- freely available
Sustainability 2017, 9(9), 1530; doi:10.3390/su9091530
3. Materials and Methods
3.2. Data Sources
3.3. Study Variables
3.4. Road Safety System Approach
3.5. European Road Safety Strategy
- A well-functioning internal market—harmonisation of transport requirements with infrastructure and industry capacity.
- Fair competition and workers’ rights—developing a competitive business environment based on collaboration.
- Reducing greenhouse gas emissions—a 30% reduction in CO2 emissions, as stipulated in the Paris COP21 agreement.
- Digital technologies—using innovations in the automotive field to increase road safety.
3.6. National Road Safety Strategy
- Developing a road-safe country for its inhabitants, tourists, and investors by progressively reducing the number of road accidents in the period 2016–2020;
- Improving road infrastructure on all road types to reduce the number of road accidents;
- Building additional motorway kilometres and express roads to reduce the number of accidents;
- Operational coordination of integrated interventions through interoperability and cooperation between intervention services;
- Improving the emergency service by renewing the telecommunications infrastructure;
- Implementation of an integrated system of information and statistical data for the continuous monitoring of road accidents and related actions;
- Continuous improvement of the quality of the emergency medical act and of the intervention system;
- Carrying out actions to raise road safety awareness among children, adolescents, and students.
4.1. Road Traffic Accidents Analysis According to the Established Variables
4.1.1. Collision Mode
4.1.2. Road Configuration
4.1.3. Conditions of Occurrence
4.1.4. Road Category
4.1.5. Type of Vehicle Involved
4.1.6. Personal Factors
4.1.7. Length of Time of the Driving License
4.2. Case Study: Assessing Accidents on a Road Section
- Type of collision: 70% of the accidents occurred as a result of collision between two cars, and 20% as a result of collision with road furniture.
- Road configuration: 90% of road accidents were caused by road alignment.
- Conditions of occurrence: 45% of accidents occurred in daylight, and 35% in low light.
- Road category: 30% in localities and 70% outside localities.
- Vehicle type involved: 65% cars, 45% animal traction, 55% bicycles. In this variable, it was considered that in an accident where there may be a collision between a car and a vehicle with animal traction, taking into account both variants is considered in the expressed percentage.
- Personal factors: most accidents have been committed by drivers in the following age categories: 26–35, 36–45, and over 56 years. In the situation of collisions with animal traction vehicles, the driver’s age is over 56 years.
- Driving license length of time: one year and over six years. In vehicles with animal traction, the experience cannot be accurately determined (the duration of use is over 15 years of use of the vehicle).
- Type of collision: 95% of accidents occurred as a result of collision between two cars and 5% as a result of collision with road furniture.
- Road configuration: 60% of road accidents were caused by road alignment, 40% due to non-compliance with the role of the emergency lane.
- Conditions of occurrence: 85% of accidents occurred in the daylight.
- Road category: 100% outside localities.
- The type of vehicle involved: 65% cars, 55% intervention vehicles, 50% auto-trailers and 35% lorries/trucks. In this variable, it was considered that in an accident there may be a collision between a car and a truck, taking into account both variants in the expressed percentage.
- Personal factors: most accidents were committed by drivers in the following age categories: 26–35, 46–55, and 56–65 years. The age of drivers of trucks, auto-trailers and intervention vehicles is in the 56–65 years category.
- Driving license length of time: over six years. The driver’s duration of use for trucks, auto-trailers, and intervention vehicles is over 10 years.
4.3. The Performance of the Romanian Road System and the Risk Factors
- Daytime usage of low beam light on all road categories—using daytime low beam lights helps reduce the number of accidents.
- The degree of use of seatbelts on all road categories—the percentage of use of seatbelts on motorway roads is more than one third higher than the one on national/European roads.
- Average running speeds—average running speed for vehicles is 33 km/h in localities, 66 km/h on national/European roads, and 124 km/h on motorways.
- The number of kilometres covered annually—cars register up to 15,000 km a year, heavy vehicles about 35,000 km, buses 50,000 km.
- The current state of the auto fleet in Romania has seen a considerable increase in vehicles over 10 years old over the last 10 years.
- Vehicle equipment contributes to driving performance on all road categories due to the comfort and special features offered to the driver (lane keep assist, rain sensors, driver fatigue warning, etc.).
- The driver’s behaviour, including: individual risk factors, administered drugs, tiredness, lack of experience, drink, hearing and visual impairment, the use of mobile phones, distributional attention, health status, and more.
- Vulnerable road traffic subjects include: pedestrian behaviour, unattended children, and older road users.
- Protective behaviours, including all tools that help reduce the likelihood of a road accident.
- Environment, including all attributes related to the environment, road condition, and other adjacent elements.
