A conflict-based safety assessment technique for rear-end crash risk at signalized intersections in a lower-middle-income country: A comparison between homogeneous and heterogeneous traffic conditions

Chauhan, Ritvik; Dhamaniya, Ashish; Arkatkar, Shriniwas; Haque, Md. Mazharul

doi:10.1016/j.ssci.2023.106075

Cited by 15 publications

(4 citation statements)

References 48 publications

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“…Tujuan pengaturan simpang dengan sinyal adalah mengoptimalkan kapasitas jalan dan menghindari terjadinya kecelakaan lalu lintas di daerah tersebut. Karena perilaku lalu lintas bersifat dinamis, maka kinerja pengaturan lalu lintas di daerah simpang perlu dievaluasi secara periodik [4], [5].…”

Section: Pendahuluanunclassified

Evaluasi Simpang Bersinyal

Khotimah,

Sarwanta,

Sunaryo

2023

jri

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Simpang Tiga Tugu Api Karangampel merupakan salah satu persimpangan yang ada di kawasan kota Indramayu yang memiliki volume lalu lintas cukup tinggi. Permasalahan pada daerah persimpangan jalan berupa lamanya tundaan yang menyebabkan terjadinya antrian. Tujuan dari penelitian ini adalah untuk mengetahui kinerja pada kondisi eksisting dan 5 tahun ke depan dan melakukan penanganan pada simpang jika terjadi masalah. Hasil analisis kinerja Simpang ini berdasarkan MKJI 1997, dan didapatkan arus kendaraan terjadi di pendekat Utara sebesar 610 smp/jam, kapasitas terbesar pada pendekat Utara yaitu 1012 smp/jam,. Derajat kejenuhan diperoleh 0,60, untuk tundaan simpang sebesar 31,64 smp/det, panjang antrian pendekat utara 60,38 m, selatan 50,72 m, pendekat barat 49,85 m. Untuk kondisi simpang 5 tahun tahun kedepan didapatkan nilai derajat kejenuhan yaitu 0,91 smp/jam, panjang antrian sebesar 98,11 meter, tundaan simpang terbesar 51,60 dt/smp. Hal ini menunjukkan bahwa tingkat pelayanan simpang buruk dan perlu pengkajian ulang terhadap tingkat pelayanan simpang. Sehingga perlu dilakukan alternatif penanganan untuk memperbaiki kinerja simpang. Usulan langkah penanganan yang dapat dilakukan yaitu dengan Perubahan waktu siklus sinyal memperoleh derajat kejenuhan 0,84, Tundaan Lalu Lintas 42,63 det/smp, Panjang antrian yaitu sebesar 67,92 m.

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Section: Pendahuluanunclassified

Evaluasi Simpang Bersinyal

Khotimah,

Sarwanta,

Sunaryo

2023

jri

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“…Surrogate safety measures (SSM) such as TTC deceleration rate to avoid a crash (DRAC), and postencroachment time (PET) are integral to collision avoidance algorithms, acting as indicators for potential collision risks [15]. The researchers in [16] utilized empirical trajectory data to investigate SSM at signalized intersections. Their study revealed that, within heterogeneous traffic, a vehicle experiences a minimum of twice as many rear-end conflicts.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Intersection Traffic Safety Utilizing V2I Communications: Design and Evaluation of Machine Learning Based Framework

Shahriar,

Kale,

Chang

2023

IEEE Access

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In recent years, improving intersection traffic safety has become a major focus for researchers. However, it remains a significant concern due to the increasing number of vehicles and the introduction of autonomous and cooperative driving systems. Advancements in artificial intelligence (AI) and vehicle-to-everything (V2X) technologies offer promising solutions to reduce collisions between vehicles. As V2X technologies are slowly being integrated into traffic safety systems, questions arise about their impact on safety effectiveness. This research area is relatively unexplored but has potential to enhance intersection safety. In this paper, we introduce the Intersection Traffic Safety Framework (ITSF), a safetyoriented system devised to mitigate collisions at road intersections. This framework achieves this through the implementation of a collision avoidance mechanism that harnesses vehicle-to-infrastructure (V2I) communications. Additionally, it incorporates a machine learning model tasked with distinguishing between vehicles posing a risk and those that do not, as part of the collision avoidance process. Furthermore, this work assesses the performance of this framework by considering critical factors like the penetration rate of V2X-enabled vehicles, end-to-end latency, and the responsiveness of drivers to safety alerts. The proposed approach proves highly effective in ensuring road users' safety with a penetration rate of 60% or higher, resulting in a significant reduction in collisions and nearly 98% accuracy in classifying risky vehicles. Additionally, the algorithm remains successful even when some drivers neglect warnings, showcasing its robustness in minimizing collision rates.

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“…Moreover, the qualities directly related to the accidents contribute significantly to the magnitude of such accidents [ 10 ]. Among these factors are the speed at which the car was hit by the other vehicle, also known as the second automobile [ 12 ], the number of cars involved in the incident [ 13 ], and the congestion level. Other external factors contributing to the seriousness of accidents include driving at night and unfavorable weather conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The demographic trends impacting road accident incidence have been thoroughly documented in high-income countries such as the United States and Sweden, thanks to the research conducted [12]. Several pieces of empirical data demonstrate, time and time again, that certain demographic groups are disproportionately represented in accidents that result in catastrophic consequences [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Comparing fatal crash risk factors by age and crash type by using machine learning techniques

Alshehri,

Alanazi,

Yosri

et al. 2024

PLoS ONE

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This study aims to use machine learning methods to examine the causative factors of significant crashes, focusing on accident type and driver’s age. In this study, a wide-ranging data set from Jeddah city is employed to look into various factors, such as whether the driver was male or female, where the vehicle was situated, the prevailing weather conditions, and the efficiency of four machine learning algorithms, specifically XGBoost, Catboost, LightGBM and RandomForest. The results show that the XGBoost Model (accuracy of 95.4%), the CatBoost model (94% accuracy), and the LightGBM model (94.9% accuracy) were superior to the random forest model with 89.1% accuracy. It is worth noting that the LightGBM had the highest accuracy of all models. This shows various subtle changes in models, illustrating the need for more analyses while assessing vehicle accidents. Machine learning is also a transforming tool in traffic safety analysis while providing vital guidelines for developing accurate traffic safety regulations.

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A conflict-based safety assessment technique for rear-end crash risk at signalized intersections in a lower-middle-income country: A comparison between homogeneous and heterogeneous traffic conditions

Cited by 15 publications

References 48 publications

Evaluasi Simpang Bersinyal

Evaluasi Simpang Bersinyal

Enhancing Intersection Traffic Safety Utilizing V2I Communications: Design and Evaluation of Machine Learning Based Framework

Comparing fatal crash risk factors by age and crash type by using machine learning techniques

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