Information and Entropy Aspects of the Specifics of Regional Road Traffic Accident Rate in Russia
Abstract
:1. Introduction
1.1. Why Was Information-Entropic Analysis Chosen as a Tool for Studying the Features of the Road Traffic Accident Rate Formation?
1.2. Outline of the Article
1.3. Study’s Limitations
2. Analysis of Previous Works
3. Initial Idea and Problem Statement
3.1. Cause–Effect Chain of the Process of the Road Traffic Accident Rate Formation
3.2. Initial Idea and Problem Statement
- ND—number of dead people in traffic accidents in year;
- P—median size of population of country (region);
- NVh—size of vehicle fleet.
- NRA—number of road traffic accidents (RTA) in the country (region), accidents/year;
- NV—number of victims of road traffic accidents in the country (region), people/year.
- (a)
- explicit (first-order problems);
- (b)
- requiring serious analysis (second-order problems);
- (c)
- totally hidden (third-order problems).
4. Theoretical Aspects of Quantitative Assessment of the Structural Specifics of the Regional Road Traffic Accident Rate in Russia
- Xi+1—the value of the characteristic of the subsequent block of the cause–effect chain of the process under study;
- Xi—the value of the characteristic of the previous block of the cause–effect chain.
- n—number of information transmission links;
- —weight coefficients, meeting the normalization condition ().
- Qi—positive share of a link in the cause–effect chain of road traffic accident rate formation;
- Q—overall positive of the process of functioning of the cause–effect chain.
- QN = ln(1/KN)—positive share of link 1, Population—Fleet of vehicles;
- QRA = ln(1/KRA)—positive share of link 2, Fleet of vehicles—Road Traffic Accidents;
- QV = ln(KV)—positive share of link 3, Road Traffic Accidents (RTA)—RTA victims;
- QD = ln(1/KD)—positive share of link 4, RTA victims—Fatality Cases.
5. Results of the Study of Information Entropy Specifics of the Regional Road Traffic Accident Rate in Russia
6. Informational Meanings of Uncertainty (Chaoticity) of the Processes of Formation of the Road Traffic Accident Rate in the Regions of Russia
7. Conclusions and Recommendations for Improving the Quality of the Federal Road Safety Management in Russia
7.1. General Conclusions
- Information-entropic analysis can be successfully used to assess the structural quality of road safety systems.
- The basis of the information entropy analysis of the road safety systems quality is the determination of the weight (relative significance) of the evaluation indices and the creation of a multi-level synthetic evaluation model. To do this, we need to use the cause–effect chain “Population P—Fleet of vehicles NVh—Road Traffic Accidents NRA—RTA Victims NV—Fatality Cases ND” of the process of formation of the road traffic accident rate and determine the relationship between the values of the characteristics of the subsequent and previous blocks of the cause–effect chain Ki.
- It is the values of the information transformation coefficients Ki that will determine the specifics of Shannon’s information entropy (the absolute value of H RS and the value of the relative entropy Hn RS) and set the weights (significance) of certain links in the cause–effect chain for specific regional road safety systems.
- Studies of the regional specifics of the processes of formation of the road traffic accident rate in the regions of the Russian Federation have shown that these specifics really exist. Regional values of the information transformation coefficients Ki differ significantly (sometimes by multiples), and their bizarre combinations have the most direct impact on the final quantitative estimate of the relative entropy Hn RS for specific regional road safety systems.
- During this research, it was determined that Hn RS RF 2021 = 0.699. The range of values for individual regions of the country is Hn RS regions 2021 = [0.631; 0.810]. At relatively high values of Hn RS → max, regional road safety systems can be characterized as relatively uncertain in terms of assessing the probability of death of people. At relatively low values of Hn RS → min, regional road safety systems can be characterized as relatively certain in terms of assessing the probability of death of people.
