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600451-00113

SWUTC Research Project Description

Manual Traffic Control for Planned Special Events and Emergencies

University: Louisiana State University

Principal Investigator:
Brian Wolshon
Gulf Coast Research Center for Evacuation and Transportation Resiliency
(225) 578-5247

Project Monitor:
Alison Catarella-Michel, P.E., P.T.O.E.
Urban Systems Associates, Inc.

Funding Source: USDOT

Total Project Cost: $62,198

Project Number: 600451-00113

Date Started: 5/1/13

Estimated Completion Date: 5/31/14

Project Summary

Project Abstract:
To understand and find ways to improve traffic control techniques during emergencies, this research aims to analyze and evaluate the use of manual traffic control. To put it plainly, at present no one can quantify the effect manual traffic control has on intersection operations. Despite being widely used both in the United States and abroad, the impact of manual traffic control has never been quantified from a scientific or engineering standpoint. The goal of this research is to conduct a quantitative analysis on the impact of manual traffic control on isolated intersections using empirical data. This research seeks to determine when it may be more beneficial to use police in lieu of signalized control, when it should be used, where it can best be implemented, and how it could be simulated for the purpose of evaluating its effect on the overall movement of traffic during emergencies, events, or routine traffic conditions.

Project Objectives:
The goal of this research is to conduct a quantitative analysis on the impact of manual traffic control on isolated intersections using empirical data. This research seeks to determine when it may be more beneficial to use police in lieu of signalized control, when it should be used, where it can best be implemented, and how it could be simulated for the purpose of evaluating its effect on the overall movement of traffic during emergencies, events, or routine traffic conditions.

  1. Conduct a full review of the existing body of knowledge on manual traffic control from both transportation and police research areas.
  2. Observe and record a statically relevant sample of isolated intersections under manual traffic control.
  3. Quantify pertinent variable data from the recorded intersection under manual control.
  4. Identify the statistically significant independent variables contributing to phase length and sequence.
  5. Mathematically replicate the observed officer behavior using logistical regression (logit).
  6. Create statistically similar intersections for each case study in a microscopic traffic simulator.
  7. Program the developed logit model into the traffic micro-simulation environment.
  8. Compare the simulated manual traffic control of the study intersections to the current signal traffic controller deployed in the field.

Task Descriptions:

Task 1: Literature Review
A complete literature review encompassing the breadth and depth of knowledge in the field, both state-of-the-art and state-of-the-practice will be conducted.

Task 2: Collect Data
Observe and record a statically relevant sample of isolated intersections under manual traffic control.  Using throughput as the key variable for a given time frame, the sample size should corresponding to ±10% at 95% confidence level.

Task 3: Assessment of Pertinent Data
Quantify pertinent variable data from the recorded intersection under manual control using an extensive database of officer actions and potential stimuli.  Variables with strong and weak correlation will be measured using an α p-value of 0.05 and 0.1, respectively.

Task 4: Replication of Officer Behavior
Mathematically replicate the observed officer behavior using logistical regression (logit).  The goodness-of-fit of the model will be represented with a ρ² value no less than 0.25.

Task 5: Create Statistically Similar Intersections
Create statistically similar intersections for each case study in a microscopic traffic simulator.  Use goodness-of-fit measures such as regression analysis with p-values no less than 0.75 and comparison test such as T-test and/or ANOVA.

Task 6: Program Logit Model
Program the developed logit model into the traffic micro-simulation environment.  A statistical analysis comparing simulated throughput and capacities of case study intersections and observed values.

Task 7: Comparison of Results
Compare the simulated manual traffic control of the study intersections to the current signal traffic controller deployed in the field.  The collection and statistical analysis of intersection throughput will be compared with the capacity under both scenarios.


Implementation of Research Outcomes:
The goal of this research was to quantify the effect of manual traffic control on intersection operations and to develop a quantitative model to describe the decision-making of police officers directing traffic for special events and emergencies. This was accomplished by collecting video data of police officers directing traffic at several special events in Baton Rouge, LA and Miami Gardens, FL. These data were used to develop a discrete choice model (logit model) capable of estimating police officer’s choice probabilities on a second-by-second basis. This model was able to be programmed into a microscopic traffic simulation software system to serve as the signal controller for the study intersections, effectively simulating the primary control decision activities of the police officer directing traffic. The research findings suggested police officers irrespective of their location, tended to direct traffic in a similar fashion; extending green time for high demand directions while avoiding gaps in the traffic stream.

Products developed by this research:

New Mathematical Model:  This research developed a new mathematical model capable of predicating the choice behavior of police officers directing traffic at signalized intersections. The model formulation is grounded on discrete choice logistic regression. Based on the current state of traffic at the intersection, the model is capable of making accurate, short term, predictions of the police officer’s future right-of-way allocation.

Presentation and Publication:  Modeling and Analysis of Manual Intersection Traffic Control for Application in Evacuation Time Estimate Studies, S. Parr and Brian Wolshon, presented at the 2014 ANS Winter Meeting and Nuclear Technology Expo, Anaheim, CA, November 2014. And published in the conference proceedings.

Publication: Selection and Allocation of Manual Traffic Control Points and Personnel during Emergencies, S. Parr, V. Dixit and B. Wolshon, Louisiana State University, published in the Journal of Emergency Management, Special NEC Issue, January 2014.

Presentation: Operation Characteristics of Manual Traffic Control for Mass Egress, S. Parr and B. Wolshon, Louisiana State University, presented at the 2014 National Evacuation Conference, New Orleans, LA, January 2014.

Presentation: Methodology for Simulating Manual Traffic Control, Scott Parr and Brian Wolshon, LSU, presented at the 95th meeting of the Transportation Research Board and submitted for publication in the Transportation Research Record, Journal of the Transportation Research Board, 2016.

Dissertation: Analysis and Modeling of Manual Traffic Control (MTC) for Signalized Intersections, Scott Parr, LSU, Dissertation, December 2014.

Impacts/Benefits of Implementation:
In special event and emergency transportation planning, manual traffic control is a frequently used technique for assisting the flow of traffic through intersections. This research took a quantitative approach to analyses and model manual traffic control using discrete choice science and traffic simulation. The model developed is adaptable for incorporation into other networks and software. Based on the work conducted during this project, transportation planners now have an additional tool, which can more accurately estimate the impact of manual traffic control on the overall movement of vehicles.

This research can lead to new regulatory policies for cities, counties, and states with regard to emergency planning requirements. Using the knowledge gained from this research, transportation planners can now optimize (in a mathematical and quantifiable sense) the allocation of police officers for manual traffic control. This would result in having more officers available for other tasks after a disaster such as providing security and search-and-rescue operations.

Web Links:
Final Technical Report