Proposal for Consulting Services for FRESNO COG TRANSPORTATION MODEL DEVELOPMENT AND SUPPORT Prepared for: Fresno Council of Governments (Fresno COG) P12-3814-WC
Table of Contents OVERVIEW... 1 DETAILED WORK PLAN... 3 Task Group A Initial Re-Calibration/Re-Validation of the MIP Model... 3 Task Group B Subsequent Re-Calibration/Re-Validation of the MIP Model... 4 Task Group C Create Script Files and Troubleshoot Modeling Problems... 9 Task Group D Review and Refine Model and Modeling Data and Assumptions... 11 Task Group E Provide Training and Attend Meetings as Requested... 11 Task Group F Activity Based Model Development... 12 MANAGEMENT APPROACH... 13 CONFLICTS OF INTEREST... 20 FEHR & PEERS QUALIFICATIONS AND RELEVANT EXPERIENCE... 21 Attachments Attachment A: Title VI Assurance Attachment B: DBE Participation Attachment C: Budget and Cost Breakdown Attachment D: Resumes
OVERVIEW Fresno COG s new travel demand forecast model has been implemented in the latest version of the leading modeling platform (Citilabs Cube) and meets or exceeds the requirements of SB 375. Fehr & Peers is proud to have led the Model Improvement Program that resulted in the new model. We understand that Fresno COG wishes to build on this substantially improved modeling base to ensure that it will continue to meet or exceed federal, state and local requirements for regional land use and transportation planning. We also understand Fresno COG s interest in incorporating new demographic and travel data that is either now, or will soon be, available, including revised demographic forecasts for the Valley, the 2010 Census, and the soon to be completed California Household Travel Survey (CHTS). Fehr & Peers is well-equipped to facilitate the integration of this wealth of new data and to recalibrate the model to reflect it fully in both the immediate term and when the new CHTS is complete. Fehr & Peers has worked with both Fresno COG and the City of Fresno on planning for FAX s new BRT system, and has served the California High-Speed Rail Authority in a variety of capacities, from environmental analysis of the Valley corridor to developing modeling tools and prototypes for HSR supportive land use and transportation systems. We are aware of both the need for, and viable means of, enhancing the transit mode choice component of the model to reflect these new modes and the transit services that will support them. We will capitalize on the mode choice modeling work we have done for state and regional agencies to expand the sensitivity of the Fresno COG model to all modes of transportation. Fehr & Peers specializes in innovative and robust integration of transportation and land use planning. We have been selected by many leading land use planners and scenario planning tool developers to enhance the transportation components of their plans and tools; this includes the developer of Envision Tomorrow. We have deep familiarity with the land use categories that are used in such tools, and are thus are in an ideal position to provide Fresno COG with a model that can make use of land use data with more or fewer categories, and readily translate from more complex to less complex rubrics. We understand that Fresno COG s priorities in the near-term relate to meeting the requirements of the 2014 RTP/SCS, integrating new interregional forecasts, recalibrating and re-validating the 4-step model, as well as updating the MIP documentation and user guide to reflect these changes and other Fresnospecific elements. In the longer term, Fresno COG wishes to see continued development and increasing application of its new Activity-Based Model. Having been involved creating this model and other AB models in the Valley we are well-suited to this task group as well. Most importantly, we understand that these needs and requirements are subject to change. Recognizing this, our key strengths for this projects 1 1
are our staff s depth, flexibility and most importantly or innovative attitude. We will respond quickly, fully and creatively to Fresno s COG for model development support, and we will also share ideas for model improvement that could benefit Fresno COG. We have a history of close and effective working relationship with Fresno COG, and we will strive to be a seamless extension of COG staff. The detailed work plan that follows comprehensively addresses tasks in the RFP as a starting point and incorporates the unique approaches and specialized tools developed by Fehr & Peers to accomplish the goals of Fresno COG over the course of three years, with special attention to the near-term need of the 2014 RTP/SCS. Meeting and exceeding the requirements in the RFP will be possible owing to Fehr & Peers decades of Valley-wide modeling experience and the deep expertise specifically relating to the Fresno COG MIP trip and activity models. 2 2
