BibTex Citation Data :
@article{MKTS71536, author = {Bambang Istiyanto and Raudina Putri}, title = {Identifikasi Lokasi Rawan Kecelakaan Menggunakan Haddon’s Matrix, EAN, dan UCL di Kabupaten Tulungagung}, journal = {MEDIA KOMUNIKASI TEKNIK SIPIL}, volume = {31}, number = {1}, year = {2025}, keywords = {Blackspot; EAN; UCL; Haddon’s matrix}, abstract = { This study aims to identify accident-prone locations (blackspot) on the Cuwiri – Karangrejo road segment in Tulungagung Regency using the Equivalent Accident Number (EAN) and Upper Control Limit (UCL) methods. Accident data from 2021 to 2024 served as the basis for analysis. The calculation results show the highest EAN value of 870 and UCL value of 201.8, indicating a very high accident risk. Out of 23 stations, 7 were identified as blackspots with the highest EAN of 138 at STA 0+600-0+900 and UCL value of 59.27. The most common type of collision was front-rear (40 cases) and the most frequent vehicle involvement was motorcycle vs motorcycle (64 cases). The main contributing factors include human error, vehicle condition, and inadequate road infrastructure. The findings are expected to support the Tulungagung Regency Government in addressing accident-prone areas. }, issn = {25496778}, pages = {61--68} doi = {10.14710/mkts.v31i1.71536}, url = {https://ejournal.undip.ac.id/index.php/mkts/article/view/71536} }
Refworks Citation Data :
This study aims to identify accident-prone locations (blackspot) on the Cuwiri – Karangrejo road segment in Tulungagung Regency using the Equivalent Accident Number (EAN) and Upper Control Limit (UCL) methods. Accident data from 2021 to 2024 served as the basis for analysis. The calculation results show the highest EAN value of 870 and UCL value of 201.8, indicating a very high accident risk. Out of 23 stations, 7 were identified as blackspots with the highest EAN of 138 at STA 0+600-0+900 and UCL value of 59.27. The most common type of collision was front-rear (40 cases) and the most frequent vehicle involvement was motorcycle vs motorcycle (64 cases). The main contributing factors include human error, vehicle condition, and inadequate road infrastructure. The findings are expected to support the Tulungagung Regency Government in addressing accident-prone areas.
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Last update: 2025-09-28 21:00:58
