Overview
For mainline roadway sections, performance metrics such as the speed, volume, and level of service (LOS) are commonly available through traffic sensors and third-party data. However, ramp metering metrics are less prevalent. The Arizona Department of Transportation (ADOT) partnered with the University of Arizona (UArizona) to develop the Statewide Mobility Analytics in Real-Time (SMART) tool, which integrates data from INRIX, loop detectors, and controller event-based data to provide a comprehensive operations analysis suite. The Queue Tracking module for the SMART tool was particularly developed to take advantage of the wealth of information contained in event-based data to provide easy access to ramp performance metrics. ADOT developed a MaxFlow Algorithm for adaptive ramp metering. This strategy slows the metering rate based on the local and downstream traffic conditions. This slowing relative to the traditional fixed rate increases the possibility of queue buildup on the ramp that could eventually impact the cross street. . The Queue Tracking module underwent three major revisions by closely coordinating the needs of ADOT with the capabilities provided by the UArizona team. During the project, ADOT and the UArizona held bi-weekly meetings to maintain progress and ensure that the performance metrics were going to provide the most benefit. The project successfully leveraged big data to effectively and efficiently improve fixed-rate and adaptive ramp metering performance in Arizona. The benefits this project brings include not only better freeway performance for the public, but it balances the ramp performance to better meet the needs of local agencies. Using the SMART tool, a small case study including seven ramps was performed for the rollout of the MaxFlow algorithm along the West-Bound I-10 in Phoenix in June of 2021. After the metering strategy changed, ADOT calculated a savings of over $800,000 in delay costs annually.
In this case study you will learn:
- How the Arizona Department of Transportation (ADOT) partnered with the University of Arizona (UArizona) to develop the Statewide Mobility Analytics in Real-Time (SMART) tool which included a queue tracking module used for ramp performance tracking.
- How using the ADOT developed MaxFlow Algorithm for adaptive ramp metering can slow the metering ramp rate based on local and downstream traffic conditions
- How the rollout of the MaxFlow algorithm along the West-Bound I-10 in Phoenix in June of 2021 was found to save over $800,000 in delay costs annually.
Background
Performance evaluation is one of the key attributes of safe and efficient freeway operations. For mainline roadway sections, performance metrics such as the speed, volume, and level of service (LOS) are commonly available through traffic sensors and third-party data. However, ramp metering metrics are less prevalent. This may be due to a lack of data collection mechanisms on ramps or the difficulty of accessing ramp data. In Arizona, ramp meters typically have a loop-based presence detector for meter actuation, and a loop-based queue detector used to assess whether the meter is causing excessive queueing. These detectors are used to operate the meter and record high-resolution controller event-based data. This event-based data provides information on exactly what is happening along the rampat a second-by-second resolution. For example, when a vehicle drives on to the presence detector, an input is sent to the controller and recorded as an event. Then, the controller sends an output to a green ramp signal that is recorded as another event. This type of data is not commonly used by practitioners due to its large data storage requirements and low data quality.
In this project, the Arizona Department of Transportation (ADOT) partnered with the University of Arizona (UArizona) to develop the Statewide Mobility Analytics in Real-Time (SMART) tool, which integrates data from INRIX, loop detectors, and controller event-based data to provide a comprehensive operations analysis suite. The Queue Tracking module for the SMART tool was particularly developed to take advantage of the wealth of information contained in event-based data to provide easy access to ramp performance metrics.
TSMO Planning, Strategies and Deployment
ADOT developed its MaxFlow Algorithm for adaptive ramp metering. This strategy slows the metering rate based on the local and downstream traffic conditions. This slowing relative to the traditional fixed rate increases the possibility of queue buildup on the ramp that could eventually impact the cross street. Balancing these arterial and freeway performance requirements is the next step in improving ramp meter performance. To achieve this balance and improve the ramp meter performance, the rollout of adaptive metering in the Phoenix region has required manually watching camera feeds and ramp meter outputs in real time to check for queuing. This manual analysis is limited by only real-time info availability, historical data availability, and cumbersome operations of an Excel file. These limitations make historical before and after comparisons impossible.
