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Suggested Citation:"1. Summary." National Academies of Sciences, Engineering, and Medicine. 2023. Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance. Washington, DC: The National Academies Press. doi: 10.17226/27264.
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Suggested Citation:"1. Summary." National Academies of Sciences, Engineering, and Medicine. 2023. Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance. Washington, DC: The National Academies Press. doi: 10.17226/27264.
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Suggested Citation:"1. Summary." National Academies of Sciences, Engineering, and Medicine. 2023. Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance. Washington, DC: The National Academies Press. doi: 10.17226/27264.
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Suggested Citation:"1. Summary." National Academies of Sciences, Engineering, and Medicine. 2023. Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance. Washington, DC: The National Academies Press. doi: 10.17226/27264.
×
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Suggested Citation:"1. Summary." National Academies of Sciences, Engineering, and Medicine. 2023. Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance. Washington, DC: The National Academies Press. doi: 10.17226/27264.
×
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Suggested Citation:"1. Summary." National Academies of Sciences, Engineering, and Medicine. 2023. Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance. Washington, DC: The National Academies Press. doi: 10.17226/27264.
×
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Suggested Citation:"1. Summary." National Academies of Sciences, Engineering, and Medicine. 2023. Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance. Washington, DC: The National Academies Press. doi: 10.17226/27264.
×
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Suggested Citation:"1. Summary." National Academies of Sciences, Engineering, and Medicine. 2023. Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance. Washington, DC: The National Academies Press. doi: 10.17226/27264.
×
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Suggested Citation:"1. Summary." National Academies of Sciences, Engineering, and Medicine. 2023. Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance. Washington, DC: The National Academies Press. doi: 10.17226/27264.
×
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Suggested Citation:"1. Summary." National Academies of Sciences, Engineering, and Medicine. 2023. Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance. Washington, DC: The National Academies Press. doi: 10.17226/27264.
×
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Suggested Citation:"1. Summary." National Academies of Sciences, Engineering, and Medicine. 2023. Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance. Washington, DC: The National Academies Press. doi: 10.17226/27264.
×
Page 11
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Suggested Citation:"1. Summary." National Academies of Sciences, Engineering, and Medicine. 2023. Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance. Washington, DC: The National Academies Press. doi: 10.17226/27264.
×
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Suggested Citation:"1. Summary." National Academies of Sciences, Engineering, and Medicine. 2023. Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance. Washington, DC: The National Academies Press. doi: 10.17226/27264.
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Page 13

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

1. SUMMARY This chapter provides a summary of the research methods and findings and serves as an introduction to this report. This chapter is also intended to serve as a standalone Summary. 1.1 Background The determination of the level-of-service (LOS) at signalized intersections is an important activity for decision-making in the allocation of resources for managing public roads, estimating the impact of new developments, and designing signal timing plans. The Highway Capacity Manual (HCM) methodology for signalized intersections is the most widely used method of estimating the LOS. The core of this methodology is a delay equation that takes as its inputs the demand volumes for various movements at an intersection, the signal timing, and other parameters related to the configuration of the intersection. For right-turn movements, the current HCM methodology instructs users to obtain field measurements of the right-turn-on-red (RTOR) flow rate or else to assume that all of the right- turning vehicles execute the movement during the green interval. A consequence of this is that the estimated delay for the right-turn movement is likely overestimated. Other estimated quantities related to characteristics of the right-turn movement, such as the pedestrian delay, are also likely to be inaccurate. Furthermore, the scenario of dual right-turn lanes has not received much attention in previous studies. In addition, there is a wide variety of guidance on whether to allow RTOR on a given right-turn movement. It is clear that RTOR should be prohibited when there is inadequate sight distance. The impacts of other intersection characteristics such as geometry, phase configuration, and so forth are less clear. There is a need to develop models of RTOR volume to permit users of the HCM methodology to estimate the RTOR rather than rely on collection of field data, which often does not include RTOR as a separate quantity. This will also be useful to predict future scenarios in which RTOR cannot be directly observed. These models need to explicitly consider the scenario of dual right- turn lanes in addition to single exclusive right-turn lanes and shared through and right-turn lanes. In addition, there is a need to synthesize existing guidance on whether to allow RTOR. This study addresses these needs through the development of models for RTOR volume prediction and the development of improved guidance for whether to allow RTOR. In addition, models of RTOR capacity are explored. 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. These are documented briefly in this summary, with full details included in Chapter 6 of the report. Separate models were developed for single, dual, and shared right-turn lanes. For volume 1

