1. Simpson County Travel Demand Model Mobility Analysis November 7, 2003

2. Study Location

3. MODEL BACKGROUND The main objective was to forecast traffic volumes on a new section of the KY 1008 bypass in Franklin, KY Project was coordinated through: The Division of Planning The Division of Multimodal Programs KYTC decided to build a full travel demand model for Simpson County for future uses such as: Air quality analysis (non-attainment) Any other transportation-related testing

4. BACKGROUND (CONT.) Additional purpose was to test mobility in Simpson County KYTC wanted to apply Texas Transportation Institute’s (TTI) Mobility Indices in a travel demand model The result was a preliminary set of procedures that could be used to quantify mobility using travel demand models

5. RESEARCH Two reports were reviewed as part of this project: The 2002 Urban Mobility Study (TTI): Outlines the definitions and procedures for determining mobility indices Includes results of indices throughout U.S. A case study of Grand Junction, Colorado: Written by TTI and the Colorado Department of Transportation Uses travel time research to derive area-wide mobility indices

6. 2002 URBAN MOBILITY STUDY Methodology can be found in Appendix B of the 2002 Urban Mobility Study report Other information included in report: Constants Formulas Sample Calculations Mobility Indices of Major Cities in U.S.

7. GRAND JUNCTION CASE STUDY Grand Junction was used to test TTI’s mobility methodology in the year 2000 Travel time was most important attribute for accurate results Travel Time Data was collected during the following periods: AM Peak PM Peak Off Peak (Free Flow Period)

8. GRAND JUNCTION CASE STUDY (cont.) Additional data collected included: Road segment distance Vehicle occupancy 24 hour traffic counts

9. SIMPSON MODEL ISSUES The KYTC wanted the TTI methodology to apply to travel demand models Much of the data collected can be obtained from a travel demand model However, the Simpson County Travel Demand Model was a 24-hour model and did not contain peak volumes Initially, it was believed that not having the peak hour traffic information may limit the use of the TTI methodology

10. ADDITIONAL INFORMATION The TTI methodology applies to Interstates and Principal Arterials Since there were not any Principal Arterials in Simpson County, Minor Arterials were used as the next ‘best’ thing in the analysis Roads such as I-65, US 31W, KY 73, KY 100, KY 383, and KY 1171 were used

12. TTI INDICES RCI – Roadway Congestion Index TRI – Travel Rate Index TTI – Travel Time Index

13. ROADWAY CONGESTION INDEX (RCI) Provides an indication of the total number of hours in a day that a road may experience congestion Therefore, a value of 20% would indicate that 20% of the daily travel along the road occurred in congested conditions Also, the RCI can be used to determine the annual person-hours of delay for a specific study area

14. RCI INPUTS The index requires the following input: Number of Lanes ADT Peak Directional Traffic Speed Estimates Estimates of Travel Delay

15. RCI PROBLEMS / SOLUTIONS As previously noted, the Simpson model did not include peak hour directional forecasts Because of this, a ‘true’ RCI calculation could not be calculated based on TTI methodology However, a similar index could be calculated by: Subtracting modeled travel time from free flow travel time Multiplying result by number of vehicles on segment This was conducted for all interstates and arterials

16. RCI RESULTS Based on this procedure, the average annual delay in Simpson County was 0.87 hours per person. In the 2002 Urban Mobility Study, the smallest RCI value was 5.0 hours/delay per person in Brownsville, Texas Considering Brownsville, Texas is nearly ten times the size of Franklin, KY, the value for Simpson County seemed reasonable

17. TRAVEL RATE INDEX (TRI) Provides an indication of the total amount of extra time required to make a trip as a result of congestion along a roadway Therefore, a value of 1.20 would indicate that it would take 20% longer to make a trip during peak periods when compared to free-flow speeds

18. TRI INPUTS The index requires the following input: Average Freeway Speed Freeway Vehicle Miles of Travel Average Arterial Speed Arterial Vehicle Miles of Travel

19. TRI FORMULA Travel Rate Index = Freeway Travel Rate Freeway Free Flow Rate Peak Period Freeway VMT x + Prin. Arterial Travel Rate Prin. Arterial Free Flow Rate Peak Period Principle Arterial VMT x Peak Period Principle Arterial VMT + Peak Period Freeway VMT

20. TRI PROCEDURE The following steps were taken to calculate TRI: Calculate average model speed for each segment Calculate VMT for each segment Calculate a Free Flow Rate per segment Assume a K-Factor to obtain a peak VMT per segment Use TRI equations to calculate study area TRI

21. TRI RESULTS Based on this procedure, the Travel Rate Index was calculated to be 1.00256 hours per person This indicates that it will take approximately 0.2% longer during peak periods than free flow periods Based on the TTI report, Anchorage, Alaska and Corpus Christi, Texas had the lowest TRI value of 1.02 Therefore, a value of 1.00256 in Franklin, a much smaller city, seems reasonable

22. Future 2025 TRI

23. RESULTS ScenarioDescription Freeway TRI Arterial TRI Overall TRI 1Base 20021.00 2Base 2025 (I-65: 4 Lanes)2.921.891.92 3E+C 2025 (I-65: 4 Lanes)2.181.151.17 4 E+C 2025 with Bypass (I-65: 4 Lanes)2.181.071.09 5Base 2025 (I-65: 6 Lanes)1.041.681.66 6E+C 2025 (I-65: 6 Lanes)1.041.09 7 E+C 2025 with Bypass (I-65: 6 Lanes)1.041.091.08

24. TRAVEL TIME INDEX (TTI) The TTI is similar to the TRI, but more complex This index includes recurring and incident congestion whereas the TRI only considers recurring congestion The TTI also requires peak direction information to determine mobility Without this information, it would be difficult to use the TTI procedures As a result, it was decided that this index would not be included as part of the Simpson County analysis

25. Geographic Application of Mobility Index Measures In these illustrations, mobility indices for TAZ’s are used to illustrate the effect that a new bypass has option has on improving the mobility service for some areas and not affecting others. 0.00 is Best >1.00 is Worst New Bypass Mobility Improved Mobility Unaffected

26. CONCLUSIONS The RCI and TRI are two indices that can potentially be used to report mobility from a small-urban travel demand model Accurate speed data (peak periods) from the model is necessary for good results The mobility indices will produce more accurate results if used on a model with hourly assignments Consider opportunities for modified approach to deal with small urban areas Consider applications to correlate mobility indices to smaller geographic units (TAZ’s)

27. QUESTIONS? COMMENTS?