Financial Summary |
|
Contract Amount: | $595,032.00 |
Suggested Contribution: | |
Total Commitments Received: | $595,000.00 |
100% SP&R Approval: | Approved |
Contact Information |
|||
Lead Study Contact(s): | Khyle Clute | ||
Khyle.Clute@iowadot.us | |||
Phone: 515-239-1646 | |||
FHWA Technical Liaison(s): | Eddie Curtis | ||
Eddie.Curtis@dot.gov | |||
Phone: 404-780-0927 | |||
Study Champion(s): | Chris Poole | ||
Chris.Poole@iowadot.us |
Organization | Year | Commitments | Technical Contact Name | Funding Contact Name |
---|---|---|---|---|
Federal Highway Administration | 2021 | $0.00 | Eddie Curtis | Kimberly Duke |
Federal Highway Administration | 2022 | $33,000.00 | Eddie Curtis | Kimberly Duke |
Federal Highway Administration | 2023 | $33,000.00 | Eddie Curtis | Kimberly Duke |
Federal Highway Administration | 2024 | $33,000.00 | Eddie Curtis | Kimberly Duke |
Georgia Department of Transportation | 2021 | $25,000.00 | Katherine D’Ambrosio Shearin | Brennan Roney |
Georgia Department of Transportation | 2022 | $25,000.00 | Katherine D’Ambrosio Shearin | Brennan Roney |
Georgia Department of Transportation | 2023 | $25,000.00 | Katherine D’Ambrosio Shearin | Brennan Roney |
Georgia Department of Transportation | 2024 | $25,000.00 | Katherine D’Ambrosio Shearin | Brennan Roney |
Iowa Department of Transportation | 2021 | $99,000.00 | Chris Poole | -- -- |
Pennsylvania Department of Transportation | 2022 | $33,000.00 | Steve Gault | Evan Zeiders |
Pennsylvania Department of Transportation | 2023 | $33,000.00 | Steve Gault | Evan Zeiders |
Pennsylvania Department of Transportation | 2024 | $33,000.00 | Steve Gault | Evan Zeiders |
Texas Department of Transportation | 2021 | $33,000.00 | Rodney Jones | Ned Mattila |
Texas Department of Transportation | 2022 | $33,000.00 | Rodney Jones | Ned Mattila |
Texas Department of Transportation | 2023 | $33,000.00 | Rodney Jones | Ned Mattila |
Utah Department of Transportation | 2021 | $99,000.00 | Mark Taylor | David Stevens |
Current traffic signal controller technology and associated methods of signal actuation make use of a “binary” (on/off) states to measure vehicle presence at fixed locations (detection zones) near an intersection. This provides a relatively limited view of demands at an intersection. For example, an active presence state on any detector typically indicates that one or more vehicles present. The controller does not know if there are one or many vehicles present in that zone. In recent years, advances in sensor technology have made it feasible to begin obtaining enhanced information about vehicle demands at intersections. Rather than only capturing vehicle presence, the distances and speeds of vehicles nearby intersections can be obtained. This capability has existed within some detection technologies (such as radar) for some time. However, signal controllers cannot directly make use of the data; because they only accept a binary input, the enhanced information bust be reduced to a simple presence impulse. Therefore, the full value of this existing technology is not being realized. Another feature recently introduced into many traffic signal controllers is a capability to transmit information to other controllers through peer-to-peer communication. This capability would permit additional enhancement of signal controllers by permitting them to communicate information to each other directly. The potential value of vehicle trajectory data can be seen in research studies, mostly in simulation, that have employed vectors of estimated of vehicle arrival times to yield methods of enhanced signal control. Tremendous opportunities for improvement in the efficiency and safety of signal control is promised with such data, providing considerable motivation for the industry to move forward. However, such concepts have not made their way into standard methods of actuated signal control currently employed at the vast majority of intersections. It is possible that substantial improvements in the performance of signalized intersections could be induced by the introduction of vehicle speed and position data from sensors, rather than using only point detection. A white paper, “Leveraging Sensor-Based Vehicle Trajectory Construction in Existing Traffic Signal Infrastructure”, in the proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, provides an example of such applications in their early stages. By introducing new control methods to leverage such mechanisms, it would be possible to add new capabilities to traffic signal controllers largely within existing infrastructure. Example applications include dilemma zone protection using real vehicle speeds, the selection of control decisions using vehicle arrival times based on more accurate estimates, and the extension of red clearance intervals based on real-time vehicle speed information. By utilizing existing infrastructure, these enhancements could be implemented in a scalable fashion across entire systems. “Vehicle Trajectory-Enhanced Advanced Signal Control”, the concept this research seeks to develop, will introduce these concepts into existing signal control infrastructure, leading to modernized signal control based on next-generation data concepts, as the successor to contact-closure methods of vehicle actuation currently in use.
