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Suggested Citation:"Chapter 4 - FAST-TrIPs Overview." National Academies of Sciences, Engineering, and Medicine. 2014. Dynamic, Integrated Model System: Sacramento-Area Application, Volume 2: Network Report. Washington, DC: The National Academies Press. doi: 10.17226/22369.
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Suggested Citation:"Chapter 4 - FAST-TrIPs Overview." National Academies of Sciences, Engineering, and Medicine. 2014. Dynamic, Integrated Model System: Sacramento-Area Application, Volume 2: Network Report. Washington, DC: The National Academies Press. doi: 10.17226/22369.
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Suggested Citation:"Chapter 4 - FAST-TrIPs Overview." National Academies of Sciences, Engineering, and Medicine. 2014. Dynamic, Integrated Model System: Sacramento-Area Application, Volume 2: Network Report. Washington, DC: The National Academies Press. doi: 10.17226/22369.
×
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Suggested Citation:"Chapter 4 - FAST-TrIPs Overview." National Academies of Sciences, Engineering, and Medicine. 2014. Dynamic, Integrated Model System: Sacramento-Area Application, Volume 2: Network Report. Washington, DC: The National Academies Press. doi: 10.17226/22369.
×
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Suggested Citation:"Chapter 4 - FAST-TrIPs Overview." National Academies of Sciences, Engineering, and Medicine. 2014. Dynamic, Integrated Model System: Sacramento-Area Application, Volume 2: Network Report. Washington, DC: The National Academies Press. doi: 10.17226/22369.
×
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Suggested Citation:"Chapter 4 - FAST-TrIPs Overview." National Academies of Sciences, Engineering, and Medicine. 2014. Dynamic, Integrated Model System: Sacramento-Area Application, Volume 2: Network Report. Washington, DC: The National Academies Press. doi: 10.17226/22369.
×
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Suggested Citation:"Chapter 4 - FAST-TrIPs Overview." National Academies of Sciences, Engineering, and Medicine. 2014. Dynamic, Integrated Model System: Sacramento-Area Application, Volume 2: Network Report. Washington, DC: The National Academies Press. doi: 10.17226/22369.
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17 C h a p t e r 4 For modeling the transit component, FAST-TrIPs is divided into two main submodules: transit assignment and simulation. Above all, the transit assignment submodule plays the role of passenger assignment for given OD pairs. For assigning transit passengers for the OD pairs, a trip-based shortest path model (Noh et al. 2011; Khani et al. 2012a; Khani et al. 2012b; Noh et al. 2012a; Noh et al. 2012b) is used by searching for a feasible path on each OD pair. The assigned passengers, including their paths, are given to and simulated through the transit simula- tion submodule in FAST-TrIPs. During the simulation, expe- rienced arrival and departure times of transit vehicles are used to simulate boarding and alighting of passengers, con- sidering transfers and other components (e.g., walking and waiting). Each passenger’s trajectory (i.e., experienced path) is recorded, and dwell time for each transit route is calculated as a function of the boardings and alightings at each stop. Results of the simulation are used in the next iteration of auto-transit vehicle simulation and are also fed back to the activity-based model in the next global iteration for updating the demand. FAST-TrIPs has an intermodal functionality, which is embed- ded in the two submodules mentioned above. It is capable of assigning and simulating the intermodal passengers in a mixed environment, modeling these movements for auto and transit passengers. The intermodal model consists of a park-and-ride assignment model for individual tours, a transit assignment and transit simulation model for the transit portion of the tour, and an interface with DynusT for the auto assignment and simulation. FaSt-trIps in the Integrated Model In the big picture, in terms of integration, FAST-TrIPs inter- faces with DynusT as well as connecting with the DaySim ABM. Before a discussion of the FAST-TrIPs model in detail, the overall functional location of the FAST-TrIPs tool in the integrated architecture should be noted. The high-level modular architecture for transit in the C10B project is given in Fig ure 1.1; Figure 4.1 briefly depicts the FAST-TrIPs operation. To operate the FAST-TrIPs module, or DTA module in a higher level, several critical inputs to FAST-TrIPs must be made. To support the transit and intermodal travel within DynusT, FAST-TrIPs reads as input the database of traveler tours and trips from DaySim. This processing of the trip and tour rosters allows FAST-TrIPs to identify both the transit trips with walk access and egress and the transit trips with auto access or egress. The latter are intermodal trips in the sense that they require simulation of both modes of travel (transit and auto) within FAST-TrIPs and DynusT. Specifically, for the purposes of FAST-TrIPs, the output from DaySim includes information for each trip segment and subsequent activity for each individual. It contains origin, des- tination, and travel mode, including differentiation for dif- ferent transit access modes, and time at which travel begins, for trips leaving from the primary tour destination, or time at which travel ends, for trips destined to the primary tour destination. This last choice, of departure time or arrival time, makes use of DaySim’s protocol that trips going toward the primary tour destination are constrained to arrive by the start of the activity at that destination. Also, trips departing from the pri- mary tour destination cannot depart before the scheduled end of the activity at the primary tour destination. Passengers are assigned to a path in the transit network according to the protocol discussed later. In addition to the assignment of transit trips, FAST-TrIPs also performs an intermodal assignment, for which an estimate of auto travel times is necessary. This allows FAST-TrIPs to choose the opti- mal combination of an auto segment and a transit segment for an intermodal trip. The auto travel times in this assign- ment are obtained from either a time-dependent auto short- est path or the most recent auto skims of the network loading FAST-TrIPs Overview

