An Evaluation Procedure for Mutually Exclusive Highway Safety Alternatives under Different Policy Objectives

The purpose of evaluating mutually exclusive alternatives is to select the one with the highest benefits for implementation. A number of analytic techniques are available for such evaluation purposes. Four such techniques: Cost Effectiveness (C/E), Benefit Cost Ratio (B/C), Internal Rate of Return (IRR), and Pay-off Period (PP) are discussed in this paper, including their theoretical foundation and data requirements, Also discussed are the measures of effectiveness (MOE) associated with each of these techniques, and how these are to be interpreted. Alternatives to be selected for implementation following such evaluation can typically be funded under different policy objectives. Three such objectives are identified in the paper: Objective A, constrained resource perspective; Objective B, investment perspective; and Objective C, face value perspective. The possible relationship between the alternative selection and program is discussed in the paper. A case study for a set of six mutually exclusive highway safety alternatives is presented using the four analytic techniques and three objectives, resulting in various possible solutions. Results show that under compatible assumptions, and for a given policy objective, the outcome of the evaluation is not affected by the choice of the analytic technique. However, for a given analytic technique, the outcome may be affected by the choice of the policy objective chosen. The principles presented are relevant for most public projects (e.g. transit, airports, etc.) involving the investment of taxpayer resources, even though the case study involves a highway safety project

Optimization Model for Allocating Resources for Highway Safety Improvement at Urban Intersections

The authors present a procedure for allocating resources for implementing safety improvement alternatives at urban intersections over a multiyear planning horizon. The procedure, on the basis of optimization techniques, attempts to maximize benefits measured in dollars saved by reducing crashes of different severity categories subject to budgetary and other constraints. It is presented in two parts: (1) a base case including the objective function and a set of mandatory constraints; and (2) additional policy constraints/special features that can be separately incorporated to the base case. Demonstration of the procedure is presented on intersections in the Detroit metropolitan region, in which economic losses resulting from traffic crashes at intersections are estimated to exceed $4 billion annually. The proposed model can allocate resources for safety improvement alternatives over a planning horizon, given a number of independent locations and a number of mutually exclusive alternatives at each location. The policy constraints provide the analyst the flexibility of adding equity, urgency, and other features to the base case. An integer programming technique is applied to solve the demonstration problem. © 2012 American Society of Civil Engineers

Evaluation procedure for mutually exclusive highway safety alternatives under different policy objectives

The purpose of evaluating mutually exclusive alternatives is to select the one with the most benefits for implementation. A number of analytic techniques are available for such evaluation purposes. This paper discusses four such techniques: cost effectiveness C/E, benefitto- cost ratio B/C, internal rate of return (IRR), and payoff period (PP), including their theoretical foundation and data requirements. Also discussed are the measures of effectiveness (MOEs) associated with each of these techniques and how they are to be interpreted. Alternatives to be selected for implementation following such evaluation can typically be funded under different policy objectives. This paper identifies three such objectives: Objective A, constrained resource perspective; Objective B, investment perspective; and Objective C, face-value perspective. The possible relationship between the alternative selection and the program is discussed in the paper. A case study for a set of six mutually exclusive highway safety alternatives is presented using the four analytic techniques and three objectives, resulting in various possible solutions. Results show that under compatible assumptions, and for a given policy objective, the outcome of the evaluation is not affected by the choice of the analytic technique. However, for a given analytic technique, the outcome may be affected by the choice of the policy objective chosen. The principles presented are relevant for most public projects (e.g., transit, airports) involving the investment of taxpayer resources, even though the case study involves a highway safety project. © 2012 American Society of Civil Engineers

Bus garage location planning with dynamic vehicle assignments: A methodology

A simultaneous vehicle scheduling and bus garage location and sizing optimization is described. The methodology's importance lies in its treating garage locations and sizes and vehicle schedules as dynamic. In other bus garage planning methodologies, vehicle schedules are assumed fixed.

Incorporating uncertainty and risk in transportation investment decision-making

This paper presents a framework for addressing uncertainty and risk for large-scale transportation investments involving public–private participation. Demand, fare/toll and demand responsive costs are considered in the uncertainty analysis. Uncertainty analysis provides information on economic feasibility of the project. A set of relaxation policies is proposed to form various Ownership, Tenure and Governance (OTG) strategies reflecting the nature and level of participation by the public and private entity. A Monte Carlo Simulation-based Value at Risk is used to quantify risk. Finally, a methodology is proposed to integrate uncertainty and risk. The framework is tested on the proposed multibillion dollar Detroit River International Crossing connecting the cities of Detroit in the USA with Windsor in Canada. The analysis provides insights to probable outcomes for this transportation infrastructure investment under different OTG scenarios

