Real-time Prediction of Arterial Vehicle Trajectories: An Application to Predictive Route Guidance for an Emergency Vehicle

Choi, Seongjin; Kim, Jiwon; Yu, Hwapyeong; Yeo, Hwasoo

doi:10.1109/itsc.2019.8917122

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…Previous studies used different ways of defining discrete locations. For instance, Choi et al (2018), Choi et al (2019a), andOuyang et al (2018) used partitioned networks, so-called cells or zones, while Choi et al (2019b) and Ziebart et al (2008a) used road links to represent trajectories. In this paper, we represent a trajectory as a sequence of links to model link-to-link route choice behaviors in urban road networks.…”

Section: Letmentioning

confidence: 99%

“…The POIs considered in Gambs et al (2012) are home, work, and other activity locations to model human activity trajectories throughout the day, rather than vehicle movement trajectories reflecting link-to-link vehicle driving behavior considered in this study. Choi et al (2019b) used a feed-forward neural network to predict the next intersection in a grid-structured road network. A set of intersections in Brisbane, Australia are treated as POI's to capture the link-to-link route choice behavior.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

TrajGAIL: Generating urban vehicle trajectories using generative adversarial imitation learning

Choi

Kim

Yeo

2021

Transportation Research Part C: Emerging Technologies

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Section: Letmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TrajGAIL: Generating urban vehicle trajectories using generative adversarial imitation learning

Choi

Kim

Yeo

2021

Transportation Research Part C: Emerging Technologies

Self Cite

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Urban Vehicle Trajectory Generation Based on Generative Adversarial Imitation Learning

Wang,

Cui,

Wong

et al. 2024

IEEE Trans. Veh. Technol.

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Evaluation of Trajectory and Destination Prediction Models: A Systematic Classification and Analysis of Methodologies and Recent Results

Júnior,

Dutra,

Neto

2024

JISA

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Predicting trajectories and destinations is of considerable relevance in the context of urban mobility, as it can be useful for suggesting detours, avoiding congestion, and optimizing people's commutes. Therefore, this research performs a classification and analysis of trajectory and destination prediction models in articles published from 2017 to 2023. These models were mapped considering: authors; the existence of more than one geographic scenario; the type of forecast; the use of semantic and contextual data; and description of the algorithms. The result consists of discussions of representative works, based on classification, with grouping of techniques. Furthermore, there is a focus on works that used contextual and/or semantic data, from which another framework was developed, specifying the titles of the articles, and whether the methodology involved the use of points or areas of interest, and a reference to how they were generated. This focus expands the previous framework, specifying the differences of a portion of published studies.

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Real-time Prediction of Arterial Vehicle Trajectories: An Application to Predictive Route Guidance for an Emergency Vehicle

Cited by 3 publications

References 29 publications

TrajGAIL: Generating urban vehicle trajectories using generative adversarial imitation learning

TrajGAIL: Generating urban vehicle trajectories using generative adversarial imitation learning

Urban Vehicle Trajectory Generation Based on Generative Adversarial Imitation Learning

Evaluation of Trajectory and Destination Prediction Models: A Systematic Classification and Analysis of Methodologies and Recent Results

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