a region"; "number of registered vehicles in a region" (thirteen most significant indicators were
chosen).
Experimental data were statistically processed using a multiple regression analysis. The results are given in a
correlation Table according to Figure 2.
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Mukhtar Kerimov et al. / Transportation Research Procedia 20 ( 2017 ) 288 – 294
Column 1
Column 2
Column 3
Column 4
Column 5
Column 6
Column 7
Column 8
Column 9
Column 10
Column 11
Column 12
Column 13
Column 14
Column 1
1
Column 2
0.70063799
1
Column 3
0.63099057
0,966139202
1
Column 4
0,77849777
0.914605299
0.945645484
1
Column 5
0,3582102
0,847114486
0,902847682
0,74599515
1
Column 6
0.61709102
0.774516558
0.865770902
0.91646835
0,6247888
1
Column 7
-0,0135398
0,344772624
0,202951121
-0,0014358
0.45645795
-0,30612092
1
Column 8
0.65764012
0.069397179
-0.0303839
0,24333514
0,3201132
0.086944174
0.2376485
1
Column 9
0.78332785
0.493777365
0,581162986
0.70484745
0,37536951
0.728487932
-0,3927218
0,3635835
1
Column 10
0,58124685
0.070144288
0,130509925
0,24571311
0.01654632
0.26873059
-0,3751756
0,377604
0,846027165
1
Column 11
-0,25558085
-0.02343702
-0,21949035
-0,308686
-0.1017598
-0.53781784
0.72638958
-0,017867
-0.79049881
-0,75188501
1
Column 12
0.94458745
0,827329764
0,791223651
0.91230383
0.51410716
0.798160825
-0.042042
0.4713666
0.769168604
0.425571364
-0,280439152
1
Column 13
-0.84165331
-0.5010039
-0,48136396
-0.5665844
-0.23833
-0,5580842
0.20443086
-0,463219
-0.86218291
-0,81740456
0,541174248
-0,737320563
1
Column 14
0.25549692
-0.30622258
-0,47889516
-0.3000792
-0.7354849
-0.30667968
-0.225046
0.6157208
-0,04727009
0.18983727
0.189347812
0.076385384
-0.27572724
1
Fig. 2. Indicators of correlation relationship between the factors which affect traffic safety in the study regions.
The analysis of the table shows that the following factors have the maximum impact on the number of traffic
accidents: "population density in a region", "length of roads" and "territory of a region". These factors are related to
the first group of indicators. The factor "number of vehicles in a region" has the greatest impact which causes 88.4 %
of all the accidents.
Figure 1 shows that individual factors are correlated. For example, the factors "number of orders on administrative
offences" and "amount of penalties paid" have a correlation of 0.96, while the "amount of penalties paid" and "number
of stationary traffic enforcement cameras – 0.95. Almost all the factors have positive correlation with traffic accidents.
The program Statgraphics was used to make a quantitative assessment of impact of the following factors: "transport
density in a region", "population density in a region" and "number of vehicles in a region" on traffic safety.
Table 1 shows statistical characteristics of the obtained dependency: values of the coefficients of the model; validity
assessment according to t-criterion and analysis of the degree of impact of the factors on the dependent variable.
The data shows that all the coefficients are statistically important and account for 94.4 % of the impact on the
dependent variable.
The mathematic model is as follows:
1198 04,10335527,1009546,105,1326 xxxy
(2)
Table 1. Statistical characteristics of mathematic model.
Standard
T
Parameter
Estimate
Error
Statistic
P-Value
CONSTANT
1326.05
698.759
1.89772
0.1540
x8
-1.09546
0.528357
-2.07333
0.1298
x9
10.5527
3.81843
2.76361
0.0699
x11
1033.04
172.974
5.97221
0.0094
Analysis of Variance
Source
Sum of Squares
Df
Mean Square
F-Ratio
P-Value
Model
5.81312E7
3
1.93771E7
34.50
0.0080
Residual
1.68483E6
3
561612,
Total (Corr.)
