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IMPROVING THE ESTIMATION ACCURACY OF DATA TRAFFIC USING DATA MINING SPATIAL AUTOREGRESSIVE BENCHMARK MODEL.

Nation-wide Annual Average Daily Traffic(AADT)data on NFAS roads across the country are destroyed. Two machine learning methods, the Artificial Neural Network and Random Forest demonstrate a substantial increase in the accuracy of estimating AADT according to five scales that is MSE, RSQ, RMse, MAE...

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Publicado en:Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 2488 - 2500
Autores principales: RAMESH, G., RAO, MALLIKARJUNA, R., SRIDEVI, NEELIMA, P.
Formato: pictorial tables/charts Journal Article
Publicado: Turkish Journal of Physiotherapy & Rehabilitation 2021
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 2021
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      pub: Turkish Journal of Physiotherapy & Rehabilitation
      place: Kizilay/ Ankara, <Blank>
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        atl: IMPROVING THE ESTIMATION ACCURACY OF DATA TRAFFIC USING DATA MINING SPATIAL AUTOREGRESSIVE BENCHMARK MODEL.
      aug:
        au:
          RAMESH, G.
          RAO, MALLIKARJUNA
          R., SRIDEVI
          NEELIMA, P.
        affil: Associate Professor, Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering & Technology Hyderabad- 500090, Telangana State, India
      sug:
        subj:
          Data Mining
          Motor Vehicles
          Neural Networks (Computer)
          Benchmarking
          Validity
          Machine Learning
          Algorithms
          Data Analysis
          Program Implementation
          Computer Communication Networks
      ab: Nation-wide Annual Average Daily Traffic(AADT)data on NFAS roads across the country are destroyed. Two machine learning methods, the Artificial Neural Network and Random Forest demonstrate a substantial increase in the accuracy of estimating AADT according to five scales that is MSE, RSQ, RMse, MAE and MAPE, using a Spatial Autoregressive Model as a benchmark. An estimated AADT of 87 variables in the area of central, adjacent traffic, population, jobs, land-use diversity, density of road networks, urban design, destination access, etc. is focused on data mining from three aspects, i.e. on road and off-road, network centrality and neighbouring influences. The variable collection for estimates is promoted by aggregation of data by different buffer sizes and linearity and singletonity statistical analysis. The interplay between the variables, variable measurements of significance are extensively explored when applying machine-learning approaches not only the estimation output but also the relationship between and variable and AADT.
      pubtype: Academic Journal
      doctype:
        pictorial
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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