estimation, four different models were developed for each lane configuration. The models address differing levels of user-supplied site-specific data input. 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. All of the models were validated by a comparison of results with field data. A subset of the 260 intersections was set aside for validation of the volume models. Similarly, a subset of intersections observed to be operating at RTOR capacity was used to validate the capacity models. Model 1B uses a statistical model form that accounts for the distribution of RTOR volumes and uses variables that are more likely to be available from field count data. Therefore, it is the most promising of the model forms for implementation. Additional models are presented that use simpler mathematical forms or fewer variables. The main products of this research are the models, which are documented in the report and in this summary. In addition, a practitioner guide was developed that contains documentation of the models along with a spreadsheet tool to provide sample calculations. Additionally, the RTOR volume calculations have been integrated into the HCM Computational Engine. The practitioner guide also includes a synthesis of guidance on whether to permit RTOR at a given location. In addition, during the course of this research a survey was distributed to develop an understanding of current practice with regard to RTOR. The survey results are presented in this report. 1.2 Findings Figure 1 presents a layout of an intersection showing the various movements that are relevant to the analysis of RTOR movements. When the signal intervals serving the various movements are considered, this provides a basic framework that is used throughout the analysis. The primary objective of this study was to develop an expression for the RTOR volume as a function of the conflicting vehicular volumes V1, V2, V3, and V4; conflicting pedestrian volumes P1 and P2; and the shadowed left turn. In addition to volume, models of capacity were also explored. 2

Relevant Movements • Subject RTOR Movement • V1 – Conflicting Through • V2 – Opposing Left V2 • V3 – Cross Street U-Turn • V4 – Through Vehicles in a Shared Lane (not shown) V3 • LT – Shadowed Left Turn V1 • P1 – Crossing Pedestrian RTOR LT • P2 – Parallel Pedestrian P1 P2 Figure 1. Layout of a typical intersection showing movements relevant to RTOR operation. 1.2.1 Literature Review, Synthesis of Previous Work, and Practitioner Survey A literature review was carried out to examine the results of prior research on the modeling of RTOR. 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. Various models from the literature were combined into one spreadsheet model, and calculations were carried out for a few different scenarios in which the total right-turn volume and different conflicting volumes were varied. Figure 2 shows an example of one such comparison, which examines 11 different models of RTOR volume and capacity for two scenarios. Because different models varied in terms of either volume or capacity, the overall volume-to-capacity (v/c) ratio is presented as a way to compare the results. In this case, the results are shown for an exclusive right-turn lane, but similar analyses were done for shared and dual right-turn lanes. The influence of other volumes besides V1 were also considered. 3

HCM 2016 HCM 2010 WisDOT Abu-lebdeh Synchro Luh Stewart Virkler SSA Virkler ASSA Canadian Liu 1 0.9 Volume-to-Capacity Ratio 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 300 600 900 1200 1500 1800 Conflicting Through Movement Volume (veh/h/ln) (a) High right-turn volume scenario. 0.4 Volume-to-Capacity Ratio 0.3 0.2 0.1 0 0 300 600 900 1200 1500 1800 Conflicting Through Movement Volume (veh/h/ln) (b) Low right-turn volume scenario. Figure 2. Influence of conflicting through movement volume V1 on right-turn v/c ratio for an exclusive right-turn lane. The research team also conducted an online survey of practitioners to assess current practices with regard to RTOR modeling. There were 46 survey respondents altogether. 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. Other interesting information about the use cases for LOS modeling and frequency of retiming were also obtained in the survey. 4