18 and simulation in DynusT. This is indicated on the right side of Figure 1.1, DynusT–FAST-TrIPs integration framework. Once the intermodal assignment has been performed, FAST-TrIPs provides DynusT with the auto portion of any intermodal trip, allowing the simulation of the auto trip through either • The assignment of an OD (a park-and-ride lot), and time of arrival at the park-and-ride lot, for trips transferring to transit headed to the primary tour destination; or • The assignment of an origin (park-and-ride lot), destina- tion, and time of departure from the park-and-ride lot, for trips returning from the primary tour destination. The auto portion of these intermodal trips is simulated in DynusT. In addition, the trip and tour records from DaySim are updated to enforce sequential trip behavior: In the simu- lation, transit trips cannot begin before the auto actually arrives at the park-and-ride lot, and vice versa. Of course, in parallel, DynusT reads the same trip and tour rosters from the intermodal assignment and determines the assignment of pure auto trips to associated paths and travel times in the network. Especially for intermodal cases, inter- modal assignment will create the auto and transit part of these intermodal trips as deciding the optimal park-and-ride lot. The auto portion of the intermodal assignment is added in the DynusT simulation, and the transit portions are loaded to the transit assignment in FAST-TrIPs. These assigned auto trips from the intermodal demand are then simulated in DynusT. As a second input shown in Figure 1.1, FAST-TrIPs uses Google’s GTFS files. The GTFS files currently allow a transit agency to provide its routes and schedules to Google Maps. However, this same route and schedule data are often made publicly available by transit agencies, allowing others to develop applications using these data. The Sacramento Regional Tran- sit District is one of the many transit agencies providing GTFS data to Google and to the public. The GTFS files contain the geographic representation of routes and stops (typically in geographic information system, or GIS, shape files). The data also contain either the formal schedule of service (in the case of GTFS), or the frequency information (in the case of traditional line files) associated with each transit route and direction. The GTFS data or line files are converted into route networks that are compatible with the DynusT road network. This process is partly auto- mated, using existing shape files for the road and transit net- works, but considerable manual processing may be necessary to adjust the network to ensure that road segments are consis- tent and that transit stop locations are placed at appropriate locations in the road network. Finally, the schedule (the so- called stop-times in GTFS) also serves as input to the transit assignment. The transit network and service data should ide- ally be based on the GTFS. This is useful because the actual service schedule and individual stops can be modeled explic- itly. This provides a more dynamic modeling of the transit pas- senger behavior than a traditional four-step model. The GTFS data can be used to represent the base year, perhaps by making some manual adjustments to the existing (2010 for the C10B implementation) GTFS schedule data to make it comparable to the base year. The detail by which transit routes are defined in a schedule- based format such as GTFS may pose a problem when dealing with future-year forecasts. A transit network for the future will require designating routes, stops, and schedules. This may be easily adapted from existing schedules if only more modest changes are envisioned. However, for more signifi- cant changes in the transit network, this could require sig- nificant effort to develop appropriate GTFS data. Whether to develop this GTFS input or simply to use a future line file (from a four-step model) would be a decision likely made jointly by the local MPO (like SACOG) or the local transit agency (like Sacramento Regional Transit). For the auto and transit vehicle simulation, as shown at the bottom of Figure 1.1, the auto, transit, and intermodal assign- ments are fed directly into a network loading and simulation, using DynusT’s simulation capability. The simulation of vehi- cle movements in the network, at a fine level of temporal and spatial resolution, is handled directly within DynusT; this simulation includes specific movements of both auto and transit vehicles in the network. There is appropriate logic in DynusT for the management of mixed vehicle types in the traffic stream, as explained earlier, with each vehicle type hav- ing its own particular speed–density characteristics. The sim- ulation also allows for the appropriate movements of transit Figure 4.1. Fast-trIPs algorithmic structure.

