Selecting and scheduling interrelated road projects with uncertain demand

ABSTRACT

In transportation systems, the existence of interrelations among components and uncertainties in various elements such as future demand usually complicates the capital budgeting process. This paper proposes a method for evaluating, selecting and scheduling interrelated road projects in an urban network under demand uncertainties. The objective is to optimally determine the selection, sequence and schedule of capacity improvement projects while minimizing the present value of total system cost, including travel time, vehicle operating and safety costs, subject to a cumulative budget flow constraint. The scheduling problem is formulated as a non-linear integer optimization problem within a genetic algorithm that minimizes the present value of the system cost over a planning horizon. The proposed model also includes a design feature which determines the type of improvement at each location. This study constitutes a useful framework for state planners and regional decision makers for the project prioritization process.

KEYWORDS:

• Project selection and scheduling
• genetic algorithm
• project interrelations
• project prioritization
• system optimization
• demand uncertainty

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Elham Shayanfar http://orcid.org/0000-0003-0856-0662

Additional information

Funding

The work presented in this paper was partly funded through the following project conducted at the University of Maryland, College Park, Maryland, USA: Optimized Development of Urban Transportation Networks, Urban Mobility and Equity Center, June 2017 – June 2018, Pricipal Investigato: Professor Paul Schonfeld. The was funding provided by the Urban Mobility and Equity Center (UMEC), which is a University Transportation Center funded by the U.S. Department of Transportation. UMEC is led by Morgan State University and includes two other university partners, namely the University of Maryland and Virginia Tech.