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Defining Bicycle Levels of Service Criteria Using Levenberg–Marquardt and Self-organizing Map Algorithms

  • Sambit Kumar Beura
  • Veera Leela Manusha
  • Haritha Chellapilla
  • Prasanta Kumar Bhuyan
Original Article

Abstract

This article proposes a bicycle level of service (BLOS) model for the assessment of urban roadway segments in mid-sized cities carrying heterogeneous traffic. The bicycling environments persisting on as many as 74 segments of four Indian cities are thoroughly analyzed. On-street bicyclists with varied demographics have rated these segments using a Likert scale ranging from ‘1’ (excellent) to ‘6’ (worst). The influences of various road attributes (geometric, traffic, and built-environmental) and bicyclists’ characteristics (socio-demographic and travel characteristics) on the perceived ratings are assessed using Spearman’s correlation analysis. Subsequently, eight significant variables are identified and used to develop a Levenberg–Marquardt neural network-based BLOS model. The most efficient but less complex model consisted of one hidden layer, three hidden neurons, and hyperbolic tangent activation function. This model produced very high values of correlation coefficient between the actual and predicted perceived ratings (i.e., 0.93 and 0.92 in the training and testing phases, respectively). The applications of Garson’s algorithm and connection-weight approaches explored that the effective width of outermost lane has the highest influence on urban street BLOS. The BLOS criteria are classified into six categories A–F (representing excellent–worst) using the self-organizing map in artificial neural network cluster technique. It was observed that most of the studied segments are offering average to worst kind of services at their present-day conditions. Thus, the influencing variables should be largely prioritized in the planning process to achieve better service levels efficiently.

Keywords

Bicycle level of service Urban road segment Heterogeneous traffic Artificial neural network Levenberg–Marquardt algorithm Self-organizing map Clustering 

Notes

Acknowledgements

The authors acknowledge the opportunity provided by the 4th Conference of the Transportation Research Group of India (4th CTRG) held at IIT Bombay, Mumbai, India between 17th December, 2017 and 20th December, 2017 to present the work that forms the basis of this manuscript.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Sambit Kumar Beura
    • 1
  • Veera Leela Manusha
    • 1
  • Haritha Chellapilla
    • 2
  • Prasanta Kumar Bhuyan
    • 1
  1. 1.Department of Civil EngineeringNational Institute of Technology RourkelaRourkelaIndia
  2. 2.Department of Civil EngineeringIndian Institute of Technology MadrasChennaiIndia

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