Data-Driven Methodology for Prioritizing Traffic Signal Retiming Operations

Dunn, Michael R.; Ross, Heidi W.; Baumanis, Carolina; Wall, Jared; Lammert, Jonathan; Duthie, Jen; Juri, Natalia Ruiz; Machemehl, Randy B.

doi:10.1177/0361198119843236

Cited by 9 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Performance measures are valuable if they can provide information that identifies challenges and improvement opportunities ( 4 – 7 ). Capacity performance measures can locate congestion problems ( 8 – 12 ) and progression performance measures give insight on how vehicles are arriving on a system ( 3 , 13 , 14 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Arrivals on Green Estimations from Vehicle Detection and Connected Vehicle Data

Saldivar-Carranza

Gayen

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Many agencies use Automated Traffic Signal Performance Measures (ATSPMs), which require vehicle detection and communication equipment, to evaluate traffic signal efficiency. ATSPMs usually rely on projections based on spatially limited vehicle trajectory samples to estimate performance. Recently, connected vehicle (CV) data has become available that provides entire vehicle trajectories that can be used to generate accurate signal performance measures without the need for projections or infrastructure upgrades. This paper analyzes over 50 intersections in Utah to evaluate how traditional detector-based arrivals on green (AOG) calculations compare with their CV-based calculations. The effects that saturation and queue- lengths have on estimations are analyzed. In general, there is close correlation between computations when queues are short and undersaturated conditions exist. However, if queues extend past the advance detector, detector-based calculations tend to overestimate AOG since vehicles are detected during green but may stop before. This impact is particularly large when the approach is oversaturated, which led to overestimations of around 40% in this study. In addition, detector-based estimations can underestimate AOG in undersaturated scenarios with short queues if vehicles reach the advance detector on red but reduce their speed afterwards, allowing them to not stop. In some cases, the detector-based technique underestimated AOG by over 50%. The findings can help practitioners understand how detector-based estimations vary by traffic conditions. This is particularly important as the industry moves toward a hybrid blend of detector- and CV-based signal performance measures. It is recommended that CV trajectories be used to measure AOG during periods with long queues, oversaturated conditions, or both.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Arrivals on Green Estimations from Vehicle Detection and Connected Vehicle Data

Saldivar-Carranza

Gayen

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…Guadamuz et al ( 14 ) evaluated signal timing performance with high-resolution controller event data on a four-intersection corridor to identify how to reallocate unused green time to improve efficiency. Using probe data at sub-mile segmentations, Day et al ( 5 ) investigated travel time in relation to the speed limit over a network of corridors, and a study by Dunn et al ( 15 ) ranked signalized corridors for retiming purposes with 15-min average probe speed data.…”

Section: Literature Reviewmentioning

confidence: 99%

Systemwide Identification of Signal Retiming Opportunities with Connected Vehicle Data to Reduce Split Failures

Saldivar-Carranza

Platte

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Road networks are significantly affected by traffic signal operations, which contribute from 5% to 10% of all traffic delay in the U.S.A. It is important for agencies to be able to systematically monitor traffic signal performance and have procedures to identify locations as proactively as possible where the redistribution of green time has the greatest potential of reducing congestion. Currently, Automated Traffic Signal Performance Measures (ATSPMs) require extensive communication networks and detectors on all phases at each intersection to effectively monitor the traffic signal performance for every traffic movement. This study utilizes commercially available connected vehicle (CV) trajectory data to identify overcapacity movements where there is availability of unused green time and no downstream blockage by an adjacent signal. Over 100 intersections, 400,000 trajectories, and 6,800,000 Global Positioning System points are evaluated using this method to prioritize signal retiming opportunities in a proposed Relative Performance Diagram (RPD) by assessing split failures and downstream blockage of all traffic movements. Out of the intersections identified as having opportunities for operational improvements, three were selected for retiming. A post-retiming review of the traffic signal performance measures showed up to 30% reductions in split failures, 53 s/veh reductions in control delay, and 21% increase in arrivals on green at the benefited movements. The techniques easily scale to any intersection where CV data is available without the need for vehicle detection or communication technology.

show abstract

“…On the signal-system side, such data resulting in signal timings (e.g., cycle time, splits, and offsets) recorded each cycle, as occurred in the field. Some of the recent studies have utilized such datasets for various signal-related purposes ( 35 , 45 ). Since these data are collected at a higher resolution than the previous group, we define them as MR data.…”

Section: Signal Retiming Procedures—review Of Existing and Proposed P...mentioning

confidence: 99%

“…Although these data sets were not utilized in the traditional signal retiming process, they helped improve the arterial management process. Similarly, based on MR data, Dunn et al ( 35 ) proposed a methodology for the identification of corridors that warrant signal retiming. In addition, Radivojevic and Stevanovic ( 36 ) proposed a methodology to quantitatively evaluate the performance of signal operating agencies, including their retiming efforts.…”

Section: Literature Reviewmentioning

confidence: 99%

Modifying Signal Retiming Procedures and Policies by Utilizing High-Fidelity Modeling with Medium-Resolution Traffic Data

Dobrota

Stevanovic

Mitrović³

2021

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Current signal retiming policies are deficient in recognizing the potential of emerging traffic datasets and simulation tools to improve signal timings. Consequently, current practice advocates the use of periodically collected (low-resolution) traffic datasets and deterministic (low-fidelity) simulation tools. When deployed in the field, such signal timings require excessive fine-tuning. The most recent trends promote the use of high-resolution data collected at 10 Hz frequency. While such an approach shows promise, the process heavily relies on specific data sets that are neither widely available nor clearly integrated into the existing signal retiming practices and procedures. Interestingly, data collected in an ongoing fashion and aggregated in several-minute bins (referred to here as medium-resolution) have not received much attention in the traditional retiming procedures. This study examines traditional signal retiming practices to provide a contextual framework for the other retiming alternatives. The authors define and classify different resolutions of various traffic data used in the signal retiming process and propose a signal retiming procedure based on widely available medium-resolution data and high-fidelity simulation modeling. The authors apply the traditional (low-resolution and low-fidelity) and a proposed (medium-resolution and high-fidelity) approach to a 28-intersection corridor in southeastern Florida. Signal timing plans developed from the proposed approach outperformed current plans from field and those plans developed in the traditional approach by reducing the average delay anywhere between 6.5 and 26%. With regard to the number of stops, changes for the traditional and proposed approaches were of much lower significance when compared with the field signal timings. The traveling speeds have been increased by 4.1%–18% by the proposed signal timings and delay was not transferred onto the neighboring streets, as was the case for plans developed by the traditional approach. Development, calibration, and validation of models within the proposed approach are more time-consuming and challenging than the modeling needs of the traditional approach. One direction of future research should address the automation of calibration and validation procedures. The other direction for future research should be related to the field evaluation of proposed signal timing plans.

show abstract

Data-Driven Methodology for Prioritizing Traffic Signal Retiming Operations

Cited by 9 publications

References 4 publications

Comparison of Arrivals on Green Estimations from Vehicle Detection and Connected Vehicle Data

Comparison of Arrivals on Green Estimations from Vehicle Detection and Connected Vehicle Data

Systemwide Identification of Signal Retiming Opportunities with Connected Vehicle Data to Reduce Split Failures

Modifying Signal Retiming Procedures and Policies by Utilizing High-Fidelity Modeling with Medium-Resolution Traffic Data

Contact Info

Product

Resources

About