19 vehicles at stops, including the following: a vehicle pulls out of the traffic stream at bus pullouts but remains in the traffic stream for curbside stops; dwell times allow for passengers to alight and to board, depending on passenger type (pedestrian, bicycle, wheelchair); a bus may bypass a stop if no passengers want to board or alight (so-called hail stop operation); and the vehicle may hold until the scheduled departure time if a vehi- cle is running ahead of schedule (“running hot”). The simulation of passenger movements in the transit net- work, however, is handled directly within FAST-TrIPs. The interaction between DynusT and FAST-TrIPs is through the link travel times that DynusT provides to FAST-TrIPs and the bus arrival, dwell, and departure times that FAST-TrIPs provides to DynusT. At the beginning of the simulation, FAST-TrIPs provides an estimate of the dwell times for each transit vehicle at each stop. During the simulation in DynusT, the transit vehicle dwells at the stop for this period of time, and then, if holding is needed, the vehicle is held until the scheduled departure time. The final simulation outputs from DynusT include the actual vehicle arrival and departure times from each stop. These outputs, in turn, can be used in the assignment to adjust passengers’ path choices, to reflect issues with service reliability, or to confirm vehicle adherence to the schedule. The assignment and simulation of transit vehicles and pas- sengers is described in the following sections. passenger assignment Transit assignment is the process that determines a path, or set of connecting subpaths, for a passenger to get from origin to destination. DaySim determines the origins and destinations for the travelers and feeds this information into the assign- ment. FAST-TrIPs handles transit passenger and intermodal passenger assignment, and DynusT handles auto assignment. From the output of DaySim, intermodal and transit trips are read directly by FAST-TrIPs. The required inputs for tran- sit assignment in FAST-TrIPs include the passenger origin, the passenger destination, and the time of departure at the origin or the time of arrival at the destination. FAST-TrIPs has a transit shortest path algorithm, called trip-based short- est path (TBSP), which finds the path from the origin to the destination of the passengers. Algorithmically, transit networks have an important fea- ture in the types of nodes. That is, in transit networks, rerout- ing (i.e., a transfer to another route) can be done only at certain stops. For example, when a passenger is on board, he or she may not consider alighting from a vehicle to transfer to another route at every stop. More particularly, it does not happen where there is no other route available at a stop or where other stops are not within walking distance from that stop. Therefore, one may consider any variation in the path at transfer stops only. This property leads to the generation of a hierarchical transit network with transfer stops at the higher level, where the non-transfer stops in the path search algo- rithm may be disregarded. In this case, instead of having mul- tiple stops and multiple links between two transfer stops, the transfer stops can be connected by a single transit trip, with associated departure and arrival times. However, the non- transfer stops are maintained in the network, as they can be used for access and egress points. In the network preparation phase, transfer links are gener- ated between a pair of stops if 1. The distance between the stops is less than a certain value (e.g., 0.25 mile), and 2. There is at least one route that serves one of the stops but not the other. After generating transfer links, transfer stops are defined to construct the network hierarchy. A transfer stop is defined as a stop from which the passenger has the option of transfer- ring to another route. With this definition, a stop is defined as a transfer stop if it is located on more than one route, or it has at least one inbound or outbound transfer link. Using this simple model, the hierarchical transit net- work, which can be very useful in transit path algorithms, is generated. A new network representation that is suitable for modeling schedule-based transit systems was used. This network struc- ture is called trip based and is defined by a graph G(N, P, T) where N is the set of nodes (or stops), P is the set of transit trips where each trip belongs to a route r in R (set of transit routes), and T is the set of transfer links. For each trip there is a list S(p), which contains the stops served by the trip as well as the associated arrival and/or departure time of the transit vehicle at that stop. Also, for each stop, there is a set A(n) containing the trips serving the stop. The main advantage of the trip-based network representation over node- or link- based structures is that the connection between two stops can be established by a single trip p if they are located on the same trip, while in both node-based and link-based networks the connection between any two stops is made using a sequence of links. Transit Trip-Based Shortest Path The trip-based network has the advantages of stop connectiv- ity, dynamic representation of service, and hierarchical struc- ture of transfer stops. On the basis of these properties, together with the data availability through GTFS and the behavior of transit users, a TBSP is developed. TBSP is a labeling algo- rithm in the schedule-based transit systems, exploiting the trip-based network format. Therefore, it has the advantage of

