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4SHRP 2 L04, Incorporating Reliability Performance Mea- sures into Operations and Planning Modeling Tools, is a cen- tral project within the second Strategic Highway Research Program (SHRP 2) Reliability focus area. The goal of this focus area is to reduce unexpected congestion and improve travel time reliability. Numerous technical measures and policies are under consideration within SHRP 2 research projects to confront the problems of traffic congestion and devise means to improve reliability. The motivation for this L04 project is the recognition that it is essential to improve planning and operations models in parallel with these devel- opments to have suitable evaluation tools for the projects and policies that are expected to improve reliability. What is lacking is a comprehensive framework and conceptually coherent set of methodologies to (1) better characterize reli- ability, and the manner in which the various sources of vari- ability operate individually and in interaction with each other in determining overall reliability performance of a net- work; (2) assess the impacts of reliability on users and the system; and (3) determine the effectiveness and value of proposed counter measures. Therefore, this model develop- ment project has a significant and practical role to play in future project investment evaluations that will use reliabil- ity improvement estimates. objectives The primary objective of this project is to develop the capability of producing measures of reliability performance as output in traffic simulation models and planning models. A secondary objective is to then examine how travel demand forecasting models can use reliability measures to produce revised esti- mates of travel patterns. The intent of this project is therefore to close this gap in the underlying conceptual foundations of travel modeling and traffic simulation, and provide practical means of generating realistic reliability measures using network simulation models. Approach The research teamâs approach centers on providing a unify- ing framework for reliability analysis, using essentially any particle-based microsimulation or mesosimulation model that produces trajectories. To address the challenges associ- ated with this task, the framework proposes to capture the sources of unreliability in network traffic performance through a combination of endogenous mechanisms (i.e., capture directly the phenomena that cause delay, such as flow break- down) and exogenous events with given probabilities. Previ- ous technical reports, particularly the Task 7 Report, Simulation Model Adaptation and Development [part of the SHRP 2 L04 Project Reference Material Report (Stogios et al. 2014)], the team elaborated on the conceptual and method- ological frameworks developed as an outcome of this project. They also presented the specific methodologies and procedures devised to incorporate reliability performance measures in supply-side (network operations) models used on their own or in conjunction with integrated demand-supply model systems for both strategic and operational planning applications. This final report is intended to provide an application- focused description of the methodology and tools developed under the L04 project to address the study objectives of assess- ing the reliability performance of a network and evaluate the effectiveness of different projects and measures to improve reliability. report organization The report is organized into three principal parts. The first part focuses on the underlying conceptual and methodologi- cal foundations of the work. The second part describes the specific framework and tools devised to perform the reliabil- ity analysis. The final part concludes with study findings and conclusions that are preceded by the application of the frame- work and tools on a real-world test network. C h A p t e r 1 Introduction
5 different levels of resolution (path, originâdestination, net- work), from the set(s) of simulated trajectories obtained for a particular scenario simulation, or from actual vehicle tra- jectories obtained through real world observations and data sources. The third part, Part 3: Applications, consists of three chap- ters. Chapter 8 describes the application of the overall meth- odology in connection with a state-of-the-art mesoscopic traffic network simulator and dynamic assignment tool to the New York City regional network. Chapter 9 presents similar information using the selected microscopic simulation tool, applied in a subset of the New York City network for which the needed data were available. The applications provide vali- dation by comparing the simulated outputs with those observed as part of a sample of GPS-equipped vehicles. Chapter 10 concludes the report with a summary of the key findings, along with directions for further research necessary to advance the state of the art as well as the state of the practice in this important area. Overall, this project has succeeded in meeting the main points articulated in the functional requirements and has shown considerable potential for general applicability to large-scale networks under realistic scenario assumptions. The approach was able to produce reasonable reliability metrics when compared with the observed trajectory data. The first part, Part 1: Research Background, consists of three chapters. Chapter 2 describes the challenges associated with incorporating reliability measures into operational and plan- ning models and provides a synthesis of existing approaches, thus placing the developments in this project against the back- drop of existing contributions. Chapter 3 focuses on incorpo- rating reliability into strategic planning tools; it is based on a report developed as the outcome of Task 11, which is now part of the SHRP 2 L04 Project Reference Material Report (Stogios et al. 2014). Chapter 4 articulates the functional requirements that have guided the development of the framework and the methods presented in the second part of the report. The second part, Part 2: Framework and Tools for Travel Time Reliability Analysis, consists of three chapters. Chapter 5 describes the data requirements and model selected for the application of the tools used in the application. Chapters 6 and 7 present the principal general-purpose tools developed as part of this project. In particular, Chapter 6 describes the scenario-based approach devised in this study to capture exogenous sources of travel time variability in a network. It is a major contribution of this study, which may be used in connection with both planning and operations models, as described in Chapter 6. Chapter 7 describes the general pur- pose Trajectory Processor designed to extract reliability per- formance indicators, including travel time distributions at
