Open AccessAn artificial neural network approach for evaluating transportation network improvements
Author(s) -
Wei ChienHung,
Schonfeld Paul M.
Publication year - 1993
Publication title -
journal of advanced transportation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 46
eISSN - 2042-3195
pISSN - 0197-6729
DOI - 10.1002/atr.5670270202
Subject(s) - artificial neural network , interdependence , time horizon , computer science , scheduling (production processes) , network planning and design , selection (genetic algorithm) , operations research , flow network , schedule , set (abstract data type) , engineering , artificial intelligence , mathematical optimization , operations management , computer network , mathematics , political science , law , programming language , operating system
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.