Abstract

Intelligent transportation systems in urban areas in developing countries are still being developed. In this process, there are plenteous reports from society that complain about traffic jams, accidents, and other conditions that require immediate handling. For instance, in Surabaya, one of the urban areas in Indonesia, there were 7,975 complaints via @e100ss Twitter's account, the transportation media in Surabaya, in December 2022. This research aims to find contexts from the traffic complaint text to determine the urgency factor from the text. Traffic urgency factors are used to determine the complaint's handling priority. Therefore, the main steps in this research are as follows. The first step is preprocessing and tokenizing the text data. The second step is calculating the term frequency (TF) from the tokenized word. Furthermore, the words with the highest TF values were categorized according to their context. In this context, a model to determine urgency was developed in this research. This research suggests that traffic urgency is moderated by the context of location, context of a group people that being reported, context of the object that mentioned in the complaint text, and context of the condition. This research also found that a specific location has the most frequent reported time.

Original languageEnglish
Title of host publication2023 8th International Conference on Business and Industrial Research, ICBIR 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages585-590
Number of pages6
ISBN (Electronic)9798350399646
DOIs
Publication statusPublished - 2023
Event8th International Conference on Business and Industrial Research, ICBIR 2023 - Bangkok, Thailand
Duration: 18 May 202319 May 2023

Publication series

Name2023 8th International Conference on Business and Industrial Research, ICBIR 2023 - Proceedings

Conference

Conference8th International Conference on Business and Industrial Research, ICBIR 2023
Country/TerritoryThailand
CityBangkok
Period18/05/2319/05/23

Keywords

  • context from text
  • time-location context analysis
  • traffic context from text
  • traffic report context
  • traffic urgency model

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