Green time usage metrics on signalized intersections and arterials using high-resolution traffic data

Guadamuz, Renato; Tang, Houjun; Yu, Zhengyao; Guler, S. Ilgin; Gayah, Vikash V.

doi:10.1016/j.ijtst.2021.06.006

Cited by 9 publications

(6 citation statements)

References 21 publications

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“…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

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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.

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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

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“…Li et al ( 13 ) utilized detector-derived SF measurements in a transportation network to locate an intersection where a split (i.e., green time) rebalance improved operations. 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

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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.

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“…In summary, researchers have developed many TSPMs so far. Still, a very limited number of developed TSPMs are related to volume-based capacity-utilization measures (e.g., green time utilization metric) ( 33 ). Also, some occupancy-based capacity utilization measures (i.e., GOR ) do not truthfully depict signal capacity utilization.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Instead, it utilizes volume data as well as saturation headway to determine traffic state (e.g., undersaturated, oversaturated) of the signal ( 32 ). Lastly, in an effort to estimate the efficiency of utilized green time across various phases, Guadamuz et al, normalized volume counts over a minute of green time and labeled such a measure as a “green time usage metric” ( 33 ). However, similar to the other similar measures, the green time usage metric is not indicative enough to reveal properties of the volume that was served (e.g., volume is only from queue, free flow, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Development and Evaluation of Performance Measures for Capacity Utilization of Traffic Signals

Dobrota

Stevanovic

Mitrović³

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Emerging high-resolution datasets allow for developing traffic signal performance measures (TSPMs) which can provide a better understanding of traffic signal operations. One of the specific objectives of signal operations is related to the measurement of capacity utilization. A fundamental measure for capacity utilization is the “degree of saturation” ( DS), and it has both volume-based and occupancy-based formulations. The former is known as “volume over capacity ratio” ( V/C), whereas the latter is known as “green occupancy ratio” ( GOR). When V/C is used to observe capacity utilization, it is not known whether the capacity is utilized from interrupted flow, free flow, or a combination of two. Similarly, the amount of time in the cycle utilized by volume (characterized with the first and last vehicle arrival time) is not revealed within V/C. On the other hand, GOR“overlooks” cases when a high number of vehicles travel without stopping or fewer vehicles depart the signal with significant startup delays and similar. In addition, because of a lack of scientific attention, GOR has often been misinterpreted as some similar, yet different TSPM. Therefore, if only V/C or GOR are used to monitor signal performance, the understanding of utilized capacity will be incomplete or misleading. This study proposes three TSPMs: “queued volume in volume to capacity ratio” ( QViV/C), “cycle utilization” ( CLU), and “volume-occupancy capacity utilization” ( VOCU) to overcome the limitations of V/C and GOR. Also, by evaluating existing occupancy-based measures, it was found that they can be used to estimate capacity utilization, including oversaturated conditions.

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Green time usage metrics on signalized intersections and arterials using high-resolution traffic data

Cited by 9 publications

References 21 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

Development and Evaluation of Performance Measures for Capacity Utilization of Traffic Signals

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