The objective of this research is to develop field-tested methods of integrating vehicle trajectory data into actuated signal control that can be directly implemented in traffic signal controllers. This research will identify the practical requirements and limitations of establishing trajectory-assisted actuated signal control. The findings will be developed into a resource toolkit that will permit implementation and further development of the methods conceived during the course of the research.
Phase I This phase includes steps necessary in preparation for real-world testing of trajectory-based control concepts, including preliminary field tests. Task 1. Literature Synthesis. Review the existing literature on traffic signal control applications including both trajectory-based methods and relevant control method that rely on equivalent information predicted from conventional detection. Identify specific methods of control enhancement that can be implemented based on vehicle trajectory data. Develop a taxonomy of control options to frame potential specific applications of trajectory data, and connect these to specific operational objectives. Task 2. System Component Review. Compile specifications of commercially available sensors capable of identifying vehicle positions and speeds in real time, and traffic signal controllers with user-programmable logic or other mechanisms for introducing code necessary to implement control algorithms. Task 3. Sensor Evaluation. Using a location where a trajectory-capable sensor has been previously deployed in the field, obtain vehicle position and speed data from the sensor and compare this data against the groundtruth measurements. Quantify the accuracy and tolerances of measurements through such sensors. Task 4. Establish Algorithm Environment. Determine the appropriate platform for implementing trajectory-enabled signal control algorithms and test to ensure that this platform will perform in the field. Task 5. Algorithm Selection and Bench Testing. Select algorithms to be included in field tests. The main focus should be a method to integrate trajectory data into phase actuation decisions (call and extensions decisions). Implement these using field equipment in a laboratory setting. Task 6. Test Location Selection and Procedure. Identify field locations for testing the operational impacts of selected algorithms. A minimum of two field locations with different types of controllers should be selected. Task 7. Preliminary Field Test. After confirming functionality with a bench test, conduct a preliminary field test of the trajectory-based control algorithm. The purpose of this test will be to determine that the sensor and control environment is capable of implementing the selected control. Phase II tasks will focus on evaluation of the control. Task 8. Phase I Report and Panel Meeting. Document findings of Tasks 1-7 in an interim report and submit for panel review. Convene a panel meeting to discuss these results and revise objectives for Phase II. Phase II This phase will execute comprehensive field tests of trajectory-based signal control. Task 9. Revised Test Scope. Based on panel input from the Phase I panel meeting, assess the scope for remaining tests to be conducted during Phase II. This will focus on what trajectory-based enhancements are most feasible for implementation. Task 10. Single-Intersection Testing. Evaluate the performance of the selected enhancements in detail at a single intersection. The focus of this evaluation will be on examining local-intersection details such as equitable service of green times. This should include a sensitivity analysis of the impacts of key control parameters on the performance of the enhanced control. Task 11. Multiple Corridor Testing. Evaluate the performance of the selected enhancements along at least two different signal corridors. The focus of this evaluation will be on the ability of such enhancements to facilitate smooth traffic flow across groups of intersections. This should include a sensitivity analysis of the impacts of key control parameters on the performance of the enhanced control. Task 12. Implementation Analysis. Assess the ability of the control enhancements to be implemented at scale. Identify mechanisms by which implementation could be achieved and key steps in that process. Task 13. Outreach and Engagement. Disseminate research findings through participation
Desired annual commitment from each partner is $33,000 per year for 3 years (FY22-24) for a total of $99,000. Alternatively, if FY21 is still available for your program, a commitment of $25,000 per year for 4 years (FY21-24) is also a possibility. Commitment covers travel for an in-person meeting one or two times throughout the life of the project. Specific meetings and locations to be determined during the project. Will be pursuing a 100% SPR Part B waiver. Additional partners are always welcome. Please reach out to the Lead Agency Contact at any time.
