An Approach To Analyze Different Route Factors Using Hadoop Framework
DOI:
https://doi.org/10.26438/ijcse/v6i12.794798Keywords:
Route factors, Hadoop Framework, Big dataAbstract
Whenever a person travelling from one place to another place. There are different routes between different regions. A person travelling from source to destination chooses a path based on different factors. Route choices from source to destination play an important role. It helps the user to choose the best route from many routes present based on different considerations. The traveller chooses the route with best factors like less time and distance. Such types of route factors are the main reasons to choose the route. We here develop a visual analytic system to display few more route choices. Here, based on the route factors the route with best factors is chosen as the best path and viewed to the user. We study and analyse route factors based on dataset. We analyse the dataset and a system with best and multiple route factors is developed using hadoop Framework.
References
[1] Y. Zheng, L. Capra, O. Wolfson, H. Yang, “Urban computing: concepts, methodologies, and applications,” ACM
Transactions on Intelligent Systems and Technology (TIST), vol. 5, no. 3, p. 38, 2014.
[2] Vaishali Y. Chandurkar “A Survey on Evaluation of Traffic Mobility Using Clustering” International Journal of Innovative Research in Computer and Communication Engineering.
[3] Alessandro Vacca a, Italo Meloni “Understanding route switch behavior: an analysis using GPS based data” Transportation Research Procedia 5 ( 2015 ) 56 – 65
[4] W. Liu, Y. Zheng, S. Chawla, J. Yuan, and X. Xing, “Discovering spatiotemporal causal interactions in traffic data streams,” in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011, pp. 1010–1018
[5] J. Yuan, Y. Zheng, X. Xie, and G. Sun, “T-Drive: enhancing driving directions with taxi drivers’ intelligence,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 1, pp. 220–232, 2013. Carlo Giacomo Prato, Victoria Gitelman and Shlomo, “Mapping patterns of pedestrian fatal accidents in Israel”, Accident Analysis and Prevention, pp.54–62, Jan 2012.
[6] H. Li, R. Guensler, and J. Ogle, “Analysis of morning commute route choice patterns using global positioning system-based vehicle activity data,” Transportation Research Record: Journal of the Transportation Research Board, vol. 1926.1, pp. 162–170, 2005.
[7] Shlomo Bekhor, Moshe E. Ben-Akiva, M.Scott Ramming, “Evaluation of choice set generation algorithms,” Springer science, 2006
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
