Driving behaviour modelling system based on graph construction

Chen, Sei-Wang; Fang, Chiung‐Yao; Tien, Chih Ting

doi:10.1016/j.trc.2012.10.004

Cited by 70 publications

(25 citation statements)

References 13 publications

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“…As stated above, in recent years, knowledge graph has been widely applied in driving behavior identification in traffic areas [14], state of illness analysis in modern medicine [15], and relationship mining between literature and authors in information science [16]. In different research areas, the implication and contents of knowledge information might vary significantly, but the elements and structure were almost consistent.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, the knowledge graph, consisting of nodes, arcs, and/or transfer possibility, has been widely used in traffic, medicine, library, information, and many other areas recently [14][15][16]. For Chen et al ' study [14], driver behavior graph was constructed to identify the driving habit of different drivers (e.g., whether it is safe or not) and to express the process of his/her whole performance during driving intuitively.…”

Section: Introductionmentioning

confidence: 99%

“…For Chen et al ' study [14], driver behavior graph was constructed to identify the driving habit of different drivers (e.g., whether it is safe or not) and to express the process of his/her whole performance during driving intuitively. Integrating medical data from different languages, medical graph could seek the best knowledge presentation scheme [15].…”

Section: Introductionmentioning

confidence: 99%

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Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction

Liang

Weng

Zhou

et al. 2018

Journal of Advanced Transportation

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This paper presents a novel method for mining the individual travel behavior regularity of different public transport passengers through constructing travel behavior graph based model. The individual travel behavior graph is developed to represent spatial positions, time distributions, and travel routes and further forecasts the public transport passenger's behavior choice. The proposed travel behavior graph is composed of macronodes, arcs, and transfer probability. Each macronode corresponds to a travel association map and represents a travel behavior. A travel association map also contains its own nodes. The nodes of a travel association map are created when the processed travel chain data shows significant change. Thus, each node of three layers represents a significant change of spatial travel positions, travel time, and routes, respectively. Since a travel association map represents a travel behavior, the graph can be considered a sequence of travel behaviors. Through integrating travel association map and calculating the probabilities of the arcs, it is possible to construct a unique travel behavior graph for each passenger. The data used in this study are multimode data matched by certain rules based on the data of public transport smart card transactions and network features. The case study results show that graph based method to model the individual travel behavior of public transport passengers is effective and feasible. Travel behavior graphs support customized public transport travel characteristics analysis and demand prediction.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction

Liang

Weng

Zhou

et al. 2018

Journal of Advanced Transportation

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“…The driving style is intended as the whole of manoeuvres that a driver performs while driving, in safe or unsafe conditions. Different drivers have different driving styles: driver's personal characteristics and habits will affect their reaction when faced with a dangerous situation while driving [8].…”

Section: Investigating Car Users' Driving Behaviourmentioning

confidence: 99%

Investigating Car Users’ Driving Behaviour through Speed Analysis

Eboli

Guido

Mazzulla

et al. 2017

PROMET

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“…While both views are relevant, none of the described are fully adequate, which is shown in the composition of driver behavior through two separate components; driving skills and driving style (Elander et al, 1993). Chen et al, (2013) further define driving skills as the attitude of a driver, while driving skills further describe an individual's driving habits. Michon (1985) argues that driving cannot be described simply as a combination of tasks, and introduces three levels of tasks that determine driving behavior, i.e., strategic (planning), tactical (manoeuvring) and operational (control).…”

Section: Introductionmentioning

confidence: 99%

Mixed-Method Design for User Behavior Evaluation of Automated Driver Assistance Systems: An Automotive Industry Case

Orlovska

Novakazi

Wickman

et al. 2019

Proc. Int. Conf. Eng. Des.

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Automotive systems are changing rapidly from purely mechanical to smart, programmable assistants. These systems react and respond to the driving environment and communicate with other subsystems for better driver support and safety. However, instead of supporting, the complexity of such systems can result in a stressful experience for the driver, adding to the workload. Hence, a poorly designed system, from a usability and user experience perspective, can lead to reduced usage or even ignorance of the provided functionalities, especially concerning Adaptive Driver Assistance Systems.In this paper, the authors propose a combined design approach for user behavior evaluation of such systems. At the core of the design is a mixed methods approach, where objective data, which is automatically collected in vehicles, is augmented with subjective data, which is gathered through in- depth interviews with end-users. The aim of the proposed methodology design is to improve current practices on user behavior evaluation, achieve a deeper understanding of driver's behavior, and improve the validity and rigor of the named results.

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Driving behaviour modelling system based on graph construction

Cited by 70 publications

References 13 publications

Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction

Individual Travel Behavior Modeling of Public Transport Passenger Based on Graph Construction

Investigating Car Users’ Driving Behaviour through Speed Analysis

Mixed-Method Design for User Behavior Evaluation of Automated Driver Assistance Systems: An Automotive Industry Case

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