7.2. Recommendations
7.3. Model Variants of the Process of Formation of the Regional Road Traffic Accident Rate in Russia (2030 and 2070)
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
No. | Region of Russian Federation | Values (2021) | |||||
HRS | Hn RS | K1 = KN | K2 = KRA | K3 = KV | K4 = KD | ||
1 | Republic of Tuva | 1.123 | 0.810 | 0.220 | 0.0065 | 1.696 | 0.138 |
2 | Republic of Ingushetia | 1.054 | 0.760 | 0.222 | 0.0016 | 1.849 | 0.151 |
3 | Republic of Crimea | 1.037 | 0.748 | 0.274 | 0.0031 | 1.401 | 0.096 |
4 | Chuvash Republic | 1.033 | 0.745 | 0.292 | 0.0031 | 1.383 | 0.083 |
5 | Karachayevo-Circassian Republic | 1.027 | 0.740 | 0.272 | 0.0029 | 1.384 | 0.106 |
6 | Vladimir Region | 1.023 | 0.738 | 0.392 | 0.0036 | 1.497 | 0.085 |
7 | Omsk Region | 1.019 | 0.735 | 0.351 | 0.0032 | 1.357 | 0.058 |
8 | Republic of Mari El | 1.019 | 0.735 | 0.355 | 0.0029 | 1.456 | 0.085 |
9 | Kirov Region | 1.018 | 0.735 | 0.385 | 0.0034 | 1.401 | 0.058 |
10 | Penza Region | 1.018 | 0.734 | 0.387 | 0.0032 | 1.448 | 0.070 |
11 | Republic of Daghestan | 1.017 | 0.733 | 0.267 | 0.0016 | 1.606 | 0.139 |
12 | Stavropol Territory | 1.013 | 0.731 | 0.339 | 0.0026 | 1.435 | 0.091 |
13 | Republic of North Ossetia—Alania | 1.010 | 0.729 | 0.423 | 0.0026 | 1.565 | 0.073 |
14 | Republic of Sakha (Yakutia) | 1.009 | 0.728 | 0.321 | 0.0022 | 1.385 | 0.073 |
15 | Republic of Kalmykia | 1.007 | 0.727 | 0.454 | 0.0034 | 1.655 | 0.117 |
16 | Smolensk Region | 1.005 | 0.725 | 0.342 | 0.0027 | 1.365 | 0.081 |
17 | Yaroslavl Region | 1.005 | 0.725 | 0.376 | 0.0032 | 1.348 | 0.067 |
18 | Kaluga Region | 1.004 | 0.724 | 0.394 | 0.0028 | 1.462 | 0.087 |
19 | Astrakhan Region | 1.003 | 0.723 | 0.364 | 0.0029 | 1.355 | 0.073 |
20 | Republic of Altai | 1.002 | 0.723 | 0.398 | 0.0034 | 1.391 | 0.081 |
21 | Kostroma Region | 1.001 | 0.722 | 0.388 | 0.0030 | 1.376 | 0.071 |
22 | Tyumen Region | 1.001 | 0.722 | 0.465 | 0.0034 | 1.450 | 0.052 |
23 | Jewish Autonomous Region | 1.001 | 0.722 | 0.369 | 0.0032 | 1.367 | 0.089 |
24 | Bryansk Region | 1.001 | 0.722 | 0.267 | 0.0022 | 1.402 | 0.146 |
25 | Irkutsk Region | 1.001 | 0.722 | 0.353 | 0.0030 | 1.371 | 0.096 |
26 | Komi Republic | 1.000 | 0.722 | 0.401 | 0.0028 | 1.383 | 0.056 |
27 | Republic of Tatarstan | 1.000 | 0.722 | 0.352 | 0.0027 | 1.318 | 0.064 |
28 | Republic of Buryatia | 0.994 | 0.717 | 0.395 | 0.0025 | 1.434 | 0.082 |
29 | Saratov Region | 0.993 | 0.717 | 0.429 | 0.0027 | 1.440 | 0.069 |
30 | Murmansk Region | 0.993 | 0.716 | 0.415 | 0.0026 | 1.386 | 0.055 |
31 | Ulyanovsk Region | 0.992 | 0.716 | 0.386 | 0.0024 | 1.377 | 0.068 |
32 | Kemerovo Region | 0.992 | 0.715 | 0.407 | 0.0023 | 1.424 | 0.067 |
33 | Kursk Region | 0.989 | 0.713 | 0.411 | 0.0029 | 1.386 | 0.081 |
34 | Khabarovsk Territory | 0.988 | 0.713 | 0.391 | 0.0026 | 1.385 | 0.085 |
35 | Krasnodar Territory | 0.987 | 0.712 | 0.393 | 0.0027 | 1.382 | 0.087 |
36 | Amur Region | 0.986 | 0.711 | 0.417 | 0.0033 | 1.349 | 0.082 |
37 | Krasnoyarsk Territory | 0.985 | 0.710 | 0.378 | 0.0025 | 1.358 | 0.086 |
38 | Altai Territory | 0.983 | 0.709 | 0.429 | 0.0023 | 1.421 | 0.070 |
39 | Vologda Region | 0.982 | 0.708 | 0.457 | 0.0026 | 1.414 | 0.058 |
40 | Samara Region | 0.979 | 0.706 | 0.392 | 0.0024 | 1.355 | 0.084 |