We have formal procedures, and deviations need to be justified We have written guidance (or guidance from another agency), but analysts can deviate from it based on their judgment Nothing is written down, but we have some generally-accepted practices that have developed over time We talk it over and make decisions on a case-by-case basis I'm the only person who works on this, so it's up to me to make a decision Other 0 1 2 3 4 5 6 7 8 9 10 Number of Responses Figure 3. Respondent policies regarding RTOR estimation procedures. 1.2.2 Data Collection For this research, data from 260 intersections across 25 states and the District of Columbia were gathered. A map showing the geographic distribution of the locations is shown in Figure 4. This dataset included 165 intersections with a single exclusive right-turn lane, 27 intersections with dual right-turn lanes, and 68 intersections with shared through and right-turn lanes. The total number of data points for all sites was over 8,000 5-minute observations, incorporating almost 700 hours of video. Originally, it was intended to collect all-new video data for this research. However, the COVID- 19 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. Archived video was obtained from 219 intersections, while new video was collected at 41 intersections. 5

6 (MD) 3 (DC) 1 Figure 4. Distribution of data collection sites for this study. Figure 5 shows an example view of the raw data. In this example, the chart shows the variation in the RTOR flow rate with respect to the conflicting left-turn flow rate. In general, as would be expected, the RTOR flow rate decreases as the amount of conflicting flow increases. However, there are several data points where both flow rates are relatively high due to variations in numerous other factors in play at each intersection. Similar observations can be made of other conflicting volumes as well. A statistical modeling approach was used to develop models to take these factors into consideration to yield methods of estimating the RTOR flow rate. 6

800 700 Total RTOR Flow Rate (veh/h/ln) 600 500 400 300 200 100 0 0 100 200 300 400 500 600 700 Conflicting Opposing Left Turn Flow Rate (veh/h/ln) Figure 5. Total RTOR flow rate versus conflicting opposing left-turn flow rate. 1.2.3 Models of RTOR Volume and Capacity The dataset assembled during this research permitted the development of several models for estimating RTOR volume and capacity. The models were developed for three different lane configurations (single exclusive, dual, and shared). Several models were developed considering the types of data that are likely to be available to analysts. A subset of the overall dataset was excluded from model estimation to validate the statistical models of RTOR volume, while a different data subset of locations that were closest to capacity was used to validate the capacity models. The models are briefly presented below in their final equation forms. 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. Four different models were estimated for each lane configuration: • Model 1A considered all of the available dependent variables. It included a more complex model form intended to capture the large number of zeros in the RTOR volume dataset. 7

• For the exclusive and shared lane scenarios, Model 1B was estimated using a subset of dependent variables that were more likely to be available to practitioners. This approach did not yield a useful model for the dual right-turn lane scenario. • Model 2 used a subset of dependent variables that were more likely to be available to practitioners, as well as a slightly simpler equation form. This yielded usable models for all three lane configurations. • Model 3 used only one dependent variable to fill the need for a simple model for a “back of the envelope” estimation; this approach also yielded models for all three lane configurations. The intent of Model 3 is to offer a model similar in simplicity to the use of a single fixed value yet permit a better estimation of the RTOR proportion. Similar models were found in the literature that estimated the number of RTOR vehicles as a fixed proportion of the total right-turn flow. During the development process, several different model forms were explored for each of the three models, and the performance of each model was validated using a separate data subset. 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. The final forms of the equations are presented in Table 1, while the terms used in these equations are explained in Table 2. 8

