A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Accident Lane Prediction Using Probabilistic Inference
In this study a Bayesian Hierarchical Model for predicting the accident lane using the average section speed and density data obtained from a sensor or GPS was developed. With the assumption that speed and density values are normally distributed in an accident situation depending on the lane the accident has occurred, we have modelled the related parameters and predicted the probability of occurrence for each lane. This method can be used in real time traffic services where accidents are recorded by users or detected by other algorithms in order to alert the drivers about the lane of the accident. The awareness to the accident lane can ease the channeling of vehicles to the non-accident lanes and can help prevent the secondary accidents.
Accident Lane Prediction Using Probabilistic Inference
In this study a Bayesian Hierarchical Model for predicting the accident lane using the average section speed and density data obtained from a sensor or GPS was developed. With the assumption that speed and density values are normally distributed in an accident situation depending on the lane the accident has occurred, we have modelled the related parameters and predicted the probability of occurrence for each lane. This method can be used in real time traffic services where accidents are recorded by users or detected by other algorithms in order to alert the drivers about the lane of the accident. The awareness to the accident lane can ease the channeling of vehicles to the non-accident lanes and can help prevent the secondary accidents.
Accident Lane Prediction Using Probabilistic Inference
Gokasar, Ilgin (author) / Aytekin, Kaan (author)
2019-06-01
3561071 byte
Conference paper
Electronic Resource
English
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