Abstract
Tram/train derailment subject to human mistakes makes investments in an advanced control room as well as information gathering system exaggerated. A disaster in Croydon in year 2016 is recent evidence of limitation of the acquired systems to mitigate human shortcoming in disrupted circumstances. One intriguing way of resolution could be is to fuse continuous online textual data obtained from tram travelers and apply the information for early cautioning of risk discovery. This resolution conveys our consideration regarding a resource of data fusion. The focal subject of this paper is to discuss about role of pre-processing ventures in a low-level data fusion that have been distinguished as a pass to avoid time and exertion squandering amid information retrieval. Inclines in online text data pre-processing is reviewed which comes about an outline suggestion that concede traveler's responses through social media channels. The research outcome shows by a case of data fusion could go about as an impetus to railway industry to effectively partake in data exploration and information investigation.
Highlights
Data is enormous in railway industry, covering both train/tram operations and infrastructure management dimensions
In regard to application-wise, data fusion is nowadays beyond the dominant and matured domain; remote sensor and signal processing, as case studies can be found in condition monitoring (Raheja et al, 2006), crime analysis (Nokhbeh Zaeem et al, 2017), forest management (Chen et al, 2005), and engineering (Steinberg, 2001)
A conclusion remark about synergy among low-level data fusion, text pre-processing and online data source is stated in the Conclusion section
Summary
Data is enormous in railway industry, covering both train/tram operations and infrastructure management dimensions. A conclusion remark about synergy among low-level data fusion, text pre-processing and online data source is stated in the Conclusion section. An efficiency and scalability of an object refinement; a subsequent step applied to processed data, can be improved through a proper pre-processing (Mitali et al, 2003) This particular benefit is challenging to gain when online text as a data source. Language dependent factors which do not have an impact to information retrieval are identified obstacle (Singh and Kumari, 2016; Nokhbeh Zaeem et al, 2017) points out the challenge in dealing with social media text data for fortification of sentiment classification especially in terms of short length and internet slang word.
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