Virtual patrolling

M. van der Vlist, Luc Wismans, Paul van Beek, Leon Suijs

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Abstract

Approximately 25 per cent of all congestion on motorways is caused by incidents. By virtual patrolling, incidents e.g. queues, accidents and car breakdowns on a road network, can be predicted or detected in an early stage. This early detection and prediction of an incident likely to happen, offers the opportunity to act faster and therefore reduce congestion compared to current practice. A module is developed capable of virtual patrolling and based on four key features: data fusion, real-time estimation of the fundamental diagram, fuzzy traffic state estimation and artificial neural networks. In CHARM, the cooperation between the Highways England (UK) and Rijkswaterstaat (NL), the potential of this module has been demonstrated in a simulation environment and in 2015 further developed, deployed and tested in real life. Extended Kalman filtering and traffic flow theory algorithms are used to fuse data from loop detectors with FVD (floating vehicle data) to provide detailed traffic state data (i.e. flow and speed for 10 seconds for every segment of approximately 200 meters). For the prediction of the future traffic state, information on the actual road capacity is necessary. Road capacity varies for weather, lightness, number of heavy vehicles et cetera. A self-adapting module is used to make a real time estimation of the fundamental diagram and road capacity. The traffic state estimation itself is based on fuzzy logic to determine to what extent the traffic state is free flow or congested. If only speed data is available, alternative simplified fuzzy logic rules are applied. The fuzzy traffic state estimations for separate segments is combined into a combined traffic state estimation. This traffic state estimation is used as input for artificial neural networks (ANN) to predict whether it is likely that a queue will form within the short-term future (within 10 minutes) on a relevant segment. This is defined as a segment that suffers from queues on a frequent basis. The results of these estimations will be used for virtual patrolling to distinguish between normal and expected congestion and other situations. Information from the modules can also be used for the detection of prediction of shockwaves. This can be of help to act faster when incidents happen and to prevent or solve shockwaves by giving (in-car) advices on speed and lane choice to car users.
Original languageEnglish
Pages (from-to)3370-3379
JournalTransportation research procedia
Volume14
DOIs
Publication statusPublished - 2016

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State estimation
traffic
Railroad cars
incident
Fuzzy logic
Neural networks
road
logic
Data fusion
Electric fuses
neural network
Accidents
Detectors
floating
road network
accident
simulation

Cite this

van der Vlist, M., Wismans, L., van Beek, P., & Suijs, L. (2016). Virtual patrolling. Transportation research procedia, 14, 3370-3379. https://doi.org/10.1016/j.trpro.2016.05.289
van der Vlist, M. ; Wismans, Luc ; van Beek, Paul ; Suijs, Leon. / Virtual patrolling. In: Transportation research procedia. 2016 ; Vol. 14. pp. 3370-3379.
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abstract = "Approximately 25 per cent of all congestion on motorways is caused by incidents. By virtual patrolling, incidents e.g. queues, accidents and car breakdowns on a road network, can be predicted or detected in an early stage. This early detection and prediction of an incident likely to happen, offers the opportunity to act faster and therefore reduce congestion compared to current practice. A module is developed capable of virtual patrolling and based on four key features: data fusion, real-time estimation of the fundamental diagram, fuzzy traffic state estimation and artificial neural networks. In CHARM, the cooperation between the Highways England (UK) and Rijkswaterstaat (NL), the potential of this module has been demonstrated in a simulation environment and in 2015 further developed, deployed and tested in real life. Extended Kalman filtering and traffic flow theory algorithms are used to fuse data from loop detectors with FVD (floating vehicle data) to provide detailed traffic state data (i.e. flow and speed for 10 seconds for every segment of approximately 200 meters). For the prediction of the future traffic state, information on the actual road capacity is necessary. Road capacity varies for weather, lightness, number of heavy vehicles et cetera. A self-adapting module is used to make a real time estimation of the fundamental diagram and road capacity. The traffic state estimation itself is based on fuzzy logic to determine to what extent the traffic state is free flow or congested. If only speed data is available, alternative simplified fuzzy logic rules are applied. The fuzzy traffic state estimations for separate segments is combined into a combined traffic state estimation. This traffic state estimation is used as input for artificial neural networks (ANN) to predict whether it is likely that a queue will form within the short-term future (within 10 minutes) on a relevant segment. This is defined as a segment that suffers from queues on a frequent basis. The results of these estimations will be used for virtual patrolling to distinguish between normal and expected congestion and other situations. Information from the modules can also be used for the detection of prediction of shockwaves. This can be of help to act faster when incidents happen and to prevent or solve shockwaves by giving (in-car) advices on speed and lane choice to car users.",
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van der Vlist, M, Wismans, L, van Beek, P & Suijs, L 2016, 'Virtual patrolling' Transportation research procedia, vol. 14, pp. 3370-3379. https://doi.org/10.1016/j.trpro.2016.05.289

Virtual patrolling. / van der Vlist, M.; Wismans, Luc; van Beek, Paul; Suijs, Leon.

In: Transportation research procedia, Vol. 14, 2016, p. 3370-3379.

Research output: Contribution to journalArticleAcademicpeer-review

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AB - Approximately 25 per cent of all congestion on motorways is caused by incidents. By virtual patrolling, incidents e.g. queues, accidents and car breakdowns on a road network, can be predicted or detected in an early stage. This early detection and prediction of an incident likely to happen, offers the opportunity to act faster and therefore reduce congestion compared to current practice. A module is developed capable of virtual patrolling and based on four key features: data fusion, real-time estimation of the fundamental diagram, fuzzy traffic state estimation and artificial neural networks. In CHARM, the cooperation between the Highways England (UK) and Rijkswaterstaat (NL), the potential of this module has been demonstrated in a simulation environment and in 2015 further developed, deployed and tested in real life. Extended Kalman filtering and traffic flow theory algorithms are used to fuse data from loop detectors with FVD (floating vehicle data) to provide detailed traffic state data (i.e. flow and speed for 10 seconds for every segment of approximately 200 meters). For the prediction of the future traffic state, information on the actual road capacity is necessary. Road capacity varies for weather, lightness, number of heavy vehicles et cetera. A self-adapting module is used to make a real time estimation of the fundamental diagram and road capacity. The traffic state estimation itself is based on fuzzy logic to determine to what extent the traffic state is free flow or congested. If only speed data is available, alternative simplified fuzzy logic rules are applied. The fuzzy traffic state estimations for separate segments is combined into a combined traffic state estimation. This traffic state estimation is used as input for artificial neural networks (ANN) to predict whether it is likely that a queue will form within the short-term future (within 10 minutes) on a relevant segment. This is defined as a segment that suffers from queues on a frequent basis. The results of these estimations will be used for virtual patrolling to distinguish between normal and expected congestion and other situations. Information from the modules can also be used for the detection of prediction of shockwaves. This can be of help to act faster when incidents happen and to prevent or solve shockwaves by giving (in-car) advices on speed and lane choice to car users.

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