2015
DOI: 10.14513/actatechjaur.v8.n1.351
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Distinction of Road Categories by Road Users

Abstract: In order to create self-explaining roads, a remarkable difference should exist between road categories, whereas within a given road category the layout should be homogenous. The paper analyses, how many and which road categories are identified and distinguished by road users. A picture sorting task was completed to find out how road users group 45 different road scenes, and how these groups correspond to road categories according current standards.

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Cited by 3 publications
(3 citation statements)
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“…Figure 4 depicts the number of road/street categories suggested in the papers included in the sample. The number of road/street categories is particularly crucial, as an effective hierarchy system requires a proper balance (Kosztolányi-Iván et al, 2019). Specifically, when a system has numerous categories (more than 10), achieving readability can be challenging.…”
Section: Descriptive Analysis and General Comments On The Slr Tablementioning
confidence: 99%
See 1 more Smart Citation
“…Figure 4 depicts the number of road/street categories suggested in the papers included in the sample. The number of road/street categories is particularly crucial, as an effective hierarchy system requires a proper balance (Kosztolányi-Iván et al, 2019). Specifically, when a system has numerous categories (more than 10), achieving readability can be challenging.…”
Section: Descriptive Analysis and General Comments On The Slr Tablementioning
confidence: 99%
“…Similarly, Paraskevopoulos et al (2022) employed space syntax analysis to classify the road network in the Athens Metropolitan Area. Other similar research trends or ideas are reflected in the articles ofIván (2014) andKosztolányi-Iván et al (2019) who placed special emphasis on user participation, to determine which and how many road network categories a hierarchy system should have. Also,Chan and Cooper (2019) investigated whether hierarchy can be used as a tool for predicting traffic flows.Another research direction focuses on utilizing advanced algorithms, particularly machine learning techniques, for the automatic classification of urban road networks.…”
mentioning
confidence: 97%
“…A forgalmi rend felülvizsgálatához először is összegyűjtöttük a legfontosabb közlekedésbiztonságot befolyásoló tényezőket [1], [2], amelyekkel a későbbiekben foglalkozni szeretnénk és erre fektettünk hangsúlyt a felülvizsgálatban. Ezek a következők voltak:…”
Section: Bevezetésunclassified