41 | Leningrad Region | 0.978 | 0.705 | 0.415 | 0.0030 | 1.399 | 0.112 |
42 | Novgorod Region | 0.977 | 0.704 | 0.445 | 0.0032 | 1.349 | 0.077 |
43 | Udmurtian Republic | 0.975 | 0.703 | 0.383 | 0.0023 | 1.329 | 0.081 |
44 | Chelyabinsk Region | 0.973 | 0.702 | 0.442 | 0.0023 | 1.387 | 0.068 |
45 | Arkhangelsk Region | 0.972 | 0.701 | 0.399 | 0.0021 | 1.354 | 0.076 |
46 | Ryazan Region | 0.971 | 0.701 | 0.497 | 0.0027 | 1.472 | 0.076 |
47 | Voronezh Region | 0.971 | 0.701 | 0.442 | 0.0027 | 1.401 | 0.090 |
48 | Republic of Mordovia | 0.968 | 0.698 | 0.422 | 0.0021 | 1.464 | 0.108 |
49 | Sakhalin Region | 0.966 | 0.697 | 0.428 | 0.0024 | 1.381 | 0.094 |
50 | Nizhny Novgorod Region | 0.964 | 0.695 | 0.493 | 0.0030 | 1.358 | 0.063 |
51 | Tambov Region | 0.963 | 0.695 | 0.478 | 0.0022 | 1.424 | 0.071 |
52 | Orel Region | 0.963 | 0.695 | 0.457 | 0.0021 | 1.410 | 0.074 |
53 | Ivanovo Region | 0.958 | 0.691 | 0.455 | 0.0021 | 1.323 | 0.055 |
54 | Chechen Republic | 0.958 | 0.691 | 0.250 | 0.0006 | 1.844 | 0.223 |
55 | Belgorod Region | 0.955 | 0.689 | 0.416 | 0.0017 | 1.388 | 0.088 |
56 | Kabardino-Balkarian Republic | 0.949 | 0.685 | 0.364 | 0.0017 | 1.430 | 0.159 |
57 | Republic of Bashkortostan | 0.947 | 0.683 | 0.451 | 0.0021 | 1.352 | 0.092 |
58 | Magadan Region | 0.944 | 0.681 | 0.559 | 0.0026 | 1.455 | 0.079 |
59 | Khanty-Mansi Autonomous Area—Yugra | 0.943 | 0.680 | 0.514 | 0.0016 | 1.458 | 0.065 |
60 | Tula Region | 0.939 | 0.678 | 0.530 | 0.0023 | 1.407 | 0.079 |
61 | Kurgan Region | 0.939 | 0.678 | 0.480 | 0.0022 | 1.369 | 0.096 |
62 | Tver Region | 0.937 | 0.676 | 0.516 | 0.0026 | 1.369 | 0.091 |
63 | Volgograd Region | 0.937 | 0.676 | 0.517 | 0.0018 | 1.416 | 0.072 |
64 | Perm Territory | 0.935 | 0.675 | 0.482 | 0.0016 | 1.414 | 0.088 |
65 | Trans-Baikal Territory | 0.935 | 0.674 | 0.412 | 0.0022 | 1.338 | 0.144 |
66 | Lipetsk Region | 0.933 | 0.673 | 0.488 | 0.0021 | 1.387 | 0.103 |
67 | Chukotka Autonomous Area | 0.926 | 0.668 | 0.273 | 0.0009 | 1.250 | 0.133 |
68 | Kaliningrad Region | 0.925 | 0.668 | 0.470 | 0.0019 | 1.268 | 0.078 |
69 | Novosibirsk Region | 0.920 | 0.664 | 0.428 | 0.0015 | 1.314 | 0.110 |
70 | Yamal-Nenets Autonomous Area | 0.916 | 0.661 | 0.468 | 0.0011 | 1.364 | 0.078 |
71 | Republic of Karelia | 0.914 | 0.660 | 0.533 | 0.0017 | 1.439 | 0.108 |
72 | Orenburg Region | 0.913 | 0.658 | 0.501 | 0.0015 | 1.364 | 0.092 |
73 | Moscow Region | 0.911 | 0.657 | 0.430 | 0.0014 | 1.341 | 0.127 |
74 | Rostov Region | 0.909 | 0.656 | 0.424 | 0.0013 | 1.375 | 0.141 |
75 | Pskov Region | 0.908 | 0.655 | 0.565 | 0.0020 | 1.420 | 0.109 |
76 | Tomsk Region | 0.906 | 0.653 | 0.388 | 0.0011 | 1.261 | 0.117 |
77 | Nenets Autonomous Area | 0.902 | 0.651 | 0.369 | 0.0013 | 1.182 | 0.115 |
78 | Republic of Khakassia | 0.898 | 0.648 | 0.517 | 0.0017 | 1.296 | 0.099 |
79 | Sverdlovsk Region | 0.896 | 0.646 | 0.531 | 0.0012 | 1.394 | 0.096 |
80 | Primorye Territory | 0.896 | 0.646 | 0.612 | 0.0020 | 1.365 | 0.080 |
81 | Republic of Adygeya | 0.878 | 0.633 | 0.529 | 0.0017 | 1.369 | 0.157 |
82 | Kamchatka Territory | 0.875 | 0.631 | 0.696 | 0.0020 | 1.377 | 0.069 |
References
- Chuaire, M.F.; Scartascini, C.; Tommasi, M. State capacity and the quality of policies. Revisiting the relationship between openness and government size. Econ. Politics 2017, 29, 133–156. [Google Scholar] [CrossRef]
