Cipra D. Revised Submittal 1

Cipra D. Revised Submittal 1 Enhancing MPO Travel Models with Statewide Model Inputs: An Application from Wisconsin David Cipra, PhD * Wisconsin Department of Transportation PO Box 7913 Madison, Wisconsin 53707-7913 Phone: 6082648722 Email: david.cipra@dot.state.wi.us Bruce Aunet Wisconsin Department of Transportation PO Box 7913 Madison, Wisconsin 53707-7913 Phone: 6082669990 Email: bruce.aunet@dot.state.wi.us Kimon Proussaloglou, PhD Cambridge Systematics Inc. 20 North Wacker Drive Suite 1475 Chicago, Illinois 60606 Phone: 3123469908 Email: kproussaloglou@camsys.com Dan Tempesta Cambridge Systematics Inc 20 North Wacker Driver, Suite 1475 Chicago, Illinois 60606 Phone: 3123469908 Email: dtempesta@camsys.com Derek Hungness, AICP HNTB Corporation 10 East Doty Street, Suite 615 Madison, Wisconsin 53703 Phone: 6082945002 Email: dhungness@hntb.com Jerry Shadewald, P.E. HNTB Corporation 10 East Doty Street, Suite 615 Madison, Wisconsin 53703 Phone: 6082945009 Email: jshadewald@hntb.com *Corresponding Author Keywords: Demand Models, Statewide, Planning Submitted: July 21, 2005 Re-Submitted: Nov 15, 2005 Word Count: 2555 (Text) + 1000 (3 Tables + 1 Figure) = 3555

Cipra D. Revised Submittal 2 ABSTRACT Original Submittal The Wisconsin Department of Transportation has developed a multimodal statewide travel demand model and eleven new Metropolitan Planning Organization (MPO) urban travel models. The statewide and MPO model development was completed over a two-year period culminating in June 2005. The primary application of the models is to provide a tool for updating Long Range Transportation Plans and developing project level forecasts for highway design purposes. An initial application of the statewide model was to utilize its forecasts (stratified by auto and truck components) for integration into each urban model at the cordon line (external) stations. The results show that utilizing external station forecasts provided by the statewide model enhances the performance of the urban models and accounts for factors that the urban models alone cannot reflect. The integration of statewide and MPO demand models provide robust project level forecasts within Metropolitan Planning areas and increase the public s confidence in the planning process. INTRODUCTION The Wisconsin Department of Transportation (WisDOT) recently completed the development of a statewide multimodal transportation demand model. The model consists of two distinct components: a passenger forecasting model and a freight and commodities movement model. On the passenger model side, WisDOT developed a statewide model utilizing elements of the MPO planning models as inputs to the statewide forecasting process. In return, the statewide model provides valuable information on interregional travel for use by MPOs in their metropolitan planning and forecasting activities. Various levels of MPO model/statewide model integration were considered. While each level presented its own set of challenges, it was acknowledged that a viable multimodal statewide model would have to feature at least a minimal degree of integration with the urbanized area model networks, zonal structures, and processes (1). The integration of the state s MPO urbanized areas presented numerous challenges. An ultimate level of MPO integration could allow for focused analysis of the roadways within the urbanized areas in a seamless automated approach as part of each statewide model run. This level of integration, though possible, was not feasible given the constraints and limited resources available to complete the project. It was determined that a practical level of MPO model integration into the statewide model would involve a feedback mechanism to adjust and smooth the statewide forecasts on key shared network segments, i.e. MPO external cordon stations. Past practice for developing traffic projections at an each MPO model external station was to complete a trend forecast generally indicating increasing traffic but growing at a decreasing rate over time. While this technique was effective for rural roadways in relatively isolated locations, locations prone to more rapid development, or influenced by major transportation projects outside MPO boundaries, became problematic. Both MPO and State transportation planners recognized

