# Evaluation of Safety Degree at Railway Crossings in Order to Achieve Sustainable Traffic Management: A Novel Integrated Fuzzy MCDM Model

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## Abstract

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## 1. Introduction

## 2. Literature Review

## 3. Materials and Methods

- Condition 1. The ratio of the weight coefficients is equal to the comparative significance between the observed criteria.
- Condition 2. The final values of the weighted coefficients should satisfy the condition of mathematical transitivity.

## 4. Results

#### 4.1. Determining the Criteria Weights

#### 4.2. Evaluation of Level Crossings Using the Fuzzy MARCOS Method

## 5. Sensitivity Analysis and Discussion

## 6. Conclusions

- reducing level crossings by merging two or more level crossings into one,
- providing necessary visibility from roads to tracks,
- technical security of level crossings,
- abolition of level crossings, i.e., their replacement with overpasses or underpasses,
- lighting of level crossings,
- application of modern technology, i.e., technical and technological improvements on the crossing infrastructure, such as the installation of various types of sensors (audio, video, radar, and lasers) for timely detection of potentially dangerous situations,
- introduction of a video surveillance system at level crossings. Video surveillance can regularly monitor the functioning of traffic at crossings, and it can react quickly and efficiently in the event of any incident. In addition, drivers of road vehicles will behave more responsibly when they know that they will be sanctioned if they do not comply with legal regulations, and
- creating a culture of safety. All users (both road and rail, and especially road users) must be aware of dangers at level crossings.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Liang, C.; Ghazel, M.; Cazier, O.; Bouillaute, L. Advanced model-based risk reasoning on automatic railway level crossings. Saf. Sci.
**2020**, 124, 104592. [Google Scholar] [CrossRef] - Blagojević, A.; Stević, Ž.; Marinković, D.; Kasalica, S.; Rajilić, S. A novel entropy-fuzzy PIPRECIA-DEA model for safety evaluation of railway traffic. Symmetry
**2020**, 12, 1479. [Google Scholar] [CrossRef] - Obradović, M.; Jevremović, S.; Trpković, A.; Milosavljević, M. Traffic-spatial analysis of road-rail crossings on state roads in the Republic of Serbia. J. Road Traffic Eng.
**2020**, 66, 35–40. [Google Scholar] - Kasalica, S.; Obradović, M.; Blagojević, A.; Jeremić, D.; Vuković, M. Models for ranking railway crossings for safety improvement. Oper. Res. Eng. Sci. Theory Appl.
**2020**, 3, 85–100. [Google Scholar] [CrossRef] - Kasalica, S.; Vukadinović, R.; Lučanin, V. Study of drivers behaviour at a passive railway crossing. Promet Traffic Transp.
**2012**, 24, 193–201. [Google Scholar] [CrossRef] [Green Version] - Djordjević, B.; Krmac, E.; Mlinarić, T.J. Non-radial DEA model: A new approach to evaluation of safety at railway level crossings. Saf. Sci.
**2018**, 103, 234–246. [Google Scholar] [CrossRef] - Pamučar, D.; Lukovac, V.; Božanić, D.; Komazec, N. Multi-criteria FUCOM-MAIRCA model for the evaluation of level crossings: Case study in the Republic of Serbia. Oper. Res. Eng. Sci. Theory Appl.
**2018**, 1, 108–129. [Google Scholar] [CrossRef] - Salmon, P.M.; Read, G.J.M.; Walker, G.H.; Goode, N.; Grant, E.; Dallat, C.; Carden, T.; Naweed, A.; Stantond, N. STAMP goes EAST: Integrating systems ergonomics methods for the analysis of railway level crossing safety management. Saf. Sci.
**2018**, 110, 31–46. [Google Scholar] [CrossRef] - Márquez, F.P.G.; Pedregal, D.J.; Roberts, C. New methods for the condition monitoring of level crossings. Int. J. Syst. Sci.
**2015**, 46, 878–884. [Google Scholar] [CrossRef] - Đorđević, D.; Atanasković, P.; Gopčević, Š.; Mikić, T. Concept of level crossing Information subsystem. In Proceedings of the Railcon 2008—Scientific-expert conference on railways, Niš, Serbia, 9–10 October 2008; pp. 277–280. [Google Scholar]
- Bester, L.; Toruń, A. Modeling of reliability and safety at level crossing including in Polish railway conditions. In Proceedings of the International Conference on Transport Systems Telematics, Katowice/Kraków/Ustroń, Poland, 22–25 October 2014. [Google Scholar]
- Lutovac, T.; Lutovac, D. Development of a diagnostic system for computer controlled level crossing systems. Info M