5.9816E7
6
Data which represents the degree of impact of factors on the dependent variable (number of accidents) is given in
Table 2.
292 Mukhtar Kerimov et al. / Transportation Research Procedia 20 ( 2017 ) 288 – 294
Table 2 – Impact of factors on the number of accidents
Source
Sum of Squares
Df
Mean Square
F-Ratio
P-Value
x8
3.67033E7
1
3.67033E7
65.35
0.0040
x9
1.39678E6
1
1.39678E6
2.49
0.2129
x11
2.00311E7
1
2.00311E7
35.67
0.0094
Model
5.81312E7
3
According to the degree of impact the factors can be rated as follows: "specific population density in a region",
"specific transport density in a region", "population of a region". The impact of factors used in the model is important.
The chart of design values of the number of accidents in the regions is given in Figure 3.
Fig. 3. Chart of design values of the number of traffic accidents of the first group of factors.
The impact on traffic safety of such factors as "number of orders on administrative offences", "amount of penalties
paid", "number of stationary traffic enforcement cameras" and "specific density of population in a region" are also
statistically important. These factors are referred to the second group of indicators. The chart with dependency of the
number of accidents on the stated factors is shown in Figure 4.
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Mukhtar Kerimov et al. / Transportation Research Procedia 20 ( 2017 ) 288 – 294
Fig. 4. Number of accidents in RF regions vs. factors of second group
Statistical characteristics of the model are given in Table 3.
Table 3. Statistical characteristics of mathematic model.
Standard
T
Parameter
Estimate
Error
Statistic
P-Value
CONSTANT
2379.24
377.889
6.29614
0.0243
x1
1543.63
193.139
7.99232
0.0153
x2
-3.91341
0.472106
-8.28924
0.0142
x3
9.40355
3.09578
3.03754
0.0934
x8
1.0307
0.153725
6.70487
0.0215
The mathematic model is as follows:
8321
0307,14035,99134,363,154324,2379 xxxxy
(3)
So the models (2) and (3) show the dependency of the number of traffic accidents on different factors. These factors
refer to management, technological and procedural aspects of the problem of traffic safety.
The research produced the results which allow validating the general concept and providing an input for developing
an efficient traffic enforcement system.
2. Conclusion
The comprehensive studies carried out in different regions of the Russian Federation resulted in a model which can
increase traffic safety. The results show that traffic safety in the RF regions is greatly affected by the availability of
automated traffic enforcement system in the street and road network. Therefore, evaluation of the functional efficiency
of the automated traffic enforcement system requires data regarding the number and type of traffic enforcement
facilities.
294 Mukhtar Kerimov et al. / Transportation Research Procedia 20 ( 2017 ) 288 – 294
The research identified functional efficiency indicators of automated traffic enforcement systems. The proposed
algorithm of evaluation of the efficiency of automated traffic enforcement systems can substantiate the applicability
of these indicators for choosing the most appropriate traffic enforcement equipment and their effective operation.
The use of traffic enforcement equipment has a beneficial effect which can be evaluated according to specific
criteria and assessed by the rate level of reduction of the number of accidents in a dangerous road section.
The results of the research work have an objective to solve a strategic problem which was stated in the Federal
Target Program "Improvement of traffic safety in 2013 - 2020".
References
Chernyaev, I.O., Kerimov, M.A., Safiullin R.N., Marusin A.V. (2015). Analysis of Functional Efficiency of Automated Traffic Enforcement
Systems”. In proceedings of III International Research and Practice Conference “Innovation in Transport and Machine Building”, part 1, 135–138.
Government of the Russian Federation (2013). Federal Target Program “Improvement of Traffic Safety in 2013 – 2020”. Available at:
http://government.ru/media/files/41d494b8c5e15981c833.pdf (viewed on: 11.05.2016).
Kerimov, M.A., Safiullin, R.N. (2016). Traffic Enforcement Facilities: Normative Regulations and Application. Мoscow: Direct-Media, 355 p.
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