Table 1. Models of RTOR volume developed during this research. Model RTOR volume expression qRTOR =  −0.167 + 5.020 ( r C ) + 0.01037 qR  × exp  2.923 + 1.389 ( r C ) − (1.290 × 10−4 ) q1, r + ( 2.489 × 10−3 ) qL , r + ( 3.360 × 10−3 ) qR Single − ( 2.517 × 10−3 ) q ped , r − 0.06377δPLCW − 0.1024δ1RCL + 0.1291δSLT  Model 1A qRTOR =  −0.167 + 5.020 ( r C ) + 0.01037 qR  × exp  2.793 + 1.486 ( r C ) − ( 2.069 × 10−4 ) q1 − ( 3.069 × 10−4 ) q2 + ( 6.990 × 10−4 ) qSL Single + ( 3.558 × 10−3 ) qR − ( 2.233 × 10−3 ) q ped − 0.05420δ1RCL  Model 1B = exp  2.497 + 1.743 ( r g ) − ( 2.025 × 10−4 ) q1 − ( 4.152 × 10−4 ) q2 + ( 9.084 × 10−4 ) qSL qRTOR Single + ( 3.869 × 10−3 ) qR − ( 2.302 × 10−3 ) q ped  Model 2 exp  −2.321 + 3.470 ( r g )  Single qRTOR = Model 3 1 + exp  −2.321 + 3.470 ( r g )  qRTOR =  −0.458 + 2.734 ( r C ) + 0.01406qR  × exp  2.670 + 1.438 ( r C ) − ( 2.870 × 10−4 ) q1, r − ( 9.837 × 10−4 ) q2, r − ( 2.733 × 10−3 ) q3, r Shared − (1.939 × 10−3 ) q ped , r + ( 3.692 × 10−3 ) qR − 0.1871δCBL − 0.2827δ1RCL  Model 1A qRTOR =  −0.459 + 2.728 ( r C ) + 0.01223qR  × exp  2.678 + 1.262 ( r C ) − (1.941× 10−4 ) q1 − ( 9.304 × 10−4 ) q2 + (1.523 × 10−4 ) qSL Shared + ( 3.607 × 10−3 ) qR − ( 2.088 × 10−3 ) q ped − 0.04132δ1RCL  Model 1B Shared = exp  2.013 + 1.725 ( r C ) − (1.180 × 10−3 ) q2 + ( 4.441× 10−3 ) qR − (1.200 × 10−3 ) q ped  qRTOR Model 2 exp  −2.462 + 2.844 ( r C )  Shared qRTOR = Model 3 1 + exp  −2.462 + 2.844 ( r C )  qRTOR = 0.245 + 5.160 ( r C ) + 0.02175qR  × exp  2.390 − 0.2293δ2 L + 0.1343δI + 1.334 ( r C ) − ( 2.461× 10−4 ) q2, r Dual* − ( 2.428 × 10−3 ) q plped , r − ( 2.224× 10−3 ) q ped , r + ( 5.260 × 10−3 ) qR − 0.04242δPLCW  Model 1A 0.245 + 5.160 ( r C ) + 0.02168qR  qRTOR = × exp  2.351 − 0.2079δ2 L + 0.1410δI + 1.467 ( r C ) − ( 2.235 × 10−4 ) q1 − ( 3.373 × 10−4 ) q2 Dual* + ( 3.348 × 10−5 ) qLT + ( 5.281× 10−3 ) qR − ( 2.670 × 10−3 ) q ped  Model 1B = exp 1.530 + 0.4177δI + 2.470 ( r C ) − ( 2.539 × 10−3 ) q2 + ( 3.582 × 10−3 ) qR qRTOR Dual − (1.736 × 10−3 ) q ped  Model 2 exp  −2.293 + 0.4159δI + 2.851( r C )  Dual qRTOR = Model 3 1 + exp  −2.293 + 0.4159δI + 2.851( r C )  * These models developed using data from both dual lanes and single exclusive lanes. 9

