Incorporating Reliability Performance Measures into Operations and Planning Modeling Tools: Reference Material (2014) / Chapter Skim
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104 PART 2 DEVELOPMENT OF UNIFYING FRAMEWORK FOR SCENARIOBASED RELIABILITY ANALYSIS CHAPTER 5 Scenario Manager This chapter provides an overview of the unifying framework for travel time reliability analysis, as developed in Phase 1 of this research project, as well as a detailed discussion of the concept of the scenario-based analysis. This chapter also describes the general functionality and use of the Scenario Manager, which along with the Trajectory Processor (presented in Chapter 6)
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105 constructed scenarios as part of a planning exercise -- for example, in conjunction with emergency preparedness planning. It would also allow generation, through Monte Carlo sampling, of hypothetical scenarios for analysis and design purposes.
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106 framework features three components: Scenario Manager, Traffic Simulation Model, and Trajectory Processor. The primary role of the Scenario Manager is to prepare input scenarios for the traffic simulation models, which is a core part of this framework as it directly affects the final travel time distributions.
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107 Figure 5.1. Core elements of reliability modeling framework.
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108 such disruptions. The objective of the scenario-based reliability analysis is to investigate variability in the output travel time distribution by controlling the input scenario (i.e., input scenarios can be generated completely at random or in a more directed manner based on a particular experimental design)
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109 iw = the weight of the thi scenario with n i i w 1 1, which is typically obtained from the scenario probability )
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110 approach. The former tries to generate all possible scenarios that could occur during the given temporal and spatial boundaries to introduce realistic variations in the resulting travel time distribution, while the latter constructs scenarios by manually choosing various combinations of scenario components.
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111 be used to produce the group-specific travel time distribution, and the group probability is obtained based on the number of scenarios in each group. Figure 5.3.
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112 Figure 5.4. Mix-and-match approach.
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113 Figure 5.5. Various scenario components and dependency relations.
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115 5.3.1.1 Structure of Scenario Throughout this document, a set of terminologies is used to describe different components in the structure of scenario, some of which are shown in Figure 5.7. In what follows, a definition of each terminology is provided.
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116 signal control, and trip cancellation; and so on. Each scenario group is assigned the probability of occurrences either during the generation process or manually by the user.
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117 Figure 5.9. Sequence of event instances representing one scenario realization.
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118 Figure 5.10. Properties of event instance.
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119 such as frequency, duration, location, and intensity are specified either parametrically or nonparametrically.
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120 Figure 5.12. Define time and space domains and estimate input parameters from historical data.
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121 Figure 5.14. Select scenario components and generate scenarios.
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122 Figure 5.15. Obtain scenario generation results and examine generated scenarios.
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123 Figure 5.16. Example of one instance of scenario consisting of weather, accidents, and demand random variation: temporal profiles represented by "rectangular pulse" with duration (width)
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