Cipra D. Revised Submittal 3 this limitation, and the statewide model represented a viable alternative source of forecasting data in those instances. Triggers for adjusting the forecasts at the external stations would need to be determined, and depending on the level of network integration, this adjustment might need to be done relatively frequently, necessitating a shared link ID between the MPO model and its corresponding statewide model link to automate the process. However, lacking a fully automated mechanism for making those adjustments, an expert panel technique was utilized to complete this task. BACKGROUND This paper outlines the importance of providing statewide travel demand model inputs (in the form of growth rates) at the external station locations of urban travel demand models. The Wisconsin statewide passenger model includes 1,642 in-state zones and 175 border state zones. The freight and long distance trip models include all of the passenger zones as well as an additional 47 zones representing the remainder of the United States, Canada, and Mexico. The statewide network includes over 200,000 links and almost 95,000 nodes. The passenger model runs the traditional four-step process and is person-trip based. It has capabilities to forecast travel on all Wisconsin minor arterials up to and including the Interstate system. Additional features include a freight model, which estimates commodity truck trips using Reebie Transearch tonnage data by commodity group, an intercity transit model, and a benefit cost module utilizing the Surface Transportation Efficiency Analysis Model (STEAM), developed by Cambridge Systematics for the Federal Highway Administration (2). The Travel Forecasting Section of the Wisconsin Department of Transportation (WisDOT) is responsible for the development, maintenance, and application of the statewide travel demand model. The model is used in conjunction with the Traffic Analysis Forecasting Information System (TAFIS), a SAS-based program developed to provide historical trend projections using a Box-Cox regression method for each Interstate, US, and State highway in Wisconsin. Local road forecasts are completed utilizing a spreadsheet-based procedure to apply the same regression method at select segment locations. The statewide and urban models were developed jointly between the consultant team of Cambridge Systematics and HNTB Corporation, the WisDOT, and the eleven Wisconsin MPOs. The models were completed in June 2005 and multiple applications are currently being developed including an integrated freight model for identifying high volume truck routes, an application using STEAM to compute relative user benefits for comparing various planning alternative scenarios, and the development of optimal inter-city passenger bus corridors in Wisconsin. In addition, the development of these models has brought about a significant side-benefit an enhanced and trusted partnership between Wisconsin MPOs and WisDOT for completing travel demand model based long range planning updates. The MPO and statewide model locations are shown in Figure 1.

Cipra D. Revised Submittal 4 Figure 1. Wisconsin Statewide Model and MPO Locations BENEFITS The first application of the new statewide model focused on integrating the statewide model forecasts with urban model forecasts at their external station locations. The statewide model provides growth rates at the external stations based on state and regional land use plans and forecasts; as differentiated by the past practice of conducting isolated historic traffic growth trend analysis at each urban model external station location. Additionally, the statewide model also accounts for changes in through-trip patterns, which had remained relatively fixed in the absence of these data. This statewide-to-mpo model external station synchronization is critical for maintaining a single set of travel forecasts on those roadways contained in both the statewide and urban models. In summary, the benefits to this new process include (1) MPOs and WisDOT will have more confidence in travel forecasts, (2) forecasts will include changes in long distance through-

Cipra D. Revised Submittal 5 trips, not always captured in urban models, (3) traffic growth at MPO cordon lines can be compared and adjusted to meet growth demands on travel estimated from the statewide model, (4) development of detailed design forecasts for specific improvement projects will use a consistent set of forecasts, and (5) public outreach meetings should be more successful. METHODOLOGY The Wisconsin multimodal statewide model provides 2030 daily traffic volume forecasts and associated growth rates for network links at the external stations of the MPO models. The links specifically located at the cordon line around each of the MPOs were compared with existing historical forecasts provided through TAFIS and local road forecasts. A spreadsheet application was developed to compare the values from each data source. An expert panel reviewed data in order to reach consensus on final growth rates, which were incorporated into the MPO urban models. In the past, the urban models have never taken into consideration the system-wide growth from a statewide model. When the urban models obtain external growth rates from the statewide model, they provide more robust and confident forecasts within the urban area. At each external cordon station, criteria (categories of forecasts) were developed and computed for review by the expert panel. The expert panel was made up of urban and regional planners and model technicians from WisDOT and participating MPOs. The application compared trend forecasts in TAFIS, statewide model forecasts, and default growth forecasts (using statewide average VMT growth projected at 1.5% per year) for each of the 204 MPO external stations. The categories of forecasts used for comparison for each external cordon line station included: 1. Forecasts for the year 2030 from the TAFIS system based on the most recent count in (either 2003, 2004, or 2005). 2. Growth forecasts from TAFIS applied to the 2000 MPO external count. 3. The statewide model forecasts and growth factors using the existing and committed highway improvement project network. 4. A 2030 forecast based on the most recent count -- annualized at 1.5 percent growth. This forecast was used for comparison purposes and used primarily on local roadways that were not included in the statewide model. 5. A manual override number from the expert panel based upon localized information of emerging growth and development in the geographical area. Two meetings were held with the expert panel to reach consensus on the areas of concern. The following aggregated annual growth rates (Table 1) were developed for each MPO to provide the expert panel with comparative information.

