Impact on Driver Behavior, Performance, Fatigue, Over Speed and Infrastructure as A Factor of Road Accident: A Review

Suprihatiningsih, Wiwit; Leman, Abdul Mutalib; Feriyanto, Dafit; Pranoto, Hadi; Shuaib, N. A.

doi:10.1088/1757-899x/864/1/012105

Cited by 3 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traffic violation has become the most important risk to road safety [ 2 , 3 ]. The distribution of traffic violations has significant spatiotemporal heterogeneity [ 4 , 5 ] affected by various factors such as people, vehicles, roads, and the environment [ 5 , 6 , 7 , 8 , 9 ]. Traffic violation features refer to the factors related to the occurrence of traffic violations and are always used to predict traffic violations.…”

Section: Introductionmentioning

confidence: 99%

GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network

Zhou

Wang

Zhang

et al. 2023

IJERPH

View full text Add to dashboard Cite

Prediction of traffic violations plays a key role in transportation safety. Combining with deep learning to predict traffic violations has become a new development trend. However, existing methods are based on regular spatial grids which leads to a fuzzy spatial expression and ignores the strong correlation between traffic violations and road network. A spatial topological graph can express the spatiotemporal correlation more accurately and then improve the accuracy of traffic violation prediction. Therefore, we propose a GATR (graph attention network based on road network) model to predict the spatiotemporal distribution of traffic violations, which adopts a graph attention network model combined with historical traffic violation features, external environmental features, and urban functional features. Experiments show that the GATR model can express the spatiotemporal distribution pattern of traffic violations more clearly and has higher prediction accuracy (RMSE = 1.7078) than Conv-LSTM (RMSE = 1.9180). The verification of the GATR model based on GNN Explainer shows the subgraph of the road network and the influence degree of features, which proves GATR is reasonable. GATR can provide an important reference for prevention and control of traffic violations and improve traffic safety.

show abstract

Section: Introductionmentioning

confidence: 99%

GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network

Zhou

Wang

Zhang

et al. 2023

IJERPH

View full text Add to dashboard Cite

show abstract

A new hand-modeled learning framework for driving fatigue detection using EEG signals

Doğan

Tuncer

Bayğın

et al. 2023

Neural Comput & Applic

View full text Add to dashboard Cite

Evaluation of the physical barrier on lane segregation at multilane highway towards motorcyclist: A case study

Khan,

Adnan,

Sulaiman

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

In Malaysia, the escalating annual road traffic accidents, primarily attributed to human factors, necessitate a closer examination of these influences. This study focuses on assessing the impact of lane segregation on motorcyclists’ behavior within multilane roadways. Conducted along the R5 highway from Kapar to Kuala Selangor, classified as a rural highway, the study evaluates the 85th percentile operating speed, crucial for multilane flow rate. Two locations, one with median physical barriers and the other with only line markings, were compared. Traffic flow and speed were observed at 5-minute intervals over 45 minutes using a laser gun and tally counter. Analysis, based on the Malaysian Highway Capacity Manual, revealed that the median barrier location promoted safe speeds and a higher motorcycle percentage. Conversely, the absence of a median barrier but with line markings resulted in elevated speeds exceeding the limit, accompanied by a lower motorcycle percentage. These findings underscore the significance of median physical barriers in reducing vehicle speed, enhancing road safety, and curbing road accidents.

show abstract

Impact on Driver Behavior, Performance, Fatigue, Over Speed and Infrastructure as A Factor of Road Accident: A Review

Cited by 3 publications

References 17 publications

GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network

GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network

A new hand-modeled learning framework for driving fatigue detection using EEG signals

Evaluation of the physical barrier on lane segregation at multilane highway towards motorcyclist: A case study

Contact Info

Product

Resources

About