A Hybrid Model using Online and Operator for Traveler Assistance Via Smartphone
Nalinee Sophatsathit

Nalinee Sophatsathit, Computer Science Program, Faculty of Science and Technology, Suan Sunandha Rajabhat University, Bangkok, Thailand.
Manuscript received on September 16, 2019. | Revised Manuscript received on 25 September, 2019. | Manuscript published on October 10, 2019. | PP: 816-823 | Volume-8 Issue-12, October 2019. | Retrieval Number: L32191081219/2019©BEIESP | DOI: 10.35940/ijitee.L3219.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper proposes a hybrid model for use by travelers on a smartphone. The principles lie in the combined deployment of 24×7 network that directly serves the travelers and links the existing phone service as an alternate backup choice. Such a redundancy will insure higher reliability in an urgent situation, where many available communication channels are often jammed by panic calls and accesses. In addition, off-line service is also provided as an optional access means to ease the network traffic, whereby important information can be immediately conveyed to the travelers in need of assistance. An information management workflow is also set up to effectively disseminate the relevant information. The proposed system is implemented and tested by volunteers. The results are compared with the predicted outcomes obtained from the preliminary test. It was found that once the users are familiar with the answers, they tend to prematurely exit the application. However, it is not the case with operator assistance as they can continue inquiring freely with the human operator. Future artificial intelligent techniques can be employed to enhance the non-human operations to become as lively as the human service.
Keywords: Traveler Assistance, Multimodel Information, Access Routes, Smartphone.
Scope of the Article: Online Learning Systems