- Besley, T.; Persson, T. The origins of state capacity: Property rights, taxation, and politics. Am. Econ. Rev. 2009, 99, 1218–1244. [Google Scholar] [CrossRef] [Green Version]
- Evans, P.B.; Rauch, J.E. A cross-national analysis of the effects of “Weberian” state structures on economic growth. Am. Sociol. Rev. 1999, 64, 748–765. [Google Scholar] [CrossRef] [Green Version]
- Bakanov, K.S.; Antonov, S.N.; Lyakhov, P.V.; Loparev, E.A.; Ermaganbetov, A.S.; Isaev, M.M.; Koblov, P.S.; Naumov, S.B.; Fattakhov, T.A.; Mazurchuk, T.M. The State and Trends of Road Safety in the Russian Federation in 2011–2020: An Analytical Review; Scientific Center for Road Safety of the Ministry of Internal Affairs: Moscow, Russia, 2022; p. 368. (In Russian) [Google Scholar]
- Kravchenko, P.A.; Zhankaziev, S.V.; Oleshchenko, E.M. Concept for ensuring zero mortality in Russia’s roads as a mechanism for fighting against causes of road traffic accidents. Transp. Russ. Fed. 2019, 4, 3–7. (In Russian) [Google Scholar]
- Bhatia, R.; Wier, M. Safety in Numbers re-examined: Can we make valid or practical inferences from available evidence? Accid. Anal. Prev. 2011, 43, 235–240. [Google Scholar] [CrossRef]
- Elvik, R. Can a safety-in-numbers effect and a hazard-in-numbers effect co-exist in the same data? Accid. Anal. Prev. 2013, 60, 57–63. [Google Scholar] [CrossRef] [Green Version]
- Elvik, R.; Bjørnskau, T. Safety-in-numbers: A systematic review and meta-analysis of evidence. Saf. Sci. 2017, 92, 274–282. [Google Scholar] [CrossRef] [Green Version]
- Davidson, R.; MacKinnon, J.G. Econometric Theory and Methods; Oxford University Press: Oxford, UK, 2003; p. 768. [Google Scholar]
- Breunig, R. Econometric Theory and Methods—By Russell Davidson and James G. MacKinnon. Econ. Rec. 2007, 83, 110–112. [Google Scholar] [CrossRef]
- Guerard, J.B. Regression Analysis and Forecasting Models. In Introduction to Financial Forecasting in Investment Analysis; Guerard, J.B., Ed.; Springer: New York, NY, USA, 2012; pp. 19–45. [Google Scholar] [CrossRef]
- Brugge, D.; Lai, Z.; Hill, C.; Rand, W. Traffic injury data, policy, and public health: Lessons from Boston Chinatown. J. Urban Health 2002, 79, 87–103. [Google Scholar] [CrossRef] [Green Version]
- Kravchenko, P.A. Road safety: A node of relations problems? Transp. Russ. Fed. 2006, 2, 61–62. (In Russian) [Google Scholar]
- Frost, J. Regression Analysis: An Intuitive Guide for Using and Interpreting Linear Models; Statistics By Jim Publishing: Online, 2020; p. 358. [Google Scholar]
- Fermi, E. Notes on Thermodynamics and Statistics; University of Chicago Press: Chicago, IL, USA, 1966; p. 182. [Google Scholar]
- Ben-Naim, A. Entropy and Information Theory: Uses and Misuses. Entropy 2019, 21, 1170. [Google Scholar] [CrossRef] [Green Version]
- The Official Website of the State Traffic Inspectorate of the Russian Federation. Indicators of the State of Road Safety. Available online: http://stat.gibdd.ru/ (accessed on 31 October 2022).
- Official Website of the Federal State Statistics Service of the Russian Federation. Available online: https://rosstat.gov.ru/folder/ (accessed on 2 November 2022).
