Correlation analysis on crash factor in Surabaya-Manyar toll road
Crash investigators can create crash-solving and preventive goals by knowing crash's elements, such as the number, type, factor, and element. Human factors, vehicle, road conditions, and environment are the four types of elements that influence the risk of a crash. Surabaya-Manyar Toll Road h...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/11753/1/P16925_99078c76caf553cfd48ebd4008636a17%205.pdf http://eprints.uthm.edu.my/11753/ https://doi.org/10.1063/5.0200160 |
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| Summary: | Crash investigators can create crash-solving and preventive goals by knowing crash's elements, such as the
number, type, factor, and element. Human factors, vehicle, road conditions, and environment are the four types of
elements that influence the risk of a crash. Surabaya-Manyar Toll Road has been the main link between Surbaya and
Jakarta since 1993. There were 149 crashes between 2014 and 2018, with seven fatalities. An investigation and prevention
strategy might be devised to improve safety on the road. This study analyzed and discussed the correlation between crash
number and volume, factor, time occurred, and road length analysis. Data on the volume of vehicles and frequency of
accidents on the Surabaya-Manyar Toll Road between 2014 and 2018 were gathered through a collaborative effort
between the Highway Patrol division of the East Java Regional Police and PT. Margabumi Matraraya, the toll road's
management company. The result showed that the vehicle factor, human factor, time 06.00-18.00, and daily average were
all significantly correlated to the crash number (0.933, 0.505, 0.984, and 0.078), while the road factor, environment
factor, and time 19.00-05.00 were not significantly correlated to the crash number (-.0539, 0.616, and -0.519). These
implications prompted the following analysis of preventive and action in order to determine the primary factor
influencing the number of crashes, which has a strong association to be investigated |
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