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Evaluation of Outlier Filtering Algorithms for Accurate Travel Time Measurement Incorporating Lane-Splitting Situations

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  • Obada Asqool

    (Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
    Center for Transportation Research, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Suhana Koting

    (Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
    Center for Transportation Research, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Ahmad Saifizul

    (Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

Malaysia has a high percentage of motorcycles. Due to lane-splitting, travel times of motorcycles are less than passenger cars at congestion. Because of this, collecting travel times using the media access control (MAC) address is not straightforward. Many outlier filtering algorithms for travel time datasets have not been evaluated for their capability to filter lane-splitting observations. This study aims to identify the best travel time filtering algorithms for the data containing lane-splitting observations and how to use the best algorithm. Two stages were adopted to achieve the objective of the study. The first stage validates the performance of the previous algorithms, and the second stage checks the sensitivity of the algorithm parameters for different days. The analysis uses the travel time data for three routes in Kuala Lumpur collected by Wi-Fi detectors in May 2018. The results show that the Jang algorithm has the best performance for two of the three routes, and the TransGuide algorithm is the best algorithm for one route. However, the parameters of Jang and TransGuide algorithms are sensitive for different days, and the parameters require daily calibration to obtain acceptable results. Using proper calibration of the algorithm parameters, the Jang and TransGuide algorithms produced the most accurate filtered travel time datasets compared to other algorithms

Suggested Citation

  • Obada Asqool & Suhana Koting & Ahmad Saifizul, 2021. "Evaluation of Outlier Filtering Algorithms for Accurate Travel Time Measurement Incorporating Lane-Splitting Situations," Sustainability, MDPI, vol. 13(24), pages 1-23, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13851-:d:702849
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    References listed on IDEAS

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    1. Dion, Francois & Rakha, Hesham, 2006. "Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates," Transportation Research Part B: Methodological, Elsevier, vol. 40(9), pages 745-766, November.
    2. Shokoohyar, Sina & Sobhani, Ahmad & Sobhani, Anae, 2020. "Impacts of trip characteristics and weather condition on ride-sourcing network: Evidence from Uber and Lyft," Research in Transportation Economics, Elsevier, vol. 80(C).
    3. Tu Peng & Xu Yang & Zi Xu & Yu Liang, 2020. "Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods," Sustainability, MDPI, vol. 12(19), pages 1-19, October.
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