DETAILED WORK PLAN Following the RFP have divided the project into six major task groups (Task Group A through Task Group F). Each is delineated and discussed below. The detailed task discussions include descriptions of key deliverables, listed separately following the task descriptions. TASK GROUP A INITIAL RE-CALIBRATION/RE-VALIDATION OF THE MIP MODEL The current MIP model utilized the best information available at the time of development; since that time new regional forecasts, parallel studies, and improved data sources have become available. This task will expand the existing transit mode choice, update the interregional forecasts, recalibrate and validate the 4- step model, and update the MIP documentation and user guide specific to Fresno reflecting these changes. Enhance Mode Choice The current MIP model reflects the modes present in the Fresno COG RTP model before the MIP began. Since then, progress in the implementation of BRT and high-speed rail, as well as the desire by COG staff to model more than local buses, have given rise to the need for expanding the modes in the model and increasing its flexibility for modeling a wider range of accessibility scenarios. The MIP model will be updated to include emerging transit modes in the Fresno region such as Bus Rapid Transit, Light Rail Transit (LRT), Amtrak, and High Speed Rail (HSR), along with feeder service to HSR and the ability to have multiple modes of access to all transit stops. Specific modes and functionality will be determined in coordination with Fresno COG staff to ensure the capabilities desired are implemented in the most effective way. The mode choice parameters will be based on regions as comparable with Fresno as possible, and functionality will be tested for potential future modes to ensure the mode choice and routing preform reasonably. Once the parameters are calibrated with the expanded capabilities, the transit component of the model will be validated using appropriate and available local data. Update Regional Forecasts Regional forecasts in the current MIP model are based on the vehicle travel from the statewide model calibrated to roughly 2003 conditions and using the MPO base and future year data from previous RTPs. Shortly after the conclusion of the MIP, the eight San Joaquin Valley (SJV) MPOs revised their regional forecasts to more accurately reflect significantly changed economic conditions. Since other MPOs outside of the SJV also have revised their forecasts, the process and data used in the current MIP model will be updated. To allow for more accurate forecasting of interregional modes such as HSR, Amtrak and 3 3
potential commuter rail services, the development for gateway interactions will be discussed with COG staff and then will be updated to a more seamless and flexible process. If an interim update to the statewide model forecast is used to develop the gateway interactions for the Fresno COG model, the statewide model used will be included in the delivery, user guide, and documentation. Calibration and Validation of 4-Step Model The MIP model is integrated with enhanced sensitivities that interact with nearly all components of the model. The transit mode choice calibration in the task above not only influences the mode splits, but also the accessibility, vehicle availability, and distribution. Similarly, the interaction with the updated regional forecast influences multiple components of the model. After enhancing the mode choice and updating the regional forecasts, the overall model validation will be evaluated. Depending on the result, calibration to one or more of the steps will most likely be necessary for the model be within the dynamic and static validation targets. Documentation Documentation provided for the MIP included the similarities, differences, comparison, and results for each of the MPO models. This task will build on the information in the MIP document but provide a standalone document specifically for the Fresno COG model. The tasks listed above will be highlighted as updates, and maps specific to those most useful to Fresno COG will be included. The User Guide will also be updated, with step-by-step instructions and screen captures. In-person or web training with COG staff during the development and at the conclusion of Task Group A will also be provided. Deliverables Memo on calibration, sources of parameters, usability, and results of mode choice enhancements Memo on source, process, assumptions, and changes to regional forecast update and electronic files used for update Electronic files for calibrated and validated model including outputs for base and forecast scenarios runs Fresno COG specific documentation for calibrated and validated model including charts and maps, validation spreadsheets TASK GROUP B SUBSEQUENT RE-CALIBRATION/RE-VALIDATION OF THE MIP MODEL The 2001 California Household Travel Survey (CHTS) is the basis for calibration of the current MIP model. It is anticipated that early in 2013 the new CHTS data will be available. Utilizing the new CHTS 2010, 4 4