- Pulyanova, K.V.; Voevodin, E.S.; Fadeev, A.I.; Fomin, E.V.; Askhabov, A.M.; Kashura, A.S. Analysis of road safety assessment methods. In IOP Conference Series: Materials Science and Engineering, Irkutsk, Russia, 27 May–1 June 2019; IOP publishing: Bristol, UK, 2019; Volume 632, p. 012015. [Google Scholar] [CrossRef]
- Martensen, H.; Diependaele, K.; Daniels, S.; Van den Berghe, W.; Papadimitriou, E.; Yannis, G.; Van Schagen, I.; Weijermars, W.; Wijnen, W.; Filtness, A.; et al. The European road safety decision support system on risks and measures. Accid. Anal. Prev. 2018, 125, 344–351. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Piankova, A.I.; Fattahov, T.A. Years of healthy life lost due to road traffic accidents in Russia. Profil. Meditsina 2017, 20, 30–36. [Google Scholar] [CrossRef]
- Fattahov, T.A.; Piankova, A.I. The Years of Life Lost as a Result of Road Traffic Accidents In Russia in 2000–2012. Probl. Sotsialnoi Gig. Zdravookhranenniiai. Istor. Med. 2018, 26, 271–275. [Google Scholar] [CrossRef] [PubMed]
- Hartley, R.V.L. Transmission of Information. Bell Syst. Tech. J. 1928, 7, 535–563. [Google Scholar] [CrossRef]
- Schrödinger, E. What is Life? Fe Physical Aspect of the Living Cell; Cambridge University Press: Cambridge, UK, 1994. [Google Scholar]
- Shannon, C.E. Mathematical Theory of Communication. Bell Syst. Tech. J. 1948, 27, 379–423. [Google Scholar] [CrossRef] [Green Version]
- Shannon, C.E. Communication Theory of Secrecy Systems. Bell Syst. Tech. J. 1949, 28, 656–715. [Google Scholar] [CrossRef]
- Wiener, N. Cybernetics: Or Control and Communication in the Animal and the Machine; Technology Press/John and Wiley and Sons: Hoboken, NJ, USA, 1948. [Google Scholar]
- Ashby, W.R. An Introduction to Cybernetics; Chapman and Hall: London, UK, 1956. [Google Scholar]
- Brillouin, L. Science and Information Theory; Academic Press: New York, NY, USA, 1956. [Google Scholar]
- Schelling, T.C. The Strategy of Conflict; Harvard University Press: Cambridge, MA, USA, 1960. [Google Scholar]
- Hurwicz, L. On informationally decentralized systems. In Decision and Organization; Radner, R., McGuire, C.B., Eds.; North-Holland: Amsterdam, The Netherlands, 1972. [Google Scholar]
- Foerster, H. Cybernetics of Cybernetics; University of Illinois: Urbana, IL, USA, 1974. [Google Scholar]
- Turchin, V.F. The Phenomenon of Science: A Cybernetic Approach to Human Evolution; Columbia University Press: New York, NY, USA, 1977. [Google Scholar]
- Luhmann, N. The world society as a social system. Int. J. Gen. Syst. 1982, 8, 131–138. [Google Scholar] [CrossRef]
- Liiv, E.H. Infodynamics. In Generalized Entropy and Negentropy; AO Juhisjelu: Tallinn, Estonia, 1998. (In Russian) [Google Scholar]
- Shchedrovitsky, G. Reflexion and relevant problems. Reflexive Process. Control 2002, 1, 41–45. [Google Scholar]
- Cover, T.M.; Thomas, J.A. Elements of Information Theory; John and Wiley and Sons: Hoboken, NJ, USA, 2006. [Google Scholar]
- Umpleby, S.A. Second order science: Logic, strategies, methods. Constr. Found. 2014, 10, 16–23. [Google Scholar]
- Müller, K.H. The multiple faces of reflexive research designs. Syst. Cybern. Inform. 2015, 13, 87–98. [Google Scholar]
- Mobus, G.E.; Kalton, M.C. Principles of Systems Science (Understanding Complex Systems); Springer: Berlin/Heidelberg, Germany, 2015; p. 791. [Google Scholar] [CrossRef]
- Novikov, D.A. Cybernetics: From Past to Future; Springer: Berlin/Heidelberg, Germany, 2016; p. 107. [Google Scholar]
- Lepskiy, V. Evolution of cybernetics: Philosophical and methodological analysis. Kybernetes 2018, 47, 249–261. [Google Scholar] [CrossRef]
- Reza, F.M. An Introduction to Information Theory; MacGraw-Hill: New York, NY, USA, 1961; p. 481. [Google Scholar]
