A network option portfolio management framework for adaptive transportation planning

A real option portfolio management framework is proposed to make use of an adaptive network design problem developed using stochastic dynamic programming methodologies. The framework is extended from Smit’s and Trigeorgis’ option portfolio framework to incorporate network synergies. The adaptive planning framework is defined and tested on a case study with time series origin–destination demand data. Historically, OD time series data is costly to obtain, and there has not been much need for it because most transportation models use a single time-invariant estimate based on deterministic forecasting of demand. Despite the high cost and institutional barriers of obtaining abundant OD time series data, we illustrate how having higher fidelity data along with an adaptive planning framework can result in a number of improved management strategies. An insertion heuristic is adopted to run the lower bound adaptive network design problem for a coarse Iran network with 834 nodes, 1121 links, and 10 years of time series data for 71,795 OD pairs.

► Presented adaptive transportation planning as a network-based option portfolio management framework. ► Customized Smit and Trigeorgis’ option space for network-based assets. ► Characterized the elements of the strategic framework for managing different types of networks, uncertainties, and projects. ► Implemented and tested a greedy heuristic for solving the adaptive network design problem for large-scale problems. ► Illustrated the portfolio management framework with an 834-node Iran intercity truck network.