Adaptive time-staged strategies for transportation investment

Typically, investments in transportation have involved large-scale, durable facilities with significant long-term consequences on both the environment and even the very lifestyle of a region. Traditional solutions to transportation problems have ignored the fact that significant changes have occurred over the last few years, however, in the demand for transportation, in the transportation technologies available, and most importantly in the way people value transportation service. The value of many current approaches to investment planning has been severely limited because they ignore these changes and the other uncertainties inherent in the planning environment. This paper presents an application of the general sequential decision model and an adaptive strategic approach to transport planning which lends itself to handling both uncertainties in future demands, technologies and values and to a more incremental staged sequential solution to the transport investment problem. A Bayesian learning model is developed which considers both the flexibility of the staged strategic approach as well as the flexibility and adaptability of technologies.