Census 2010, local traffic count and transit ridership data, and other data not available during the MIP, the MIP model will be calibrated and validated using the enhanced mode share and updated regional forecasts developed in Task Group A. This task will modify the model process to better integrate the GIS functionality of the Cube platform, develop a concordance and means of translation between the level of land use category detail, improve the method to estimate school enrollment, calibrate and validate the model with CHTS data, test the model for sensitivity to the Ds and implement the most appropriate method for in-line implementation, and update the MIP documentation and user guide specific to Fresno reflecting the changes from the model product from Task Group A. GIS Integration Cube 6 provides a significant enhancement to the integration of GIS within the modeling system over the capabilities implemented in the MIP model with Cube 5. To capitalize on these enhancements, the highway and transit networks, TAZs and political boundaries, and parcels or related geographic units used in Envision Tomorrow will be combined into a master geodatabase. Having the model data in GIS format allows for more precise values and also the potential for integrating and linking multiple sets of data for analytics such as buffering and mapping input and output data efficiently. Below are examples of a stick network similar to the current MIP model, and the network converted to a true GIS file. The latter is clearer the more effective base for conveying model information to other planners, policymakers and the general public. Stick network 5 5
GIS Network Land Use Detail Land use detail in the current MIP model utilizes more categories than the Envision Tomorrow visioning and forecasting process produces. Sources of data for existing and future control total comparisons often have more detail, but forecasting the level of detail currently in the model may not always be practical. To address the difference, we will discuss with COG staff the required level of detail for regional planning and local planning, and develop equivalencies and processes to translate between the multiple categories. The anticipated level of detail is 5 residential categories and 6 employment categories for use in both the 4-step and activity model. The cross-classification factors used to convert the residential units into population and housing units by income and size will also be updated based on Census data so that the population is consistent with the revised land use categories. An example of equivalencies between multiple levels of employment categories is shown in the table below. EXAMPLE LAND USE CATEGORY AGGREGATION STRUCTURE CTPP NAICS SJV MIP Category Aggregation 11 Agriculture, Forestry, Fishing and Hunting Ag_Mining 21 Mining, Quarrying, and Oil and Gas Extraction 23 Manufacturing Construction CSTDM Activity Primary and Secondary 6 6
NAICS EXAMPLE LAND USE CATEGORY AGGREGATION STRUCTURE SJV MIP Category CTPP Aggregation 31-33 Construction Manufacturing CSTDM Activity 42 Wholesale Trade Wholesale Wholesale 22 Utilities 48-49 Transportation and Warehousing Trans / Util. 44-45 Retail Trade Retail Retail 51 Information Information 52 Finance and Insurance 53 Real Estate and Rental and Leasing 54 Professional, Scientific, and Technical Services 55 Management of Companies and Enterprises Administrative and Support and Waste Management and 56 Remediation Services FIRE Prof Sci, Admin 92 Public Administration Government 61 Educational Services 62 Health Care and Social Assistance 71 Arts, Entertainment, and Recreation 721 Accommodation 722 Food Services Edu / Health Arts/Rec/Accom/ Food Trans / Util. Office Education and health Leisure and hospitality 81 Other Services (except Public Administration) Other Service Other Service xx Military employment, all industries Armed Forces Military School Enrollment School trip attractions in the MIP model are generated by the enrollment at school sites. Since the planning of school sites and sizes is typically outside of the MPOs influence, the model currently adjusts employment at the existing school sites. This may cause new residential areas to be modeled as traveling long distances for school, when in reality there may be a school developed within or near new residential developments. Utilizing GIS capabilities, maps similar to the ones below can be developed to identify locations where there are increases in school age population to increase the student enrolment at existing schools and/or the need for new schools. These maps and assumptions can be reviewed with school districts and local jurisdiction staff. 7 7
