Skip to main content

Currently Skimming:

2. Literature Review
Pages 14-35

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 14...
... Therefore, it is essential to develop proper RTOR volume and capacity estimation models for correct estimation of LOS at signalized intersections. 2.2 History of Right-Turn-on-Red RTOR has been common in the United States for many years.
From page 15...
... This does not appear in the sixth edition of the HCM. Even where RTOR volumes are available from field data, practitioners performing tasks such as signal retiming or intersection redesign face challenges in applying the HCM recommendations: • The observed RTOR volume is often less than the RTOR capacity, but standard traffic counting techniques do not determine how frequently the signal fully serves the RTOR demand or quantify the number of seconds when the RTOR movement is in starvation.
From page 16...
... proposed a method to calculate RTOR capacity using the HCM. Presence of an exclusive right-turn lane, proportion of right turns using a shared lane, duration of red interval, volume of conflicting traffic, and presence of pedestrians were the major factors affecting RTOR capacity.
From page 17...
... This study used computer simulation as the analytical approach for estimating RTOR capacity. Critical parameters influencing RTOR operations and performance included the RTOR arrival distribution, signal change interval, length of right-turn lane, discharging headway of conflicting traffic, turning headway, discharging time, and gap acceptance for RTOR vehicles.
From page 18...
... Because only the unsaturated green time of the conflicting traffic is usable by RTOR vehicles, the authors proposed the following adjusted capacity model: 3600  Vct0  3600  VcU 0  cA = exp  − − exp  −  Equation 5 tf  3600  tf  3600  where: c A = adjusted RTOR capacity (pcph of unsaturated red) Vc = conflicting traffic volume (veh/h)
From page 19...
... proposed analytical models for predicting RTOR volume and capacity at signalized intersections. The study considered total right-turn volume during red phase, blockage by same approach vehicles, and blockage by conflicting traffic, and the model used the following equation for the average number of unblocked right-turning vehicles during a particular red interval:  p⋅k  =b min  , p ⋅t Equation 7 1 − p  where: k = maximum number of vehicles in the right-turn bay t = total number of vehicles arriving during red = an n + ar p = right-turn proportion in the rightmost lane = ar t ar = expected right turners arriving during red = vr ⋅ ( C − R p ⋅ f r )
From page 20...
... = b ⋅ m m = expected number of cycles per hour c = total RTOR capacity = C1 + C2 The study evaluated the models using the CORSIM simulation program. The evaluation analysis produced unbiased results when there was no impedance from the conflicting traffic but showed limited bias when the flows on the cross street were critical for the RTOR volumes.
From page 21...
...   Equation 11  p  C Here, N RTOR is the expected RTOR volume in veh/h, X r is the demand volume-to-capacity ratio for the shared lane of the subject approach, p is the ratio of through vehicles to the total volume in the shared lane (veh/h) , and C is the average cycle length during the analysis period in seconds.
From page 22...
... The capacity associated with shadowed left turns is a function of signal interval of the shadowed left turn and the follow-up time: g SHLT 3600 c3 = Equation 15 C tf where g SHLT is the effective green duration for the shadowed protected left-turn phase and t f is the follow-up time, both expressed in seconds. The researchers used CORSIM to validate the volume estimation model and found that the estimated RTOR volumes were within the confidence intervals of the simulated RTOR volumes 75% of the time.
From page 23...
... The capacity of a curb lane during regime A is as follows:   qtc1   qt f2   exp  −  1 − exp  −  c Acurb  =λ ⋅  q1 ⋅  3600  + q1 ⋅ q2 exp  − q (tc1 + t f1 )  ⋅  3600    qt q 3600   2  1 − exp  − f1    qt f1     1 − exp  −     3600    3600   Equation 17  qtc2   exp  −   q 2  3600   + ⋅ 2 q  qt f2   1 − exp  −   3600   During regime B, there is no conflicting traffic, and a simpler capacity estimation model was proposed: 23
From page 24...
... The above equations assume exclusive right-turn lanes. When the left lane of the subject approach is a shared through and right-turn lane, the authors proposed the following adjusted capacity expression:  3600 ⋅ ω left left  =cshared min  , c A + cB  Equation 19  C  Here, ω is the average number of unblocked RTOR vehicles per cycle, C is the cycle length in seconds, and cBleft is the RTOR capacity for left-side shared lane during regime B in veh/h, from Equation 17.
From page 25...
