Road Operations Orchestration Enhanced with Long-short-term Memory and Machine Learning (Position Paper)

Fuji Foo, Poh Peng, Robert Lin, Wenwey Hseush

Abstract

Road traffic management has been a priority for urban city planners to mitigate urban traffic congestion. In 2018, the economic impact to US due to lost productivity of workers sitting in traffic, increased cost of transporting goods through congested areas, and all of that wasted fuel amounted to US$87 billion, an average of US$1,348 per driver. In land scare Singapore, congestion not only translates to economic impact, but also strain to the infrastructure and city land use. While techniques for traffic prediction have existed for many years, the research effort has mainly been focused on traffic prediction. The downstream impact on how city administration should predict and react to incidents and/or events has not been widely discussed. In this paper, we propose Artificial Intelligence enabled Complex Event Processing to only identify and predict incidents, but also to enable a swift response through effective deployment of critical resources to ensure well-coordinated recovery action before any incident develop into crisis.

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Paper Citation


in Harvard Style

Foo F., Peng P., Lin R. and Hseush W. (2019). Road Operations Orchestration Enhanced with Long-short-term Memory and Machine Learning (Position Paper).In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-377-3, pages 311-316. DOI: 10.5220/0007951603110316


in Bibtex Style

@conference{data19,
author={Fuji Foo and Poh Peng and Robert Lin and Wenwey Hseush},
title={Road Operations Orchestration Enhanced with Long-short-term Memory and Machine Learning (Position Paper)},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2019},
pages={311-316},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007951603110316},
isbn={978-989-758-377-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Road Operations Orchestration Enhanced with Long-short-term Memory and Machine Learning (Position Paper)
SN - 978-989-758-377-3
AU - Foo F.
AU - Peng P.
AU - Lin R.
AU - Hseush W.
PY - 2019
SP - 311
EP - 316
DO - 10.5220/0007951603110316