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Macroscopic traffic performance indicators based on floating car data: formation, pattern analysis, and deduction

Macroscopic traffic performance indicators based on floating car data: formation, pattern analysis, and deduction

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Urban traffic is an important part of urban activities. With the rapid development of the economy and the advancement of urbanization in many cities, urban road systems experienced serious traffic congestions, which increases the traffic delay and fuel consumption, aggravates the vehicle exhaust and noise, and seriously damages the urban environment. In order to evaluate the congestion conditions of cities, macroscopic measurements are required to provide quantified indications on evaluating the traffic performance of cities.

Chapter Contents:

  • 3.1 Introduction
  • 3.2 A macroscopic traffic performance indicator: network-level trip speed
  • 3.2.1 The mathematical form of the NLT speed
  • 3.2.2 The empirical data for analyses
  • 3.2.3 Descriptive analyses of influential factors
  • 3.2.4 Correlative relationships between variables
  • 3.3 Methods of time series analysis
  • 3.3.1 The concept and basic features of the time series
  • 3.3.2 The exponential smoothing method
  • 3.3.3 The ARIMA method
  • 3.3.4 The support vector machine (SVM) method
  • 3.4 Analyses of the NLT speed time series
  • 3.4.1 Evaluation criteria of the modeling performance
  • 3.4.2 The decomposition of the NLT speed time series
  • 3.4.3 The analysis based on exponential smoothing methods
  • 3.4.4 The analysis based on ARIMA models
  • 3.4.5 The analysis based on a hybrid ARIMA–SVM Model
  • 3.5 Conclusions
  • References

Inspec keywords: traffic information systems

Other keywords: urban road systems; pattern analysis; congestion conditions; urban traffic; floating car data; traffic performance; macroscopic traffic performance indicators; vehicle exhaust; urban activities; fuel consumption; traffic delay; traffic congestions

Subjects: Traffic engineering computing

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