Tag Archives: signal

Evaluating the Use of Central Traffic Signal Control Systems

MnDOT sought to determine the full range of intersection control information (ICI) currently used in the state and how it could best be made accessible for state transportation system needs. Researchers created the Regional Database of Unified Intersection Control Information, a machine-readable, cloud-based unified ICI system. They determined steps MnDOT could take toward more effective use of its central traffic signal control system, such as mitigating traffic disruption around construction zones and participating more fully in emerging technologies such as vehicle information systems and vehicle automation.

What Was the Need?

Traffic signal control has evolved since the 1950s from simple time-based signal protocols to current dynamic systems that allow adjustment of signals to traffic conditions. Intersection control information (ICI) is increasingly important to transportation agencies, researchers and private companies involved in developing traffic models and technologies. 

Historically, the availability of traffic signal control information in Minnesota and traffic data formats have varied across jurisdictions. Nationally, increased use of central traffic signal control systems (CTSCS) has supported recent trends toward more dynamic traffic models and control, as well as toward advances in automated intelligent vehicles. This quickly evolving environment makes the creation of a unified, standardized system of ICI in Minnesota both feasible and necessary. 

MnDOT sought to examine the use of CTSCS to manage all aspects of traffic near construction zones more strategically and effectively in order to mitigate the frequent and often severe disruption of traffic these zones can cause. This project was initiated to determine the state of Minnesota’s ICI systems and to develop guidance for reaching MnDOT’s goal of a unified ICI and better statewide traffic management through CTSCS. 

A bird’s-eye view of a diverging diamond interchange in Bloomington, Minnesota. Two diamond-shaped formations of many converging and diverging lanes of traffic are seen on either side of a multilane highway.
Unified ICI can identify all the parameters traffic signal controllers need to effectively manage challenging highway configurations like this diverging diamond interchange in Bloomington.

What Was Our Goal?

This project had three objectives:

  • Deliver guidance and tools to collect ICI from all Twin Cities metro area jurisdictions and automate the importation of this information into each jurisdiction’s CTSCS and signal performance measure (SPM) applications. A stated priority was the ability to import this data into MnDOT’s digital products used in construction design.
  • With the help of all stakeholders, define the most inclusive format to represent all required information.
  • Design a Regional Database of Unified Intersection Control Information (RDUICI), and propose methods and tools for importing and exporting data between the RDUICI and all CTSCS and SPM applications by local jurisdictions. 

“A unified set of intersection control information is valuable for developing a regional signal timing database to model construction project impacts and provide standardized information for use with connected vehicle technologies.”

—Kevin Schwartz, Signal Optimization Engineer, MnDOT Metro Traffic Engineering

What Did We Do?

Researchers distributed surveys to signal operators and transportation model builders to identify the contents and develop the format of the unified ICI. 

A survey sent to 153 signal professionals sought to learn how operators in diverse jurisdictions store and distribute ICI. Responses from 42 participants helped researchers assess the availability of ICI and the degree of effort a regional unified ICI would require.

A second survey was sent to 58 designers, modelers and planners who have experience working with MnDOT signal information to learn about ICI’s various uses; 25 people responded. Researchers also interviewed a selected group of signal operators and modelers to gain more detailed information. 

A four-sided traffic signal hangs from a pole over an intersection. The traffic light illuminated on the visible side is red.
Most of Minnesota’s traffic signals use complex controllers to manage traffic, responding to information gathered from multiple sources, such as loop detectors and other sensors.

These surveys and in-depth interviews allowed researchers to create intersection models of varying complexity to drive the identification and categorization parameters of the proposed unified ICI. Researchers developed a complete unified ICI for a diverging diamond interchange, a complex interchange that is difficult to represent with traditional intersection models. Researchers also developed a relational database schema for containing the data set in a machine-readable format. This schema is a starting point for developing a system for standardizing the management and availability of ICI across jurisdictions. 

What Did We Learn?

Researchers documented all intersection signal control codes in use. They showed the feasibility of a unified ICI and demonstrated it through the example of a fully coded diverging diamond interchange. They learned that some data in older formats would need to be digitized to be included.

“Identifying the needs of different stakeholder groups allowed us to produce an organized, comprehensive format for intersection control information.”  

—John Hourdos, Research Associate Professor, Minnesota Traffic Observatory, University of Minnesota

Further investigation and communications with the software developers of MaxView, MnDOT’s CTSCS, showed researchers that current systems could not be used to store the entire unified ICI. While the systems contain much of the unified ICI data set, some detailed geometric information is missing that is critical to understanding the intersection control. MaxView also contains information that is not readable by other systems. 

Because of these challenges, researchers suggested managing unified ICI through a custom-built, centralized cloud repository. This solution would only require that vendors develop tools for exporting the information they have in a unified ICI format. The cloud repository would then be accessible to signal control vendors and to MnDOT, and security would remain intact. 

What’s Next?

MnDOT now has the full range of intersection signal control data used across the state. Researchers have determined it can be imported, stored and delivered through a cloud-based method. With these findings, the agency can begin to consider projects that use CTSCS for construction zone disruption mitigation and intelligent vehicle technologies.

