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From page 161... ... 161 1. Specifics of Abm-Dta Integration 1.1 Specifics of ABM-DTA Equilibration Versus Aggregate Models 1.1.1 Two-Way Linkage Between Travel Demand and Network Supply Since the technologies of microsimulation have been brought to a certain level of maturity on both the demand side [activity-based model (ABM) Read the entire page →
From page 162... ... 162 for each person, the daily schedule (i.e., a sequence of trips and activities) is formed without gaps or overlaps. Read the entire page →
From page 163... ... 163 Microsimulation ABM Microsimulation DTA List of individual trips Aggregate LOS skims for all possible trips Figure 1.3. Integration of ABM and DTA (aggregate feedback) Read the entire page →
From page 164... ... 164 applications where day-to-day learning and evolution may be more important than the final states.  Exploiting advanced concepts from agent-based modeling for integrating behavior processes in a network context, with special purpose data structures geared to the physical and behavioral processes modeled. Read the entire page →
From page 165... ... 165 brought to a level of temporal resolution that is sufficient for controlling the constraints (e.g., 5 min) Read the entire page →
From page 166... ... 166  Realistic activity timing and duration: Activities in the daily schedule have to be placed according to behaviorally realistic temporal profiles (SHRP 2 C04 report) Read the entire page →
From page 167... ... 167 The mutual core ensures synchronization of time-related ABM and DTA components that operates along the temporal dimension and is designed to achieve a full schedule consistency at the individual level. The ABM model generates tours with origins, destinations, and trip departure times based on expected travel times (from the DTA) Read the entire page →
From page 169... ... 169 0 24 Activity i=0 Activity i=1 Activity i=2 Trip i=1 Trip i=2 Trip i=3 Activity i=3 Departure Arrival Duration Travel id iT i i Schedule  i  Figure 1.5. Individual daily schedule consistency. Read the entire page →
From page 170... ... 170 Microsimulation ABM Microsimulation DTA List of individual trips Individual trajectories for the current list of trips Consolidation of individual schedules (inner loop for departure / arrival time corrections) Sample of alternative origins, destinations, and departure times Individual trajectories for potential trips Figure 1.6. Read the entire page →
From page 171... ... 171 = planned activity duration, = planned departure time for trip to the activity, = planned arrival time for trip to the activity, = actual time for trip to the activity that is different from expected, = schedule weight (priority) for activity duration, = schedule weight (priority) Read the entire page →
From page 172... ... 172 all subsequent trips. The trip departure adjustment and trip arrival adjustment can be interpreted as lateness versus the planned schedule if it is less than 1 and earliness if it is greater than 1. Read the entire page →
From page 173... ... 173 Table 1.2. Recommended Weights for Schedule Adjustment Activity Type Duration Trip Departure (to Activity) Read the entire page →
From page 174... ... 174 These considerations give rise to a concept of presampling of destinations, where the same subset of destinations is reused for each individual at each global iteration of ABM-DTA equilibration. Assume that the modeled region has 30,000 micro analysis zones (MAZs) Read the entire page →
From page 175... ... 175  Aggregate LOS skims by departure time period will be used as the last remaining option; behaviorally, it can be thought of as using an Advice from an advanced navigation device. Updating individual travel times, cost, and reliability for accumulated observed choices means taking full advantage of individual microsimulation. Read the entire page →
From page 176... ... 176 problem of not having observed data on preferred arrival time (PAT) that is discussed below in Section 1.3.1] Read the entire page →
From page 177... ... 177  Enforcement. These methods are specific to microsimulation and designed to ensure convergence of crisp individual choices by suppressing or avoiding Monte Carlo variability. Read the entire page →
From page 178... ... 178 microsimulation models include tour mode and destination choice with a fixed set of generated tours. However, this strategy becomes problematic for more complex decision chains where there are structural impacts of prior choices on subsequent choices in the model chain. Read the entire page →
From page 179... ... 179  Steps in progressing through global iterations. For example, one can envision six global iterations with freezing additional 20% of households at each iteration, starting from the third iteration. Read the entire page →
From page 180... ... 180  Full replication of the model outcome with fixed inputs -- that is, if the discretizing procedure is run several times with the same choice model, with fixed input variables, the results will be identical, while the Monte Carlo technique will be characterized by inherent variability (so-called Monte Carlo error) of the results. Read the entire page →
From page 181... ... 181 The discretizing procedure in the model application is done by maximizing the entropy function over the crisp choices while the fractional probabilities are given by the core choice model:           Nn Ci nin Nn Ci n in in nn in iP iP E lnlnmax     (1.8) The discretizing approach considers the whole matrix of probabilistic outcomes of the core choice model and tries to find the structurally closest matrix of discrete numbers that also fits to the marginal totals of the original matrix. Read the entire page →
From page 182... ... 182  The better the core model is in terms of likelihood function (i.e., the closer the modeled probabilities to the observed choices) , then the closer the discretizing outcome will be to the observed choices. Read the entire page →
From page 183... ... 183 option to ensure a fixed-point solution. MSA has many possible technical variations. Read the entire page →
From page 184... ... 184  Original output of microsimulation procedure (individual household/person/tour/trip characteristics) cannot be meaningfully averaged between iterations since it represents a unique set of discrete values associated with a different list of agents at each iteration. Read the entire page →
From page 185... ... 185 route choice and associated path-building procedures have to be applied for each network user (auto) individually. Read the entire page →
From page 186... ... 186 since it does not yet have full information on the number/location of destinations visited along the tour, so the exact timing and LOS information is only indicative at this point in the simulation. However, trip-level choice of departure time that is conditional on the entire-tour TOD choice can be refined to 5 min, since the choice structure is one-dimensional and more details about each particular trip origin and destination can be used. Read the entire page →
From page 187... ... 187 and the minimum duration for maintenance activity is 5 min, this means that the earliest possible departure time for the second trip (from the maintenance activity location to shop location) Read the entire page →
From page 188... ... 188 every 5 min would be impractical. Some additional options can be considered by using the sampling approach suggested earlier. Read the entire page →
From page 189... ... 189 Moreover, for more advanced integration schemes, the individual driver and travel party characteristics including VOT and VOR can be carried over from the ABM to DTA. The corresponding structure of the auto nest in mode choice is presented in Figure 1.7. Read the entire page →
From page 190... ... 190 From this point of view, multiple simulations with different demand and network scenarios that are based on conventional route choice by the shortest average time will only satisfy the second condition, and not the first one. The technical implementation of such a model depends on the treatment of route choice and its placement between the demand model and network simulation tool, which can done in several different ways, as shown in Figure 1.8. Read the entire page →

From page 161...

... 161 1. Specifics of Abm-Dta Integration 1.1 Specifics of ABM-DTA Equilibration Versus Aggregate Models 1.1.1 Two-Way Linkage Between Travel Demand and Network Supply Since the technologies of microsimulation have been brought to a certain level of maturity on both the demand side [activity-based model (ABM)