Cipra D. Revised Submittal 6 TABLE 1 Summary of Annual Traffic Growth Rates by Mode and MPO Annual Growth Rate MPO Auto Truck Total Location 0.68% 2.48% 1.03% Rock 0.63% 2.45% 0.77% La Crosse 0.81% 2.32% 1.04% Fond du Lac 0.61% 2.25% 0.94% Eau Claire 0.89% 2.44% 1.00% Green Bay 0.55% 1.33% 0.64% Sheboygan 0.63% 1.73% 0.73% Appleton/Oshkosh 0.93% 2.17% 1.13% Dane Table 1 provides aggregate growth rates for each respective MPO s external stations. Aggregated, these traffic growth rate results were generally consistent with the rate of population and employment growth being experienced by the respective regions. There were two cordon line stations that were manually adjusted (overrides) based upon comments from the MPOs. The remaining cordon line stations were not adjusted. The application was very beneficial because it provided a comparative tool to utilize the statewide model and compare growth rates with the urban models in a standard and uniform fashion. Since all the models were built using the same Wisconsin modeling standards, including National Household Transportation Survey (NHTS) data, it was clear that both the MPOs and WisDOT would be able to achieve consensus on future forecast values. All of the MPO models were then re-run with the revised external station data and updated for continuing use in the long-range transportation planning process. RESULTS There were 204 external stations screened by the expert panel. This screening was completed over the course of two meetings, totaling approximately eight hours of time. To expedite the process, the GEH statistic was adopted for use as a threshold in determining which stations required in-depth review. The statistic, developed by Geoff E. Havers (GEH), is used by British engineers to assess goodness of fit between model results and observed traffic volumes on network links. It is a form of a Chi Squared statistic that incorporates both relative and absolute errors (3), and is useful in describing the significance of difference between two numbers while minimizing problems which might be related to the scaling of the values. The GEH statistic was computed as follows for this application: GEH = SQR ((V1-V2) 2 / (V1 + V2)/2) where:

Cipra D. Revised Submittal 7 V1 = WisDOT TAFIS Growth Station Volume V2 = Wisconsin Statewide Model Station Volume A GEH threshold of 40 was used to trigger a more detailed analysis by the expert panel. The panel completed the review of all 204 forecasts. Table 2 summarizes the results of that review. TABLE 2 Summary of MPO Model External Station Data Sources Forecast Category External Stations Percent Current TAFIS 6 2.9% TAFIS Growth 41 20.1% Statewide Forecast 85 41.7% Annualized 1.5 % Growth 70 34.3% Override 2 1.0% Total 204 100.0% Table 2 includes both those MPO external station locations included in the statewide model and small volume MPO stations not in the statewide model. The Statewide model network includes only those roadways classified as Major Collectors or higher. In total, 46 MPO model external station locations were not included as links in the statewide model. For those relatively low volume roadways, historic count data were also not available, and a 1.5% annualized growth factor was applied to the base year count. There were 19 cases (less than 10 percent of all locations) where the GEH threshold was exceeded. In those cases, the statewide model forecast was significantly lower than the historical-based forecast from TAFIS. These cases (Table 3) were examined and consensus on the appropriate forecast value was reached by the expert panel. The results of this exercise provided important feedback to the statewide model developers to find ways to enhance the performance of the statewide model in 19 locations and at those locations with different forecasts from TAFIS. An abbreviated review process will be repeated with each annual update of the TAFIS, but the expert panel will likely not be re-convened for each TAFIS update review. Future planned enhancements to this process include developing a more dynamic interaction between the urban and statewide models, including appropriate logic checks, to further automate the process and reduce the reliance on an expert panel review to select the best available forecast value.

