In the field of transportation engineering, traffic signal optimization plays a crucial role in ensuring the efficient flow of vehicles and improving overall mobility on arterials, collectors, and central business districts. Traditionally, signal optimization has been a time-consuming and resource-intensive process that involved on-site visits and manual data collection. However, with advancements in technology, a new process has emerged involving Automated Traffic Signal Performance Measures (ATSPMs). These are revolutionizing signal optimization.
Traffic signal optimization involves determining the optimal timing for traffic signals to achieve safe, smooth traffic flows and reduce delays. Accurate performance measures are essential to successfully manage traffic signals, providing insights into the effectiveness of signal operations. Performance measures also allow traffic engineers to make data-driven decisions. In a recent case study, the collaboration between Mead & Hunt’s engineering consulting services and Miovision’s ATSPMs resulted in significant improvements in traffic efficiency, cost-effectiveness, and stakeholder satisfaction. By leveraging big data and remote connectivity, this project demonstrated the potential of data-driven approaches in enhancing signal optimization.
Project Overview: Re-timing Riva Road
Mead & Hunt, in conjunction with Miovision’s ATSPMs, undertook the task of re-timing a corridor along Riva Road in Anne Arundel County, Maryland. This corridor consisted of eight intersections and faced challenges due to changes in the Annapolis High School schedule and diverse land uses. The presence of a high school, office complexes, hotels, retail stores, and a car dealership contributed to congestion during peak times. Also, a major freeway interchange and a Park & Ride lot added to the complexity of coordinating traffic movements. The objective: optimize signal timings for the corridor within a short time period, without the need for on-site visits.
Traditional Signal Operations Process vs. Objectives and Performance-Based Process
In the traditional signal operations process, there is a trigger to modify traffic signal timings. Triggers could come from public complaints or the natural life cycle, such as recalibrating signal timing every three to five years. Data is collected, engineers perform modeling and operational design, and results are implemented and evaluated. Problems arise when response to a trigger is reactive as opposed to proactive, as no iterative process exists that directly involves performance measures—engineers don’t have a clear line of sight into what problem they are solving. Issues and errors are caught in the evaluation stage and at that point are costly to fix.
With the objectives and performance-based process, the agency identifies what it wants based on predetermined goals and data. To achieve signal retiming, we must prioritize the four main conflicting objectives. Then we consider how to refine our objectives and strategies with targeted tactics to reach signal optimization. This case study focused on this performance-based process, which uses performance measures throughout the signal optimization process—not just at the end.
As a metaphor for the signal optimization process, think of an orange. If we haven’t retimed a traffic signal in a long time, we are starting with a fresh orange. It is easy to squeeze the orange and think of the juice as benefits from the signal retiming project. In the first signal retiming project, we get a lot of juice—the benefits are easy to notice. However, if you are performing the signal retiming projects on a more consistent basis, you are squeezing the same orange more often. After a few times, there isn’t much juice left, thus we aren’t getting the results we want. It is more about going through the motions and checking the boxes that it was complete. Orange squeezed? Check. Except marking a task as complete doesn’t equal efficiency.
Enter the modified process using big data and ATSPMs. Big data is referenced because ATSPMs can be in different forms and data can come from different places, including aggregated crowdsourcing data like location-based services and connected vehicle data. This modified process allows us to grab a fresh, new orange with all its juice available to squeeze. The process can also be adjusted over time by adding more data sources, more advanced computation methodologies or algorithms, and we can grab that new orange more often.
Cost & Benefits
For approximately $3,000 per intersection, the case study involved the entire signal optimization process, a before/after study, one year of cloud-based signal performance measures at all intersections, and one year of cloud-based continuous counts for the major intersections. While the majority of this case study includes remote work, adding in field visits, we are still at or below the average of $4,500 per intersection noted in FHWA’s Every Day Counts-4 Innovation ATSPM factsheet. However, we are getting greater value with our signal optimization process along with leveraging investments in Intelligent Transportation Systems (ITS). Also, the agency has a full year’s worth of ATSPM data for the intersections included in the study, which will continue to help with maintenance and data-driven decision making.
The results showed a decrease in the median travel time in the morning and afternoon peak periods, better travel time reliability, decreased split failures, and a favorable user cost savings, fuel cost savings, and carbon dioxide (CO2) cost savings. This produced a 14:1 benefit-to-cost ratio. Cost included one year of cloud-based ATSPMs for all intersections, one year of cloud-based continuous counts for the three major intersections, and engineering consulting services. This is important because usually ATSPM costs are not incorporated into the optimization costs for the benefit-to-cost ratio—therefore, a consultant can provide these services in the contract for a year and still have a significant benefit-to-cost ratio.
Remote Operation & Fast Turnaround
A key advantage of leveraging ATSPMs in signal optimization involves the ability to conduct the entire project remotely. This eliminates the need for on-site visits, saving time and resources. The Mead & Hunt team could successfully analyze ATSPM data and develop new timing plans without physically visiting the corridor. This remote operation not only enhanced efficiency but also allowed for quick adjustments and fine-tuning based on real-time data.
The traditional signal optimization process can take several months to complete. However, by incorporating ATSPMs, the Mead & Hunt team achieved a significantly faster turnaround time. The project on Riva Road, which would have taken two to four months using traditional methods, was completed in weeks. This expedited timeline owes to the efficient data analysis facilitated by Miovision’s ATSPMs and the engineering team’s expertise in signal optimization.
Conclusions
The implementation of next-generation signal timing optimization process using ATSPMs yielded substantial improvements in the performance of the Riva Road corridor. This translates into the following primary benefits produced by the case study:
- It allows for a customized approach to consistently gain greater benefits,
- There is accountability because the optimization is tied to goals and objectives, and if those are not achieved, it is time to try a different approach,
- There is increased value to including the ATSPMs in the cost of the signal optimization and still achieving a highly favorable benefit-to-cost ratio,
- There is increased quality from the more robust data set and more opportunities to correct any optimization issues before a strategy is implemented, and
- It allows agencies to conduct signal optimizations more often without having to start at square one with a new optimization project.
For more information on this case study or a copy of the case study, contact Justin Effinger at justin.effinger@meadhunt.com.
