New Starts Transit Modeling Improvements in a Tour-Based Framework Suzanne Childress (corresponding author), Erik Sabina (DRCOG) Manish Jain (AECOM)

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1 New Starts Transit Modeling Improvements in a Tour-Based Framework Suzanne Childress (corresponding author), Erik Sabina (DRCOG) Manish Jain (AECOM) The Denver Regional Council of Governments (DRCOG) employs a tour-based model called Focus for regional transportation planning. As the model has been functioning well for over a year, we are ready to take the next step in using the model for transit project evaluation. DRCOG and RTD expect to use Focus for Federal Transit Administration (FTA) New Starts applications in the near future, so DRCOG has gathered a panel of experts to help us evaluate how the model will stand up under FTA New Starts scrutiny. This panel has been re-examining transit-related elements of the model to identify key improvements for greater realism based on observed data and also to ensure suitability for FTA New Starts application technical support. Solutions to several of the challenges in using tour-based models for New Starts analysis have been proposed in San Francisco by Freedman, Castligione, and Charlton 1, especially in calculating user benefits using FTA s SUMMIT software. This paper addresses more of the issues related to modifying the model for general improvement and New Starts applications. As DRCOG delved into the model complexities for New Starts, a number of questions arose that did not have immediately apparent answers in past FTA advice or good practice guidance. The goal of this paper is to raise these outstanding questions and offer some possible solutions. The three major topics we encountered were: coefficient and value-of-time considerations, mode choice structural difficulties, and drive to transit egress modeling detail. The topics need to addressed prior to using the model for an FTA New Starts application. In addition to deciding on structural changes to the model, the process of examining the model for FTA New Starts also will include extensive model validation work. Due to a lack of space, this paper will not address this essential work, but it should be noted that the comparison of modeled data to observed data is perhaps the most important part of readying a model for New Starts application. DRCOG also will perform future year forecasts prior to official modeling to ensure that the model has "the ability to produce useful forecasts the crucial property for a model set that is about to be used to make forecasts that, for New Starts projects, may cost hundreds of millions of dollars or more." 2 Consistency of Coefficients and Variables As we began considering the issues for using the Focus model in New Starts, an issue that arose immediately was the consistency of coefficients and variables across different model components. The most important coefficient relationship to maintain across model components is the implied value-of-time created by the ratio of coefficients on time and cost. The other important coefficient relationships to be maintained include ratio of in-vehicle and out-of-vehicle time components. In the trip-based models, FTA has recommended that to avoid the generation of spurious mobility benefits and disbenefits" the coefficients in path building and mode choice should as consistent as possible. FTA has noted that complexities in the mode choice model structures could result in "unexpected" and "unexplainable" decisions. 3 1 Freedman, Joel, Joe Castiglione, and Billy Charlton. "Analysis of New Starts Project by Using Tour-Based Model of San Francisco, California." Transportation Research Record (2006): Discussion-piece #16: Calibration and Validation of Travel Models for New Starts Forecasting. FTA. June 6,2006: Accessed 11/24/ Discussion piece #9: Semi-independent forecasts of Ridership and User Benefits for New Starts. FTA. June 6, Accessed 11/24/2011

2 A simple example of the path choice-mode choice interaction illustrates why coefficient consistency has been recommended. Suppose that transit paths were built with a large negative coefficient on fares, as compared to the coefficient on in-vehicle travel time; in other words, the value-of-time is low. Then the path built may have a very long travel time. Next, if the mode choice model shows a high value-of-time in which the coefficient on invehicle time is more negative than the fare coefficient, the path would be rejected because of its long in-vehicle time. In reality, however, a different high value-of-time path may have been attractive in the mode choice model. The difficulty DRCOG encountered with implementing coefficient consistency came about because the mode choice models include greater levels of disaggregation. For example, the Focus model has different coefficients in the mode choice models for five tour purposes and three income groups. The tour mode choice estimated implied values of time ranging from $14 per hour for work-high income tours to $2 per hour for other-low income tours. Currently, the path-building and assignment use only one flat value of time of $12 per hour. Table 1 shows the values-of-time used throughout the model. To achieve total consistency, DRCOG would have to build and assign paths by time-of-day, mode, income, and purpose, which seems excessive in terms of the actual differences in the paths created. In other words, paths generated using a value-of-time of $6 per hour versus $8 per hour will not be different for the vast major of trip interchanges. Skimming at this level of detail also could result in thousands of matrices. The question remains: how much disaggregation of path building/skimming is needed to adequately represent the variety of path choice decisions? Our current solution is shown in Table 1. Two sets of paths, for each time period, will be built and assigned to be as consistent as possible with the mode choice groupings: one for higher values-of-time and one for lower values-of-time. The higher value-of-time paths would be sent into higher value-of-time purposes in the mode choice model. Table 1: Component Values of Time Model Component Skimming and Assignment Mode Choice Models Purpose-Income Group Current Modeled Valueof-Time (1997$) Path-Grouping (Planned) For Value-of-Time Work/ Medium, High Income $12/hr High Non-Work, Low Income-Work $12/hr Low Work- Low Income $4.87/hr Low Work- Medium Income $10.90/hr High Work- High Income $14.45/hr High School $4.00/hr Low Escort $6.00/hr Low Other - Low Income $2.40/hr Low Other- Medium Income $2.40/hr Low Other - High Income $4.54/hr Low Work-Based Subtours $6.67/hr High The proposed process to create consistency in path choice and mode choice is as follows. First, create two sets of paths as shown in Table 1, derived from calibrating the path-building parameters based on observed paths - an approach that has become standard in adjusting models in review with FTA 4. This will involve calibrating 4 Pathbuilder Calibration with Data on Ridership Patterns. William Woodford. September Accessed 11/4/2011.

