RESEARCH ARTICLE


Data Processing Techniques for Real-Time Traveler Information: Use of Dedicated Short-Range Communications Probes on Suburban Arterial



Jinhwan Jang1, *
1 Next Generation Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology, 283 Goyangdae-Ro, Illsanseo-Gu, Goyang-Si, Gyeonggi-Do10223, Republic of Korea


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Creative Commons License
© 2020 Jinhwan Jang.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to the author at the Next Generation Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology, 283 Goyangdae-Ro, Illsanseo-Gu, Goyang-Si, Gyeonggi-Do10223, Republic of Korea; E-mail: jhjang@kict.re.kr


Abstract

Background:

As wireless communication technologies evolve, probe-based travel-time collection systems are becoming popular around the globe. However, two problems generally arise in probe-based systems: one is the outlier and the other is time lag. To resolve the problems, methods for outlier removal and travel-time prediction need to be applied.

Methods:

In this study, data processing methods for addressing the two issues are proposed. After investigating the characteristic of the travel times on the test section, the modified z-score was suggested for censoring outliers contained in probe travel times. To mitigate the time-lag phenomenon, a recurrent neural network, a class of deep learning where temporal sequence data are normally treated, was applied to predict travel times.

Results:

As a result of evaluation with ground-truth data obtained through test-car runs, the proposed methods showed enhanced performances with prediction errors lower than 13% on average compared to current practices.

Conclusion:

The suggested methods can make drivers to better arrange their trip schedules with real-time travel-time information with improved accuracy.

Keywords: Travel time, Probe, Outlier, Prediction, DSRC, Data processing techniques.