An artificial neural network approach for evaluating transportation network improvements

Chien-Hung Wei, Paul M. Schonfeld

研究成果: Article

9 引文 斯高帕斯(Scopus)

摘要

As demand increases over time, new links or improvements in existing links may be considered for increasing a network's capacity. The selection and timing of improvement projects is an especially challenging problem when the benefits or costs of those projects are interdependent. Most existing models neglect the interdependence of projects and their impacts during intermediate periods of a planning horizon, thus failing to identify the optimal improvement program. A multiperiod network design model is proposed to select the best combination of improvement projects and schedules. This model requires the evaluation of numerous network improvement alternatives in several time periods. To facilitate efficient solution methods for the network design model, an artificial neural network approach is proposed for estimating total travel times corresponding to various project selection and scheduling decisions. Efficient procedures for preparing an appropriate training data set and an artificial neural network for this application are discussed. The Calvert County highway system in southern Maryland is used to illustrate these procedures and the resulting performance.

原文English
頁(從 - 到)129-151
頁數23
期刊Journal of Advanced Transportation
27
發行號2
DOIs
出版狀態Published - 1993 一月 1

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Economics and Econometrics
  • Mechanical Engineering
  • Computer Science Applications
  • Strategy and Management

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