... The unsaturated portion of opposing movement green, gu, is as follows: sc g c − qc c gu = Equation 23 sc − qc For exclusive lanes, the opposed turn saturation flow rate is 1800 sexcl = Equation 24 e0 The opposed turn equivalent is given by  0.5 g s g + n for cars  u u f e0 =  Equation 25  0.5 g + 1 for heavy vehicles  su gu + n f In the above equations, the terms are as follows: sshared = saturation flow rate of shared lane (veh/h) sexcl = saturation flow rate of exclusive lane (veh/h)
From page 26...
... V ∑ exp   Equation 26  3600  where: s = RTOR saturation flow rate V = flow rate of conflicting traffic h = individual time headway of conflicting traffic tc = RTOR critical gap tf = RTOR follow-up time t = selected time interval = tc , tc + t f , tc + 2t f , etc. The second approach was a macroscopic model that used the following linear equation to estimate RTOR saturation flow rate: ln( s + γ)
From page 27...
... Equation 30 where RTORexp is the expected RTOR volume (veh/h) and RTORCap is the RTOR capacity, given by    G     RTOR = Cap α max  1 −  × S − Vc  , 0   Equation 31    C     The value α is the ratio of the saturation headway of conflicting through vehicles and the saturation headway of RTOR vehicles.
From page 28...
... led to the development of empirical RTOR volume estimation models for intersections and interchanges with different lane configurations (Hawley and Bruggeman 2009)
From page 29...
... Also, the RTOR capacity may be lower when there are large pedestrian volumes crossing the subject approach. 2.7 Right-Turn-on-Red Studies Using Queuing Theory Diegel (1994)
From page 30...
... Synchro models RTOR by calculating an RTOR saturation flow rate using the signal timing, volumes of the subject approach, and volumes of the conflicting movements (Trafficware 2017)
From page 31...
... When the RTOR saturation flow rate sRTOR is known, Synchro calculates the RTOR volume as follows: r =vRTOR min( sRTOR , v) ⋅ Equation 41 C Here, v is the adjusted lane group volume in veh/h and r is the effective red duration in seconds.
From page 32...
... φ0 = proportion of free vehicles in opposing traffic λ = a parameter, given by  φ0 q0 0.98  1− ∆ q when q0 ≤  0 0 ∆0 λ=  Equation 43  0.98φ0 when q0 > 0.98  ∆ 0 (1 − ∆ 0 q0 ) ∆0 Before using the above formula, the maximum filter turn saturation flow rate, sumax, is compared to the unopposed saturation flow rate, sLV , for light vehicles, where sb′ sLV = Equation 44 eLV Here, sb′ denotes the adjusted lane saturation flow rate and eLV denotes the turn equivalent for light vehicles, which is equal to 1.05 through car units per vehicle (TCU/veh)
From page 33...
... Reference Synopsis Outcomes Modeling Right-Turn Capacity RTOR Approach Lane Volume Configuration Mamlouk et al. Observational study of Recommendations on N/A N/A N/A N/A 1976 RTOR safety and factors whether RTOR should be influencing RTOR in permitted or prohibited by Indiana rule Akçelik 1981 Capacity analysis for Opposed turn saturation Gap Exclusive and  N/A opposed turns at flow rates for shared and acceptance shared signalized intersections exclusive lanes Chadda and Evaluation of pedestrian A set of countermeasures for N/A N/A N/A N/A Schonfeld 1985 safety with RTOR ensuring pedestrian safety when RTOR is allowed Luh and Lu Computation of RTOR A method to calculate Gap Exclusive and  N/A 1990 capacity using the HCM RTOR capacity by acceptance shared modifying the HCM-based capacity computation procedure for stop-sign controlled right turns Diegel 1994 Queuing theory-based Service time distribution and Queuing N/A N/A N/A approach to RTOR RTOR process classification theory operations using queuing theory Stewart and RTOR saturation flow Gap size requirements for Gap Exclusive  N/A Hodgson 1995 rate estimation using gap- RTOR maneuver and acceptance acceptance theory regression equation for estimating RTOR saturation flow rates Virkler and Evaluation of HCM SSA Changes in v/c ratios and N/A N/A N/A N/A Maddela 1995 and shadowing procedures LOS using left-turn for RTOR capacity shadowing and HCM SSA computation procedures Liu 1995 Simulation study on Models for estimating Gap Exclusive and  N/A RTOR characteristics and RTOR capacity using acceptance, shared capacity probability and regression- probabilistic based approaches and regression Abu-lebdeh et Estimation of RTOR Percent error in delay Regression Exclusive N/A  al.
From page 34...
... capacity resulting from of RTOR saturation flow allowing RTOR rate Akçelik and Calculation of opposed Saturation flow rate model Gap Exclusive  N/A Associates 2011 turn capacity using a for opposed turns acceptance (SIDRA) direct method To summarize the literature review, four different approaches to RTOR capacity modeling were found in the literature.


This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More information on Chapter Skim is available.