Incorporating Reliability Performance Measures into Operations and Planning Modeling Tools (2014) / Chapter Skim
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7RESEARCH BACKGROUND The chapters in this part of the report discuss the fundamental issues of incorporating travel time reliability into modeling tools, investigate the feasibility of incorporating such into planning models, and identify the functional requirements for incorporating travel time reliability into simulation models.
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8exacerbated recurrent congestion (if the baseline capacity is not adequate to accommodate even the average demand)
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9 There are pros and cons associated with each method. The vision emerging from this research is that both methods are useful and could be hybridized to account for different sources of travel time variation in the most adequate and computationally efficient way.
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10 of input parameters (such as parameterized probability of occurrence of a certain event)
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11 driving style) as a reliability component.
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12 Obtaining behavioral realism in individual choices requires taking into account travelers' subjective perceptions of reliability, as well as the entire set of highway LOS attributes. Subjective perceptions of travel attributes can be quite different from their objective measurements.
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13 the higher the share of trucks in the traffic, the higher the variability of travel times. A related issue that has not yet been fully explored is the associated willingness to pay for travel time savings and reliability improvements.
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14 reliability in both directions of the modeling: from supply to demand (impact of reliability on travel choices) and from demand to supply (generation of reliability measures as a result of travel decisions)
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15 Respective Roles of Planning and Operation Models in Addressing Reliability It is unrealistic to expect that it will be possible to establish one particular set of reliability measures associated with one particular method of incorporating reliability into demand and network simulation tools -- that is, "one size fits all." First, as existing practice shows, there are different modeling tasks associated with highway planning and operations analysis that lead to different modeling frameworks and scales. Second, the team has distinguished between state-of-the-art, which reflects the best and theoretically consistent solutions available regardless of their complexity, and state-of-the-practice, which reflects numerous current constraints associated with the network size, reasonable runtime, data availability, and complexity for model use and analysis of results in a practical setting.
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16 Specific Impacts of Congestion and Travel Time Reliability on Individual Travel Behavior Travel time reliability has been generally recognized as an important missing component in the previous generation of travel demand models and network simulation tools. However, as important as it is, reliability is not the only additional issue or variable that needs to be incorporated into existing travel models to better address and account for congestion.
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17 presence of congestion that makes travel time unstable, the process of traveler learning and adaptation associated with reaching equilibrium becomes longer and fuzzier. Integrating demand and supply models, with explicit consideration of reliability, has been addressed in the course of the current project, as well as part of the SHRP 2 C04 project.
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18 • Day-to-day fluctuations due to an inherent randomness of individual behavior (people do not repeat the same trips exactly every day) , as well as to variations on the activity supply side, for example, not the same business meeting in the office every day (Factor 6)
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19 namely, demand- versus supply-side, exogenous versus endogenous, and systematic versus random. Examples in each cell of the resulting taxonomy are shown in Table 2.1.
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20 in response to information) as part of the range of behavioral parameters that determine supply-side relations (such as gap acceptance and lane changing in micro- simulation models)
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21 using conventional network simulation tools. It should be noted that a deterministic route choice does not mean deterministic travel times.
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22 Specifics of ABM-dtA equilibration Versus Aggregate Models Two-Way Linkage Between Travel Demand and Network Supply The two-way linkage between travel demand and network supply has been described on the TF Resources website as follows: Since the technologies of microsimulation have been brought to a certain level of maturity on both the demand side (activitybased model, or ABM) and supply (network)
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23 ABM-dtA Integration principles The emphasis in the L04 project is on truly integrating the demand and network models and not merely connecting them through aggregate measures in an iterative application. This approach is based on the following principles: • A fully disaggregate approach implemented at the individual level (travel tours by person)
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24 several trips and related out-of-home activities and essentially represents a fragment of the individual daily schedule. In reality, the observed individual schedules are always consistent in the sense that they obey time-space constraints and have a logical continuous timeline, in which all activities and trips are sequenced with no gaps and no overlaps.
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25 of demand-supply equilibration, they are limited to achieving stability in terms of average travel times. There is no control for consistency within the individual daily schedule.
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26 on the basis of the feasibility of their adjusted schedules and the magnitude of the adjustments introduced by the DTA. The team's research on equilibration of the integrated models has resulted in new procedures for directing the convergence algorithm toward a mutually consistent solution through selection of the fraction of individuals or households whose schedules may be replanned in each iteration.
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27 Adjustment of individual daily schedule can be formulated as an entropy-maximizing problem of the following form (Equation 3.1)
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28 adjustment formulation allows for a joint treatment of deviations from the planned start times, end times, and durations. The constraints express the schedule consistency rule as shown in Figure 3.5.
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29 Approaches to Quantifying reliability and Its Impacts Construction of User-Centric Network Reliability Measures In summary, the following two important aspects of the problem need to be taken into account when the user's perspective on reliability (and performance in general) is compared with the highway operations perspective: • The user perspective can be different and include many perceived components and weights compared with the physical measures of average travel time and reliability.
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30 (e.g., the entire day) , taking into account expected (average)
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31 profiles is a way to endogenize PAT within the demand modeling (scheduling) framework.