Calibration and Validation with CHTS Data Due to the many changes in the data inputs described in the tasks above, and the release of new household survey data, the model will be calibrated and validated. Beginning with updates to land use to reflect 2010 conditions followed by estimation of trip generation rates, vehicle availability and mode choice parameters, and friction factor curves, the model will be calibrated. The traffic counts that are being collected as part of other projects, along with transit ridership data, will be integrated into the GIS database. The model will be validated statically and dynamically to ensure that it both reflects the existing conditions and also responds in the correct direction and magnitude to potential changes. Smart Growth Sensitivities Once the initial calibration and validation are complete, the smart growth sensitivity of the model will be tested. It is likely that the model is not fully sensitive to all of the potential Ds due to the use of TAZs and the interaction at the sub-zone or nearby zones. Leveraging the latest research as described in the Caltrans report Improved Data and Tools for Integrated Land Use-Transportation Planning in California (2012), processes will be calibrated to correct for these insensitivities through an in-line processor. The benefit of an in-line processor (as opposed to the current post processor) is that the summary scripts and resulting roadway volumes and VMT will reflect the changes. The Ds processor will be implemented with the capability to be turned on or off for each scenario. The Ds post-processing spreadsheets will also be updated to reflect the newly calibrated and validated model. After the post-processor is calibrated and implemented, the validation will be checked. If needed, the model will be further calibrated until both the smart growth sensitivity and validation targets are achieved. 8 8
Documentation The changes to the model in Task Group B will be document in a memo to highlight the results before and after the CHTS update. To provide a self-contained document rather than an addendum to the previous version, the documentation resulting from Task Group A will be revised to reflect the changes and results of the CHTS and other modifications of Task Group B, and maps highlighting the GIS integration will be included. The User Guide will also be updated, with step-by-step instructions and screen captures. In-person or web training with COG staff during the development and at the conclusion of Task Group B will also be provided. Deliverables Updated Cube Catalog with revised scripts and input data Spreadsheets and/or maps used for summary and presenting information Memo documenting changes as a result of the CHTS 2010 and other tasks Updated self-contained documentation and User Guide to reflect TASK GROUP C CREATE SCRIPT FILES AND TROUBLESHOOT MODELING PROBLEMS Travel models contain a wealth of information that can be helpful in the decision making process. To review and extract summary information or perform subsequent analysis with model data, scripts are developed. In this task we will coordinate with COG staff to identify refinements to current postprocesses, summaries or processes requested by reviewing or member agencies, and troubleshooting major or minor problems related to the model or model data. Scripts will be implemented in the Cube Catalog to improve ease of use and scenario planning and comparison. Examples of post-processing scripts not currently in the model application that might be beneficial are: trip distance and time distribution by purpose outputs by zone for residential, office, and commercial VMT for SB 226 summary of boarding and alighting by stop, line ridership, and capacity analysis Troubleshooting the model is facilitated by a full understanding the scenario and task being performed and knowledge of the details of the model. To assist COG staff in troubleshooting, a check-list with associated maps or tables will be developed for the main components of the model including input data, model processes, and model outputs. Examples of common checks are shown in the table below. 9 9
Purpose/Goal Recommended Procedure Checks Red Flags Preliminary Checks Base and Future year land use assumptions Compare generated Trip Ends to Land Use Network (Path trace) Base year validation (static and dynamic) Growth rates Link v/c ratios Reduction in future land use Trip ends significantly lower or higher than expected Counter-intuitive route choice Not commensurate with historical trends >1.4 Intersection turning movements Furness, Difference, or O/D Consistent adjustment method across all study intersections in study area Approach link volumes Resulting turn movements Balanced system Unexplained volume decrease Volume growth substantially greater than population and employment growth Disproportionate left or right turns Excessive sources/sinks (unless expected due to presence of midblock access points) Project Trip Distribution Select zone/ Multi-class assignment Select zone(s) centroid connector volumes Bandwidth plots Assigned SZ volume volume from single class assignment Unreasonable trip lengths Skewed trip distribution to or from subset of zones Project contribution/ Proportional shares Multi-class assignment / Select link Link volumes Sum of component volumes volume from single class assignment of total trip table Subarea extraction Subarea trip table External O-D's Subarea X-X flows Deliverables Updated Cube Catalog with revised or new scripts Spreadsheets and/or maps used for summary and presenting information Memo with table similar to the one above by task with reference to script, spreadsheet, and/or map to use in checking model results Updated documentation and User Guide to reflect scripts and processes 10 10