Cipra D. Revised Submittal 8 TABLE 3 High GEH Urban Model Links MPO Base Year (2001) Forecast Year Current TAFIS TAFIS Growth Statewide Forecast Annualized 1.5% Override GEH Forecast Selection Eau Claire 28,300 2030 52,345 54,990 37,138 48,870 83.2 TAFIS Growth Eau Claire 1,290 2030 3,022 2,800-1 2,490 74.9 TAFIS Growth Eau Claire 6,030 2030 9,587 9,310-1 8,770 136.5 TAFIS Growth Eau Claire 14,980 2030 29,194 29,260 21,246 23,810 50.4 TAFIS Growth Eau Claire 3,910 2030 7,239 7,430 12,188 7,650 48.0 TAFIS Growth Eau Claire 9,990 2030 14,240 21,030 3,890 15,380 153.5 Annualized 1.5% Fond du Lac 2,960 2035 4,110 4,480 30,072 4,770 194.7 TAFIS Growth Fond du Lac 9,700 2035 14,010 17,150 30,804 15,610 88.2 TAFIS Growth Fond du Lac 32,700 2035 50,480 52,710 31,250 52,710 104.7 TAFIS Growth Appleton/Oshkosh 8,080 2035 14,520 15,380 9,335 12,810 54.4 TAFIS Growth Appleton/Oshkosh 7,420 2035 13,450 14,130 8,326 11,770 54.8 TAFIS Growth Appleton/Oshkosh 6,460 2035 19,270 11,570 4,651 10,250 76.8 TAFIS Growth Appleton/Oshkosh 39,860 2035 68,670 75,920 53,040 63,240 90.1 Current TAFIS Appleton/Oshkosh 35,580 2035 68,000 69,850 48,796 56,450 86.4 TAFIS Growth Green Bay 23,420 2035 42,150 61,900 34,904 38,850 52,025 122.7 Override Green Bay 39,600 2035 59,950 80,280 51,696 65,700 111.3 Annualized 1.5% Rock 43,950 2035 92,750 115,530 74,947 74,000 131.5 Current TAFIS Rock 52,190 2035 96,200 94,150 78,406 87,880 53.6 TAFIS Growth Rock 12,470 2035 21,620 24,090 15,587 20,990 60.4 TAFIS Growth Sheboygan 23,150 2035 38,680 50,680 27,193 39,570 119.0 Current TAFIS Another adjustment being considered includes completing a statewide model run under high growth socio-economic scenarios, which may enhance the performance of the statewide model forecasts. Lastly, options to improve the process include organizing a Wisconsin Model User s Group which would include MPO staff persons to discuss integration issues as they arise, enhancing the current process, and keeping all interested agencies abreast of the progress being made on the statewide model and MPO model updates. CONCLUSIONS In this paper, we addressed some of the issues involved in the integration of MPO models with the statewide passenger travel demand model. The integration of statewide travel model forecasts with local urban travel models was proposed as a means of providing benefits for long-range transportation planning in Wisconsin. The statewide and urban models were developed and integrated electronically with WisDOT s historical forecast system called TAFIS (Traffic Analysis Forecasting Information System). This application allowed direct comparison of historical and statewide model forecasts at external cordon line locations within Metropolitan Planning Organizations in Wisconsin. The comparison improved travel

Cipra D. Revised Submittal 9 demand forecasting in MPO areas and facilitated consensus building on travel forecasts for major highway improvement projects. It was apparent to the expert panel that integrating the statewide growth rate projections with the urban growth projections would be extremely helpful in the public outreach phase of the long-range transportation planning process.

Cipra D. Revised Submittal 10 REFERENCES 1. Guidebook on Statewide Travel Forecasting, U.S. Department of Transportation, Federal Highway Administration, March 1999. 2. Surface Transportation Efficiency Analysis Model (STEAM 2.0) Users Manual, U.S. Department of Transportation, Federal Highway Administration, http://www.fhwa.dot.gov/steam/ 3. United Kingdom Highways Agency, Design Manual for Roads and Bridges, Volume 12, Section 2, Part 1 http://www.archive2.officialdocuments.co.uk/document/deps/ha/dmrb/vol12/sect2/12s2p1t.pdf