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1. Summary
Pages 1-13

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From page 1...
... To support the development of new models, data collection was carried out that captured nearly 700 hours of video documenting a wide range of conditions at 260 intersections in 25 different states and in the District of Columbia. Several models of RTOR volume and capacity were developed in this study.
From page 2...
... Some models made use of more independent variables, while alternative models used fewer independent variables. For capacity estimation, two models were developed for each lane configuration, including a model based on proposed mathematical expressions from the literature and an alternative model using new functions based on gap-acceptance theory with curve fitting supported by microsimulation data to arrive at appropriate model forms.
From page 3...
... A total of 21 prior studies were identified in the literature that included proposed models of either RTOR volume or capacity or that provided significant discussion. The report summarizes the proposed models in Chapter 2, while Chapter 4 includes a comparison of the model outputs from 12 of these studies under similar input data.
From page 4...
... Twelve questions gathered information from respondents about how their work was involved with traffic signal operation and what sort of practices their agencies had for modeling RTOR. The results of the survey indicated that few agencies had well-established practices for estimating RTOR, as demonstrated by the results of one question shown in Figure 3.
From page 5...
... However, the COVID19 pandemic occurred right as this new data collection was intended to take place, and the reduction in volumes greatly reduced the opportunity for new data collection. Instead, a set of archived video data, along with a set of new video data collected at a limited scale, was used to provide the necessary data.
From page 6...
... A statistical modeling approach was used to develop models to take these factors into consideration to yield methods of estimating the RTOR flow rate.
From page 7...
... 1.2.3.1 RTOR Volume Models Statistical models of RTOR volume were developed using a systematic process that tested a wide range of independent variable combinations to identify models that provided the best goodness of fit while also requiring that the correlations of the independent variables produced sensible results. For example, the models were checked to ensure that factors that would be expected to increase the RTOR volume are positively correlated, and factors those that would be expected to decrease RTOR volume are negatively correlated.
From page 8...
... The final recommended equation forms take into consideration the model performance, determined by the root mean square error (RMSE) , along with the models' usability by practitioners and applicability in existing methodologies for estimating LOS.
From page 9...
...  * These models developed using data from both dual lanes and single exclusive lanes.
From page 10...
... The advantage of using models of RTOR volume is that the outputs can be used directly in existing models of LOS for signalized intersections. The HCM methodology allows users to reduce the total right-turn volume by the RTOR volume, which tends to reduce the estimated delay of the right-turn movement (without the correction, the delay for the movement is typically overestimated)
From page 11...
... Model 1 was based on expressions found in the literature review, while Model 2 was developed using a new set of equations combining a gap acceptance approach with forms of equations for certain elements developed by fitting curves onto microsimulation data of a right-turn movement at capacity. The resulting models are presented in Table 3 and Table 4, while the terms used in the formulas are explained in Table 5.
From page 12...
... tc2 RTOR critical gap, gap closed by vehicles in the left lane (s) t f1 RTOR follow-up time, gap closed by vehicles in the rightmost lane (s)
From page 13...
... 1.3 List of Project Deliverables This research yielded the following deliverables: • The project final report, including this executive summary • A practitioner guide with an accompanying spreadsheet tool • Integration of the RTOR volume models into the HCM Computational Engine • A presentation summarizing the report • Notes on implementation of research findings and products, contained in the final chapter of the report • Prioritized recommendations for future research, contained in the final chapter of the report • A draft article for TR News 13


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