Multi-entity perspective transportation infrastructure investment decision making

Investment in new large transportation infrastructure is capital-intensive and irreversible in nature. Private sector participation in infrastructure investment has gained popularity in recent times because of scarcity of resources at the public sector, and because of the ability of the private sector to build, operate, maintain such facilities, and share future uncertainties. In such cases, there are multiple entities each with different objectives in the project. Traditional techniques used to determine feasibility of such projects and do not consider two critical elements. These are the need (1) to identify major entities involved in these projects and their individual objectives, and (2) the importance of analyzing measures of effectiveness of each entity in a multi-objective context. A framework is proposed to address these issues along with a set of relaxation policies to reflect the nature and level of participation by the entities.First, the feasibility of each single entity perspective is determined and next, a multi-objective optimization (MOO) is proposed reflecting the perspectives of all entities. The MOO results in pareto-optimal solutions to serve as tradeoff between the participation levels of the multiple entities. The Analytic Hierarchy Process (AHP) is used as a tool to narrow down number of options for decision makers for further consideration. AHP and MOO are integrated to determine the feasibility of strategies from multi-entity perspectives. The framework is examined on the proposed multibillion dollar international river crossing connecting the city of Detroit in the U.S. and the city of Windsor in Canada. This methodology provides a decision making process tool for large-scale transportation infrastructure investment consisting of multiple entities. © 2013

Optimal resource allocation for the purchase of new buses and the rebuilding of existing buses as a part of a transit asset management strategy for state DOTs

187 p.State Departments of Transportation (DOT) that provide much of the matching support to local transit agencies required for federal funding for the purchase of new buses, are duly concerned about the escalating costs of new buses and the lack of sufficient funds to keep up with their replacement costs. The authors present an asset management strategy in this report that can be used by state DOTs to (1) allocate capital dollars for the dual purpose of purchasing new buses and rebuilding existing buses within the constraints of a fixed budget, when the needs of all the constituent agencies in a peer group are considered, and (2) distribute funds among the agencies in an equitable manner. The proposed procedure includes two optimization models. Model 1 attempts to maximize the weighted fleet life of all the buses that are being purchased and rebuilt for a given peer group. Model 2 is designed to maximize the Remaining Life (RL) of the entire peer group comprising the existing buses as well as those being replaced or rebuilt. Three case studies are presented in the report to demonstrate the application of the models: two with medium sized buses and one with large sized buses. Besides replacement, three other program options are considered: two levels of rehabilitation, and one level of remanufacturing. Necessary budgetary and fleet data were provided by the Michigan Department of Transportation. The case studies show that the proposed method is viable, and can be used for the designated purpose with fleet data currently available with state DOTs. The case studies also identify major shortfalls in funding, and help to underscore the need of increased funding levels to improve the quality of the fleet.US Department of Transportatio

Single-stage integer programming model for long-term transit fleet resource allocation

The writers present a procedure for resource allocation among transit agencies for transit fleet management, specifically focusing on the purchase of new buses and rebuilding of existing buses. The model is formulated as a nonlinear optimization problem of maximizing the total weighted average remaining life of the fleet subject to budgetary, policy, and other constraints. The problem is solved using integer programming and its application is demonstrated through a case study using actual transit fleet data from the Michigan DOT. This proposed model is an extension of earlier research on a two-stage sequential optimization method, solved by linear programming. The proposed model has a single-stage structure designed to attain a better solution by allocating resources among different improvement options and different agencies in a single step. A comparison of the results by the two methods shows that while both approaches are viable, the single-stage approach produces better results. The proposed model, as demonstrated in the case study is considered more robust, compact, efficient and suitable for both short-term and long range planning. © 2010 ASCE

Optimal resource allocation among transit agencies for fleet management

Most transit agencies require government support for the replacement of their aging fleet. A procedure for equitable resource allocation among competing transit agencies for the purpose of transit fleet management is presented in this study. The proposed procedure is a 3-dimensional model that includes the choice of a fleet improvement program, agencies that may receive them, and the timing of investments. Earlier efforts to solve this problem involved the application of 1- or 2-dimensional models for each year of the planning period. These may have resulted in suboptimal solution as the models are blind to the impact of the fleet management program of the subsequent years. Therefore, a new model to address a long-term planning horizon is proposed. The model is formulated as a non-linear optimization problem of maximizing the total weighted average remaining life of the fleet subjected to improvement program and budgetary constraints. Two variants of the problem, one with an annual budget constraint and the other with a single budget constraint for the entire planning period, are formulated. Two independent approaches, namely, branch and bound algorithm and genetic algorithm are used to obtain the solution. An example problem is solved and results are discussed in details. Finally, the model is applied to a large scale real-world problem and a detailed analysis of the results is presented