**2013**, 11, 11–17. [Google Scholar] - Rudin-Brown, C.M.; Lenné, M.G.; Edquist, J.; Navarro, J. Effectiveness of traffic light vs. boom barrier controls at road–rail level crossings: A simulator study. Accid. Anal. Prev.
**2012**, 45, 187–194. [Google Scholar] [CrossRef] [PubMed] - Lenné, M.G.; Rudin-Brown, C.M.; Navarro, J.; Edquist, J.; Trotter, M.; Tomasevic, N. Driver behaviour at rail level crossings: Responses to flashing lights, traffic signals and stop signs in simulated rural driving. Appl. Ergon.
**2011**, 42, 548–554. [Google Scholar] [CrossRef] [PubMed] - Evans, A.W. Fatal accidents at railway level crossings in Great Britain 1946–2009. Accid. Anal. Prev.
**2011**, 43, 1837–1845. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Hu, S.-R.; Li, C.-S.; Lee, C.-K. Investigation of key factors for accident severity at railroad grade crossings by using a logit model. Saf. Sci.
**2010**, 48, 186–194. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Park, Y.-J.P.; Saccomanno, F.F. Collision frequency analysis using tree- based stratification. Transp. Res. Rec. J. Transp. Res. Board
**2005**, 1908, 121–129. [Google Scholar] [CrossRef] - Saccomano, F.; Fu, L.; Miranda-Moreno, M.L. Risk-based model for identifying highway-rail grade crossing blackspots. Transp. Res. Rec. J. Transp. Res. Board
**2004**, 1862, 127–135. [Google Scholar] [CrossRef] [Green Version] - Austin, R.; Carson, J. An alternative accident prediction model for highway-rail interfaces. Accid. Anal. Prev.
**2002**, 34, 31–42. [Google Scholar] [CrossRef] - Pamucar, D.; Ecer, F. Prioritizing the weights of the evaluation criteria under fuzziness: The fuzzy full consistency method—FUCOM-F. Facta Univ. Ser. Mech. Eng.
**2020**, 18, 419–437. [Google Scholar] - Simić, J.M.; Stević, Ž.; Zavadskas, E.; Bogdanović, V.; Subotić, M.; Mardani, A. A novel CRITIC-Fuzzy FUCOM-DEA-Fuzzy MARCOS model for safety evaluation of road sections based on geometric parameters of road. Symmetry
**2020**, 12, 2006. [Google Scholar] [CrossRef] - Pamučar, D.; Stević, Ž.; Sremac, S. A new model for determining weight coefficients of criteria in MCDM models: Full consistency method (FUCOM). Symmetry
**2018**, 10, 393. [Google Scholar] [CrossRef] [Green Version] - Božanić, D.; Tešić, D.; Kočić, J. Multi-criteria FUCOM–Fuzzy MABAC model for the selection of location for construction of single-span Bailey bridge. Decis. Mak. Appl. Manag. Eng.
**2019**, 2, 132–146. [Google Scholar] [CrossRef] - Nenadić, D. Ranking dangerous sections of the road using MCDM model. Decis. Mak. Appl. Manag. Eng.
**2019**, 2, 115–131. [Google Scholar] [CrossRef] - Beliakov, G.; Pradera, A.; Calvo, T. Aggregation Functions: A Guide for Practitioners; Springer: Heidelberg, Germany, 2007; Volume 221. [Google Scholar]
- Stević, Ž.; Stjepanović, Ž.; Božičković, Z.; Das, D.K.; Stanujkić, D. Assessment of conditions for implementing information technology in a warehouse system: A novel fuzzy piprecia method. Symmetry
**2018**, 10, 586. [Google Scholar] [CrossRef] [Green Version] - Đalić, I.; Stević, Ž.; Karamasa, C.; Puška, A. A novel integrated fuzzy PIPRECIA–interval rough SAW model: Green supplier selection. Decis. Mak. Appl. Manag. Eng.
**2020**, 3, 126–145. [Google Scholar] [CrossRef] - Memiş, S.; Demir, E.; Karamaşa, Ç.; Korucuk, S. Prioritization of road transportation risks: An application in Giresun province. Oper. Res. Eng. Sci. Theory Appl.
**2020**, 3, 111–126. [Google Scholar] [CrossRef] - Stanković, M.; Stević, Ž.; Das, D.K.; Subotić, M.; Pamučar, D. A new fuzzy MARCOS method for road traffic risk analysis. Mathematics
**2020**, 8, 457. [Google Scholar] [CrossRef] [Green Version] - Petrović, G.; Mihajlović, J.; Ćojbašić, Ž.; Madić, M.; Marinković, D. Comparison of three fuzzy MCDM methods for solving the supplier selection problem. Facta Univ. Ser. Mech. Eng.
**2019**, 17, 455–469. [Google Scholar] [CrossRef] - Pamucar, D.; Deveci, M.; Canıtez, F.; Božanić, D.I. A fuzzy full consistency Method-Dombi-Bonferroni model for prioritizing transportation demand management measures. Appl. Soft Comput.
**2020**, 87, 105952. [Google Scholar] [CrossRef] - Pamucar, D. Normalized weighted Geometric Dombi Bonferoni Mean Operator with interval grey numbers: Application in multicriteria decision making. Rep. Mech. Eng.
**2020**, 1, 44–52. [Google Scholar] [CrossRef] - Sałabun, W.; Urbaniak, K. A new coefficient of rankings similarity in decision-making problems. In Proceedings of the International Conference on Computational Science, Amsterdam, The Netherlands, 3–5 June 2020; pp. 632–645. [Google Scholar]
- Subotić, M.; Stević, B.; Ristić, B.; Simić, S. The selection of a location for potential roundabout construction–a case study of Doboj. Oper. Res. Eng. Sci. Theory Appl.
**2020**, 3, 41–56. [Google Scholar] [CrossRef] - Shekhovtsov, A.; Kołodziejczyk, J.; Sałabun, W. Fuzzy model identification using monolithic and structured approaches in decision problems with partially incomplete data. Symmetry
**2020**, 12, 1541. [Google Scholar] [CrossRef]