Subjects: Highway Operations, Capacity, and Traffic Control
General Information |
|
Study Number: | TPF-5(483) |
Lead Organization: | Iowa Department of Transportation |
Contract Start Date: | Feb 01, 2022 |
Solicitation Number: | 1545 |
Partners: | FHWA, GADOT, IADOT, PADOT, TX, UT |
Status: | Contract signed |
Est. Completion Date: | Feb 28, 2026 |
Contract/Other Number: | |
Last Updated: | Aug 12, 2024 |
Contract End Date: | Feb 28, 2026 |
Financial Summary |
|
Contract Amount: | $595,032.00 |
Total Commitments Received: | $595,000.00 |
100% SP&R Approval: |
Contact Information |
|||
Lead Study Contact(s): | Khyle Clute | ||
Khyle.Clute@iowadot.us | |||
Phone: 515-239-1646 | |||
FHWA Technical Liaison(s): | Eddie Curtis | ||
Eddie.Curtis@dot.gov | |||
Phone: 404-780-0927 |
Organization | Year | Commitments | Technical Contact Name | Funding Contact Name | Contact Number | Email Address |
---|---|---|---|---|---|---|
Federal Highway Administration | 2021 | $0.00 | Eddie Curtis | Kimberly Duke | (202)366 -1713 | Kimberly.Duke@dot.gov |
Federal Highway Administration | 2022 | $33,000.00 | Eddie Curtis | Kimberly Duke | (202)366 -1713 | Kimberly.Duke@dot.gov |
Federal Highway Administration | 2023 | $33,000.00 | Eddie Curtis | Kimberly Duke | (202)366 -1713 | Kimberly.Duke@dot.gov |
Federal Highway Administration | 2024 | $33,000.00 | Eddie Curtis | Kimberly Duke | (202)366 -1713 | Kimberly.Duke@dot.gov |
Georgia Department of Transportation | 2021 | $25,000.00 | Katherine D’Ambrosio Shearin | Brennan Roney | 404-347-0595 | broney@dot.ga.gov |
Georgia Department of Transportation | 2022 | $25,000.00 | Katherine D’Ambrosio Shearin | Brennan Roney | 404-347-0595 | broney@dot.ga.gov |
Georgia Department of Transportation | 2023 | $25,000.00 | Katherine D’Ambrosio Shearin | Brennan Roney | 404-347-0595 | broney@dot.ga.gov |
Georgia Department of Transportation | 2024 | $25,000.00 | Katherine D’Ambrosio Shearin | Brennan Roney | 404-347-0595 | broney@dot.ga.gov |
Iowa Department of Transportation | 2021 | $99,000.00 | Chris Poole | -- -- | -- | Transfer.Research@iowadot.us |
Pennsylvania Department of Transportation | 2022 | $33,000.00 | Steve Gault | Evan Zeiders | 717-787-8460 | evzeiders@pa.gov |
Pennsylvania Department of Transportation | 2023 | $33,000.00 | Steve Gault | Evan Zeiders | 717-787-8460 | evzeiders@pa.gov |
Pennsylvania Department of Transportation | 2024 | $33,000.00 | Steve Gault | Evan Zeiders | 717-787-8460 | evzeiders@pa.gov |
Texas Department of Transportation | 2021 | $33,000.00 | Rodney Jones | Ned Mattila | 512-416-4727 | ned.mattila@txdot.gov |
Texas Department of Transportation | 2022 | $33,000.00 | Rodney Jones | Ned Mattila | 512-416-4727 | ned.mattila@txdot.gov |
Texas Department of Transportation | 2023 | $33,000.00 | Rodney Jones | Ned Mattila | 512-416-4727 | ned.mattila@txdot.gov |
Utah Department of Transportation | 2021 | $99,000.00 | Mark Taylor | David Stevens | 801-589-8340 | davidstevens@utah.gov |
