DEBS18: PROCEEDINGS OF THE 12TH ACM INTERNATIONAL CONFERENCE ON DISTRIBUTED AND EVENT-BASED SYSTEMS

MUHAMMAD INTIZAR ALI

doi:10.1145/3210284.3220507

DEBS18: PROCEEDINGS OF THE 12TH ACM INTERNATIONAL CONFERENCE ON DISTRIBUTED AND EVENT-BASED SYSTEMS

MUHAMMAD INTIZAR ALI

Computer Science

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

We propose a sequence-to-sequence based method to predict vessels destination port and estimated arrival time. We consider this problem as an extension of trajectory prediction problem, that takes a sequence of historical locations as input and returns a sequence of future locations, which is used to determine arrival port and estimated arrival time. Our solution first represents the trajectories on a spatial grid covering Mediterranean Sea. Then, we train a sequence-to-sequence model to predict the future movement of vessels based on movement tendency and current location. We built our solution using distributed architecture model and applied load balancing techniques to achieve both high performance and scalability.

Original language	English (Ireland)
Title of host publication	Grand Challenge: Vessel Destination and Arrival Time Prediction with Sequence-to-Sequence Models over Spatial Grid
Number of pages	4
DOIs	https://doi.org/10.1145/3210284.3220507
Publication status	Published - 1 Jan 2018

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Nguyen, DD,Le Van, C,Ali, MI,

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10.1145/3210284.3220507

Cite this

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title = "DEBS18: PROCEEDINGS OF THE 12TH ACM INTERNATIONAL CONFERENCE ON DISTRIBUTED AND EVENT-BASED SYSTEMS",

abstract = "We propose a sequence-to-sequence based method to predict vessels destination port and estimated arrival time. We consider this problem as an extension of trajectory prediction problem, that takes a sequence of historical locations as input and returns a sequence of future locations, which is used to determine arrival port and estimated arrival time. Our solution first represents the trajectories on a spatial grid covering Mediterranean Sea. Then, we train a sequence-to-sequence model to predict the future movement of vessels based on movement tendency and current location. We built our solution using distributed architecture model and applied load balancing techniques to achieve both high performance and scalability.",

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N2 - We propose a sequence-to-sequence based method to predict vessels destination port and estimated arrival time. We consider this problem as an extension of trajectory prediction problem, that takes a sequence of historical locations as input and returns a sequence of future locations, which is used to determine arrival port and estimated arrival time. Our solution first represents the trajectories on a spatial grid covering Mediterranean Sea. Then, we train a sequence-to-sequence model to predict the future movement of vessels based on movement tendency and current location. We built our solution using distributed architecture model and applied load balancing techniques to achieve both high performance and scalability.

AB - We propose a sequence-to-sequence based method to predict vessels destination port and estimated arrival time. We consider this problem as an extension of trajectory prediction problem, that takes a sequence of historical locations as input and returns a sequence of future locations, which is used to determine arrival port and estimated arrival time. Our solution first represents the trajectories on a spatial grid covering Mediterranean Sea. Then, we train a sequence-to-sequence model to predict the future movement of vessels based on movement tendency and current location. We built our solution using distributed architecture model and applied load balancing techniques to achieve both high performance and scalability.

U2 - 10.1145/3210284.3220507

DO - 10.1145/3210284.3220507

M3 - Conference Publication

BT - Grand Challenge: Vessel Destination and Arrival Time Prediction with Sequence-to-Sequence Models over Spatial Grid

ER -

DEBS18: PROCEEDINGS OF THE 12TH ACM INTERNATIONAL CONFERENCE ON DISTRIBUTED AND EVENT-BASED SYSTEMS

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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