This Technical Summary pertains to Report 2019-14, “Evaluation of a Central Traffic Signal System and Best Practices for Implementation,” published March 2019.  Visit the MnDOT research project page for more information.

Using SMART-Signal Data to Predict Red Light Running at Intersections

This project developed a methodology using traffic data collected by the SMART-Signal system to identify intersections prone to red light running and, therefore, serious crashes. This methodology could help MnDOT prioritize intersections for safety improvements.

“The essence of this project was to develop a toolbox that traffic engineers can use to determine an intersection’s safety performance,” said Henry Liu, Research Professor, University of Michigan Transportation Research Institute.

Liu served as the study’s principal investigator.

“This research provides a way to classify intersections that have a higher potential for red light running,” Mick Rakauskas, Former Research Fellow, HumanFIRST Program, University of Minnesota

What Was the Need?

Engineers traditionally measure an intersection’s safety using the number of crashes that actually occur there. However, collisions are rare and somewhat random events, and it can take a long time to collect enough data to accurately assess a single location’s safety.

Traffic conflicts—“close calls” in which one or both drivers must brake, swerve or take some other evasive action to avoid a crash—happen much more often than collisions do. As a result, many research projects use traffic conflicts as an alternative measure of safety.

Red light running (RLR) is one of the most common and dangerous causes of traffic conflicts at signalized intersections. While not every RLR event leads to a collision, it is often the first step in a process that ends in one.

Additionally, crashes caused when drivers run red lights are typically right-angle crashes, which are frequently severe. About 45 percent of right-angle collisions result in injury compared to about 25 percent of other crash types. Reducing right-angle-crash frequency can therefore significantly improve overall road safety and reduce costs related to traffic collisions.

MnDOT’s Safety Group wanted to determine whether it was possible to objectively and automatically identify intersections where RLR events are most likely to occur. Developing a methodology to identify the most dangerous intersections would help MnDOT prioritize locations for safety improvements.

What Was Our Goal?

Several previous MnDOT research projects had developed the SMART-Signal system, an automatic system that collects data from traffic signal controllers at signalized intersections. MnDOT has installed the system at more than 100 intersections in the Twin Cities. This project sought to develop tools that use SMART-Signal data to evaluate safety performance at intersections.

What Did We Do?

flowchart
This flowchart shows the methodology for determining whether an RLR event will result in a crossing conflict.

Researchers analyzed SMART-Signal data collected at the intersection of Boone Avenue and Trunk Highway 55 (TH 55) in Golden Valley between December 2008 and September 2009. This intersection is equipped with both stop-bar detectors and advance detectors located about 400 feet upstream of the intersection. Researchers used stop-bar-actuation data and details about traffic signal phases to identify RLR events at the intersection.

However, since most intersections are equipped only with advance detectors, this method cannot be used to measure RLR events at all intersections. As an alternative, re-searchers used vehicle-speed and traffic-volume data from the advance detectors, along with recorded traffic-signal-phase information from SMART-Signal, to identify potential RLR events. They compared these potential events to actual RLR events identified using stop-bar data and developed a formula to predict whether an RLR event would occur. This formula can be applied at intersections of major and minor roads that are not equipped with stop-bar detectors.

Researchers then used data from a minor road to develop a method that identified whether an RLR event would lead to a traffic conflict. In this method, an intersection is first divided into four conflict zones (two in each direction). When a vehicle from the main road enters the intersection, the method enables researchers to calculate when the vehicle enters and leaves each of the conflict zones it passes through. Then they determine whether a vehicle from the minor road is in the same conflict zone. Using this methodology, researchers estimated the number of daily traffic conflicts at other inter-sections on TH 55. These estimates were based on data collected in 2009 and between 2012 and 2015.

Finally, researchers developed a regression model to evaluate whether adding the number of predicted traffic conflicts to a more standard model that used average annual daily traffic (AADT) would correlate with the number of actual collisions at that site. They evaluated the model using data from seven four-legged intersections and two T-intersections on TH 13 and TH 55.

What Did We Learn?

The formula for predicting RLR events matched observations 83.12% of the time, based on more than 2,000 data points.

The number of daily crossing conflicts at TH 55 intersections ranged from 7.9 (at Glenwood Avenue in 2009) to 51.2 (at Winnetka Avenue in 2013).

While limited data were available for the regression model (as no site had more than four years of SMART-Signal data available, and there were only 11 crashes in total), the model suggests that estimated average traffic conflicts and minor-road AADT both contribute to accurate prediction of right-angle-crash frequency, while major-road AADT does not. Due to the limited data available, however, these conclusions should be considered preliminary.

What’s Next?

While there are currently no plans for follow-up studies, additional research efforts could include continuing to evaluate and improve the prediction model as more data are collected, and installing video cameras at intersections to validate the proposed methodologies.


This Technical Summary pertains to Report 2017-08, “Estimation of Crossing Conflict at Signalized Intersection Using High-Resolution Traffic Data,” published March 2017.