**Figure 2.**Graphical representation of the Šamac-Doboj section with the structure of all level crossings.

**Figure 3.**Overview of level crossings considered in the multi-criteria decision-making (MCDM) model.

**Figure 4.**The total number of serious accidents, accidents, incidents, fatalities, and injuries in the last five years.

**Figure 5.**Steps of the fuzzy MARCOS (measurement of alternatives and ranking according to compromise solution) method.

**Figure 9.**Results of sensitivity analysis in simulation of weight values of criteria through 30 scenarios.

**Figure 10.**Spearman’s correlation coefficient (SCC) and WS for obtained ranks through simulated values of criterion weights in 30 scenarios.

**Table 1.**Calculation and results obtained by applying the fuzzy-PIPRECIA (pivot pairwise relative criteria importance assessment) method for determining the criteria weights of traffic safety group.

$\overline{{\mathit{s}}_{\mathit{j}}^{}}$ | $\overline{{\mathit{k}}_{\mathit{j}}^{}}$ | $\overline{{\mathit{q}}_{\mathit{j}}^{}}$ | $\overline{{\mathit{w}}_{\mathit{j}}^{}}$ | DF | |
---|---|---|---|---|---|

C11 | (1,1,1) | (1,1,1) | (0.19,0.24,0.29) | 0.240 | |

C12 | (0.71,0.82,0.98) | (1.02,1.18,1.29) | (0.77,0.85,0.98) | (0.15,0.2,0.29) | 0.208 |

C13 | (0.64,0.76,0.9) | (1.1,1.24,1.36) | (0.57,0.68,0.9) | (0.11,0.16,0.26) | 0.171 |

C14 | (1.14,1.29,1.35) | (0.65,0.71,0.86) | (0.67,0.96,1.38) | (0.13,0.23,0.4) | 0.242 |

C15 | (0.44,0.54,0.67) | (1.33,1.46,1.56) | (0.43,0.66,1.04) | (0.08,0.16,0.3) | 0.170 |

SUM | (3.44,4.15,5.3) | ||||

$\overline{{s}_{j}^{\prime}}$ | $\overline{{k}_{j}^{\prime}}$ | $\overline{{q}_{j}^{\prime}}$ | $\overline{{w}_{j}^{\prime}}$ | DF | |

C11 | (0.9,1,1.1) | (0.9,1,1.1) | (0.76,1.21,1.67) | (0.11,0.21,0.36) | 0.216 |

C12 | (1.01,1.12,1.18) | (0.82,0.88,0.99) | (0.84,1.21,1.5) | (0.12,0.21,0.32) | 0.211 |

C13 | (0.56,0.69,0.77) | (1.23,1.31,1.44) | (0.83,1.06,1.22) | (0.12,0.18,0.26) | 0.185 |

C14 | (1.16,1.28,1.33) | (0.67,0.72,0.84) | (1.19,1.39,1.5) | (0.17,0.24,0.32) | 0.241 |

C15 | (1,1,1) | (1,1,1) | (0.15,0.17,0.22) | 0.174 | |

SUM | (4.62,5.87,6.88) |

**Table 2.**Final values of all criteria after applying the fuzzy FUCOM (full consistency method)–fuzzy Heronian mean operator–fuzzy PIPRECIA model.

