Predicting the Segment-Based Effects of Heterogeneous Traffic and Road Geometric Features on Fatal Accidents
Inter-urban roads in Indonesia are characterized mainly by distinct road geometry and heterogeneous traffic features. The accident database from the Republic of Indonesia National Traffic Police recorded a substantial number of fatal accidents and fatalities along inter-urban roads. This study a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2022-01-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/4450 |
| _version_ | 1797972803902767104 |
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| author | Martha Leni Siregar Tri Tjahjono Nahry Yusuf |
| author_facet | Martha Leni Siregar Tri Tjahjono Nahry Yusuf |
| author_sort | Martha Leni Siregar |
| collection | DOAJ |
| description | Inter-urban
roads in Indonesia are characterized mainly by distinct road geometry and heterogeneous
traffic features. The accident database from the Republic of Indonesia National
Traffic Police recorded a substantial number of fatal accidents and fatalities
along inter-urban roads. This study aimed to analyze the effects of traffic
heterogeneity and road geometry features on fatal accidents along inter-urban
roads in South Sulawesi, Indonesia. Segment-based accident analysis was adopted
to minimize bias due to the large standard deviations of road lengths.
Vehicle-specific speeds, speed standard deviations, and volumes of six vehicle
categories, road surface condition, and road geometry were the classified
predicting factors. A machine learning technique was adopted to produce
predictions of the classification problem. A total of 1,068 road segment
observations from 2013–2016 were used to build and validate the model. Model
generalization was carried out using the out-of-sample 2019 data. With 26
potential predictors, three machine learning techniques based on the ensembles
of regression trees were used to avoid removing potential predictors
altogether. The results indicate that road-related features show the greatest
importance in predicting the number of fatal accidents. Among the speed
features, the average speed of angkots and speed standard deviation of
motorcycles showed the greatest importance. The average daily traffic (ADT) of
pickups had the greatest importance among other vehicle-specific ADTs. |
| first_indexed | 2024-04-11T03:54:13Z |
| format | Article |
| id | doaj.art-7c483974c5bc449fbf62b0d61fd240c6 |
| institution | Directory Open Access Journal |
| issn | 2086-9614 2087-2100 |
| language | English |
| last_indexed | 2024-04-11T03:54:13Z |
| publishDate | 2022-01-01 |
| publisher | Universitas Indonesia |
| record_format | Article |
| series | International Journal of Technology |
| spelling | doaj.art-7c483974c5bc449fbf62b0d61fd240c62023-01-02T01:04:49ZengUniversitas IndonesiaInternational Journal of Technology2086-96142087-21002022-01-011319210210.14716/ijtech.v13i1.44504450Predicting the Segment-Based Effects of Heterogeneous Traffic and Road Geometric Features on Fatal AccidentsMartha Leni Siregar0Tri Tjahjono1Nahry Yusuf2Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, IndonesiaDepartment of Civil and Environmental Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, IndonesiaDepartment of Civil and Environmental Engineering, Faculty of Engineering, Universitas Indonesia, Kampus UI Depok, Depok 16424, IndonesiaInter-urban roads in Indonesia are characterized mainly by distinct road geometry and heterogeneous traffic features. The accident database from the Republic of Indonesia National Traffic Police recorded a substantial number of fatal accidents and fatalities along inter-urban roads. This study aimed to analyze the effects of traffic heterogeneity and road geometry features on fatal accidents along inter-urban roads in South Sulawesi, Indonesia. Segment-based accident analysis was adopted to minimize bias due to the large standard deviations of road lengths. Vehicle-specific speeds, speed standard deviations, and volumes of six vehicle categories, road surface condition, and road geometry were the classified predicting factors. A machine learning technique was adopted to produce predictions of the classification problem. A total of 1,068 road segment observations from 2013–2016 were used to build and validate the model. Model generalization was carried out using the out-of-sample 2019 data. With 26 potential predictors, three machine learning techniques based on the ensembles of regression trees were used to avoid removing potential predictors altogether. The results indicate that road-related features show the greatest importance in predicting the number of fatal accidents. Among the speed features, the average speed of angkots and speed standard deviation of motorcycles showed the greatest importance. The average daily traffic (ADT) of pickups had the greatest importance among other vehicle-specific ADTs.https://ijtech.eng.ui.ac.id/article/view/4450fatal accidentsheterogeneous trafficmachine learningsegment-based effectsspeed standard deviation |
| spellingShingle | Martha Leni Siregar Tri Tjahjono Nahry Yusuf Predicting the Segment-Based Effects of Heterogeneous Traffic and Road Geometric Features on Fatal Accidents International Journal of Technology fatal accidents heterogeneous traffic machine learning segment-based effects speed standard deviation |
| title | Predicting the Segment-Based Effects of Heterogeneous Traffic and Road Geometric Features on Fatal Accidents |
| title_full | Predicting the Segment-Based Effects of Heterogeneous Traffic and Road Geometric Features on Fatal Accidents |
| title_fullStr | Predicting the Segment-Based Effects of Heterogeneous Traffic and Road Geometric Features on Fatal Accidents |
| title_full_unstemmed | Predicting the Segment-Based Effects of Heterogeneous Traffic and Road Geometric Features on Fatal Accidents |
| title_short | Predicting the Segment-Based Effects of Heterogeneous Traffic and Road Geometric Features on Fatal Accidents |
| title_sort | predicting the segment based effects of heterogeneous traffic and road geometric features on fatal accidents |
| topic | fatal accidents heterogeneous traffic machine learning segment-based effects speed standard deviation |
| url | https://ijtech.eng.ui.ac.id/article/view/4450 |
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