4.4. Improvement of Road Safety System
- Curves are the cause of the most accidents when considering the road configuration. Thus, 2353 deaths and 8917 injuries were recorded in the year 2016.
- During the entire analysed period, accidents occurring in the daylight are those that occur most frequently. The number of road accidents according to the configuration in 2012 is 26,928, and in 2013 and 2014 there was a decrease in the number of accidents. However, in 2015, these kinds of accidents become more frequent again. In studies [28,29,30], Wells, Mikulik, Zhao, daylight is an important factor in monitoring road safety.
- More than 60% of serious accidents took place on national roads and streets, resulting in three quarters of the number of serious injuries and deaths.
- 55% of the injuries produced on DN69/E671 resulted in the death of at least one person.
- 85% of the accidents produced on the A1 motorway resulted in the death of at least one person.
- It is seen from the literature that physical and mental health influence the number of accidents.
- By properly monitoring the health status of drivers, the number of accidents could be lower.
- Road quality and country strategy contribute to improving road performance and safety.
Conflicts of Interest
- Savolainen, P.; Mannering, F.; Lord, D.; Quddus, M. The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accid. Anal. Prev. 2011, 43, 1666–1676. [Google Scholar] [CrossRef] [PubMed]
- Kumar, S.; Toshniwal, D. A data mining framework to analyze road accident data. J. Big Data 2015, 2, 2–26. [Google Scholar] [CrossRef]
- Yannis, G.; Papadimitriou, E.; Chaziris, A.; Broughton, J. Modelling road accident injury under-reporting in Europe. Eur. Trans. Res. Rev. 2014, 6, 425–438. [Google Scholar] [CrossRef]
- Bin Islam, M.; Kanitpong, K. Identification of factors in road accidents through in-depth accident analysis. IATSS Res. 2008, 32, 58–67. [Google Scholar] [CrossRef]
- Karlaftis, M.; Tarko, A. Heterogeneity considerations in accident modelling. Accid. Anal. Prev. 1998, 30, 425–433. [Google Scholar] [CrossRef]
- Pardillo-Mayora, J.M.; Domínguez-Lira, C.A.; Jurado-Pina, R. Empirical calibration of a roadside hazardousness index for Spanish two-lane rural roads. Accid. Anal. Prev. 2010, 42, 2018–2023. [Google Scholar] [CrossRef] [PubMed]
- Shankar, V.; Mannering, F.; Barfield, W. Statistical analysis of accident severity on rural freeways. Accid. Anal. Prev. 1996, 28, 391–401. [Google Scholar] [CrossRef]
- Yau, K.K.W. Risk factors affecting the severity of single vehicle traffic accidents in Hong Kong. Accid. Anal. Prev. 1996, 36, 333–340. [Google Scholar] [CrossRef]
- Alonso, F.; Esteban, C.; Sanmartín, J.; Useche, S. Reported prevalence of health conditions that affect drivers. Cogent Med. 2017, 4, 1303920. [Google Scholar] [CrossRef]
- Lal, S.K.L.; Craig, A. A critical review of the psychophysiology of driver fatigue. Biol. Psychol. 2001, 55, 173–194. [Google Scholar] [CrossRef]
- Russo, F.; Biancardo, S.A.; Dell’Acqua, G. Road safety from the perspective of driver gender and age as related to the injury crash frequency and road scenario. Traffic Inj. Prev. 2014, 15, 25–33. [Google Scholar] [CrossRef] [PubMed]
- Useche, S.; Gómez, V.; Cendales, B. Stress-related Psychosocial Factors at Work, Fatigue, and Risky Driving Behavior in Bus Rapid Transport (BRT) Drivers. Accid. Anal. Prev. 2017, 104, 106–114. [Google Scholar] [CrossRef] [PubMed]
- Westerman, S.J.; Haigney, D. Individual differences in driver stress, error and violation. Pers. Indiv. Diff. 2000, 29, 981–998. [Google Scholar] [CrossRef]
- Wåhlberg, A.E. Some methodological deficiencies in studies on traffic accident predictors. Accid. Anal. Prev. 2003, 35, 473–486. [Google Scholar] [CrossRef]
- Ratanavaraha, V.; Suangka, S. Impacts of accident severity factors and loss values of crashes on expressways in Thailand. IATSS Res. 2014, 37, 130–136. [Google Scholar] [CrossRef]
- Cioca, L.-I.; Ivascu, L.; Rada, E.-C.; Torretta, V.; Ionescu, G. The study of sustainable development and technological impact on co2 reducing conditions: Case study of Romania. Sustainability 2015, 7, 1637–1650. [Google Scholar] [CrossRef]
- Ambrus, R.; Izvercian, M.; Ivascu, L.; Artene, A. The link between competitiveness and sustainability of enterprises. In Proceedings of the 4th BE-ci International Conference on Business & Economics, Brno, Cehia, 9–10 January 2017. [Google Scholar]
- European Commission. Road Safety: New Statistics Call for Fresh Efforts to Save Lives on EU Roads. Available online: http://europa.eu/rapid/press-release_IP-16-863_en.htm (accessed on 20 May 2017).
