Retracted: Prediction of Recovery Time of Urban Traffic Accident Based on Active Flow-Split

He, Yuan; Liu, Zupeng; Du, Shanyi

doi:10.1109/icscse.2016.0012

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…At present, the main research methods in predicting the impact of highway accidents are the traffic wave theory [18,19], vehicle arriving-departure model [20,21], regression model [22,23], decision tree model [5], and ANNs [23,24]. Yu et al [18] analyzed the accumulation and dissipation process of the accident location on two-lane highway, using the shockwave theory as a foundation, and finally estimated the spatialtemporal impact of the accident.…”

Section: Post-impact Prediction Of Traffic Accidentmentioning

confidence: 99%

“…F dissipate � true (18) If distance(s e , s j ) < L maxA /2 (19) en T hrA � t i − t e (20) End if (21) End if (22) Update L aveA ←((j − 1) * L aveA + distance(s e , s j ))/j (23) Break (24) End for (25) For better analysis of the model performance, the accuracy in each interval is calculated, respectively, as given in Table 7. It can be figured out that MAPE and RMSE are relatively low in intervals with abundant records, e.g., [25,30) and [40,50).…”

Section: Clean-up Time Analysismentioning

confidence: 99%

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Online Traffic Accident Spatial-Temporal Post-Impact Prediction Model on Highways Based on Spiking Neural Networks

Peng

2021

Journal of Advanced Transportation

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Traffic accident management as an approach to improve public security and reduce economic losses has received public attention for a long time, among which traffic accidents post-impact prediction (TAPIP) is one of the most important procedures. However, existing systems and methodologies for TAPIP are insufficient for addressing the problem. The drawbacks include ignoring the recovery process after clearance and failing to make comprehensive prediction in both time and space domain. To this end, we build a 3-stage TAPIP model on highways, using the technology of spiking neural networks (SNNs) and convolutional neural networks (CNNs). By dividing the accident lifetime into two phases, i.e., clean-up phase and recovery phase, the model extracts characteristics in each phase and achieves prediction of spatial-temporal post-impact variables (e.g., clean-up time, recovery time, and accumulative queue length). The framework takes advantage of SNNs to efficiently capture accident spatial-temporal features and CNNs to precisely represent the traffic environment. Integrated with an adaptation and updating mechanism, the whole system works autonomously in an online manner that continues to self-improve during usage. By testing with a new dataset CASTA pertaining to California statewide traffic accidents on highways collected in four years, we prove that the proposed model achieves higher prediction accuracy than other methods (e.g., KNN, shockwave theory, and ANNs). This work is the introduction of SNNs in the traffic accident prediction domain and also a complete description of post-impact in the whole accident lifetime.

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Section: Post-impact Prediction Of Traffic Accidentmentioning

confidence: 99%

Section: Clean-up Time Analysismentioning

confidence: 99%

Online Traffic Accident Spatial-Temporal Post-Impact Prediction Model on Highways Based on Spiking Neural Networks

Peng

2021

Journal of Advanced Transportation

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“…Esta estrategia hace estimar los tiempos de disipación de los efectos de un accidente de tránsito, con una eficiencia estadística superior al 95%. En la valoración de la relación entre los accidentes de tránsito y la operación del tráfico en áreas urbanas, es posible citar el trabajo mostrado en [10], donde se resalta cómo el tiempo de recuperación del tráfico luego de un accidente puede ser estudiado a través del análisis de vehículos que arriban vs. vehículos que salen del punto de limitación de flujo. Estos autores proponen modelos matemáticos, basados en optimización, que permiten estimar la acumulación de vehículos; sin embargo, no se presentan validaciones en condiciones reales.…”

Section: Introductionunclassified

Prototipo de herramienta tecnológica para el registro, almacenamiento, transmisión y análisis de la información de accidentes de tránsito

Flórez-Moreno¹,

Reyes-Contreras²,

Villamizar-Camelo³

et al. 2018

ITECKNE

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La importancia del correcto registro, almacenamiento y seguimiento de información asociado a accidentes de tránsito es clave para la toma de decisiones en áreas tan diversas como salud pública, políticas viales, seguros, entre otros. La legislación colombiana ha delegado a las entidades responsables del tránsito la implementación de herramientas tecnológicas que faciliten la documentación del Informe Policial de Accidentes de Tránsito. El presente proyecto desarrolla una solución fundamentada en un aplicativo software que permite digitalizar el registro, hasta ahora manual, de la información de los accidentes y su posterior visualización y análisis en los sistemas de información. La investigación demuestra que existen mejoras en los tiempos de registro de la información y en la confiabilidad de los datos. Se requiere profundizar en los procesos de formación de los usuarios en competencias digitales para su uso, así como en la mejora del proceso de levantamiento del croquis del accidente.

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Reducing the Impact of Vehicles on the Environment by the Modernization of Transport Infrastructure

Makarova

Mavrin

Magdin

et al. 2020

Lecture Notes in Networks and Systems

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Retracted: Prediction of Recovery Time of Urban Traffic Accident Based on Active Flow-Split

Cited by 4 publications

References 3 publications

Online Traffic Accident Spatial-Temporal Post-Impact Prediction Model on Highways Based on Spiking Neural Networks

Online Traffic Accident Spatial-Temporal Post-Impact Prediction Model on Highways Based on Spiking Neural Networks

Prototipo de herramienta tecnológica para el registro, almacenamiento, transmisión y análisis de la información de accidentes de tránsito

Reducing the Impact of Vehicles on the Environment by the Modernization of Transport Infrastructure

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