Understanding Spatial–Temporal Patterns in Trespassing on Railway Property
Trespassing on railway tracks is a growing problem in rail transport, with multiple causal factors, including increasing urbanisation, high-frequency rail traffic, higher volumes of traffic, etc. The predominant factor is human behaviour (lack of knowledge about trespassing, poor decision-making by...
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MDPI AG
2025-06-01
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| Series: | Safety |
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| Online Access: | https://www.mdpi.com/2313-576X/11/2/55 |
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| author | Silvestar Grabušić Danijela Barić Stefano Ricci |
| author_facet | Silvestar Grabušić Danijela Barić Stefano Ricci |
| author_sort | Silvestar Grabušić |
| collection | DOAJ |
| description | Trespassing on railway tracks is a growing problem in rail transport, with multiple causal factors, including increasing urbanisation, high-frequency rail traffic, higher volumes of traffic, etc. The predominant factor is human behaviour (lack of knowledge about trespassing, poor decision-making by road users and others). This research aims to analyse the available data to determine the frequency, patterns, and factors contributing to trespassing on railway tracks and to identify potential locations with the highest recorded trespassing. This is achieved by conducting a case study using data from various sources on trespassing from 2001 to 2023 on the Italian railway network. The methodology of this study consists of data collection on trespassing, data cleaning, and three-step analysis (description of variables used, and application of R programming language for descriptive statistics, correlation, and association analysis). The outcome of this study is the description of the data collecting process of trespassing on the Italian railway network, the identification of temporal factors, e.g., month, day, and hour of trespassing, and spatial factors, e.g., location and railway line where trespassing occurs most frequently, and a list of current and planned prevention measures on the Italian railway network. In the future, trespassing locations can be analysed according to the topology of risk. |
| format | Article |
| id | doaj-art-a76447bcc6834a4c97b34288ed3fc02e |
| institution | Kabale University |
| issn | 2313-576X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Safety |
| spelling | doaj-art-a76447bcc6834a4c97b34288ed3fc02e2025-08-20T03:27:22ZengMDPI AGSafety2313-576X2025-06-011125510.3390/safety11020055Understanding Spatial–Temporal Patterns in Trespassing on Railway PropertySilvestar Grabušić0Danijela Barić1Stefano Ricci2Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, CroatiaFaculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, CroatiaDepartment of Civil, Building and Environmental Engineering, Sapienza University of Rome, Via Eudossiana 18, 00185 Roma, ItalyTrespassing on railway tracks is a growing problem in rail transport, with multiple causal factors, including increasing urbanisation, high-frequency rail traffic, higher volumes of traffic, etc. The predominant factor is human behaviour (lack of knowledge about trespassing, poor decision-making by road users and others). This research aims to analyse the available data to determine the frequency, patterns, and factors contributing to trespassing on railway tracks and to identify potential locations with the highest recorded trespassing. This is achieved by conducting a case study using data from various sources on trespassing from 2001 to 2023 on the Italian railway network. The methodology of this study consists of data collection on trespassing, data cleaning, and three-step analysis (description of variables used, and application of R programming language for descriptive statistics, correlation, and association analysis). The outcome of this study is the description of the data collecting process of trespassing on the Italian railway network, the identification of temporal factors, e.g., month, day, and hour of trespassing, and spatial factors, e.g., location and railway line where trespassing occurs most frequently, and a list of current and planned prevention measures on the Italian railway network. In the future, trespassing locations can be analysed according to the topology of risk.https://www.mdpi.com/2313-576X/11/2/55trespassingrailwaysafetyhuman factorsspatial patternstemporal patterns |
| spellingShingle | Silvestar Grabušić Danijela Barić Stefano Ricci Understanding Spatial–Temporal Patterns in Trespassing on Railway Property Safety trespassing railway safety human factors spatial patterns temporal patterns |
| title | Understanding Spatial–Temporal Patterns in Trespassing on Railway Property |
| title_full | Understanding Spatial–Temporal Patterns in Trespassing on Railway Property |
| title_fullStr | Understanding Spatial–Temporal Patterns in Trespassing on Railway Property |
| title_full_unstemmed | Understanding Spatial–Temporal Patterns in Trespassing on Railway Property |
| title_short | Understanding Spatial–Temporal Patterns in Trespassing on Railway Property |
| title_sort | understanding spatial temporal patterns in trespassing on railway property |
| topic | trespassing railway safety human factors spatial patterns temporal patterns |
| url | https://www.mdpi.com/2313-576X/11/2/55 |
| work_keys_str_mv | AT silvestargrabusic understandingspatialtemporalpatternsintrespassingonrailwayproperty AT danijelabaric understandingspatialtemporalpatternsintrespassingonrailwayproperty AT stefanoricci understandingspatialtemporalpatternsintrespassingonrailwayproperty |