An artificial neural network approach for evaluating transportation network improvements

Chien‐Hung ‐H Wei, Paul M. Schonfeld

研究成果: Article同行評審

10 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)129-151
期刊Journal of Advanced Transportation
出版狀態Published - 1993

All Science Journal Classification (ASJC) codes

  • 汽車工程
  • 經濟學與計量經濟學
  • 機械工業
  • 電腦科學應用
  • 策略與管理


深入研究「An artificial neural network approach for evaluating transportation network improvements」主題。共同形成了獨特的指紋。