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Appl. Sci. 2017, 7(9), 878; doi:10.3390/app7090878

A Novel Approach to Extract Significant Patterns of Travel Time Intervals of Vehicles from Freeway Gantry Timestamp Sequences

1
Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan
2
Intelligent Transport System Research Center, China Engineering Consultants, INC., Taipei 10637, Taiwan
This paper is an extended version of our paper published in 2017 IEEE International Conference on Applied System Innovation as titled ”A Novel Approach to Extract Signticant Time Intervals of Vehicles from Superhighway Gantry Timestamp Sequences”.
Department of Medical Research, China Medical University Hospital, China Medical University,
*
Author to whom correspondence should be addressed.
Received: 8 August 2017 / Revised: 12 August 2017 / Accepted: 14 August 2017 / Published: 28 August 2017
(This article belongs to the Special Issue Selected Papers from IEEE ICASI 2017)
View Full-Text   |   Download PDF [24196 KB, uploaded 30 August 2017]   |  

Abstract

It is attractive to extract and determine the key features of traffic patterns for mitigating road congestion and predicting travel time of vehicles in traffic analysis. Based on the previous work that is a scalable approach via a Hadoop MapReduce programming model, this paper aims to extract significant patterns of travel time intervals of vehicles from freeway traffic in Taiwan, and meanwhile to compute the statistics of these patterns from the point of view one may concern. Experimental resources are the records of timestamp gantry sequences of vehicles passed in five months from 2016/11 to 2017/3 that were downloaded from the Traffic Data Collection System, one of Taiwan government open data platforms. To select one specific gantry sequence for demonstration, the longest sequence on the trip within the Taiwan National Freeway No. 5 is selected. Experimental results show that some statistics of vehicle travel time intervals according to 24 h per day are computed for illustration. These statistics can not only provide clues to experts to analyze traffic congestions, but also help drivers how to avoid rush hours. Furthermore, this work is able to handle a larger amount of real data and be promising for further traffic and transportation research in the future. View Full-Text
Keywords: traffic analysis; maximal repeat; timestamp sequence; Hadoop; MapReduce traffic analysis; maximal repeat; timestamp sequence; Hadoop; MapReduce
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Wang, J.-D.; Hwang, M.-C. A Novel Approach to Extract Significant Patterns of Travel Time Intervals of Vehicles from Freeway Gantry Timestamp Sequences. Appl. Sci. 2017, 7, 878.

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