12 th TRB Conference on Transportation Planning Applications May 17-21, 2009 Presenters: Jin Ren and Aziz Rahman Automatically Balancing Intersection Volumes in A Highway Network 12 th TRB Conference on Transportation Planning Applications May 17-21, 2009 Presenters: Jin Ren and Aziz Rahman 12 th TRB Conference on Transportation Planning Applications May 17-21, 2009 Presenters: Jin Ren and Aziz Rahman 12 th TRB Conference on Transportation Planning Applications May 17-21, 2009 Presenters: Jin Ren and Aziz Rahman
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
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
Current Balancing Techniques 1.Manual Adjustment: match the volumes departing one intersection to those arriving at the downstream intersection, or vice versa 2.EMME Demand Adjustments: create a trip table and run traffic assignment based on intersection volumes 3.VISUM T-Flow Fuzzy Technique: create a trip table to emulate intersection turning volumes
Pros and Cons of Each Technique 1.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
T-Flow Fuzzy Example 1
T-Flow Fuzzy Example 2
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
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
Formation of Intersection Turn Matrix
Doubly Constrained Balancing Method -Factors for origins (in) and destinations (out) -Bi-Proportional Algorithm 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 t ij bjbj aiai Algorithm assumption: Formula:
Schematics to Intersection Balancing 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 % Err < NoYes
Equations for Intersection Balancing Doubly constrained: 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 m th Iteration: Row wise m th Iteration: Column wise
Successive Averaging or Maximizing and Iterative Balancing Diagram Non Balanced Vol. Avg. Link level In & Out Vol. Form Intersection Turns Matrix Balance Intersection In & Out Vol. Apply Doubly Constrained for Turns Vol. Adjustment Calculate %Error % Error Change? New Turn Vol. %Error<0.001? Balanced Vol Yes No Yes No
Layout Unbalanced Intersection Volumes Assumption: Averaging in/out link volumes are supposed to be equal.
Doubly Constrained Balancing Method: doubly constrained intersection arrivals and departures
Example 1 Balancing Statistics T-Flow Fuzzy TechniqueSuccessive Average Technique
Example 2 Balancing Statistics T-Flow Fuzzy TechniqueSuccessive Average Technique
Statistical Comparisons Findings: SA/IB Example 1 and Example 2 are both better than T-Flow. TESTSR2R2 RMSESlopeMean Rel Err% VOLUME DELTA T-Flow Fuzzy Ex (-3.0%) SA/IB Ex T-Flow Fuzzy Ex (-2.5%) SA/IB Ex
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