The Queue Tracking module in the SMART tool was developed to overcome these limitations and streamline ramp meter performance assessment. The module is powered by a combination of Microsoft SQL Server Database stored procedures and data processing using the R programming language. The event-based data were normalized to a continuous timeline using innovative algorithms, so meaningful visualizations were successfully developed to answer ADOT’s technical questions quickly. The analysis shows three types of metrics:
- total before and after performance
- location specific performance
- metering rates
The total before and after performance analysis includes average queue durations throughout the day, the percent of time that queueing occurred, and total queue durations. The location specific performance analysis allows for comparison of multiple ramps along a corridor which is especially useful for adaptive metering that considers upstream or downstream traffic in the metering rate decision process. The metering rate analysis provides a view of the behavior of adaptive ramp metering by displaying the average ramp metering rate throughout the day, as well as showing the percent of time metering.
The Queue Tracking module excels at evaluating ramp operational performance measures and is complemented by other existing modules in the SMART tool where users can concurrently measure lane-by-lane freeway flow rates and speeds, freeway delays and delay cost, speed-flow curve relationships, capacity analysis, and real-time ramp metering rate tune-up.
Communications Planning and Execution
This project originated out of a need for further information voiced by ADOT and other local agencies. The original manual reporting process was shared as a basis for the module. The Queue Tracking module underwent three major revisions before its current state, and this was made possible by closely coordinating the needs of ADOT with the capabilities provided by the UArizona team. During the project, ADOT and the UArizona held bi-weekly meetings to maintain progress and ensure that the performance metrics were going to provide the most benefit. Both ADOT and UArizona were involved in biannual multi-state ramp metering peer exchange meetings to collect feedback from more than ten state DOTs. The result was a collaboration of innovation that promoted learning for all parties and a tool capable of improving ramp metering performance for the public.
Outcome, Benefit and Learnings
The outcome of this project was to successfully leverage big data (high-resolution data) to effectively and efficiently improve fixed-rate and adaptive ramp metering performance in Arizona. This outcome was largely achieved by developing tools to identify performance quickly and easily according to several metrics. With this increase in available information, ADOT can evaluate the impacts of changes made to ramp meters, and iteratively tune the meters to their best performance with minimal manpower.The benefits this project brings include not only better freeway performance for the public, but it balances the ramp performance to better meet the needs of local agencies. Using the SMART tool, a small case study including seven ramps was performed for the rollout of the MaxFlow algorithm along the West-Bound I-10 in Phoenix in June of 2021. After the metering strategy changed, ADOT calculated a savings of over $800,000 in delay costs annually. The costs were estimated with the value of an hour of delay being the average wage for Phoenix which was $24.29 per hour, as reported by the Bureau of Labor Statistics (U.S. Bureau of Labor Statistics, 2018). Furthermore, the impact on ramps was only a 16-minute increase in daily queue durations, minimizing impacts on local streets. An added benefit is time savings for ADOT engineers and learning for all involved. For example, the small ramp study on I-10 mentioned in previous paragraph was performed with the SMART tool in approximately half an hour. Additionally, the time it takes to evaluate five ramp meters along a corridor has been reduced from eight hours to ten minutes. This type of analysis was previously performed manually in Excel and by watching traffic cameras, but now it can be performed with just a few clicks. In this case, it would have taken an ADOT traffic engineer 400 hours to evaluate all 250+ ramps statewide. Now this effort has been significantly reduced to 8.33 hours.
While traditional traffic data such as speeds and volumes have a long history of use at DOTs, event-based data is relatively new and less commonly used. Understanding the limitations of discrete data in a world of averaged data was crucial for meaningful results. This module also opens the door for other similar types of analyses such as realtime monitoring and historical event analysis for meter status or detector errors.