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Abstract

Being negatively impressed by the data published by the European Commission in CARE (Community database on Accidents on the Roads in Europe), where Poland is presented as the European Country with the highest rate of fatalities in road crashes involving cyclists during 4 years period (2009–2013), the Authors decided to analyse available data. Bikes become a more and more popular means of transport and the way of active recreation. In Warsaw, the share of bicycle trips rises 1 to 3% per year. The aforementioned, together with increasing traffic density, caused 4233 registered injuries among cyclists in 2018 in Poland. In 286 cases the accidents were direct reasons for the cyclists’ death. Considering these facts, it becomes extremely important to point the most influencing factors and conditions contributing to cyclists’ serious accidents. Onedimensional or two-dimensional statistics are not sufficient to find all important associations between the road conditions and the number of cyclists’ accidents. To overcome that the association analysis is applied. The results of the analysis can contribute to increasing the knowledge and safety of transport.
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Bibliography


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Authors and Affiliations

Hubert Anysz
1
ORCID: ORCID
Paweł Włodarek
1
ORCID: ORCID
Piotr Olszewski
1
ORCID: ORCID
Salvatore Cafiso
2
ORCID: ORCID

  1. Warsaw University of Technology, Faculty of Civil Engineering, Al. Armii Ludowej 16, 00-637 Warsaw, Poland
  2. University of Catania, Department of Civil Engineering and Architecture, Viale Andrea Doria 6, 95131 Catania, Italy
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Abstract

The homogenous properties – as flats are – have the set of key features that characterizes them. The area of a flat, the number of rooms and storey number where it is located, the technical state of a building, and the state of the vicinity of the blocks of flats assessed. The database comprises 222 flats with their transaction prices on the secondary estate market. The analysed flats are located in a certain quarter of Wrocław city in Poland. The database is large enough to apply machine learning for successful price predictions. Their close locations significantly lower the influence of clients’ assessments of the attractiveness of the location on the flat’s price. The hybrid approach is applied, where classifying precedes the solution of the regression problem. Dependently on the class of flats, the mean absolute percentage error achieved through the calculations presented in the article varies from 4,4 % to 7,8 %. In the classes of flats where the number of cases doesn’t allow for machine predicting, multivariate linear regression is applied. The reliable use of machine learning tools has proved that the automated valuation of homogenous types of properties can produce price predictions with the error low enough for real applications.
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Authors and Affiliations

Hubert Anysz
1
ORCID: ORCID
Monika Podwórna
2
Nabi Ibadov
1
ORCID: ORCID
Kunibert Lennerts
3
Kostiantyn Dikarev
4

  1. Warsaw University of Technology, Faculty of Civil Engineering, Al. Armii Ludowej 16, 00-637 Warsaw, Poland
  2. Wrocław University of Science and Technology, Faculty of Civil Engineering , Wyb. Wyspiańskiego 27, 50-370 Wrocław, Poland
  3. Karlsruhe Institute of Technology, Institute of Technology and Management in Construction, Gotthard-Franz-Street 3, 76131 Karlsruhe, Germany
  4. Prydniprovska State Academy of Civil Engineering and Architecture, Department of Construction Technology, 24a, Chernyshevskogo St., Dnipro, 49005, Ukraine

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