- European Commission. A Road Transport Strategy for Europe. Available online: https://ec.europa.eu/transport/road_safety (accessed on 20 May 2017).
- National Institute of Statistics (NIS). Economic Statistics—Transportation. Available online: http://statistici. insse.ro/shop/?lang=en (accessed on 10 May 2017).
- Traffic Police. Statistics. Available online: https://www.politiaromana.ro/ro/structura-politiei-romane/unitati-centrale/directia-rutiera/statistici (accessed on 10 May 2017).
- Salata, F.; Golasi, I.; Bombelli, E.; de Lieto Vollaro, E.; Nardecchia, F.; Pagliaro, F.; Gugliermetti, F.; de Lieto Vollaro, A. Case Study on Economic Return on Investments for Safety and Emergency Lighting in Road Tunnels. Sustainability 2015, 7, 9809–9822. [Google Scholar] [CrossRef]
- Ladeira, R.M.; Malta, D.C.; Morais, O.L.; Montenegro, M.M.; Soares, A.M.; Vasconcelos, C.H.; Mooney, M.; Naghavi, M. Road traffic accidents: Global Burden of Disease study, Brazil and federated units, 1990 and 2015. Rev. Bras. Epidemiol. 2015, 3, 157–170. [Google Scholar] [CrossRef]
- Carozzi, S.; Elorza, M.E.; Moscoso, N.S.; Ripari, N.V. Methodologies for estimating the indirect costs of traffic accidents. Rev. Med. Inst. Mex. Seguro Soc. 2017, 55, 441–451. [Google Scholar] [PubMed]
- Jama, H.H.; Grzebieta, R.H.; Friswell, R.; McIntosh, A.S. Characteristics of fatal motorcycle crashes into roadside safety barriers in Australia and New Zealand. Accid. Anal. Prev. 2011, 43, 652–660. [Google Scholar] [CrossRef]
- Soehodho, S. Public transportation development and traffic accident prevention in Indonesia. IATSS Res. 2017, 40, 76–80. [Google Scholar] [CrossRef]
- Steenberghen, T.; López, E. Overcoming barriers to the implementation of alternative fuels for road transport in Europe. J. Clean. Prod. 2008, 16, 577–590. [Google Scholar] [CrossRef]
- Wells, P. Deaths and injuries from car accidents: An intractable problem? J. Clean. Prod. 2007, 15, 1116–1121. [Google Scholar] [CrossRef]
- Mikulik, J. Road accidents in the Czech Republic. IATSS Res. 2009, 33, 72–74. [Google Scholar] [CrossRef]
- Zhao, S. Road accidents in the China. IATSS Res. 2009, 33, 75–79. [Google Scholar] [CrossRef]
- Dalla Chiara, B.; Pellicelli, M. Sustainable road transport from the energy and modern society points of view: Perspectives for the automotive industry and production. J. Clean. Prod. 2016, 133, 1283–1301. [Google Scholar] [CrossRef]
- Tähkämö, L.; Halonen, L. Life cycle assessment of road lighting luminaires—Comparison of light-emitting diode and high-pressure sodium technologies. J. Clean. Prod. 2015, 93, 234–242. [Google Scholar] [CrossRef]
- Feniser, C.; Burz, G.; Mocan, M.; Ivascu, L.; Gherhes, V.; Otel, C.C. The Evaluation and Application of the TRIZ Method for Increasing Eco-Innovative Levels in SMEs. Sustainability 2017, 9, 1125. [Google Scholar] [CrossRef]
- Chubukov, A.; Kapitanov, V.; Monina, O.; Silyanov, V.; Brannolte, U. Simulation of regional mortality rate in road accidents. Trans. Res. Proc. 2017, 20, 112–124. [Google Scholar] [CrossRef]
- Cheng, A.S.K.; Liu, P.Y.; Tulliani, N. Relationship between driving-violation behaviours and risk perception in motorcycle accidents. Hong Kong J. Occup. Ther. 2015, 25, 32–38. [Google Scholar] [CrossRef]
- Torretta, V.; Rada, E.C.; Schiavon, M.; Viotti, P. Decision support systems for assessing risks involved in transporting hazardous materials: A review. Saf. Sci. 2017, 92, 1–9. [Google Scholar] [CrossRef]
- Vilela da Silva, L.O.; Braga, M.G.C. Violent events on the road: Risk perception of traffic-related and non traffic-related situations. Accid. Anal. Prev. 2017, 2. [Google Scholar] [CrossRef] [PubMed]
- Ivascu, L.; Cioca, L.-I. Opportunity Risk: Integrated Approach to Risk Management for Creating Enterprise Opportunities. In Proceedings of the 2nd International Conference on Psychology, Management and Social Science, Shenzhen, China, 23–24 January 2014. [Google Scholar]
- Korchagin, V.; Ljapin, S.; Rizaeva, J.; Konovalova, V. Subsystem of road accident consequences elimination. methodology of subsystem efficiency improvement. Trans. Res. Proc. 2017, 20, 316–320. [Google Scholar] [CrossRef]
