Travel time measure specification by functional approximation: Application of radial basis function neural networks

Hilmi Berk Celikoglu*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

10 Citations (Scopus)

Abstract

In this study, in the purpose of providing a dynamic procedure for reliable travel time specification, the performance of a neural functional approximation method is analysed. The numerical analyses are carried out on the succeeding sections of a freeway segment inputting data obtained from microwave radar sensor units located successively at the cross-sections of a freeway segment of approximately 4 km. Measurements on traffic variables, i.e., vehicle counts, speed, and occupancy, for the reference time periods are processed. The structure of the employed radial basis function neural networks are configured considering the data of a three-lane freeway segment obtained by succeeding sensors located in side-fired position. Travel time measures approximated by the neural models are compared with the corresponding field measurements obtained by probe vehicle. Results prove neural model's performance in representing spatiotemporal variation of flow dynamics as well as travel times. Adaptability of the proposed travel time specification procedure to real-time intelligent control systems is a possible future extension.

Keywords

  • Intelligent Transportation System
  • Neural networks
  • Traffic flow
  • Travel time

Fingerprint

Dive into the research topics of 'Travel time measure specification by functional approximation: Application of radial basis function neural networks'. Together they form a unique fingerprint.

Cite this