Multidimensional Data and Model Uncertainties in Comparing Heterogeneous Benefits of Distributed Transportation Projects

Abstract

Investments in transportation projects are typically justified by diverse potential benefits, including safety, environmental, energy savings, congestion mitigation, and others. There is a need for such benefits to be comparable early in transportation programming, to allocate scarce resources to preliminary engineering. This paper discusses quantitative methods to aid in prioritizing locations of future highway projects. The paper adopts 15 quantitative metrics including crash rate, emergency route access, environmental issues, level of service (LOS), volume-to-capacity ratio, traffic flow, intermodal access, heavy truck usage, unemployment rate, right-of-way use, use of alternative transportation modes, bridge sufficiency rating, and cost effectiveness. This effort contributes to real-world transportation programming and priority setting via analysis of the crash avoidance and other benefits and costs that are expected before project designs are available. The innovation of this paper is in two areas: (1) estimation of broad-spectrum benefits across several categories including crashes avoided, travel time saved, fuel uses avoided, and emissions avoided; and (2) development of a software tool that compares project benefits to prefeasibility estimates of project costs. The developed software represents the uncertainty of the results using numerical intervals. The paper illustrates how project selection is informed with sparse and/or early information on a large number of potential locations and needs. With interval analysis of uncertainty, decision makers might conclude that existing evidence is sufficient to distinguish among competing needs. The methods are demonstrated for 53 project locations ranging in their prefeasibility cost estimates from $2 million to $130 million.