Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
18 Overview A case study is provided to demonstrate how TFGuide can be used by different audiences (transportation planners, travel forecasters, and decision makers). In this case study, a medium- size metropolitan planning organization (MPO) sought to perform a major transit corridor study, leading to the development of a Locally Preferred Alternative for possible New/Small Starts funding from the Federal Transit Administration. This case study demonstrates how the user would interact with and provide responses to TFGuide. The remainder of the case study identifies the roles for the transportation planner, travel forecaster, and decision maker at a transportation agency in interacting with TFGuide. These roles are intended to be complementary. TFGuide can be operated by a single audience (such as the travel forecaster) with input from the other audiences (transportation planner and decision maker), since this may be more efficient than having each audience participate with TFGuide directly. Role of the Transportation Planner A transportation planner defines the requirements and needs of a planning program. In this case study, the agency is a medium-size MPO and the planning program is a major transit corridor study. Profile The first data to be input for an agency are the time zone and the region size, found under the profile option (in the upper-left corner). In this case study, the agency is in a region with a population of 500,000 to 600,000 people (see Figure 15). Program or Plan The transportation planner is typically responsible for identifying needs for each planning program or plan. If the transportation planner identifies that travel forecasts are needed for a specific program or plan, then the planner would seek advice on the appropriate tool and its development from the travel forecaster (also often referred to as the transportation demand modeler). In this case study, the transportation planner will define the program or plan. Figure 16 presents the categories of transportation planning programs and plans and the selection of Major Transit Corridor Study for this case study. Only one program or plan can be selected for each scenario. Reviewing a Case Study C H A P T E R 5
Reviewing a Case Study 19 Requirements In Step 2, the transportation planner identifies the requirements and priorities of the pro- gram or plan. The best way to complete this step is with the transportation planner and travel forecaster working together to define the details needed to apply a travel-forecasting tool. Figure 17 shows the following requirements selected in TFGuide for the major transit corridor study: ⢠This will be a detailed planning analysis. ⢠The program will follow federal standards as a planning study. ⢠The demographics by income group are an essential element for equity analysis. ⢠This is a corridor study. ⢠The transit network is multimodal (complex). ⢠The spatial detail is traffic analysis zones. ⢠The analysis should be done for peak time periods. ⢠The analysis considers transit fares and auto(mobile) operating costs. Figure 15. Medium-size MPO selection for major transit corridor study.
Figure 16. Planning program selection for Major Transit Corridor Study. First Screen Figure 17. Requirements selection for major transit corridor study (two screens). (continued)
Reviewing a Case Study 21 Second Screen Figure 17. (Continued).
22 Method Selection for Travel Forecasting: User Guide ⢠The methods should be sensitive to traveler demographic characteristics, highway and transit network supply, and transit operations. ⢠The travel markets are resident passenger and special markets. The directions shown in the left panel of Figure 17 tell the user to choose at least one require- ment from each category (general, level of detail, and scope). A transportation planner can receive better customized recommendations by specifying additional requirements. The transportation planner also needs to identify the priorities by assigning weights to each requirement. The weights are used in the scoring for recommendations and can be adjusted when the user reviews the recommendations. For this case study, the demographic detail of income groups is identified as a high-priority requirement and weighted by a factor of 3. Performance Metrics In Step 3, the transportation planner identifies the performance metrics to support the program or plan. This is typically done by the transportation planner, sometimes in combination with the deci- sion maker; there are six categories of performance metrics and 34 individual metrics. The transpor- tation planner or decision maker may be interested in some metrics that are not available, so the travel forecaster may need to interpret the potential performance measures to obtain the information on the metrics that are not available. Additional performance metrics can be added to the system if users identify metrics of interest. This flexibility has been built into TFGuide to allow changes over time. On the left bar of the performance metrics screen, the transportation planner is directed to identify at least one performance metric (Figure 18). Each performance metric that is selected will help to customize the recommendations. Figure 18 presents the four performance metric selections for the major transit corridor study: (1) travel costs by income group, (2) access to jobs, (3) vehicle miles traveled, and (4) transit ridership. Community measures include the travel costs by income group, an essential element that the transportation planner identified as a priority, so these measures are weighted by a factor of 3. Role of the Travel Forecaster The travel forecaster has three primary responsibilities when using TFGuide: ⢠To review the requirements and performance metrics specified by the transportation planner. ⢠To identify the current agency methods (this is optional). ⢠To review and assess the recommendations provided by TFGuide. The travel forecaster should agree with the specifications for requirements and performance metrics before proceeding. This may need to be discussed so the transportation planner and travel forecaster can come to consensus on the requirements for a program or plan. The travel forecaster should also specify current agency methods because this prevents exist- ing methods from being included in the cost and resources needed to implement the recommen- dations. The travel forecaster can choose to add methods that the agency has previously reviewed and determined to be unsuitable so that these may also be excluded from the recommendations. The recommendations are customized as much as possible within the confines of a decision- support system. As a result, the travel forecaster should assess these recommendations to deter- mine the best fit for their agencyâs specific needs. The travel forecaster should review the details of all recommendations, consider the potential benefits that a method may bring to other
Reviewing a Case Study 23 programs and plans, and test the scored recommendations by adjusting the weights for each element. Following this assessment, the travel forecaster should identify the preferred recommendation and detail the scope, schedule, and budget outlined in TFGuide for this recommendation. The travel forecaster can also identify whether this work can be done in-house or with consultant support, allowing the budget to be segmented for in-house or consultant support. Constraints In Step 4, the travel forecaster establishes an initial budget and schedule for the work. These constraint settings can be adjusted so that recommendations will be rescored based on how well they meet the constraints. Figure 19 presents the two primary constraints for each program or plan: (1) budget (in dollars) and (2) schedule (in months). The travel forecaster will input First Screen Figure 18. Performance metric selection for major transit corridor study (three screens). (continued on next page)
24 Method Selection for Travel Forecasting: User Guide budget and schedule to compare against the cost and timeline required by the selected methods. Although budget and schedule are identified as constraints, they do not preclude methods that exceed the budget or schedule. Instead, they contribute to a higher score if they are within budget or within schedule, based on the ratio of the budget to cost or schedule to timeline. Current Agency Methods (Optional) In Step 5, there is an option to filter the recommendations by excluding any methods and resources (expertise, data, hardware, and software) that the agency has already invested in. Cur- rent methods and resources are excluded from the cost and timeline of any recommendation, but they are available to support method packages. Figure 20 presents the current methods and resources that the agency has already developed or purchased. If the resources required for a method are not identified as current resources of the agency, then the method will include the Second Screen Figure 18. (Continued).