20 processing a subset of stops and finds the shortest path in a shorter amount of time compared to traditional shortest path algorithms. The general form of the algorithm is shown in Figure 4.2; the form can be either label-setting or label- correcting, but the label-correcting form is used for the appli- cation in this project. The variables in Figure 4.2. TBSP algorithm are defined as follows: PATi = Preferred arrival time to stop i; seqip = Sequence number of stop i on trip p; dpi = Departure time of trip p at stop i (usually the same as arrival in printed schedules and GTFS data); vijp = In-vehicle time from stop i to stop j using trip p; tij = Transfer time from stop i to stop j (typically equal to the walking time between two stops); ai = Arrival/departure time label of stop i; wip = Waiting time at stop i for trip p, equal to the differ- ence between the departure time of trip p at stop i and the arrival time label of stop i; li = Label of stop i, equal to the travel time (or cost) from the origin stop to stop i in forward algorithms, and the travel time (or cost) from stop i to the des- tination stop in backward algorithms; pi = Predecessor stop of i; mi = Mode (trip number or transfer link) used to reach stop i in forward algorithms, or to leave stop i in backward algorithms; cpi = Utility (a function of travel time or cost) of trip p at stop i to reach the destination; T(i) = Set of transfer links at stop i; R(i) = Set of routes at stop i; p(i) = Set of trips at stop i; and SE = Scan eligible list, containing the stops with tempo- rary labels. The TBSP algorithm in Figure 4.2 is a forward-labeling algorithm starting from the origin with t as the planned departure time (PDT). With very few modifications, a back- ward algorithm can be developed with search from the desti- nation in a backward direction. In this backward case, a preferred arrival time (PAT) is used for the destination. In the proposed forward algorithm, the label of each stop is the earliest arrival time. In general, a generalized cost function can be used to gener- ate different variations of the shortest path algorithm. In this case, different weights are applied to different components of the trip. This approach is used to model the inconvenience of transfers and waiting times compared with in-vehicle time. Assuming the weight ak is applied to the kth element of the passenger trip, the least-cost path algorithm is developed based on the shortest path algorithm with changes as shown below: 13- If (li + at.tij < lj): 14- lj = li + at.tij, aj = ai + tij, pj = i, mj = "T" 19- If li + aw.wip + av.vijp < lj: 20- lj = li + aw.wip + av.vijp, aj = djp, pj = i, m, = p 1- Initialization: 2- Get the origin (O) and the departure time (τ) 3- i=O, li=0, pi=Φ, ai=τ, mi=Φ, SE={i} 4- lj=∞, pj=-, aj=∞, mi=Φ; j≠i 5- Termination Criterion: 6- If SE=Φ, stop! 7- Stop Selection: 8- Select i=Argminj {lj| j SE}, if Label Setting 9- Select i=The first stop in SE, if Label Correcting 10- SE=SE\{i} 11- Updating the labels: 12- t T(i): 13- If (li+tij<lj): 14- lj=li+tij, aj=ai+tij, pj=i, mj= ˝T˝ 15- SE=SE {j} 16- r R(i): 17- Select p=Argminq {wiq|dqi≥ai} 18- j S(p) with seqj>seqi: 19- If li+wip+vijp<lj: 20- lj= li+wip+vijp, aj=djp, pj=i, mj=p 21- If j Nt: 22- SE=SE {j} 23- Break the loop! Figure 4.2. tBsP algorithm.

