# C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 A Probabilistic Approach Determining When to Turn on/off.

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C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 A Probabilistic Approach Determining When to Turn on/off Signal Coordination Rasool Andalibian Center for Advanced Transportation Education and Research April 2014

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Outline  Background and Problem Statement  Signal Coordination: Common Practice  Stop Probabilistic Model  Model Outputs  Summary and Conclusions

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Problem Statement  Major signalized arterials are generally coordinated during peak periods.  They run free (actuated) during non-peak periods.  Traffic demand level is a key element to consider.  At what demand level signal coordination is warranted?

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Signal Coordination Strategy  Signal Timing Manual: intersections in close proximity with large amount of traffic on coordinated street.  MUTCD: Traffic signal within 0.5 mile of each other  FHWA: I ntersections close together (i.e., within ¾ mile): advantageous to coordinate them. At greater distances (over ¾ mile), consider the traffic volumes and potential for platoons

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Research Objectives  Develop a probabilistic model that predicts the number of stops for non-coordinated signalized arterials.  Develop # stop thresholds using the model that can guide engineers to decide when signals should be coordinated.

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Previous Work  TRB 2013: Performance Assessment on Non-coordinated Signalized Arterials and Guidelines for Signal Coordination

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Stop Prediction Model

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Probabilistic Model: Basic Equations i i = direction of travel a = intersection index

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Probability of Making Stops

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Stop Probability: Stop Example #1 #3 #2

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Stop Probability: Stop Example Probability of making 1 stop

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Stop Probability: Stop Example Probability of making 1 stop

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Probability Distribution of Stops n=4 n=10 1.6 2.0 2.4 4 5 6

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Traffic Volume vs. g/C Ratio Intersection Inventory

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Traffic Volume vs. g/C Ratio Total entry traffic volume varies from 100 to 5000 vph Total Volume Distribution MajorMinor 0.50 0.550.45 0.600.40 0.650.35 0.700.30 MajorMinor 0.60 / 0.400.55 / 0.45 0.70 / 0.300.60 / 0.40 0.80 / 0.200.65 / 0.35 Volume Distribution Directionality

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Traffic Volume vs. g/C Ratio

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Stop Thresholds

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Results of Stop Thresholds

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 250 to 350 vphpl Model Outputs: Recommendation for Signal Coordination  Establishing various stop thresholds results in different level of traffic volumes.  Considering more than 0.5n and 0.6n stops with the probability of 0.5 and 0.6 the recommended traffic volume for signal coordination would be:

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 ITE Survey  A survey conducted on the ITE Community Website: When Signals are Coordinated Florida: 250 vphplFlorida: 250 vphpl San Diego: 300 vphplSan Diego: 300 vphpl Portland: 300 vphplPortland: 300 vphpl Sacramento: 350 vphplSacramento: 350 vphpl

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Summary and Findings  Lack of consistency in traffic demand in signal coordination practice.  This study looks at signal coordination from number of stops standpoint.  A probabilistic stop-base model is developed predicting the distribution of stops.

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Summary and Findings Cont.  The number of stops is a function of number of intersections and average g/C ratio of all intersections.  An attempt is made to relate actuated g/C ratios and traffic volumes.  Establishing various stop-base thresholds leads to different traffic level for signal coordination.  The author’s threshold is : 50 to 60 percent of drivers making more than 05n and 0.6n stops.

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 Summary and Findings Cont.  The recommended traffic level to trigger signal coordination is 250 to 350 vphpl.  ITE survey shows that the results of this study is compatible with state-of-the-practice.

C.A.T.E.R Center for Advanced Transportation Education and Research Class Seminar Spring 2014 THANK YOU 24QUESTION? “Signals are coordinated according to speed limit thus, NEVER SPEED UP!” Rasool Andalibian

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