TASK GROUP D REVIEW AND REFINE MODEL AND MODELING DATA AND ASSUMPTIONS The update, calibration, and validation of the model outlined in the previous tasks, together with ongoing coordination with COG staff in applying the model, will result in insights that will point to potential areas for refining the model processes, data, and assumptions. The GIS integration task will aid in reviewing and updating the model land use and transportation network data. Maps created during the initialization and post-process comparison stages will be used to map and compare data from various scenarios, and to implement the quality on roadway network and other checks that resulted from the previous task. Model processes leveraging the GIS integration allow for dynamic updates to the area type and terrain variables in the roadway network, which influence the capacity and speed of the facilities. With the understanding gained via the Smart Growth testing, it will become possible to identify desirable thresholds of land use development by TAZ can be used to as a basis TAZ refinement. Capitalizing on the GIS integration, refinements to TAZ boundaries can easily be coordinated with Census and Envision Tomorrow geography files, and TAZ level data can be summarized to reflect the updated zone system. As COG staff gain familiarity with their new model, the potential of developing GIS functions to perform tasks that currently performed outside of the model will be discussed. Deliverables Memos describing potential areas for improvement Updated land use and demographic files Updated Cube Application Web and/or in-person training Updated User Guide highlighting model data review process TASK GROUP E PROVIDE TRAINING AND ATTEND MEETINGS AS REQUESTED Web and in-person meetings, discussions, memos, documentation, and User Guides will be translated into a training session to assist in the professional development of COG staff. The training materials will provide both information on model functionality and hands-on exercises. Training material will be developed in coordination with COG staff and will consist of data sets to facilitate error checking, reviewing and updating of model data input data, running the model and post-processers, summarizing and mapping results for reasonableness checking, and for comparison of scenarios. 11 11
To take advantage of the latest methods and software, we will evaluate updates to modeling software and discuss with software developers, and share with COG staff the capabilities of transportation modeling software, data, and analysis methods. Since the 4-step model is currently implemented in Cube Cloud Services, both desktop and Cloud software and technical approaches will be reviewed with COG staff. The potential of sharing the model through the Cloud, utilizing the ArcGIS Online services for web mapping, and other productivity or collaboration enhancing techniques will be evaluated and discussed with COG staff. New model features will be presented at Fresno County Model Steering Committee meetings for adoption by the committee. Meetings with the committee will also be conducted to present potential software enhancements, training opportunities, and other topics as requested by COG staff. Deliverables Training outline, guidebooks, and sample data sets Memos updating COG staff on updated or new transportation modeling software and recommendations on adoption of new approaches Attending and presenting at Fresno Model Steering Committee meetings TASK GROUP F ACTIVITY BASED MODEL DEVELOPMENT Land use, network, TAZ, and other recommendations resulting from previous tasks will be discussed and prioritized with COG staff. Potential enhancements that will serve both models include: Implementing GIS functionality in the AB model so both systems utilize the same GIS input data sets. Coordinate refined zone system, population synthesis and sub-taz buffer analysis from AB model with 4-step model to enhance 4-step model sensitivity to Ds variables Investigate network detail, particularly in developing areas Integrate Envision Tomorrow output with AB model population synthesis Further utilize cloud computing for scenario planning to reduce run time Deliverables Memos discussing potential refinements and benefits to modeling and planning process Updated input data and Cube Catalog for AB and 4-step models Updated documentation and User Guide for AB and 4-step models 12 12