Table 2. Explanation of terms used in Table 1. Term Explanation qRTOR Right-turn-on-red flow rate (veh/h/ln) qR Total right-turn flow rate (veh/h/ln) r C red-to-cycle ratio q1 Total conflicting thru flow rate (veh/h/ln) q1,r Conflicting thru flow rate during red (veh/h/ln) q2 Total opposing left-turn flow rate (veh/h/ln) q2,r Opposing left-turn flow rate during red (veh/h/ln) q3,r U-turn flow rate during red (veh/h/ln) qSL Total shadowed left-turn flow rate (veh/h/ln) qSL , r Shadowed left-turn flow rate during red (veh/h/ln) q ped Total conflicting pedestrian flow rate (ped/h) q ped , r Conflicting pedestrian flow rate during red (ped/h) q plped , r Parallel pedestrian flow rate during red (ped/h) δ2L Presence of two or more right-turn lanes (Indicator variable) δPLCW Presence of parallel pedestrian crosswalk (Indicator variable) δ1RCL One receiving lane (Indicator variable) δSLT Shadowed left turn is present (Indicator variable) δCBL Presence of conflicting bicycle lane (Indicator variable) δI Subject approach is an interchange ramp (Indicator variable) 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). Software tools that are derived from this methodology include a field for right- turn volume where the model outputs can be entered. Prior to the development of the present models, the analyst would typically have to measure these volumes in the field. This is often not done as part of a turning movement count activity, or in some cases field data may not exist (for example, if analyzing a proposed intersection or future year volume set). The models estimated in this research will enable analysts to enter a reasonable estimate for the RTOR volume for the given lane configuration. 1.2.3.2 RTOR Capacity Models In addition to models of RTOR volume, this research also examined the estimation of RTOR capacity. To begin, the overall phase sequence was analyzed to determine the intervals where there are opportunities for RTOR to take place, including those where this turning movement is limited by conflicting traffic flow. In brief, for intervals with conflicting flow, two subintervals are defined: the earlier subinterval, when the conflicting flow is releasing a queue at the 10

saturation flow rate, where there are no gaps; and the later subinterval, after the end of saturation flow, where the number of gaps is a function of the overall flow rate of the conflicting movement. The resulting methodology is flexible with respect to the local phase sequence at an intersection since the analyst would select the appropriate equation for each interval individually. For each lane configuration (single, shared, and dual), two models were developed. 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. Table 3. RTOR capacity Model 1 by scenario and interval. Scenario, Interval Equation g SHLT 3600 Single, c1 = Interval 1 C tf  Vt  exp  − c c  c{2,3} = Vc  3600  g − g s  Vc t f  C Single, 1 − exp  −  Intervals 2 and 3  3600  Dual, Interval 1 Same as (Single, Interval 1) but applied to each lane separately c{2,3 = } c left curb A + c A , where  cleft  S  q2 =⋅ ( exp − qtc2 3600 ) q ⋅q + 1 2 exp  − (  q (tc2 + t f2 )  1 − exp − qt f1 3600 ) ⋅  ( 1 − exp − qt f2 3600 q )  ( 3600  1 − exp − qt 3600 ( f2 )) 2 ( q 2 exp − qtc1 3600  + 1 ⋅  ) ( q 1 − exp − qt f 3600  1  )  ( exp − qtc1 3600 ) q ⋅q ( 1 − exp − qt f2 3600 ) ccurb = S  q1 ⋅ ( + 1 2 exp − q (tc1 + t f1 ) 3600 ⋅ )  ( 1 − exp − qt f1 3600 q ) ( ( 1 − exp − qt f1 3600 )) 2 ( q 2 exp − qtc2 3600  + 2⋅  ) Dual, Intervals 2 and 3 ( q 1 − exp − qt f 3600  2  ) g SHLT 3600 1 (1 − p ) 3600 Shared, = c1 × Interval 1 C tf Vs p C  Vt  exp  − c c  = c{2,3} Vc  3600  g c − g q × 1 (1 − p ) 3600  Vc t f  C Vs p C Shared, 1 − exp  −  Intervals 2 and 3  3600  11