Current traffic signal controller technology and associated methods of signal actuation make use of a “binary” (on/off) states to measure vehicle presence at fixed locations (detection zones) near an intersection. This provides a relatively limited view of demands at an intersection. For example, an active presence state on any detector typically indicates that one or more vehicles present. The controller does not know if there are one or many vehicles present in that zone. In recent years, advances in sensor technology have made it feasible to begin obtaining enhanced information about vehicle demands at intersections. Rather than only capturing vehicle presence, the distances and speeds of vehicles nearby intersections can be obtained. This capability has existed within some detection technologies (such as radar) for some time. However, signal controllers cannot directly make use of the data; because they only accept a binary input, the enhanced information bust be reduced to a simple presence impulse. Therefore, the full value of this existing technology is not being realized. Another feature recently introduced into many traffic signal controllers is a capability to transmit information to other controllers through peer-to-peer communication. This capability would permit additional enhancement of signal controllers by permitting them to communicate information to each other directly. The potential value of vehicle trajectory data can be seen in research studies, mostly in simulation, that have employed vectors of estimated of vehicle arrival times to yield methods of enhanced signal control. Tremendous opportunities for improvement in the efficiency and safety of signal control is promised with such data, providing considerable motivation for the industry to move forward. However, such concepts have not made their way into standard methods of actuated signal control currently employed at the vast majority of intersections. It is possible that substantial improvements in the performance of signalized intersections could be induced by the introduction of vehicle speed and position data from sensors, rather than using only point detection. A white paper, “Leveraging Sensor-Based Vehicle Trajectory Construction in Existing Traffic Signal Infrastructure”, in the proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, provides an example of such applications in their early stages. By introducing new control methods to leverage such mechanisms, it would be possible to add new capabilities to traffic signal controllers largely within existing infrastructure. Example applications include dilemma zone protection using real vehicle speeds, the selection of control decisions using vehicle arrival times based on more accurate estimates, and the extension of red clearance intervals based on real-time vehicle speed information. By utilizing existing infrastructure, these enhancements could be implemented in a scalable fashion across entire systems. “Vehicle Trajectory-Enhanced Advanced Signal Control”, the concept this research seeks to develop, will introduce these concepts into existing signal control infrastructure, leading to modernized signal control based on next-generation data concepts, as the successor to contact-closure methods of vehicle actuation currently in use.
The objective of this research is to develop field-tested methods of integrating vehicle trajectory data into actuated signal control that can be directly implemented in traffic signal controllers. This research will identify the practical requirements and limitations of establishing trajectory-assisted actuated signal control. The findings will be developed into a resource toolkit that will permit implementation and further development of the methods conceived during the course of the research.