- Pincus, S. Approximate Entropy as a Measure of System Complexity. Proc. Natl. Acad. Sci. USA 1991, 88, 2297–2301. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lin, Z.Z.; Wen, F.S. Entropy weight based decision-making theory and its application to black-start decision-making. Proceeding CSU EPSA 2009, 21, 26–33. [Google Scholar]
- Jing, L.P.; Michael, K.N. An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data. IEEE Trans. Knowl. Data Eng. 2009, 19, 1026–1041. [Google Scholar] [CrossRef]
- Qi, Y.; Wen, F.; Wang, K.; Liand, L.; Singh, S.N. A fuzzy comprehensive evaluation and entropy weight decision-making based method for power network structure assessment. Int. J. Eng. Sci. Technol. 2010, 2, 92–99. [Google Scholar] [CrossRef]
- Petrov, A.; Petrova, D. Atmospheric pollution in cities of Russia: Statistics, causes and characteristics. In IOP Conference Series: Earth And Environmental Science; IOP publishing: Bristol, UK, 2017; Volume 72, p. 12007. [Google Scholar] [CrossRef] [Green Version]
- Sethna, J.P. Entropy, Order Parameters, and Complexity; Clarendon Press: Oxford, UK, 2017; p. 371. [Google Scholar]
- Majda, A.; Kleeman, R.; Cai, D. A mathematical framework for quantifying predictability through relative entropy. Methods Appl. Anal. 2002, 9, 425–444. [Google Scholar]
- Kleeman, R. Measuring dynamical prediction utility using relative entropy. J. Atmos. Sci. 2002, 59, 2057–2072. [Google Scholar] [CrossRef]
- Han, T.S. Information-Spectrum Methods in Information Theory; Springer: Berlin/Heidelberg, Germany, 2003; Volume 538. [Google Scholar]
- Fedoseev, S.A.; Stolbov, V.Y.; Gitman, M.B.; Pustovoyt, K.S. Improving the quality of the industrial enterprise management based on the network-centric approach. R-Economy 2015, 4, 608–617. [Google Scholar] [CrossRef] [Green Version]
- Shenker, O. Information vs. entropy vs. probability. Euro. J. Phil. Sci. 2020, 10, 5. [Google Scholar] [CrossRef]
- Buchdahl, H.A. The Concepts of Classical Thermodynamics; Cambridge University Press: Cambridge, UK, 1966; p. 223. [Google Scholar]
- Kostic, M.M. The elusive nature of entropy and its physical meaning. Entropy 2014, 16, 953–967. [Google Scholar] [CrossRef] [Green Version]
- Ribeiro, M.; Henriques, T.; Castro, L.; Souto, A.; Antunes, L.; Costa-Santos, C.; Teixeira, A. The Entropy Universe. Entropy 2021, 23, 222. [Google Scholar] [CrossRef] [PubMed]
- Petrov, A.I. Entropy Method of Road Safety Management: Case Study of the Russian Federation. Entropy 2022, 24, 177. [Google Scholar] [CrossRef] [PubMed]
- Petrov, A.I.; Kolesov, V.I.; Petrova, D.A. Theory and Practice of Quantitative Assessment of System Harmonicity: Case of Road Safety in Russia before and during the COVID-19 Epidemic. Mathematics 2021, 9, 2812. [Google Scholar] [CrossRef]
- Mohammed, A.A.; Ambak, K.; Mosa, A.M.; Syamsunur, D. A review of the traffic accidents and related practices worldwide. Open Transp. J. 2019, 13, 65–83. [Google Scholar] [CrossRef] [Green Version]
- Smeed, R.J. Some Statistical Aspects of Road Safety Research. J. R. Stat. Soc. Ser. A 1949, 112, 1–34. [Google Scholar] [CrossRef]
- Smeed, R.J. Variations in the pattern of accident rates in different countries and their causes. Traffic Eng. Control 1968, 10, 364–371. [Google Scholar]
- Rumar, K. Transport Safety Visions, Targets and Strategies: Beyond 2000; European Transport Safety Council ETSC: Brussels, Belgium, 1999; Available online: https://archive.etsc.eu/documents/etsl1.pdf (accessed on 31 January 2023).
- Kolesov, V.; Petrov, A. Cybernetic Modeling in Tasks of Traffic Safety Management. Transp. Res. Procedia 2017, 20, 305–310. [Google Scholar] [CrossRef]
- Road Safety Profiles of the Subjects of the Russian Federation 2021. Available online: https://media.mvd.ru/files/embed/4478063 (accessed on 19 October 2021). (In Russian).