21 The result of the TBSP is a shortest path tree from the origin to all destinations. So the path to a specific destination will be found by tracking the predecessors from the destination stop to the origin stop. The path is then attached to the passenger and is passed to the simulation model. passenger Simulation The passenger simulation model is a high-resolution model, capable of simulating the path taken by individuals in the tran- sit and intermodal networks. The main inputs are the paths generated in the transit assignment submodule. In fact, there are three categories of data inputs to the passenger simulation: • Transit network, including stops, routes, and schedule; • Transit vehicle simulation results, including the actual arrival/departure of transit vehicles at each stop; and • Passengers, including information about each passenger and the passenger’s assigned path. There are two main modules to capture the behavior of pas- sengers and their interactions with transit vehicles. The first module captures access, egress, and transfer behavior of pas- sengers. In the same way the simulation captures the move- ment of a passenger from his alighting stop to either his destination or the next boarding stop (in case of a transfer). The detailed information of the passenger’s trip is recorded. The second module takes care of the boarding and alighting of passengers whenever a transit vehicle arrives to a stop. There- fore, an event-based simulation is used for this part, when an event is the simulated arrival of a transit vehicle to a transit stop. By considering factors such as the number of boarding and alighting passengers and type of transit vehicle, a dwell time value is calculated for the transit vehicle at each stop. For each transit vehicle, based on the type of route, a capacity is assumed. All of the information regarding the boarding, alight- ing, and passenger load of the vehicles is written in the output files and can be used in the next iteration. The model also has post-processing functions for calculating skim tables, sum- mary statistics, and convergence measures. Passenger Movements in Simulation The primary engine that performs simulations is the simula- tion engine within DynusT, which handles auto and transit vehicle movements. However, FAST-TrIPs handles passenger movements within transit networks. During the simulation itself, FAST-TrIPs and DynusT’s mesoscopic simulator do not communicate directly. Rather, the simulation of the passenger movements is done through post-processing of the vehicle trajectories from DynusT. This design decision was made so as to maintain the computa- tional efficiencies of DynusT yet still have a means of deter- mining passenger movements. This passenger simulation process is illustrated in Figure 4.3. As described previously, DynusT mesoscopic simulator handles all vehicle movements, including autos and transit vehicles. Among many other outputs, DynusT generates vehi- cle trajectories for the transit vehicles that are simulated, and it also records the times that autos depart their origins and arrive at their destinations. These outputs, shown on the left side of Figure 4.3, serve as the critical inputs to the transit and intermodal travel simulation in FAST-TrIPs. FAST-TrIPsDynusT Output Transit Passenger Assignment Transit vehicle arrival Passenger Accounting (Simulation) Vehicle Pax 1 Pax 3 Pax 6 … … Passenger arrival time, stop, boarding behavior Transit Skims and Passenger Measures Passenger experience Stop Pax 4 Pax 8 Pax 12 … … Passenger arrival from auto Transit Vehicle Operating StatisticsTransit vehicle movements Feedback to next iteration Auto arrivals at Park-and- Ride Lots Transit Vehicle Trajectories …… …… Figure 4.3. transit passenger simulation in Fast-trIPs.