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32 Table 3.5. Recommended Values of Parameters for Generalized Cost Function with Reliability travel purpose examples of population/travel Model Coefficients and Derived Measures Household Income, $/year Car occupancy Distance, Miles time Coefficient Cost Coefficient Cost for SD(T)
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33 (continued) Table 3.5.
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34 variations if used in the scheduling procedure and departure time optimization)
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35 Incorporating reliability into Network Simulation This section presents a concise overview of each method of quantification of travel time reliability from the perspective of its inclusion in an operational network simulation model. This means that the reliability measure of interest has to be incorporated into the route choice and generated at the O–D level to feed into the demand model.
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36 The first approach employs the network simulation model to produce travel time distributions for each trip departure time bin (30 min)
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37 This particular form is chosen since it is logical to expect that the variation should tend to zero when average travel time tends to the minimal (free-flow) time.
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39 the standard deviation per mile is contrasted to the average time per mile and takes the following form (Equation 3.20)
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40 matrix) , both the link-level and O–D-level travel time variances can be calculated in a way that gives rise to the following estimation method for scaling parameter (Equation 3.21)
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41 Incorporating average travel time in the feedback mechanism has become a routine part of travel demand and traffic assignment models. Traffic assignment models operate with (average)
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42 2. Internal feedback of travel time distributions (and any derived measure of reliability)
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43 demand scenarios in turn are combined with scenarios for special events and day-to-day variation scenarios that are also assumed independent. Network simulation scenarios are combined with scenarios for weather conditions and scenarios for work zones that are also assumed independent.
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44 in aggregate demand (trip table) based on the observed variation in link traffic counts.
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45 A model of this type naturally lends itself to a traffic simulation incorporating reliability. The probability of the event occurring during the simulation run is estimated based on the frequency for each venue.
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46 profiles. However, improving travel demand models and network simulation tools in this direction is closely intertwined with a general improvement of individual microsimulation models.
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47 C h A p t e r 4 Introduction This chapter describes the framework and the functional requirements for the inclusion of travel time reliability estimates in transportation network modeling tools, with particular focus on stochastic traffic simulation models. The framework identifies phenomena and behaviors that account for the observed variability in network traffic performance, and unifies all particle-based simulations at the microscopic and mesoscopic levels.
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48 Depending on the model's intended purpose, data availability, and resource constraints for executing a particular study, appropriate assumptions can be formulated and inputs specified regarding (1) the demand-side and supply-side characteristics, and (2)
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49 Functional requirements Traffic operation models need to model variations in demand and supply sides as well as capture traffic physics. They are also expected to support system management decision making to control reliability, produce reliability-related measures, and retain flexibility to adapt to various agency and policy environments.
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50 When density goes above a threshold, the vehicle-to-vehicle interactions become a dominant factor. While density can be considered a result of these other variables, at a certain threshold, density might itself be an independent random variable contributing to instability, such as flow breakdown.
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51 Quantifying Travel Time Variability As one of the key functional requirements is concerned with producing reliability-related output, the operations models need to generate travel time distributions by link, path, and trip (i.e., O–Ds) , as well as reliability performance measures for the entire system.
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52 Constructing Travel Time Distributions To quantify travel time variability, the traffic simulation tools need to support various uncertainty analysis methods such as Monte Carlo simulation, sensitivity analysis, and scenario planning. Monte Carlo method.
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53 from the vehicle trajectories. The travel time per mile can therefore be computed for each vehicle.
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54 Figure 4.7. CHART network.
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55 trajectories in the output of a simulation model enables construction of the path- and O–D-level travel time distributions of interest, as well as the extraction of link-level distributions. As such, the key building block for producing measures of reliability in a traffic network simulation model is particle trajectories and the associated experienced traversal times through entirety or part of the travel path.
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56 The next line shows the time instance, relative to the departure time, at which the vehicle exited nodes 102, 160, 102, 103, 151, 97, 89, 4, 3, 24, 5, 27, 28, 32, 35, 39, 40, and 11, which are 0.80, 0.90, 1.60, 2.20, 3.00, 3.40, 3.80, 5.00, 5.50, 5.90, 6.00, 6.30, 6.70, 7.10, 7.30, 7.60, 8.20, and 8.40 minutes, respectively. The next line shows the travel times on links 102→160, 160→102, 102→103, 103→151, 151→97, 97→89, 89→4, 4→3, 3→24, 24→5, 5→27, 27→28, 28→32, 32→35, 35→39, 39→40, and 40→11, which are 0.80, 0.10, 0.70, 0.60, 0.80, 0.40, 0.40, 1.20, 0.50, 0.40, 0.10, 0.30, 0.40, 0.40, 0.20, 0.30, 0.60, and 0.20 minutes, respectively.
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57 Variation among Vehicles 1. Perform one simulation run.
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58 remains relatively stable. Therefore, travel time is more sensitive to the departure time in the periods with high volatility.
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59 as inherent or "random" is likely to remain substantial. This has important implications for how the models are used to produce reliability estimates and how these measures are interpreted and, in turn, used operationally.
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