- Population of the Russian Federation by Regions and Municipalities. 2021. Available online: https://rosstat.gov.ru/compendium/document/13282 (accessed on 20 October 2021). (In Russian)
- Wu, K.; Nan, Q.; Wu, T. Philosophical Analysis of the Meaning and Nature of Entropy and Negative Entropy Theories. Complexity 2020, 2020, 8769060. [Google Scholar] [CrossRef]
- Wu, K. Philosophical classification of information forms. Potential Sci. 1984, 3, 33–35. [Google Scholar]
- Wu, K.; Qi, L. Introduction to Philosophical Information; Shaanxi People’s Press: Xi’an, China, 1987. [Google Scholar]
- Wu, K. On in-Itself Information; Academic Monthly: Shanghai, China, 1986; Volume 19. [Google Scholar]
- Wu, K.; Jian, W.; Tianqi, W. An Introduction to the Philosophy of Information; Xi’an Jiaotong University Press: Xi’an, China, 2019. [Google Scholar]
- Fagnant, D.J.; Kockelman, K. Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transp. Res. Part A Policy Pract. 2015, 77, 167–181. [Google Scholar] [CrossRef]
- Millard-Ball, A. Pedestrians, Autonomous Vehicles, and Cities. J. Plan. Educ. Res. 2016, 38, 6–12. [Google Scholar] [CrossRef] [Green Version]
- Wang, J.; Zhang, L.; Huang, Y.; Zhao, J. Safety of Autonomous Vehicles. J. Adv. Transp. 2020, 2020, 8867757. [Google Scholar] [CrossRef]
- Petrov, A.; Petrova, D. Assessment of Spatial Unevenness of Road Accidents Severity as Instrument of Preventive Protection from Emergency Situations in Road Complex. In IOP Conference Series: Materials Science and Engineering; IOP Publishing: Bristol, UK, 2016; Volume 142, p. 012116. [Google Scholar] [CrossRef]
- Rosencrantz, H.; Edvardsson, K.; Hansson, S.O. Vision Zero—Is it irrational? Transp. Res. Part A Policy Pract. 2007, 41, 559–567. [Google Scholar] [CrossRef]
- Johansson, R. Vision Zero—Implementing a policy for traffic safety. Saf. Sci. 2009, 47, 826–831. [Google Scholar] [CrossRef]
- Zwetsloot, G.I.J.M.; Kines, P.; Wybo, J.-L.; Ruotsala, R.; Drupsteen, L.; Bezemer, R.A. Zero Accident Vision based strategies in organisations: Innovative perspectives. Saf. Sci. 2017, 91, 260–268. [Google Scholar] [CrossRef]
- IRTAD Road Safety Database. 2022. Available online: https://trimis.ec.europa.eu/project/international-road-traffic-and-accident-database (accessed on 8 November 2022).
- | P | NVh | NRA | NV | ND |
P | 1.0 | 0.9823 | 0.9272 | 0.8909 | 0.7707 |
NVh | 0.9823 | 1.0 | 0.9282 | 0.9015 | 0.7974 |
NRA | 0.9272 | 0.9282 | 1.0 | 0.9892 | 0.8088 |
NV | 0.8909 | 0.9015 | 0.9892 | 1.0 | 0.8622 |
ND | 0.7707 | 0.7974 | 0.8088 | 0.8622 | 1.0 |
- | HR | TR | SignRTA | SvRTA |
HR | 1.0 | 0.8203 | 0.3125 | 0.2962 |
TR | 0.8203 | 1.0 | 0.4262 | 0.3759 |
SignRTA | 0.3125 | 0.4262 | 1.0 | 0.9662 |
SvRTA | 0.2962 | 0.3759 | 0.9662 | 1.0 |
Links in the Cause–Effect Chain of Road Traffic Accident Rate Formation | |||
---|---|---|---|
Link Population P—Fleet of vehicles NVh | Link Fleet of vehicles NVh—Road Traffic Accidents NRA | Link Road Traffic Accidents NRA—RTA Victims NV | Link RTA Victims NV—Fatality Cases ND |
Coefficients of information transformation Ki | |||
K1 = KN | K2 = KRA | K3 = KV | K4 = KD |
- | K1 = KN | K2 = KRA | K3 = KV | K4 = KD |
K1 = KN | 1.0 | −0.2540 | −0.2278 | −0.2854 |
K2 = KRA | −0.2540 | 1.0 | 0.1681 | −0.3197 |
K3 = KV | −0.2278 | 0.1681 | 1.0 | 0.4680 |
K4 = KD | −0.2854 | −0.3197 | 0.4680 | 1.0 |
Values of Coefficients of Information Transmission between Links Ki | |||
---|---|---|---|
K1 = KN | K2 = KRA | K3 = KV | K4 = KD |
[0.220; 0.696] | [0.0006; 0.0065] | [1.182; 1.849] | [0.052; 0.223] |
Numerical Values Ki (2021)/Representative Regions | ||
---|---|---|
Ki | Minimum Value Ki/Region | Maximum Value Ki/Region |
KN | 0.220—Republic of Tuva | 0.696—Kamchatka Territory |
KRA | 0.0006—Chechen Republic | 0.0065—Republic of Tuva |
KV | 1.182—Nenets Autonomous Area | 1.849—Republic of Ingushetia |
KD | 0.052—Tyumen Region | 0.223—Chechen Republic |
Population P | Fleet of Vehicles NVh | Road Traffic Accidents NRA | RTA Victims NV | Fatality Cases ND | |||
---|---|---|---|---|---|---|---|
332,609 | 73,084 | 474 | 804 | 111 | |||
