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 ## Introduction
 The ABM3 model system is based on the ActivitySim platform. The current version of ActivitySim follows the ABM2+ resident model structure closely since they are both based on the Coordinated Travel Regional Activity-based Modeling Platform (CT-RAMP). The figure below shows the resident model structure. In order to understand the flow chart, some definitions are required. These are described in more detail below.
 * *Tour*: A sequence of trips that start and end at an anchor location. In ActivitySim, anchors are home or work.
-* *Primary destination*: The “main” activity of the tour; this activity determines the tour purpose. It also divides the tour into two "legs"; the sequence of trips from the anchor location to the primary destination is the outbound leg, and the sequence of trips from the primary destination back to the anchor location is the inbound or return leg.
+* *Primary destination*: The “main” activity of the tour; this activity determines the tour purpose. It also divides the tour into two "legs"; the sequence of trips from the anchor location to the primary destination is the outbound leg, and the sequence of trips from the primary destination back to the anchor location is the inbound or return leg.
 * *Mandatory activity:* Work or school
 * *Non-mandatory activity*: Any out of home activity that is not work or school, including maintenance activities such as shopping as well as discretionary activities such as out-of-home recreation and eating out.
 * *Fully joint tour*: A tour in which two or more household members travel together to all out-of-home activity locations and return home together. In other words, no household member is picked-up or dropped-off en route.
 * *Intermediate stop*: An out-of-home activity location on the tour other than the anchor location or the primary destination. Intermediate stops are made on the way from the anchor location to the primary destination (outbound) or on the way from the primary destination back to the anchor location (inbound).
-* *Tour mode*: The “main mode” or “preferred mode” of the tour. This is an abstract concept used categorize the tour with respect to accessibility and constrain the availability of modes for trips on the tour to ensure some consistency of modes used for each trip.
+* *Tour mode*: The “main mode” or “preferred mode” of the tour. This is an abstract concept used categorize the tour with respect to accessibility and constrain the availability of modes for trips on the tour to ensure some consistency of modes used for each trip.
 The resident model design is shown below.
-`![](images/design/resident_model_design.png)`
+![](images/design/resident_model_design.png)
 The first model in the sequence is disaggregate accessibilities. This is a recent addition to ActivitySim in which the tour destination choice model is run for a prototypical sample population covering key market segments and destination choice logsums from the model are written out for each tour in the population. These destination choice logsums are then merged with the actual synthetic population and used as accessibility variables in downstream models such as auto ownership, coordinated daily activity patterns, and tour frequency. are mandatory location choice; this model is run for all workers and students regardless of whether they attend work or school on the simulated day. Next a set of long-term and mobility models are run. The first model in the sequence predicts whether an autonomous vehicle is owned by the household. This model conditions the next model, which predicts the number of autos owned. If an autonomous vehicle is owned, multiple cars are less likely. Next, the mandatory (work and school) location choice models are run. The work location choice models includes a model to predict whether the worker has a usual out-of-home work location or exclusively works from home. If the worker chooses to work from home, they will not generate a work tour. An external worker identification model determines whether each worker with an out-of-home workplace location works within the region or external to the region. If they work external to the region, the external station is identified. Any primary destination of any work tours generated by the worker will be the external station chosen by this model. A work location choice model predicts the internal work location of each internal worker, and a school location choice model predicts the school location of each student.
 Next, a set of models predicts whether workers and students have subsidized transit fares and if so, the percent of transit fare that is subsidized, and whether each person in the household owns a transit pass. A vehicle type choice model then runs, which predicts the body type, fuel type, and age of each vehicle owned by the household; this model was extended to predict whether each vehicle is autonomous, conditioned by the autonomous vehicle ownership model. Next, we predict whether each household has access to a vehicle transponder which can be used for managed lane use. We assume that all vehicles built after a certain year (configurable by the user) are equipped with transponders. Next we predict whether each worker has subsidized parking available at work. Finally we predict the telecommute frequency of each worker, which affects downstream models including the daily activity pattern model, the non-mandatory tour frequency model, and stop frequency models.
