Understanding the Contributions of Operations, Technology, and Design to Meeting Highway Capacity Needs (2012)

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

12 C h a p t e r 1 project Background The primary objectives of SHRP 2 Capacity Project C05 were threefold: 1. Quantify the capacity benefits, individually and in com­ bination, of operations, design, and technology improve­ ments at the network level for both new and existing facilities. 2. Provide transportation planners with the information and tools to analyze operational improvements as an alterna­ tive to traditional construction. 3. Develop guidelines for sustained service rates (SSRs) to be used in planning networks for limited access highways and urban arterials. Taken together, these objectives support the development of methodologies to effectively determine the expected capacity gain from candidate operational improvements relative to the capacity gain from construction of an additional lane. A variety of questions must be considered in efforts to address these objectives: • Who is the audience? • What is the range of operational strategies that should be considered? • What performance metrics should be used? • How can the operational effects of a particular strategy be fairly compared with the performance impacts of a traditional construction project? • How can sustainable service flow rates be characterized and modeled? • What tools can be used to implement the new meth od­ ologies? Who Is the Audience? The audience for this project is diverse in many respects. Groups that will benefit from a better understanding of the contributions of operations, technology, and design to meet highway capacity needs include the following: • Decision makers, who will use the analysis results to make public investment decisions; • Traffic engineers and transportation planners, who will use the methodologies developed in this project to plan and evaluate alternative operational improvement strategies; • Civil engineers, who will use the methodologies to evaluate the adequacy of their highway design; • Researchers and educators, who will use the methodologies to advance their research, improve their understanding of traffic phenomena, and train future transportation profes­ sionals; and • ITS designers, who are interested in exploring the benefits of current and future ITS technologies. What Improvement Strategies Should Be Considered? The domains of operations, design, and technology all repre­ sent fertile ground for developing strategies that can effec­ tively enhance corridor and/or network performance while also forestalling the need for new construction. The focus of this project was limited to strategies that have the potential to increase capacity or supply. Strategies to reduce or manage demand are worthy of consideration but were not explicitly incorporated into this project work. What Performance Metrics Should Be Used? The traffic performance measures that are particularly effec­ tive for a point analysis—for example, the computed volume/ capacity ratio or level of service at an intersection—are often not especially meaningful in the context of a corridor, sub­ area, or network. Thus, it is desirable and helpful to consider multiple performance measures to assess capacity gains at both the local and network levels. A number of measures were Introduction

13 considered and are supported by the analysis methodologies that were developed. How Can the Capacity-Enhancing Abilities of the Improvement Strategies Be Characterized? Figure 1.1 conceptually illustrates a way to use the analysis methodologies developed in this project to define the rela­ tionship between lane miles added onto a network in the case of a construction project and the effective lane miles added by a nonconstruction improvement strategy. As Figure 1.1 illustrates, a set of nonconstruction improvements that result in reducing the network travel time from, say, A to B, is equiv­ alent to the addition of D minus C lane miles, increasing the network capacity by (D - C)/C%. How Can Sustainable Service Flow Rates Be Characterized and Modeled? Recent research indicates that highway capacity shows all properties of a random variable (1–5). The capacity of a high­ way facility is the result of driver behavior and therefore varies with driver population (i.e., by types of vehicles, motivation or trip purposes, experiences, familiarity with the freeway section or with the traffic operation at the specific time). In addition, highway capacity is a matter of systematic variability (e.g., due to accidents, incidents, weather, and work zones) that is the rea­ son for most of the congestion delay on freeways. The amount of this delay increases with increasing demand. However, only in a limited part of the network does demand exceed the nor­ mal capacity of the infrastructure so that it becomes the main contributor to delay. Modeling the stochastic nature of capacity on both freeway and arterial networks is an important advancement in improv­ ing the reliability of travel demand forecasts and operational analyses. What Tools Can Be Used to Implement the New Methodologies? The primary product of this project is new or enhanced analysis methodologies. Even so, their practical applicability in a real­ world environment depends on the ability to implement them in useful and usable tools. Therefore, an important question at the start of this project was what assignment/simulation tools can be considered for this purpose? Several options were considered by using a decision support approach. One simple theoretical approach to quantifying network­ wide capacity would be to solve a max­flow min­cut optimiza­ tion problem, but this method cannot account for the complex traffic­flow dynamics in a time­varying and complex traffic network. Dynamic traffic assignment (DTA) modeling tools were targeted because of their unique ability to evaluate network performance under time­varying demand and supply condi­ tions created by various operations­based, design­based, and technology based strategies. A range of network analysis tools are available, and all were initially considered for use in this project: DYNASMART­P (Dynamic Network Assignment Simulation Model for Advanced Roadway Telematics: Plan­ ning version), DynusT, DTALite, DYNAMIT, IDAS, Integra­ tion, VisSim, Paramics, SCRITS, and EMME/3. For a number of pragmatic reasons unique to this project, DYNASMART­P was selected and used as the platform for developing and testing the improved analysis methodologies described in this report. However, there is no obvious reason preventing the incorporation of these methodologies into any of the other analysis tools identified, and in fact this has already happened with at least one of the tools (DTALite). references 1. Brilon, W., J. Geistefeldt, and M. Regler. Randomness of Capacity— Idea and Application. Proc., 5th International Symposium on High- way Capacity and Quality of Service, Yokohama, Japan, Vol. 1, Japan Society of Traffic Engineers, Tokyo, 2006, pp. 147–157. 2. Brilon, W., J. Geistefeldt, and H. Zurlinden. Implementing the Concept of Reliability for Highway Capacity Analysis. Presented at 86th Annual Meeting of the Transportation Research Board, Washington, D.C., 2007. 3. Brilon, W., J. Geistefeldt, and M. Regler. Reliability of Freeway Traffic Flow: A Stochastic Concept of Capacity. Proc., 16th International Symposium on Transportation and Traffic Theory, College Park, Md., 2005, pp. 125–144. 4. Brilon, W., and M. Ponzlet. Variability of Speed­Flow Relationships on German Autobahns. Transportation Research Record 1555, TRB, National Research Council, Washington, D.C., 1996, pp. 91–98. 5. Elefteriadou, L., F. L. Hall, W. Brilon, R. P. Roess, and M. G. Romana. Revisiting the Definition and Measurement of Capacity. Proc., 5th Inter- national Symposium on Highway Capacity and Quality of Service, Yokohama, Japan, Vol. 2, Japan Society of Traffic Engineers, Tokyo, 2006, pp. 391–399. Effect of Non-Construction Improvements on Network Travel Time N et w or k Tr av el T im e Equivalent Capacity Gain Added Network Lane Miles A B C D Figure 1.1. Equivalent capacity gain concept.