12 th TRB Conference on Transportation Planning Applications May 17-21, 2009

1. Automatically Balancing Intersection Volumes in A Highway Network 12th TRB Conference on Transportation Planning Applications May 17-21, 2009 Presenters: Jin Ren and Aziz Rahman 12th TRB Conference on Transportation Planning Applications May 17-21, 2009 Presenters: Jin Ren and Aziz Rahman 12th TRB Conference on Transportation Planning Applications May 17-21, 2009 Presenters: Jin Ren and Aziz Rahman 12th TRB Conference on Transportation Planning Applications May 17-21, 2009 Presenters: Jin Ren and Aziz Rahman

2. Presentation Outline • Need for Balanced Volumes • Current Balancing Techniques • New Automatic Balancing Techniques • Formation of Intersection Turn Matrix • Doubly Constrained Method • Successive Averaging or Maximizing and Iterative Balancing • Statistical Comparisons of Methods • Conclusion

3. Need for Balanced Volumes • Existing base highway network simulation in Synchro and VISSIM • Unbalanced upstream and downstream post-processed future flow • Build simulation confidence in audience • Ensure simulation model run results not wacky • Take into account mid-block driveway traffic in simulation

4. Current Balancing Techniques • Manual Adjustment: match the volumes departing one intersection to those arriving at the downstream intersection, or vice versa • EMME Demand Adjustments: create a trip table and run traffic assignment based on intersection volumes • VISUM T-Flow Fuzzy Technique: create a trip table to emulate intersection turning volumes

5. Pros and Cons of Each Technique • Manual Adjustment: a) uses a simple spreadsheet or Synchro b) time-consuming if numerous balancing iterations required 2. VISUM T-Flow Fuzzy Technique: emulate turns with balanced volumes, but intra-zonal traffic causes turning volume losses

6. T-Flow Fuzzy Example 1

7. T-Flow Fuzzy Example 2

8. Why Introduce New Methods? • Develop a statistically sound technique • Reduce labor time on balancing • Generate more accurate turning volumes • Create an automatic process which is user-friendly and affordable • Build confidence in simulation with the balanced volumes

9. New Automatic Balancing Techniques • Successive Averaging/Iterative Balancing: iteratively average downstream and upstream link volumes and then balance intersections • Successive Maximizing/Iterative Balancing: iteratively maximize downstream and upstream link volumes and then balance intersections

10. Formation of Intersection Turn Matrix

11. ai and bj adjustments made to each O-D pair volume in order to achieve the target values Oi and Dj required by the growth factors for the origins and destinations Doubly Constrained Balancing Method -Factors for origins (in) and destinations (out) -Bi-Proportional Algorithm Formula: tij ai Algorithm assumption: bj

12. ai and bj adjustments made to each O-D pair volume in order to achieve the target values Oi and Dj required by the growth factors for the origins and destinations Schematics to Intersection Balancing %Err < 0.001 Yes No

13. ai and bj adjustments made to each O-D pair volume in order to achieve the target values Oi and Dj required by the growth factors for the origins and destinations Equations for Intersection Balancing Doubly constrained: mth Iteration: Row wise mth Iteration: Column wise

14. Successive Averaging or Maximizing and Iterative Balancing Diagram Non Balanced Vol. Avg. Link level In & Out Vol. Form Intersection Turns Matrix New Turn Vol. Balance Intersection In & Out Vol. Apply Doubly Constrained for Turns Vol. Adjustment Calculate %Error Yes %Error<0.001? Balanced Vol No % Error Change? Yes No

15. Layout Unbalanced Intersection Volumes Assumption: Averaging in/out link volumes are supposed to be equal.

16. Doubly Constrained Balancing Method: doubly constrained intersection arrivals and departures

17. Example 1 Balancing Statistics T-Flow Fuzzy Technique Successive Average Technique

18. Example 2 Balancing Statistics T-Flow Fuzzy Technique Successive Average Technique

19. Statistical Comparisons Findings: SA/IB Example 1 and Example 2 are both better than T-Flow.

20. Conclusion An innovative mathematical method is presented with two practical examples Successive averaging/iterative balancing technique shows better goodness of fit statistics Automatic balancing technique saves time in traffic simulation process The spreadsheet method can be implemented cost-effectively Capacity constraint can be incorporated in the balancing algorithm in future