3 path-building parameters for transit against transit on-board survey data and possibly against household travel survey data. Next, using the calibrated transit paths, calibrate the tour mode choice and trip mode choice models against the household on-board surveys from Ideally, the models would be re-estimated based on paths which have been calibrated against observed data and other adjustments suggested by the FTA New Starts process. As the field moves towards distributed values-of-time, as in the SHRP C10 project, DRCOG will need to examine what this implies about relationships with path building and assignment 5. Should DRCOG also implement distributed values-of-time in both the mode choice and the path-building? Furthermore, what do value-of-time consistency issues imply for DRCOG's new dynamic traffic assignment model? Mode Choice Structural Concerns FTA New Starts DRCOG's disaggregate tour-based model structure has allowed for much greater behavioral realism and sensitivity to policy issues. However, the structure of the model presents some new hurdles in the FTA New Starts process. Four difficulties were encountered with the model structure and New Starts: calibration of the tour mode choice model to ultimately match observed on-board transit trips interpretation of coefficients and constants use of the SUMMIT software to calculate user benefits from a build alternative Modeling is always a trade-off between representing the complexity of the actual decisions people make and the computational cost of representing the decision. During programming of the model many choices had been made to make the model run faster. A typical trip-based SUMMIT application holds the trip tables constant and calculates user benefits from the trip mode choice model only. SUMMIT was designed when trip-based models were the only regional models in use, so there should be no expectation that it should work well in the tour-based context. With tour-based models, the fixed trip table approach is difficult to accomplish because each model component is linked to the next. Mode choice logsums bind the model components together, sending detailed accessibilities throughout the model chain. Each consecutive model component is conditional on the results from all the previous components. The linked relationship of the model components makes New Starts activities like fixing trip tables, using SUMMIT, calibration and interpreting coefficients more difficult than it was in trip-based modeling. The tour-trip mode choice relationship is a good example of the linked, conditional nature of model components. Focus uses two mode choice models: a tour mode choice model and a trip mode choice model, following the model structure developed by Bradley and Bowman for Sacramento. 6 When assigning a mode to a tour from the household survey, a hierarchy is followed based on the modes of each trip on the tour, as follows: 1. Drive To Transit 2. Walk To Transit 3. School Bus 4. Shared Ride Shared Ride 2 6. Drive Alone 5 Incorporation of Variable Value-of-Time in DAYSIM Mode Choice Models Summary. Lemp, Jason Petersen, Eric, Rossi, Tom. SHRP C10 Project Files. January 11, Bradley, Mark and Bowman, John. "Technical Memo Number 4: Mode Choice Models."SACSIM/05:Activity-Based Travel Forecasting Model for SACOG- Featuring DaySim- the Person Day Activity Travel Simulator. Prepared for the Sacramento Area Council of Governments. August 2, 2006.