Local Values | Global Values | Rank | |
---|---|---|---|

C11 | (0.15,0.223,0.326) | (0.048,0.094,0.163) | 4 |

C12 | (0.134,0.205,0.305) | (0.043,0.086,0.153) | 7 |

C13 | (0.114,0.173,0.263) | (0.037,0.073,0.132) | 8 |

C14 | (0.15,0.234,0.363) | (0.048,0.098,0.182) | 2 |

C15 | (0.113,0.165,0.259) | (0.037,0.069,0.13) | 9 |

C21 | (0.082,0.126,0.205) | (0.015,0.031,0.069) | 15 |

C22 | (0.125,0.21,0.341) | (0.023,0.052,0.115) | 11 |

C23 | (0.11,0.184,0.309) | (0.02,0.045,0.104) | 13 |

C24 | (0.168,0.298,0.518) | (0.031,0.073,0.174) | 3 |

C25 | (0.113,0.182,0.326) | (0.021,0.045,0.11) | 12 |

C31 | (0.082,0.126,0.205) | (0.022,0.05,0.116) | 10 |

C32 | (0.125,0.21,0.341) | (0.028,0.068,0.16) | 6 |

C33 | (0.11,0.184,0.309) | (0.026,0.067,0.161) | 5 |

C34 | (0.168,0.298,0.518) | (0.037,0.101,0.249) | 1 |

C35 | (0.113,0.182,0.326) | (0.021,0.043,0.097) | 14 |

$\mathit{f}({\tilde{\mathit{K}}}_{\mathit{i}}^{-})$ | $\mathit{f}({\tilde{\mathit{K}}}_{\mathit{i}}^{+})$ | K- | K+ | fK- | fK+ | Ki | Rank | |
---|---|---|---|---|---|---|---|---|

LC1 | (0.02,0.13,0.86) | (0.05,0.37,2.43) | 3.609 | 1.286 | 0.236 | 0.662 | 1.029 | 6 |

LC2 | (0.02,0.14,0.86) | (0.05,0.39,2.43) | 3.659 | 1.303 | 0.239 | 0.671 | 1.061 | 5 |

LC3 | (0.02,0.13,0.93) | (0.04,0.36,2.62) | 3.733 | 1.329 | 0.243 | 0.684 | 1.108 | 4 |

LC4 | (0.03,0.15,0.94) | (0.05,0.43,2.65) | 4.006 | 1.427 | 0.261 | 0.734 | 1.298 | 1 |

LC5 | (0.02,0.12,0.88) | (0.04,0.34,2.49) | 3.531 | 1.257 | 0.230 | 0.647 | 0.980 | 8 |

LC6 | (0.03,0.15,0.91) | (0.05,0.42,2.57) | 3.907 | 1.391 | 0.255 | 0.716 | 1.227 | 3 |

LC7 | (0.02,0.13,0.85) | (0.04,0.37,2.41) | 3.573 | 1.272 | 0.233 | 0.655 | 1.006 | 7 |

LC8 | (0.03,0.15,0.95) | (0.05,0.41,2.67) | 3.969 | 1.413 | 0.259 | 0.727 | 1.271 | 2 |

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**MDPI and ACS Style**

Blagojević, A.; Kasalica, S.; Stević, Ž.; Tričković, G.; Pavelkić, V.
Evaluation of Safety Degree at Railway Crossings in Order to Achieve Sustainable Traffic Management: A Novel Integrated Fuzzy MCDM Model. *Sustainability* **2021**, *13*, 832.
https://doi.org/10.3390/su13020832

**AMA Style**

Blagojević A, Kasalica S, Stević Ž, Tričković G, Pavelkić V.
Evaluation of Safety Degree at Railway Crossings in Order to Achieve Sustainable Traffic Management: A Novel Integrated Fuzzy MCDM Model. *Sustainability*. 2021; 13(2):832.
https://doi.org/10.3390/su13020832

**Chicago/Turabian Style**

Blagojević, Aleksandar, Sandra Kasalica, Željko Stević, Goran Tričković, and Vesna Pavelkić.
2021. "Evaluation of Safety Degree at Railway Crossings in Order to Achieve Sustainable Traffic Management: A Novel Integrated Fuzzy MCDM Model" *Sustainability* 13, no. 2: 832.
https://doi.org/10.3390/su13020832