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Statistical evaluation of data requirement for ramp metering performance assessment

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  • Ma, Xiaobo
  • Karimpour, Abolfazl
  • Wu, Yao-Jan

Abstract

Ramp metering is known to be an effective freeway control measure that ensures the overall efficiency and safety of a highway system by regulating the inflow traffic on-ramps. Therefore, agencies are required to frequently assess the performance of their ramp meters. However, one major challenge for the agencies conducting ramp metering performance assessments is the lack of knowledge about data requirements. Data requirements consist of the information regarding the duration of data collection for accommodation time (the time needed for the users to get familiar with the change) and for the evaluation time. In this paper, a non-parametric statistic approach is proposed that is robust to the underlying distribution of the random variable. Meaning that the accuracy of the model is insensitive to the data distribution. For validation purposes, three active ramps along State Route 51, in the Phoenix Metropolitan area, Arizona are selected as the case study. ADOT altered their ramp control strategy from fixed-time to responsive control and is attempting to know the extent of data required for assessing its new ramp metering strategy. For this particular case study, the results suggest that two months’ worth of data is the minimum data sufficient for a ramp metering assessment. The proposed assessment approach can be transferred to other ramp metering applications to help traffic engineers efficiently tune up ramp metering strategies.

Suggested Citation

  • Ma, Xiaobo & Karimpour, Abolfazl & Wu, Yao-Jan, 2020. "Statistical evaluation of data requirement for ramp metering performance assessment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 248-261.
    • Handle: RePEc:eee:transa:v:141:y:2020:i:c:p:248-261
    DOI: 10.1016/j.tra.2020.09.011
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    References listed on IDEAS

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    1. Alonso, Borja & Ibeas, Ángel & Musolino, Giuseppe & Rindone, Corrado & Vitetta, Antonino, 2019. "Effects of traffic control regulation on Network Macroscopic Fundamental Diagram: A statistical analysis of real data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 136-151.
    2. Zhang, Lei & Levinson, David, 2010. "Ramp metering and freeway bottleneck capacity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(4), pages 218-235, May.
    3. Li, Hao & Gao, Kun & Tu, Huizhao, 2017. "Variations in mode-specific valuations of travel time reliability and in-vehicle crowding: Implications for demand estimation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 250-263.
    4. Ghasri, Milad & Hossein Rashidi, Taha & Waller, S. Travis, 2017. "Developing a disaggregate travel demand system of models using data mining techniques," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 138-153.
    5. Levinson, David & Zhang, Lei, 2006. "Ramp meters on trial: Evidence from the Twin Cities metering holiday," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(10), pages 810-828, December.
    6. Kharoufeh, Jeffrey P. & Goulias, Konstadinos G., 2002. "Nonparametric identification of daily activity durations using kernel density estimators," Transportation Research Part B: Methodological, Elsevier, vol. 36(1), pages 59-82, January.
    7. Gao, H. Oliver & Johnson, Lynn Schooley, 2009. "Methods of analysis for vehicle soak time data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(8), pages 744-754, October.
    8. Kim, Suji & Park, Sungjin & Jang, Kitae, 2019. "Spatially-varying effects of built environment determinants on walking," Transportation Research Part A: Policy and Practice, Elsevier, vol. 123(C), pages 188-199.
    9. Malec, Peter & Schienle, Melanie, 2014. "Nonparametric kernel density estimation near the boundary," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 57-76.
    10. Malec, Peter & Schienle, Melanie, 2012. "Nonparametric Kernel density estimation near the boundary," SFB 649 Discussion Papers 2012-047, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    11. Guerrieri, Marco & Mauro, Raffaele, 2016. "Capacity and safety analysis of hard-shoulder running (HSR). A motorway case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 162-183.
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    1. Salvatore Trubia & Salvatore Curto & Salvatore Barberi & Alessandro Severino & Fabio Arena & Giovanni Pau, 2021. "Analysis and Evaluation of Ramp Metering: From Historical Evolution to the Application of New Algorithms and Engineering Principles," Sustainability, MDPI, vol. 13(2), pages 1-19, January.

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