Reviewing a Case Study 25 cost of the additional resources needed. Description of resources/methods will be displayed when the user hovers over an individual resource or method. The description of the software resource Transportation Network Planning Package appears in Figure 20 as it would when the text is hovered over in TFGuide. Menu of Recommendations TFGuide evaluates the performance metrics and sensitivities specified by the agency in Fig- ures 15 through 20, along with details on the requirements, to determine which methods best meet the needs for the scenario. Figure 21 shows some of the menu of recommendations, ranked Third Screen Figure 18. (Continued).
26 Method Selection for Travel Forecasting: User Guide Figure 19. Constraint settings for major transit corridor study. Figure 20. Current methods for major transit corridor study.
Reviewing a Case Study 27 by score. Some of the recommendations are mutually exclusive (e.g., direct-demand models and pivot-point and incremental models), and others are combined to work together (e.g., frequency- based transit assignment and time of day [disaggregate]). The direct-demand model is shown as a single method because its score is high relative to the requirements and constraints. The menu of recommendations screen includes a summary of the requirements, performance metrics, constraints, and agency methods (left side of Figure 21). The screen also includes indi- vidual characteristic weights. The menu of recommendations reflects the transportation plan- nerâs selection of income as an important demographic detail and travel cost by income group as an important community measure; each receives a weight of 3. The transportation planner can change the weights and constraints interactively to determine the effect on the menu of recommendations. If the transportation planner decides that the sen- sitivity to transit network supply and operations is most critical, and weights by a factor of 5, then the recommendations favor methods that meet this sensitivity requirement. Figure 22 presents the recommendations using a weight of 5 on sensitivities. The score of each method or method package is detailed by component; an example is shown in Figure 23 for the frequency-based transit assignment and time of day (disaggregate) recom- mendation. These details include current and additional resources needed to implement these methods. Each method is shown separately and the cumulative contribution is shown for each component. The cost for each method and the resources required are shown along with a list of current resources that are required for implementing this set of methods. Figure 21. Menu of recommendations for major transit corridor study, weighted by demographic detail and community measures.
Figure 22. Menu of recommendations for major transit corridor study, weighted by sensitivities. Frequency-Based Transit Assignment and Time of Day (Disaggregate) Figure 23. Scoring details of a method package for major transit corridor study.
Reviewing a Case Study 29 Role of the Decision Maker Decision makers at a transportation agency are the program managers, directors, executive directors, boards, or committees that are responsible for approving the program or plan in the scenario. Decision makers may be different for each program. The role of the decision maker is twofold. First, the decision maker should review and approve the budget and schedule con- straints for the program or plan, following an estimate provided by the transportation planner and travel forecaster. Second, the decision maker should review and approve the preferred recommendation from the travel forecaster. Outcome Initially, TFGuide recommends a direct-demand model with the highest score of 24 based on the weighting by a factor of 3 for demographic detail and community measures. The transit corridor case study has limited time and resources ($100,000 within 12 months), so methods with lower cost and timeline constraintsâsuch as the direct-demand modelâwill score higher if they also meet many of the other requirements. Three other recommendations receive relatively high scores (22) in the case study: ⢠Time of day (disaggregate) and frequency-based transit assignment score lower on cost because the methods cost more to develop and require an advanced travel-forecasting and statistics background that the direct-demand models do not require. They take longer to develop, so they also score lower on schedule. Schedule-based transit assignments are not in wide use, so they score lower on industry adoption. Both time of day and frequency-based transit assign- ment methods are sensitive to auto operating costs, so these methods score higher for pricing policies; these are also sensitive to highway network supply and score higher on sensitivities than direct-demand models. ⢠Pivot-point and incremental models score lower on cost because they require an observed origin-destination trip table (at a cost of $40,000) that the direct-demand models do not require. They take longer to develop, so they also score lower on schedule. Pivot-point and incremental models are sensitive to both transit fares and auto operating costs, whereas direct-demand models are only sensitive to transit fares, so these methods score higher for this element. ⢠Destination choice and multiclass equilibrium traffic assignment score lower on cost because the methods cost more to develop and require an advanced travel-forecasting and statistics background and truck counts to develop. These methods take longer to develop and score lower on schedule. Both destination choice and multiclass assignment methods are sensitive to auto operating costs and score higher for pricing policies than direct-demand models. The process to compare each component of the scoring with the highest recommendation can reveal useful information. In this case study, the preferred recommendation may be the highest score (direct-demand model), but the agency may decide that the lack of sensitivity for auto operating costs or highway network supply are important considerations. As a result, these elements could be weighted higher to produce a different recommendation.
