Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways

Lee, S; Ngoduy, D; Keyvan-Ekbatani, M

Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways

Lee, S

Ngoduy, D

Keyvan-Ekbatani, M

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Publication Type:: Journal Article
Citation:: Transportation Research Part C: Emerging Technologies, 2019, 106 pp. 360 - 377
Issue Date:: 2019-09-01

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Field	Value	Language
dc.contributor.author	Lee, S https://orcid.org/0000-0003-2356-3677	en_US
dc.contributor.author	Ngoduy, D https://orcid.org/0000-0002-0446-5636	en_US
dc.contributor.author	Keyvan-Ekbatani, M	en_US
dc.date.issued	2019-09-01	en_US
dc.identifier.citation	Transportation Research Part C: Emerging Technologies, 2019, 106 pp. 360 - 377	en_US
dc.identifier.issn	0968-090X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135458
dc.description.abstract	© 2019 The current paper proposes a novel stochastic procedure for modelling car-following behaviours on a multi-lane motorway. We develop an integrated multi-lane stochastic continuous car-following model where a deep learning architecture is used to estimate a probability of lane-changing (LC) manoeuvres. To the best of our knowledge, this work is among the very few papers which exploit deep learning to model driving behaviour on a multi-lane road. The objective of this study is to establish a coupled stochastic continuous multi-lane car-following model using Langevin equations to cope with probabilistic characteristics of LC manoeuvres. In particular, a stochastic volatility, derived from LC manoeuvres is introduced in a multi-lane stochastic optimal velocity model (SOVM). In additions, Convolutional Neural Network (CNN) is applied to estimate a probability of LC manoeuvres in the integrated multi-lane car-following model. Furthermore, imaged second-based trajectories of the lane-changer and surrounding vehicles are used to identify whether LC manoeuvres occur by using the CNN. Finally, the proposed method is validated using a real-world high-resolution vehicle trajectory dataset. The results indicate that the prediction of the integrated SOVM is almost identical to the observed trajectories of the lane-changers and the following vehicles in the initial and the target lane. It has been found that the proposed multi-lane SOVM can tackle the unpredictable fluctuations in the velocity of the vehicles in the acceleration/deceleration zone.	en_US
dc.relation.ispartof	Transportation Research Part C: Emerging Technologies	en_US
dc.relation.isbasedon	10.1016/j.trc.2019.07.023	en_US
dc.subject.classification	Logistics & Transportation	en_US
dc.title	Integrated deep learning and stochastic car-following model for traffic dynamics on multi-lane freeways	en_US
dc.type	Journal Article
utslib.citation.volume	106	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	106	en_US

Abstract:

© 2019 The current paper proposes a novel stochastic procedure for modelling car-following behaviours on a multi-lane motorway. We develop an integrated multi-lane stochastic continuous car-following model where a deep learning architecture is used to estimate a probability of lane-changing (LC) manoeuvres. To the best of our knowledge, this work is among the very few papers which exploit deep learning to model driving behaviour on a multi-lane road. The objective of this study is to establish a coupled stochastic continuous multi-lane car-following model using Langevin equations to cope with probabilistic characteristics of LC manoeuvres. In particular, a stochastic volatility, derived from LC manoeuvres is introduced in a multi-lane stochastic optimal velocity model (SOVM). In additions, Convolutional Neural Network (CNN) is applied to estimate a probability of LC manoeuvres in the integrated multi-lane car-following model. Furthermore, imaged second-based trajectories of the lane-changer and surrounding vehicles are used to identify whether LC manoeuvres occur by using the CNN. Finally, the proposed method is validated using a real-world high-resolution vehicle trajectory dataset. The results indicate that the prediction of the integrated SOVM is almost identical to the observed trajectories of the lane-changers and the following vehicles in the initial and the target lane. It has been found that the proposed multi-lane SOVM can tackle the unpredictable fluctuations in the velocity of the vehicles in the acceleration/deceleration zone.

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http://hdl.handle.net/10453/135458