4 7. Bike 8. Walk The tour is assigned the mode of the lowest ranked trip from this list. For example, if a tour includes trips that are Shared Ride 2, Drive-to-Transit, and Drive Alone, the tour will be assigned the Drive-to-Transit mode. The lower the rank assigned to the tour, the more constrained are the choices available for any trip on the tour. In operation, as a result of the hierarchy, the trip mode choice is constrained by tour mode to only allow higher ranked trips given a tour-mode. So for example, if the tour mode choice is drive alone, the only allowed trip modes are drive alone, bike, or walk. In the 1997 TBI household survey, the large majority of trips (83%) have the same mode as the tour. As a result, the trip mode choice model mostly predicts that the trip mode will match the tour mode. This means that the trip mode choices cannot be examined in isolation. The relationship of tour and trip mode choices makes the representation of FTA user benefits somewhat difficult because one must calculate benefits as derived from tour mode choice and trip mode choice, when in some sense they are one loosely linked model. DRCOG's imperfect solution to running SUMMIT in this linked framework is to start with the recommendations developed for San Francisco 1. Through conversations with FTA, DRCOG will define a suitable framework for SUMMIT application. Another difficulty in the FTA New Starts process with a tour-based model is how to calibrate the mode choice models. One step in readying a model for New Starts forecasting is calibration against a transit-on-board survey. When calibrating the model, the ultimate goal is to have modeled trip mode choices match observed trip mode choices by travel markets (socio-economic, access modes, time of day, trip purpose and origin-destination). The difficulty arises because to calibrate the trip results, one may need to modify coefficients and constants in the tour mode choice model to improve the calibration. In the past, we have dealt this with creating a probability matrix of tour mode by trip mode from the household survey. This matrix allows one to predict trip mode choice result changes when making a change to the tour mode choice. An example of how the tour-trip probability matrix can be used to predict trip mode choice from tour mode choice changes, is as follows. The probability of choosing a Drive-to-Transit trip on a Drive-to-Transit tour is 61%.. Thus, if one increased the number of Drive-to-Transit tours by calibrating the alternative specific constants, one could expect only around 61% of the increase to filter down to Drive-to-Transit trips. As is apparent from this example, the tour-trip mode choice linked model relationship increases the complexity of the already difficult task of FTA New Starts mode choice calibration. It is standard FTA practice to expect coefficients and constants to "tell a story". This story is much more complex in disaggregate models. For example, the in-vehicle time coefficient in the trip mode choice model is truly conditional on the previously selected tour mode, tour time of day, and intermediate stop location. As mentioned before, the tour and trip mode choice models are great in terms of behavioral realism, but may be difficult to work with in some ways in the context of FTA New Starts. Drive-to-Transit Modeling Fully representing Drive-to-Transit tours is very computationally expensive because they are highly complex. To properly model Drive-to-Transit skims in origin-destination format, it is necessary to build a path from the tour destination to the tour origin that returns to the same park-n-ride lot selected for the outbound leg of the tour. In other words, return skims need to be created based on a Walk-to-Transit component from the destination to the park-n-ride lot and a Drive component from the park-n-ride lot to the tour origin. The path builder would need to ensure that the same park-n-ride lot is used for both legs of the tour. The resulting trip tables should also be structured to assign the trips to the highway and transit networks at the appropriate time-of-day and in the correct orientation.

5 The traditional trip based models address the complex nature of Drive-to-Transit choice by modeling trips in Production-Attraction format which does not specifically model the return leg of the tour. In DRCOG s initial cut of the model, Drive-to-Transit tours were modeled in Production-Attraction format to reduce model run times. In this scheme, Drive-to-Transit tours are not considered in the trip mode choice model and the tour is converted to two trips with the tour origin and destination as the trip ends. However, this led to the confusing outcome that Walk-to-Transit and all auto trips were assigned in true origin-destination format, but Drive-to- Transit was not. For FTA New Starts process, it is important to represent this complexity because the tour mode choices could change between the build and baseline alternative and this may introduce model asymmetry. An additional complexity arises for Drive-to-Transit tours: how to correctly represent intermediate stop locations on the tour? For example, suppose the model predicted a shopping stop on a Drive-to-Transit halftour. Does the stop happen on the driving leg or the transit leg of the half-tour? A simple binary choice model that says whether the intermediate stop occurs on the drive leg or the transit leg would be required. Unfortunately, no such model was estimated to determine where the intermediate stop is made on a Drive-to- Transit tour. The current solution to this problem is to not allow intermediate stops on Drive-to-Transit tours. DRCOG will implement, however, modeling Drive-to-Transit trips in origin-destination format by passing in synthetically constructed skims on the tour destination to tour origin half-tour. Conclusion As DRCOG begins to ready the model for FTA New Starts analysis, we have uncovered many interesting questions about how to improve our tour-based model in the context of New Starts. We hope that our experiences will be useful to modelers in the future. Our main findings are the following. When designing and implementing tour-based mode choice models, one must carefully consider the interaction between the value-of-time used in various model steps. To the extent possible, the same number of paths should be built and assigned as there are values-of-time in the mode choice model. Similarly, to the extent possible, the relationship between various components of generalized cost should be maintained through the various modeling steps. Maintaining consistent relationships will only become more difficult as greater disaggregation is implemented in choice models, static assignment, and dynamic traffic assignment. Realistic drive to transit modeling requires careful attention to the egress end of the trip that is not traditionally considered in the trip-based modeling. Modelers need to consider the longer term drive-to-transit egress issues when building a tour-based model. Finally, DRCOG found that although the mode choice model structure is behaviorally realistic, it places more burden on the analyst to understand model results and coefficients because the values come from linked components that cannot be explained in isolation.