Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia

Research Article

The Bootstrap Stratified Random Sampling in Finite Population for Traffic Survey Data

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  • @INPROCEEDINGS{10.4108/eai.2-8-2019.2290544,
        author={Kristiana  Yunitaningtyas and Indahwati  Indahwati and Muhammad  Nur Aidi and Santi  Susanti},
        title={The Bootstrap Stratified Random Sampling in Finite Population for Traffic Survey Data},
        proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia},
        publisher={EAI},
        proceedings_a={ICSA},
        year={2020},
        month={1},
        keywords={bootstrap finite population stratified random sampling traffic survey},
        doi={10.4108/eai.2-8-2019.2290544}
    }
    
  • Kristiana Yunitaningtyas
    Indahwati Indahwati
    Muhammad Nur Aidi
    Santi Susanti
    Year: 2020
    The Bootstrap Stratified Random Sampling in Finite Population for Traffic Survey Data
    ICSA
    EAI
    DOI: 10.4108/eai.2-8-2019.2290544
Kristiana Yunitaningtyas1,*, Indahwati Indahwati1, Muhammad Nur Aidi1, Santi Susanti2
  • 1: Department of Statistics, IPB University, Indonesia
  • 2: Transportation Agency, Sukabumi City
*Contact email: kristiana_yunitaningtyas@apps.ipb.ac.id

Abstract

Traffic survey is an important technique used to measure traffic density and gas emissions produced by vehicles but generally it is carried out for a long period of time. This study aims to apply stratified random sampling to traffic survey data so as to improve the process of data collection and efficiency with a high degree of accuracy. The data is divided into strata based on traffic density and is implemented using the direct bootstrap resampling technique by paying attention to the finite population correction factor. The bootstrap in finite population is expected to resolve the overestimate variance due to the standard bootstrap. Evaluation is done by looking at the criteria of validity, reliability, and accuracy of the bootstrap statistics. The results indicated that the bias and variance decrease when bootstrap replication is large. Bootstrap sample size of 32 produced the lowest distribution of bias, adjusted variance, and MSE value.