© Siemens AG All Rights Reserved A New Method of Split and Cycle Time Calculation as Part of the Adaptive Network Control Method Sitraffic Motion Juergen Mueck (presentation, with input from Qiao Ge) Dr. Andreas Poschinger and Qiao Ge (paper) J. Mueck, A. Poschinger: Siemens AG, Germany Q. Ge: ETH Zuerich, Switzerland

IC MOL CTE © Siemens AG All Rights Reserved Juergen MueckPage 2 Content  Context: Adaptive Network Control  Requirements we have to meet  Algorithmic Solution  Split Calculation  Cycle Time Selection  Conclusions

IC MOL CTE © Siemens AG All Rights Reserved Juergen MueckPage 3 Context: Split and Cycle Time Calculation within Sitraffic Motion Hierarchical Architecture Central Network Control Local Traffic Actuated Control Central Optimization  Optimize every 5-15 min  Signal program choice  Stage sequences  Cycle times  Green split  Offset Local controllers  Control every second  Traffic actuation  PT Priorisation

IC MOL CTE © Siemens AG All Rights Reserved Juergen MueckPage 4 Customer requirements we have to meet Many customers have very detailed requirements regarding split and cycle time calculation, for example:  Split must be calculated as optimal as possible  Assign free parts of split (buffer times) to  Pedestrians (inner city traffic)  Side roads (promoting Public Transport)  Main roads (making coordination more robust)  Impose min/max times and other restrictions  Common cycle time must be sufficiently high, but only when needed...  Cycle time selection must be fast but robust Do all this with an algorithm, which is comprehensible can be changed in configuration at any time doesn’t use stochastic optimization (  traceability, calibration, speed) Example with four sub junctions

IC MOL CTE © Siemens AG All Rights Reserved Juergen MueckPage 5 Split and cycle time calculation, based on stages Basics For each controller:  Start with the minimum possible cycle time, incl. minimum durations  Find the signal group with the smallest Green Reserve Rate (R) first, then extend the stage containing this signal group with 1 second. SG 2 has the smallest R minimum required cycle time 1s to stage 3 Update and Repeat until for all signal groups Green Reserve Rate

IC MOL CTE © Siemens AG All Rights Reserved Juergen MueckPage 6 Split and cycle time calculation, based on stages Applying Multiple-Criteria Decision Matrix +1s Assessment per signal group (“min wins”) Assessment per stage (“min wins”)   Maximum Signal Length  Green Reserve Rate  Green Distribution Factor Initial signal plan (= min cycle time)  Min signal & stage lengths Final signal plan with required cycle time / target cycle time Maximum stage lengths # One stage wins Increasing stage length improves sg assessment stage not relevant  99 maximum reached  100

IC MOL CTE © Siemens AG All Rights Reserved Juergen MueckPage 7 Decision Matrix Example (1): Which stage to extend?  One signal group has green time in more than one stages  Main /Help Signal Group  Boundary Condition  Approach: Stage based decision matrix If SG 4 has the min. R, then extend Stage 1 or Stage 2?

IC MOL CTE © Siemens AG All Rights Reserved Juergen MueckPage 8 Decision Matrix Example (2): Decision step Stage 1Stage 2Stage 3Stage 4Stage 5 Signal Group R SG R R R R Stage 1Stage 2Stage 3Stage 4Stage 5 R SG R R R R Sorted by Stages (ascending order of R): Original:

IC MOL CTE © Siemens AG All Rights Reserved Juergen MueckPage 9 Interim result: “Stage based cycle time” Found cycle time fulfills (if possible)  Min stage durations  Min green times  Necessary green times The found cycle time is definitely the minimum necessary cycle time.... but with regard to applying trends or forecasting to the found cycle time: Min constraints and coupled green times in stages have biased the found cycle times.  Hybrid approach by using a second cycle time calculation method and “merging” the results So, the cycle time calculated this way can not be used as base for trend analysis.

IC MOL CTE © Siemens AG All Rights Reserved Juergen MueckPage 10 Split and Cycle time, based on stages (part 1) Overall picture on cycle time and split calculation, showing the hybrid cycle time approach Cycle time forecast Split (cont.) for se- lected Cycle time = Calculation step Cycle time, by conflict groups Cycle time calculation based on “conflict groups” (= cg)  Def. “cg” ≡ “Set of conflicting signal groups”  Enumerate through all cg’s  Use intergreen times only Found cycle time  is by that much lower, but  is without discontinuities and bias  feasible for trend analysis

IC MOL CTE © Siemens AG All Rights Reserved Juergen MueckPage 11 Cycle time forecast Cycle time, by conflict groups Idea:  Transfer the relative change during the time horizon from conflict based cycle time to the stage based one Please refer to the paper for the theoretical fundation of the transfer process Split and Cycle time, based on stages (part 1) with

IC MOL CTE © Siemens AG All Rights Reserved Juergen MueckPage 12 Post processing of the identified “best” cycle time Found cycle time is feasible to recognize evolving peak traffic fast enough Some “finalizing”...  Final cycle time has to be one out of a given set (Discretizing)  Cycle time required by all intersections in an area have to be merged (usually by a “max” operation...)  Some hysteresis is applied (Stability)  Downward changes are delayed (Stability)

IC MOL CTE © Siemens AG All Rights Reserved Juergen MueckPage 13 Conclusions Split calculation Compared to the previous, heuristic method, based on a steepest gradient approach (not shown here) Simulation results show that:  Constraints are safely met (if consistent)  Calculation is much faster  Initial parameters do well  saves time & money!  Positive feed back from field applic.  Performance improvement ≈ 5% (weighting of number of stops and delay) Cycle time calculation First findings allow to expect that  the approach could be a way to sufficiently handle cycle time selection in practice  both from a clear algorithmic point of view and  a practical point of view (few calibration parameters) But more experience in the field is necessary to have sufficient input for the final assessment.

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