- Cioca, L.I.; Moraru, R.I. The importance of occupational health and safety in the framework of corporate social responsibility. Manag. Sustain. Dev. 2010, 2, 71–77. [Google Scholar]
- Cioca, M.; Cioca, L.I.; Buraga, S.C. Spatial [Elements] decision support system used in disaster management. In Proceedings of the IEEE International Conference on Digital Ecosystems and Technologies, Cairns, Australia, 21–23 February 2007; p. 235. [Google Scholar]
|Variable||Variable Type||Variable Implication|
|Collision mode||Between vehicles||This variable highlights the severity of accidents according to the number of factors involved in the accident.|
|Vehicle and pedestrian|
Crossing the railway
|The most important road configurations that are involved in road accidents are these types. These are found on all road categories.|
|Condition of occurrence||Daylight|
|The intensity of light within a day was considered to form the three categories of values for the condition of an accident occurrence.|
|Road category||Occurred on motorways|
Occurred in localities
Occurred outside of localities
|For this variable, the main values were considered depending on the intensification of the use of each type of road|
|Type of involved vehicle||car, van, bike, moped & motorcycle, animal traction, auto-trailer, intervention vehicle, and lorry/truck||All vehicle categories were considered as values for this variable. The type of vehicle involved in an accident is important for improving the road system on the direction of action|
|Personal factors||Age of the driver|
Gender of the driver
|The two values for the variable were considered relevant for the improvement of the national road system.|
|Length of time of the driving license||Period of years||The length of time measured in years was considered for the evaluation of this variable.|
|Category of Public Roads||Improvement Status||UM||2012||2013||2014||2015||2016|
|County and communal||Total||km||67,298||67,599||67,912||68,314||68,468|
|Collision Mode||2012||2013||2014||2015||2016||Variation (%) 2012–2016|
|Collisions between vehicles||11,932||11,128||11,432||13,540||13,690||+14.7%|
|Collisions between a vehicle and a pedestrian||8791||8301||8576||8995||9010||+2.4%|
|Collisions involving only one vehicle||6205||5398||5347||6409||6423||+3.5%|
|Dynamics compared to 2010, year 2010 = 100%||+3.5%||−4.5%||−2.5%||+11.3%||+12%||-|
|Characteristic||No. of Deaths||No. of Injured|
|Crossing the railway||147||135|
|Conditions of Accident Occurrence||2012||2013||2014||2015||2016||Variation (%) 2012–2016|
|The Place Where Accidents Occur||2012||2013||2014||2015||2016||Variation (%) 2012–2016|
|Occurred on motorways||131||136||129||175||201||+53%|
|Occurred in localities (excluding motorways)||22,108||20,541||21,080||23,921||24,568||+11.12%|
|Occurred outside of localities (excluding motorways)||4689||4150||4146||4848||4354||−7.2%|
|Km of motorway||550||644||683||747||747||+35.8%|
|Density of road accidents reported per km of motorway||4.19||4.73||5.29||4.26||3.71||+14.5%|
|Variable Type||2012||2013||2014||2015||2016||Variation (%) 2012–2016|
|Moped & Motorcycle||860||598||559||560||589||−32%|
|Involved||Involved with Guilt||Involved||Involved with Guilt|
|Road Category||The Main Cause||Percentage Cause/Road Type|
|Street||No priority to pedestrians||21%|
|No priority to vehicles||15%|
|Unlawful crossing of pedestrians||23%|
|Bicycle rider’s deviations||12%|
|National road||Speed not adapted to road conditions||25%|
|The irregular crossing of pedestrians||13%|
|County Road||Speed not adapted to road conditions||30%|
|The irregular crossing of pedestrians||15%|
|Bicycle rider’s deviations||10%|
|Communal road||Bicycle rider’s deviations||25%|
|Speed not adapted to road conditions||18%|
|Deviations of the drivers of animal traction vehicles||13%|
|Motorway||Failure to observe the distance between vehicles||25%|
|Speed not adapted to road conditions||20%|
|Attention distraction with other activities||15%|
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