Values of coefficients of information transmission between links in the cause–effect chain | |||||||
K1 = KN | K2 = KRA | K3 = KV | K4 = KD | ||||
0.220 | 0.0065 | 1.696 | 0.138 | ||||
Positive of a link in the cause-and-effect chain | |||||||
1.515 | 5.038 | 0.528 | 1.980 | ||||
Relative weight of positive of a corresponding link in the chain | |||||||
0.167 | 0.556 | 0.058 | 0.219 | ||||
Estimated value [] | |||||||
−1.788 | −0.587 | −2.842 | −1.521 | ||||
Estimated value [·] | |||||||
−0.299 | −0.326 | −0.166 | −0.332 | ||||
Estimated entropy value = 1.123 | |||||||
Estimated value of relative entropy Hn RS = 0.810 |
Population P | Fleet of Vehicles NVh | Road Traffic Accidents NRA | RTA Victims NV | Fatality Cases ND | |||
---|---|---|---|---|---|---|---|
312,704 | 217,792 | 443 | 610 | 42 | |||
Values of coefficients of information transmission between links in the cause–effect chain | |||||||
K1 = KN | K2 = KRA | K3 = KV | K4 = KD | ||||
0.696 | 0.0020 | 1.377 | 0.069 | ||||
Positive of a link in the cause-and-effect chain | |||||||
0.362 | 6.198 | 0.320 | 2.676 | ||||
Relative weight of positive of a corresponding link in the chain | |||||||
0.038 | 0.649 | 0.033 | 0.280 | ||||
Estimated value [] | |||||||
−3.274 | −0.433 | −3.397 | −1.273 | ||||
Estimated value [·] | |||||||
−0.124 | −0.281 | −0.114 | −0.356 | ||||
Estimated entropy value = 0.875 | |||||||
Estimated value of relative entropy Hn RS = 0.631 |
Population P | Fleet of Vehicles NVh | Road Traffic Accidents NRA | RTA Victims NV | Fatality Cases ND | |||
---|---|---|---|---|---|---|---|
145,557,576 | 60,136,889 | 133,277 | 181,656 | 14,871 | |||
Values of coefficients of information transmission between links in the cause–effect chain | |||||||
K1 = KN | K2 = KRA | K3 = KV | K4 = KD | ||||
0.413 | 0.002 | 1.363 | 0.082 | ||||
Positive of a link in the cause-and-effect chain | |||||||
0.884 | 6.112 | 0.310 | 2.503 | ||||
Relative weight of positive of a corresponding link in the chain | |||||||
0.090 | 0.623 | 0.032 | 0.255 | ||||
Estimated value [] | |||||||
−2.407 | −0.473 | −3.455 | −1.366 | ||||
Estimated value [·] | |||||||
−0.217 | −0.295 | −0.109 | −0.349 | ||||
Estimated entropy value = 0.969 | |||||||
Estimated value of relative entropy Hn RS = 0.699 |
Actual Statistics (2021) | |||||||||
---|---|---|---|---|---|---|---|---|---|
Population P | Fleet of Vehicles NVh | Road Traffic Accidents NRA | RTA Victims NV | Fatality Cases ND | |||||
145,557,576 | 60,136,889 | 133,277 | 181,656 | 14,871 | |||||
Actual values (2021) | |||||||||
Human Risk HR | Transport Risk TR | RTA Significance | RTA Severity | Entropy H RS | Relative Entropy Hn RS | ||||
10.21 | 24.73 | 0.112 | 8.20 | 0.969 | 0.699 | ||||
Expected (forecast) values (2030) | |||||||||
Human Risk HR | Transport Risk TR | RTA Significance | RTA Severity | Entropy H RS | Relative Entropy Hn RS | ||||
≈4.00 | ≈7.00 | ≈0.080 | ≈5.00 | 0.927 | 0.669 | ||||
Required statistics for this (2030) | |||||||||
Population P | Fleet of Vehicles NVh | Road Traffic Accidents NRA | RTA Victims NV | Fatality Cases ND | |||||
150,000,000 | 85,500,000 | 75,000 | 120,000 | 6000 |
Imagined values (2070) | |||||||||
Population P | Fleet of Vehicles NVh | Road Traffic Accidents NRA | RTA Victims NV | Fatality Cases ND | |||||
170,000,000 | 85,000,000 | 20,000 | 23,800 | 560 | |||||
Imagined values (2070) | |||||||||
Human Risk HR | Transport Risk TR | RTA Significance | RTA Severity | Entropy H RS | Relative Entropy Hn RS | ||||
≈0.33 | ≈0.66 | ≈0.028 | ≈2.4 | 0.856 | 0.618 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Petrov, A.I. Information and Entropy Aspects of the Specifics of Regional Road Traffic Accident Rate in Russia. Information 2023, 14, 138. https://doi.org/10.3390/info14020138
Petrov AI. Information and Entropy Aspects of the Specifics of Regional Road Traffic Accident Rate in Russia. Information. 2023; 14(2):138. https://doi.org/10.3390/info14020138
Chicago/Turabian StylePetrov, Artur I. 2023. "Information and Entropy Aspects of the Specifics of Regional Road Traffic Accident Rate in Russia" Information 14, no. 2: 138. https://doi.org/10.3390/info14020138