Authors:
Fuji Foo
1
;
Poh Ju Peng
1
;
Robert Kuo-Chung Lin
1
and
Wenwey Hseush
2
Affiliations:
1
Certis Group and Singapore
;
2
BigObject and Taiwan
Keyword(s):
Complex Event Processing, In-memory Computing, Working Memory.
Related
Ontology
Subjects/Areas/Topics:
Business Analytics
;
Data Engineering
;
Predictive Modeling
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 ac
tion before any incident develop into crisis.
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