Browsing Han Publications by Author "Uniyal, S."

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Predicting Traffic Phases from Car Sensor Data
Heyns, E.E. ; Uniyal, S.
2019, Article in monograph or in proceedings (Proceedings: 15th World Conference on Transport Research)This research aims to predict traffic density using driver behaviour as collected from the CAN bus. The hypothesis is that driver behavior is influenced by traffic density in such a way that an approximation of the traffic density can be determined from changes in the driver behavior. Machine learning will be employed to correlate a selection of commonly available sensors on cars to the traffic density. Challenges in the processing of the data for this purpose will be outlined. The data for this study is collected from five passenger cars and nineteen trucks driving the A28 highway in Utrecht region in the Netherlands. The results show that traffic density can be detected using driver behaviour. An overall accuracy of over 95\% is achieved with a precision of 92%. The recall rate however is low most likely caused by overfitting due to the unbalanced data set. The results still look promising and more training data should improve the results.
Predicting traffic phases from car sensor data
Uniyal, S. ; Heyns, E.E. ; Dugundji, Elenna
2019, Article in monograph or in proceedings (Transportation Research Procedia)This research is an explorative study to look for the potential to predict traffic density from driver behavior using signals collected from the Controller Area Network (CAN) bus. The hypothesis is that driver behavior is influenced by traffic density in such a way that an approximation of the traffic density can be determined from changes in the driver behavior. Machine learning will be employed to correlate a selection of commonly available sensors on cars to the traffic density. Challenges in the processing of the data for this purpose will be outlined. The data for this study is collected from five passenger cars and nineteen trucks driving on the A28 highway in Utrecht region in the Netherlands. This study is restricted to straight roads in order to isolate the steering behavior attributable to the traffic state influences rather than following the curve in the road. The results are encouraging that the correlation between driver behavior and traffic density can be established. An overall accuracy of over 95% is achieved with a precision of 92%. The recall rate however is low most likely caused by over-fitting due to the unbalanced dataset. The results still look promising and more training data should improve the results.
Predicting traffic phases from car sensor data using machine learning
Heyns, E.E. ; Uniyal, S. ; Dugundji, Elenna ; Tillema, F. ; Huijboom, C.N.
2019, Article in monograph or in proceedings (Shakshuki, Elhadi (ed.), The 10th International Conference on Ambient Systems, Networks and Technologies (ANT 2019))This research is an explorative study to look for the potential to predict traffic density from driver behaviour using signals collected from the Controller Area Network (CAN) bus. The hypothesis is that driver behaviour is influenced by traffic density in such a way that an approximation of the traffic density can be determined from changes in the driver behaviour. Machine learning will be employed to correlate a selection of commonly available sensors on cars to the traffic density. Challenges in the processing of the data for this purpose will be outlined. The data for this study is collected from five passenger cars and nineteen trucks driving on the A28 highway in Utrecht region in the Netherlands. This study is restricted to straight roads in order to isolate the steering behaviour attributable to the traffic state influences rather than following the curve in the road. The results are encouraging that the correlation between driver behaviour and traffic density can be established. An overall accuracy of over 95% is achieved with a precision of 92%. The recall rate however is low most likely caused by over-fitting due to the unbalanced data set. The results still look promising and more training data should improve the results. This research is part of the broader project VIA NOVA which aims to investigate the use of car-sensor data for traffic and road asset management.
Predicting traffic phases from car sensor data
Heyns, E.E. ; Uniyal, S. ; Huijboom, C.N.
2019, Article in monograph or in proceedings (Paper number ITS-1974, 13th ITS European Congress)This research is an explorative study to look for the potential to predict traffic density from driver behavior using signals collected from the Controller Area Network (CAN) bus. The hypothesis is that driver behavior is influenced by traffic density in such a way that an approximation of the traffic density can be determined from changes in the driver behavior. Machine learning will be employed to correlate a selection of commonly available sensors on cars to the traffic density. Challenges in the processing of the data for this purpose will be outlined. This study is restricted to straight roads in order to isolate the steering behavior attributable to the traffic state influences rather than following the curve in the road. The results are encouraging that the correlation between driver behavior and traffic density can be established. An overall accuracy of over 95% is achieved with a precision of 92%. The recall rate however is low most likely caused by over-fitting due to the unbalanced dataset.

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