22 In FAST-TrIPs, these data are used in a simulation of tran- sit passenger travel. This simulation likewise follows a high temporal resolution, using a 1-second interval. With this as the simulation clock, the simulation methods in FAST-TrIPs handle the bookkeeping associated with the persons on board each vehicle and at the transit stops. This is done through a set of lists keeping track of passengers on a vehicle and pas- sengers in a stop at any point in time. The movement of passengers in each time interval follows the following transit assignment logic: • A passenger who arrives at a stop during that time interval, from a given access mode (walk or auto access), is loaded into the queue at the stop. Note that passengers using auto access are not generated until the DynusT outputs indicate that the auto has arrived at the park-and-ride lot. • A transit vehicle that arrives at a stop during that time inter- val is processed, in the sense that alighting passengers are removed from the vehicle and boarding passengers are added to the vehicle. The first method processes the passengers on the vehicle k. Those passengers on board whose final stop is s will be taken off this list and processed as an alighting passenger. Statistics for the alighting passenger (access time, egress time, in-vehicle time, transfer time, number of trans- fers, etc.) are written out to a file to compute experienced transit skims and summary statistics for the transit mode. A second method processes the list of passengers in the queue at the stop s. Those passengers desiring to board this vehi- cle k are then placed on board the vehicle, in a first-in–first- out basis, until either all passengers desiring to board have been admitted on board, or the vehicle capacity is reached. In the latter case, passengers are denied boarding due to the capacity constraints. • An alighting passenger is sent to the destination, by a given egress mode, or to a transfer stop. As these movements occur, FAST-TrIPs keeps track of the time associated with access, waiting, on board, transferring, and more in order to generate passenger statistics. Once pas- sengers arrive at their destinations, their travel statistics are accumulated, and the passengers’ experienced skims from the transit network are also generated. These skims and tran- sit passenger summary statistics follow the structure of simi- lar outputs for vehicle trips from DynusT. These in turn can be used to affect the transit passenger assignment in the next iteration. In addition, transit vehicle operating statistics (on- time performance, travel times, hours in operation, etc.) can also be derived from the trajectories as outputs from the DynusT simulation. In the DynusT–FAST-TrIPs integration model, three types of convergence apply. DynusT and FAST-TrIPs, have their own individual convergence methods that use a relative gap mea- sure to assess user equilibrium. DynusT uses the simulation- based relative gap measure. The convergence of FAST-TrIPs is estimated by the number of passengers who are denied from boarding due to capacity constraints. Finally, for the combined DynusT–FAST-TrIPs model, a relative gap mea- sure comparing dwell time changes from one iteration to the next is applied. Intermodal Assignment and Simulation The intermodal assignment and simulation model in FAST- TrIPs models passenger tours with a combination of auto and transit modes; in other words, passengers who drive from their origin to access transit routes have the opportunity to select their transit path among a big set of transit options. Travelers are constrained to park their cars at the transit stations, some- thing that limits their feasible choices to park-and-ride facili- ties only. Furthermore, they have to return to the same facility to pick up their car in the return portion of the tour. These constraints make the intermodal assignment a difficult prob- lem containing different choice problems. In the SHRP 2 C10B implementation, the intermodal problem has been decomposed to a tour-based park-and-ride choice (called the intermodal assignment model) and transit/ auto path choice model (similar to auto or transit passen- gers). Given a passenger tour, including the activities and time windows between the activities, the intermodal assign- ment model finds the optimal park-and-ride location consid- ering the travel cost in the whole tour. More specifically, the park-and-ride location has to 1. Be reachable in the time window between two consecutive activities, and 2. Have the minimum tour travel cost compared with other park-and-rides. The algorithm that solves the intermodal problem is a combination of a shortest path in the auto network using the PDT from origin to all park-and-rides and a backward transit shortest path to the destination using the PAT. This gives the travel cost of the bimodal trip from the origin to the destina- tion. With the similar concept, travel cost for the second half of the tour is calculated and the optimal park-and-ride is assigned to the tour with a specific arrival time to make the mode transfer. Then the trips with auto or transit mode are extracted and fed to the transit assignment model in FAST- TrIPs or DynusT. To make the algorithm computationally efficient, a TBSP is used for the transit network that takes into account the service schedule in GTFS.

23 In the transit assignment, intermodal passenger trips are modeled similarly to other passenger trips with the differ- ence that either the origin or the destination is a park-and- ride location. The assignment model generates the path for passengers, and the passengers are simulated at the same time with other passengers. One important thing to be considered in simulating intermodal passengers is the actual arrival to the park-and-ride and the transfer to transit mode. Because the first part of an intermodal trip is traveled by auto, an auto trip is simulated in DynusT considering the roadway congestion, and the actual arrival of the passenger to the transit station is recorded. This time is used for the simulation of the transit part, and the feasibility of transit path is verified before start- ing the simulation. Finally, FAST-TrIPs produces the outputs of the intermodal tours in separate files from transit and sends them back to the demand model.

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TRB’s second Strategic Highway Research Program (SHRP 2) Report S2-C10B-RW-2: Dynamic, Integrated Model System: Sacramento-Area Application, Volume 2: Network Report describes the theoretical background and methodology for the integration of DynusT, a mesoscopic dynamic traffic assignment model, and FAST-TrIPs, a public transit passenger assignment and simulation model.

The SHRP 2 Capacity Project C10B also produced the summary report Dynamic, Integrated Model System: Sacramento-Area Application, Volume 1: Summary Report that provides an integrated model that simulates individuals’ activity patterns, travel, and their vehicle and transit trips as they move through the transportation system. A unique feature of this model is the simulation of transit vehicles as well as individual person tours using transit.

C10B model files and data, start-up guide, and network users guide for the Sacramento proof-of-concept application are available.

Software Disclaimer: This software is offered as is, without warranty or promise of support of any kind either expressed or implied. Under no circumstance will the National Academy of Sciences or the Transportation Research Board (collectively "TRB") be liable for any loss or damage caused by the installation or operation of this product. TRB makes no representation or warranty of any kind, expressed or implied, in fact or in law, including without limitation, the warranty of merchantability or the warranty of fitness for a particular purpose, and shall not in any case be liable for any consequential or special damages.

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