Incorporating Reliability Performance Measures into Operations and Planning Modeling Tools (2014) / Chapter Skim
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61 FRAMEWORK AND TOOLS FOR TRAVEL TIME RELIABILITY ANALYSIS This part of the report describes the modeling tools and the general methodology/process of how to use the tools and interpret the results.
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62 Scenario Manager The Scenario Manager is essentially a preprocessor of simulation input files for capturing exogenous sources of travel time variation. Recognizing the importance of the scenario definition and the complexity of identifying relevant exogenous sources, the Scenario Manager provides the ability to construct scenarios that entail any mutually consistent combination of external events.
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63 quantify user-centric reliability measures, the experienced travel time and the departure time of each vehicle are extracted from the vehicle trajectory. By comparing the actual and the preferred arrival time, the probability of on-time arrival can be computed.
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64 Trajectory Travel Time Data and Sources The specific analysis approach in the proposed reliability evaluation framework requires a special type of travel time data, which was not available until recent technological developments made this possible. In particular, the requirement for trajectory-based travel times for individual vehicles, which are then analyzed over their time and space dimensions and various aggregate metrics, may almost exclusively be satisfied by vehicle probe data.
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65 • Provide travel time at disaggregated levels (e.g., vehicle travel time) and at fine time intervals (e.g., link/path travel time for every 5 minutes)
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66 Introduction Purpose and Objectives Distinguishing exogenous sources of variation both on the demand and the supply sides from endogenous sources of variation lie at the foundation of this conceptualization and approach. It should be recognized, however, that unlike regimes, which are typically mutually exclusive states with distinct properties of a physical system, these sources of variation can operate simultaneously and often will.
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67 encompassing any systematic variations. While exogenous sources of variation are captured through scenarios by the Scenario Manager, endogenous variation sources are captured in the traffic simulation model, depending on the modeling capability of the selected tool.
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68 distribution from each simulation run and constructs the overall travel time distribution aggregated over multiple scenarios. While chaining these three modules completes the necessary procedures for performing a scenario-based reliability analysis, there are two feedback loops worth mentioning to further incorporate behavioral aspects of travelers into the reliability modeling framework.
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69 Monte Carlo Approach This approach uses Monte Carlo simulation to prepare input scenarios aimed at propagating uncertainties in selected scenario components X into uncertainties in the generated scenarios Si (i = 1, .
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70 Realization N : Work zone (SN) Realization 3 : No event (S3)
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71 Independent Weather Planned Special Event Scheduled Traffic Control Incident Work zone Incident Management Strategies Weather-responsive Traffic Management (WRTM) Strategies Extra Demand/ Schedule Adjustment Day-to-day Random Variation Sc en ar io G en er at io n Pr oc es s A affects B; the attributes of B are dependent on the attributes of A
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72 Implementation of Scenario Manager The main role of the Scenario Manager is to prepare a set of scenarios that will be used as input to the traffic simulation models. The implementation of the Scenario Manager is done in two steps: scenario specification and scenario generation.
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73 SG 1 External Events Scenario Groups (SG) Traffic Management Demand-side Factors Rain Collision VMS SCENARIO COMPONENTS Weather Work zone Incident Signal Control VMS Pricing Day-to-day Variation Demand Adjustment Signal control Low variation Low variation Work zone Trip cancelation Low variation High variation High variation SG 2 SG 3 SG 4 SG 5 SG 6 SG 7 Figure 6.8.
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74 illustrates these event attributes in a time-space-intensity diagram using an example of a collision event. Scenario Generation Weather Scenario Modeling weather events in a fully parametric manner is a nontrivial task; it requires theoretical models that characterize complex weather phenomena, and identifying such models is beyond the scope of this study.
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75 rate parameter l, which is the expected number of events that occur per unit of time. As previously mentioned in the report, the incident rate is not constant over time but depends on the prevailing weather condition.
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76 Figure 6.12. Example of spatial distribution pattern of incidents: Distributed based on lane-miles of roads.
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77 This method might be used only when the source region, where the incident data are collected and parameters (e.g., incident rates) are estimated, fully covers the target region, where the incident scenarios will be generated.
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78 and various database-related tasks are performed in the left panel. The step-by-step procedures for generating scenarios are as follows: Step 1.
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79 Figure 6.16. Launch scenario generation tool.
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80 Figure 6.18. Obtain scenario generation results and examine generated scenarios.
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81 distributions across multiple scenarios in the Trajectory Processor later.
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82 C h A p t e r 7 Introduction To promote the use of end-to-end travel time reliability measures in the professional community for regionwide transportation operations planning, it is important and critically necessary to develop a flexible visualization platform for analyzing microscopic and mesoscopic dynamic simulation results, particularly in tracking vehicular movement, path, and time-dependent trip-related statistics. As a generic visualization platform for travel time reliability, the vehicle Trajectory Processor designed in this project aims to apply new methods of communication between transportation practitioners, decision makers, and the public.
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83 settings, this module will generate individual scenario-specific O–D travel time statistics (scenario-specific average O–D travel time and standard deviation) and aggregated O–D travel time statistics (aggregated average O–D travel time and standard deviation)
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84 Figure 7.1. System architecture.
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85 4. Calculate travel time PDF/CDF and Planning Time Index/ Buffer Time Index, for individual scenarios and in combination, based on prespecified MOE settings.
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86 Figure 7.2. Data flowchart.
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87 Figure 7.3. Example O–D statistics user interface.
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88 Table 7.2. Vehicle Trajectory Path Analysis: Comparison Between GPS and Google Routing Paths Internal Vehicle ID Departure Time (2010-5-3)
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89 Figure 7.7. Another comparison of path from TomTom GPS traces and Google Earth.
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90 Processing Vehicle Trajectory Files from VisSim and Aimsun Through Map Matching Vehicle Trajectory File in VisSim and Aimsun Usually, the vehicle trajectory generated by traffic assignment and simulation software packages includes the vehicle movement information. However, this information often represents in node IDs/link IDs used by the underlying transportation planning network.
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91 comparable include many conventional metrics such as the mean and standard deviation of travel times, percentiles, and Buffer Index, as presented in Type B in Table 7.3. For O–Dlevel analysis, therefore, both Type A and Type B measures can be used.
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