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Thanapong Champahom, Panuwat Wisutwattanasak, Chamroeun Se, Chinnakrit Banyong, Sajjakaj Jomnonkwao and Vatanavongs Ratanavaraha
Among several approaches to analyzing crash research, the use of machine learning and econometric analysis has found potential in the analysis. This study aims to empirically examine factors influencing the single-vehicle crash for personal cars and truc...
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Paolo Intini, Nicola Berloco, Vittorio Ranieri and Pasquale Colonna
(1) Run-off-road (ROR) crashes are a crucial issue worldwide, resulting in a disproportionate number of traffic deaths. In safety research, macro-level analysis on large datasets is usually conducted by linking explanatory variables to ROR crash frequenc...
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Francesca Russo, Roberta Di Pace, Gianluca Dell?Acqua, Stefano de Luca
Pág. 1088 - 1096
In general in case of crash situations the quality of collected data is very limited and several information are usually unreliable. Thus it is recognised that a significant effort is required in order to improve the quality of the crash prediction model...
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Xuchun S. Tu
Pág. 1920 - 1930
A review of the crash history has identified that the run-off-road crash is one of key crash types in Queensland, Australia. Hazards on both sides of a carriageway are identified a potential risk exposed to road users. As such, a proper roadside design p...
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Francesca La Torre, Ceki Erginbas, Robert Thomson, Giuseppina Amato, ... Graham Hemmings
Pág. 4237 - 4246
Run Off Road (ROR) crashes are road accidents that often result in severe injuries or fatalities. To reduce the severity of ROR crashes, ?forgiving roadsides? need to be designed and this includes identifying situations where there is a need for a Vehicl...
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