Table 4. RTOR capacity Model 2 by scenario and interval. Scenario, Interval Equation Single, Interval 1 Same as (Single, Intervals 2 and 3) but gs = 0 g − gs 3600  t −1  ci c′ = c′ , where= exp  − c ⋅ VC  and C tf  500  qC (1 − P ) 3600 Single, gs = Intervals 2 and 3 s 3600 − ( q 3600 )( CP g ) Dual, Interval 1 Same as (Single, Intervals 2 and 3) but gs = 0 and applied to each lane separately Dual, Intervals 2 and 3 Same as (Single, Intervals 2 and 3) but applied to each lane separately Shared, Interval 1 Same as (Shared Intervals 2 and 3), gs = 0 g − gs 3600  4 p + 0.3tc − 1  =ci c′ , where = c′ 0.01exp [ 4.3 p ] exp  − ⋅ VC  and C tf  1000  qC (1 − P ) 3600 Shared, gs = Intervals 2 and 3 s 3600 − ( q 3600 )( CP g ) Table 5. Explanation of terms used in Table 3 and Table 4. Term Explanation ci Capacity of interval i cleft and ccurb Capacity of left lane and curb lane in dual right-turn lane group respectively q1 Conflicting volume in rightmost lane (veh/h) q2 Conflicting volume in left lane (veh/h) q Total conflicting volume q1 + q2 C Cycle length (s) g Effective green time on conflicting approach (s) g SHLT Effective green for shadowed left-turn phase (s) gs queue service time on conflicting approach (s) Vc Total conflicting volume (veh/h) Vs Total shared lane volume (veh/h) tc critical gap (s) tf follow-up time (s) tc1 RTOR critical gap, gap closed by vehicles in the rightmost lane (s) 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) t f2 RTOR follow-up time, gap closed by vehicles in the left lane (s) s Saturation flow rate on the conflicting approach (veh/h) sRTOR Saturation flow rate for the RTOR movement (veh/h) S Cycle split for portion of cycle where RTOR occurs under gaps (intervals 1 and 2) p Ratio of through vehicles to total volume in shared lane P Proportion of vehicles arriving on green on the conflicting approach 12

These formulas are able to provide the capacity associated with a particular interval where RTOR may potentially take place. The next step, integration of the capacity expressions with a delay estimate, is a topic for future study. While one possible application would be to use the sum of capacity estimates in the existing methodology, the present model assumes that all of the capacity occurs during green. In reality, however, the capacity provided by RTOR also occurs in other intervals. Thus, it stands to reason that the delay expression may need to be adjusted before these expressions can be applied. 1.2.4 Development of Practitioner Guide A practitioner guide was developed as part of this research and is presented as a separate document. This guide includes a summary description of the models developed during the study and is accompanied by a spreadsheet tool that enables users to calculate the model results for their own scenarios. To support site selection guidance, an additional review was undertaken to identify recommendations on whether to allow or prohibit RTOR at a given location. This effort included about 30 additional sources, including several state manuals on traffic control devices or similar documents. These resources were synthesized into a listing of considerations for RTOR site selection. 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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Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance Get This Book
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 Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance
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The determination of the level-of-service (LOS) at signalized intersections is an important activity for decision-making in the allocation of resources for managing public roads, estimating the impact of new developments, and designing signal timing plans. There is a need to develop models of right-turn-on-red (RTOR) volume to permit users of the Highway Capacity Manual methodology to estimate the RTOR rather than rely on collection of field data, which often does not include RTOR as a separate quantity.

NCHRP Web-Only Document 368: Right-Turn-on-Red Operation at Signalized Intersections with Single and Dual Right-Turn Lanes: Evaluating Performance, from TRB's National Cooperative Highway Research Program, addresses these needs through the development of models for RTOR volume prediction and the development of improved guidance for whether to allow RTOR.

The document is supplemental to NCHRP Research Report 1068: Right-Turn-on-Red Site Considerations and Capacity Analysis: Practitioner's Guide.

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