Federal Highway Administration Office of Operations and Resource Center. Some material in this monograph was compiled from previous studies that were made possible under National Cooperative Highway Research Program project 3-79a, INDOT State Planning and Research (SPR) projects, Indiana LTAP projects, and USDOT through Small Business Innovation Research (SBIR) projects with Traffax, Inc., and through a joint research project with Marshall University. We would like to thank Rick Schuman and colleagues at Inrix, Inc., for provision of sample vehicle trajectory data in Chapter 7. We are grateful to these sponsors and research partners for their support over the years.The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein, and do not necessarily reflect the official views or policies of the sponsoring organizations. These contents do not constitute a standard, specification, or regulation. CopyrightCopyright 2015 by Purdue University. All rights reserved.Print ISBN: 978-1-62260-376-3 ePUB ISBN: 978-1-62260-377-0 ABSTRACT INTEGRATING TRAFFIC SIGNAL PERFORMANCE MEASURES INTO AGENCY BUSINESS PROCESSESThis report discusses uses of and requirements for performance measures in traffic signal systems facilitated by high-resolution controller event data. Uses of external travel time measurements are also discussed. The discussion is led by a high-level synthesis of the systems engineering concepts for traffic signal control, considering technical and nontechnical aspects of the problem. This is followed by a presentation of the requirements for implementing data collection and processing of the data into signal performance measures. The remaining portion of the report uses an example-oriented approach to show a variety of uses of performance measures for communication and detector system health, quality of local control (including capacity allocation, safety, pedestrian performance, preemption, and advanced control analysis), and quality of progression (including evaluation and optimization).

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The most significant energy consuming infrastructures and the greatest contributors to greenhouse gases for any developed nation today are electric and freight/passenger transportation systems. Technological alternatives for producing, transporting, and converting energy for electric and transportation systems are numerous. Addressing costs, sustainability, and resilience of electric and transportation needs requires longterm assessment since these capital-intensive infrastructures take years to build with lifetimes approaching a century. Yet, the advent of electrically driven transportation, including cars, trucks, and trains, creates potential interdependencies between the two infrastructures that may be both problematic and beneficial. We are developing modeling capability to perform long-term electric and transportation infrastructure design at a national level, accounting for their interdependencies. The approach combines network flow modeling with a multiobjective solution method. We describe and compare it to the state of the art in energy planning models. An example is presented to illustrate important features of this new approach.Keywords infrastructure resilience, investment planning, optimisation, infrastructure robustness, long-term planning, national infrastructure systems, energy infrastructure, transport infrastructure, infrastructure interdependencies, electric vehicles, infrastructure design, network flow modelling, energy planning Disciplines Electrical and Computer Engineering | Power and Energy | Systems and Communications | Transportation Engineering CommentsThis is a manuscript of an article published as Ibanez, Eduardo, . "Resilience and robustness in longterm planning of the national energy and transportation system.Abstract: The most significant energy consuming infrastructures and the greatest contributors to greenhouse gases for any developed nation today are electric and freight/passenger transportation systems. Technological alternatives for producing, transporting, and converting energy for electric and transportation systems are numerous. Addressing costs, sustainability, and resilience of electric and transportation needs requires long-term assessment since these capital-intensive infrastructures take years to build with lifetimes approaching a century. Yet, the advent of electrically driven transportation, including cars, trucks, and trains, creates potential interdependencies between the two infrastructures that may be both problematic and beneficial. We are developing modeling capability to perform long-term electric and transportation infrastructure design at a national level, accounting for their interdependencies. The approach combines network flow modeling with a multiobjective solution method. We describe and compare it to the state of the art in energy planning models. An example is presented to illustrate important features of this new approach. Author

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Agencies often find it difficult to justify investments in active traffic management. Historically, it has been a challenge to obtain data that would help make the case for those investments. While new data sources have emerged recently, there remains very little documentation of the potential long term benefits from signal retiming using associated performance measures. This study presents a use case for an active traffic management strategy on a signalized corridor over a 5-year period, during which traffic volumes increased by approximately 36%, and offset optimization was performed every 2-3 years. Despite the considerable volume growth, arrivals on green were increased by more than 41%, and the percentage of vehicles arriving on green increased by 10%, a gain of 6 percentage points. Furthermore, drivers experienced an average of 5% reduction in travel time and travel time reliability costs after each optimization. This resulted in a total user benefit over the 5-year period of approximately $3.6 million. Agencies can utilize these strategies to quantitatively assess how traffic performance and signal timing degrade over time, in a manner similar to physical infrastructure assets. The results highlight the benefits and associated business case of adopting a long-term active traffic management strategy, based on datadriven performance monitoring and decision making.