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Browsing by Author "Heyns, E.E."
Now showing items 1-10 of 10
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Investigation on identifying roadanomalies using in-vehicle sensors for cooperative applications and roadasset management 2020, Article / Letter to editor (Transportation Research Record : Journal of the Transportation Research Board, (2020)) -
TRR - Journal paper: Investigation on identifying road anomalies using in-vehicle sensors for cooperative applications and road asset management 2020, Article / Letter to editor (Transportation Research Record : Journal of the Transportation Research Board, vol. 2674, iss. 7, (2020), pp. 585-595)
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Identification and classification of slippery winter road conditions using commonly available vehicle variables 2019, Article / Letter to editor (Transportation Research Record : Journal of the Transportation Research Board, vol. 2673, iss. 2, (2019), pp. 60-70)Extreme winter weather conditions severely affect the transportation sector. Technologies such as Road Weather Information Systems provide live data on the road surface conditions to assist the road authorities in providing safe mobility. The main problem is, however, the limited number of such systems that have been deployed, resulting in fragmented informa- tion about road conditions. This paper addresses the problems associated with the limited quantity of information concerning slippery winter road conditions by presenting a proof-of-concept for a system that not only detects slippery winter road con- ditions, but also predicts the type of slippery surface (ice, snow and slush) via vehicle-based systems. The concept demon- strated in this paper makes use of commonly available variables, which are, longitudinal slip ratios, longitudinal acceleration and the ambient temperature to identify such situations. The developed system employs a Fuzzy Inference System that is not only capable of identifying slippery conditions but is also capable of classifying surfaces based on the extent of slipperiness. This provides the road authorities with several moving sensors (vehicles traveling on a particular road) compared with the few fixed sensors currently available. This could deliver a pool of information to assist the road authorities to efficiently han- dle their staff and equipment so that appropriate equipment reaches the right place at the right time.
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Investigation of pothole detection using in-vehicle data for cooperative applications 2019, Article in monograph or in proceedings (ITS World Congress 2019)
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Predicting traffic phases from car sensor data 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.
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Predicting traffic phases from car sensor data using machine learning 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.
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Predicting Traffic Phases from Car Sensor Data 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.
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Predicting traffic phases from car sensor data 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. -
Early detection of highway congestion from probe car data 2018, Article in monograph or in proceedings (Book of Abstracts StochMod18)Highway congestion is an increasingly pressing societal problem, both in terms of cost (manyproductive hours lost) and safety (highway congestion increases the risk of accidents). While thereis a plethora of research on detecting and predicting traffic flow state from floating car data fromdata generators such as in-car navigation systems, little research has been done on how more de-tailed vehicle-generated data such as available on the vehicle CAN-bus (breaking, steering, etc.)could be translated into earlier or better quantification of the traffic flow state. The hypothesisunderlying my research is that the the data generated by participating cars can be modelled asa complex system of which the spatio-temporal complexity can be quantified; that a rise in thespatio-temporal complexity could be an early indicator of perturbed traffic flow; and that theaggregate patterns of multiple participating cars on a given road segment could in turn be mod-elled as such a system where the rise in spatio-temporal complexity is a good measure of thecongestion-proneness of the traffic condition. This information could be fused with historical dataon congestion probabilities to provide better congestion prediction with applications towards co-operative (cooperative adaptive speed control) or externally managed (variable message signs)traffic management strategies.
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Monitoring winter road conditions using commonly available vehicle variables for cooperative applications 2018, Article in monograph or in proceedings (ITS World Congress 2018)
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