Incorporating Reliability Performance Measures into Operations and Planning Modeling Tools (2014) / Chapter Skim
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93 p A r t 3 This part of the report describes two case studies incorporating travel time reliability into microscopic and mesoscopic models and summarizes the findings and conclusions of this research project.
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94 deterministic scenarios from existing historical sources. This case study uses the former approach: a set of random scenarios are constructed using Monte Carlo sampling for each category.
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95 Table 8.1. Scenario Components and Input Parameters Weekday or Weekend exogenous Sources Scenario CaseWeather Incident Day-to-Day Demand Variation Frequency: poisson ()
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96 fraction of link capacity lost due to the incident)
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97 resolutions. As discussed in Table 7.3 in Chapter 7, different reliability metrics can be used to assess the reliability performance at different levels of the system: network-level, O–Dlevel, and path level.
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98 Table 8.3. Network-Level Performance Measures, Departure Time Interval 7 a.m.
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99 Table 8.4. Network-Level Performance Measures, Departure Time Interval 8 a.m.
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100 Table 8.5. Network-Level Performance Measures, Departure Time Interval 9 a.m.
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101 Figure 8.3. Mean travel time per mile (network-level)
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102 Figure 8.5. 80th percentile travel time per mile (network-level)
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103 Zone ID: 685 Zone ID: 605 Figure 8.7. Selected origin–destination (O–D)
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104 Figure 8.9. Standard deviation of travel times (O–D-level)
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105 Figure 8.11. Buffer Index (O–D-level)
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106 Figure 8.13. Mean travel time (path-level)
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107 Figure 8.15. Planning Time Index (path-level)
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108 Table 8.6. O–D-Level Performance Measures, Departure Time Interval 7 a.m.
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109 Table 8.7. O–D-Level Performance Measures, Departure Time Interval 8 a.m.
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110 Table 8.8. Path-Level Performance Measures, Departure Time Interval 7 a.m.
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111 consecutive days from May 2, 2010, to May 17, 2010, in New York, to perform this comparison. We selected the same path used in the path-level analysis (see Figure 8.12)
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112 Figure 8.18. Simulation versus observation: 80th percentile travel time (path-level)
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113 Figure 8.20. Simulation versus observation: Buffer Index (path-level)
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114 C h A p t e r 9 The purpose of this chapter is to demonstrate how microsimulation tools can be used in performing reliability analyses using the framework and tools developed under this project. The Aimsun simulation software was used to perform the microsimulation task.
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115 and 5 p.m. (Tables 9.4, 9.5, and 9.6)
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116 Table 9.1. Network-Level, Departure Time Interval 7 a.m.
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117 Table 9.2. Network-Level, Departure Time Interval 8 a.m.
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118 Table 9.4. Network-Level, Departure Time Interval 2 p.m.
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119 Figure 9.4. Scatter plot: Standard deviation of travel time per mile.
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120 Figure 9.6. Scatter plot: 95th percentile travel time per mile.
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121 Table 9.7. Origin (3457817)
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122 Table 9.9.
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123 Figure 9.8. Average travel time (3457817–3475128)
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124 Figure 9.10. 80th percentile travel time (3457817– 3475128)
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125 area, such as in the meso-experiment, overall average times would not be as sensitive to local incidents as much, since there would be many of the model trips that are far removed from the incident and that would operate under normal travel conditions. 44 Fundamental difference in the microsimulation and mesosimulation tools.
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126 Table 9.12. Departure Time Interval 2 p.m.
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127 Figure 9.15. Planning Time Index.
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128 C h A p t e r 1 0 The SHRP 2 L04 research project has addressed the need for a comprehensive framework and a conceptually coherent set of methodologies to (1) better characterize travel time reliability and the manner in which the various sources of variability operate individually and in interaction with each other in determining the overall reliability performance of a network, (2)
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129 By necessity, the quantification of travel time variability (that characterizes the reliability of travel in a network) entails representing the variability of travel times through the network's links and nodes along the travel paths followed by travelers, and taking into account the correlation between link travel times.
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130 The vehicle Trajectory Processor is then introduced to extract reliability-related measures from the vehicle trajectory output of the simulation models. It produces and helps visualize reliability performance measures (travel time distributions, indicators)
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131 to prototype the key concepts -- namely, those of a Scenario Manager and a Trajectory Processor -- and demonstrated them with two commonly used network modeling software platforms. Agency Adoption Throughout this study, it has become clear that reliability as an evaluation and decision factor is here to stay.
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132 reliability should be included in mode choice and time- of-day choice and (through these choices or in a different way) also be incorporated into the other travel choices such as destination choice and trip frequency choice.
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133 Abdelghany, K., and H Mahmassani.
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134 Dong, J., and H
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135 Parsons Brinckerhoff, Northwestern University, Mark Bradley Research & Consulting, University of California at Irvine, Resource System Group, University of Texas at Austin, F Koppelman, and GeoStats.
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