-Next the daily and tour level models are run. The first daily model is the daily activity pattern model is run, which predicts the general activity pattern type for every household member. Then[BS3] [JF4]  Mandatory tours are generated for workers and students, the tours are scheduled (their location is already predicted by the work/school location choice model), a vehicle availability model is run that predicts which household vehicle would be used for the tour, and the tour mode is chosen. After mandatory tours are generated, a school pickup/dropoff model forms half-tours where children are dropped off and/or picked up at school. The model assigns chaperones to drive or ride with children, groups children together into “bundles” for ride-sharing, and assigns the chaperone task to either a generated work tour or generates a new tour for the purpose of ridesharing. Fully joint tours – tours where two or more household members travel together for the entire tour - are generated at a household level, their composition is predicted (adults, children or both), the participants are determined, the vehicle availability model is run, and a tour mode is chosen. The primary destination of fully joint tours is predicted, the tours are scheduled, the vehicle availability model is run, and a tour mode is chosen. Next, non-mandatory tours are generated, their primary destination is chosen, they are scheduled, the vehicle availability model is run, and a tour mode is chosen for each. At-work subtours are tours that start and end at the workplace. These are generated, scheduled (with constraints that the start and end times must nest within the start and end time of the parent work tour), a primary destination is selected, the vehicle availability model is run, and a tour mode is chosen.
+Next the daily and tour level models are run. The first daily model is the daily activity pattern model is run, which predicts the general activity pattern type for every household member. Then[BS3] [JF4]  Mandatory tours are generated for workers and students, the tours are scheduled (their location is already predicted by the work/school location choice model), a vehicle availability model is run that predicts which household vehicle would be used for the tour, and the tour mode is chosen. After mandatory tours are generated, a school pickup/dropoff model forms half-tours where children are dropped off and/or picked up at school. The model assigns chaperones to drive or ride with children, groups children together into “bundles” for ride-sharing, and assigns the chaperone task to either a generated work tour or generates a new tour for the purpose of ridesharing. Fully joint tours – tours where two or more household members travel together for the entire tour - are generated at a household level, their composition is predicted (adults, children or both), the participants are determined, the vehicle availability model is run, and a tour mode is chosen. The primary destination of fully joint tours is predicted, the tours are scheduled, the vehicle availability model is run, and a tour mode is chosen. Next, non-mandatory tours are generated, their primary destination is chosen, they are scheduled, the vehicle availability model is run, and a tour mode is chosen for each. At-work subtours are tours that start and end at the workplace. These are generated, scheduled (with constraints that the start and end times must nest within the start and end time of the parent work tour), a primary destination is selected, the vehicle availability model is run, and a tour mode is chosen.
 At this point, all tours are generated, scheduled, have a primary destination, and a selected tour mode. The next set of models fills in details about the tours - number of intermediate stops, location of each stop, the departure time of each stop, and the mode of each trip on the tour. Finally, the parking location of each auto trip to the central business district (CBD) is determined.
 After the model is run, the output files listed above are created. The trip lists are then summarized into origin-destination matrices by time period and vehicle class or transit mode and assigned to the transport network. Skims are created based on congested times, and the model system is iterated multiple times until either some convergence threshold is attained, or a predetermined number of iterations is reached.
 ActivitySim is used to represent all internal travel and internal-external made by residents of the SANDAG region (modeled area).  The decision-makers in the model system include both persons and households. These decision-makers are created (synthesized) for each simulation year and land-use scenario, based on Census data and forecasted distributions of households and persons by key socio-economic categories.  The decision-makers are used in the subsequent discrete-choice models in a microsimulation framework where a single alternative is selected from a list of available alternatives according to a probability distribution.  The probability distribution is generated from a logit model which considers the attributes of the decision-maker and the attributes of the various alternatives. The application paradigm is referred to as Monte Carlo simulation, since a random number draw is used to select an alternative from the probability distribution. The decision-making unit is an important element of model estimation and implementation and is explicitly identified for each model specified in the following sections.
-A key advantage of using the micro-simulation approach is that there are essentially no computational constraints on the number of explanatory variables that can be included in a model specification.  However, even with this flexibility, the model system will include some segmentation of decision-makers.  Segmentation is a useful tool to both structure models (for example, each person type segment could have their own model for certain choices) and to characterize person roles within a household.  Segments can be created for persons as well as households.
+A key advantage of using the micro-simulation approach is that there are essentially no computational constraints on the number of explanatory variables that can be included in a model specification.  However, even with this flexibility, the model system will include some segmentation of decision-makers.  Segmentation is a useful tool to both structure models (for example, each person type segment could have their own model for certain choices) and to characterize person roles within a household.  Segments can be created for persons as well as households.