Phase I This phase includes steps necessary in preparation for real-world testing of trajectory-based control concepts, including preliminary field tests. Task 1. Literature Synthesis. Review the existing literature on traffic signal control applications including both trajectory-based methods and relevant control method that rely on equivalent information predicted from conventional detection. Identify specific methods of control enhancement that can be implemented based on vehicle trajectory data. Develop a taxonomy of control options to frame potential specific applications of trajectory data, and connect these to specific operational objectives. Task 2. System Component Review. Compile specifications of commercially available sensors capable of identifying vehicle positions and speeds in real time, and traffic signal controllers with user-programmable logic or other mechanisms for introducing code necessary to implement control algorithms. Task 3. Sensor Evaluation. Using a location where a trajectory-capable sensor has been previously deployed in the field, obtain vehicle position and speed data from the sensor and compare this data against the groundtruth measurements. Quantify the accuracy and tolerances of measurements through such sensors. Task 4. Establish Algorithm Environment. Determine the appropriate platform for implementing trajectory-enabled signal control algorithms and test to ensure that this platform will perform in the field. Task 5. Algorithm Selection and Bench Testing. Select algorithms to be included in field tests. The main focus should be a method to integrate trajectory data into phase actuation decisions (call and extensions decisions). Implement these using field equipment in a laboratory setting. Task 6. Test Location Selection and Procedure. Identify field locations for testing the operational impacts of selected algorithms. A minimum of two field locations with different types of controllers should be selected. Task 7. Preliminary Field Test. After confirming functionality with a bench test, conduct a preliminary field test of the trajectory-based control algorithm. The purpose of this test will be to determine that the sensor and control environment is capable of implementing the selected control. Phase II tasks will focus on evaluation of the control. Task 8. Phase I Report and Panel Meeting. Document findings of Tasks 1-7 in an interim report and submit for panel review. Convene a panel meeting to discuss these results and revise objectives for Phase II. Phase II This phase will execute comprehensive field tests of trajectory-based signal control. Task 9. Revised Test Scope. Based on panel input from the Phase I panel meeting, assess the scope for remaining tests to be conducted during Phase II. This will focus on what trajectory-based enhancements are most feasible for implementation. Task 10. Single-Intersection Testing. Evaluate the performance of the selected enhancements in detail at a single intersection. The focus of this evaluation will be on examining local-intersection details such as equitable service of green times. This should include a sensitivity analysis of the impacts of key control parameters on the performance of the enhanced control. Task 11. Multiple Corridor Testing. Evaluate the performance of the selected enhancements along at least two different signal corridors. The focus of this evaluation will be on the ability of such enhancements to facilitate smooth traffic flow across groups of intersections. This should include a sensitivity analysis of the impacts of key control parameters on the performance of the enhanced control. Task 12. Implementation Analysis. Assess the ability of the control enhancements to be implemented at scale. Identify mechanisms by which implementation could be achieved and key steps in that process. Task 13. Outreach and Engagement. Disseminate research findings through participation
Desired annual commitment from each partner is $33,000 per year for 3 years (FY22-24) for a total of $99,000. Alternatively, if FY21 is still available for your program, a commitment of $25,000 per year for 4 years (FY21-24) is also a possibility. Commitment covers travel for an in-person meeting one or two times throughout the life of the project. Specific meetings and locations to be determined during the project. Will be pursuing a 100% SPR Part B waiver. Additional partners are always welcome. Please reach out to the Lead Agency Contact at any time.
Subjects: Highway Operations, Capacity, and Traffic Control
Title | File/Link | Type | Private |
---|---|---|---|
Quarterly Report: June 2024 | TPF-5(483)_2024_Q2.pdf | Progress Report | Public |
Quarterly Progress Report: March 2024 | TPF-5(483)_2024_Q1.pdf | Progress Report | Public |
Quarterly Report: December 2023 | TPF-5(483)_2023_Q4.pdf | Progress Report | Public |
Quarterly Report: September 2023 | TPF-5(483)_2023_Q3.pdf | Progress Report | Public |
Quarterly Report: June 2023 | TPF-5(483)_2023_Q2.pdf | Progress Report | Public |
Quarterly Report: March 2023 | TPF-5(483)_2023_Q1.pdf | Progress Report | Public |
Quarterly Report: December 2022 | TPF-5(483)_2022_Q4.pdf | Progress Report | Public |
Quarterly Report: September 2022 | TPF-5(483)_2022_Q3.pdf | Progress Report | Public |
Quarterly Report: June 2022 | TPF-5(483)_2022_Q2.pdf | Progress Report | Public |
Quarterly Report: March 2022 | TPF-5(483)_2022_Q1.pdf | Progress Report | Public |
Acceptance Letter | TPF-5(483) Integration of New Traffic Signal Actuation Concepts using Enhanced Detector Information | Memorandum | Public |
Title | File/Link | Type | Private |
---|---|---|---|
Approved Waiver Memo | Approval of SPR Waiver Pooled Fund Solicitation#1545.pdf | Memorandum | Public |