Validation of Left-Turn Delay at Two-Way Stop-Controlled Intersections

2000 ◽  
Vol 1710 (1) ◽  
pp. 181-188 ◽  
Author(s):  
Sarah A. Simpson ◽  
Judson S. Matthias

Control delay for left-turning vehicles at unsignalized intersections was observed in the field and compared with average control delay calculated from the methodologies presented in the 1997 update of the Highway Capacity Manual (HCM). Unsignalized intersections with two-way left-turn lanes on the major street were observed in the peak and offpeak hours, and control delays were recorded for the one-stage and twostage left-turn processes. Next, the methodologies presented in the HCM were used to calculate the control delay for both processes and compared with the observed data. These comparisons were used as the basis for validation of the HCM methodologies regarding left-turn control delay at unsignalized intersections. From the comparisons, the calculated delay closely corresponds with the observed data, with a total approach volume at the intersection of approximately 2,500 vehicles per hour or less. Once the total approach volume increases above this level, the calculated values rapidly increase and the actual observed control delays gradually increase at a much lower rate. As a result, the observed and calculated delays are different when the intersection handles more than 2,500 approach vehicles in an hour. Statistical analyses were performed on the data to determine if the average observed control delay was related to the calculated control delay. Statistically, the observed control delay and the calculated control delay at the 95 percent confidence level show that the two data sets yield similar results for off-peak conditions. However, during the peak hour, when the total approach volumes are higher, the 95 percent confidence interval yields different results. Hence, the HCM procedures produce, on average, greater control delay estimates than the field observations when the total approach volumes are high.

Author(s):  
Zihang Wei ◽  
Yunlong Zhang ◽  
Xiaoyu Guo ◽  
Xin Zhang

Through movement capacity is an essential factor used to reflect intersection performance, especially for signalized intersections, where a large proportion of vehicle demand is making through movements. Generally, left-turn spillback is considered a key contributor to affect through movement capacity, and blockage to the left-turn bay is known to decrease left-turn capacity. Previous studies have focused primarily on estimating the through movement capacity under a lagging protected only left-turn (lagging POLT) signal setting, as a left-turn spillback is more likely to happen under such a condition. However, previous studies contained assumptions (e.g., omit spillback), or were dedicated to one specific signal setting. Therefore, in this study, through movement capacity models based on probabilistic modeling of spillback and blockage scenarios are established under four different signal settings (i.e., leading protected only left-turn [leading POLT], lagging left-turn, protected plus permitted left-turn, and permitted plus protected left-turn). Through microscopic simulations, the proposed models are validated, and compared with existing capacity models and the one in the Highway Capacity Manual (HCM). The results of the comparisons demonstrate that the proposed models achieved significant advantages over all the other models and obtained high accuracies in all signal settings. Each proposed model for a given signal setting maintains consistent accuracy across various left-turn bay lengths. The proposed models of this study have the potential to serve as useful tools, for practicing transportation engineers, when determining the appropriate length of a left-turn bay with the consideration of spillback and blockage, and the adequate cycle length with a given bay length.


Author(s):  
Abishai Polus ◽  
Sitvanit Shmueli

Roundabouts are replacing conventional unsignalized intersections in many parts of the world and could become more widespread in the United States, although there are some limitations as well as clear advantages. Models for entry capacity into the rotary were developed. Entry capacity depends on the geometric characteristics of the roundabout, particularly the diameter of the outside circle of the intersection. The geometric characteristics determine the speed of vehicles around the central island and, therefore, have an impact on the gap-acceptance process and consequently the capacity. Traffic conditions that impede entry capacity involve the flow around the roundabout. Flow and geometric data from six small to medium-sized roundabouts were analyzed. Individual and aggregated entry-capacity models were calibrated by using the diameter and circulating flows as explanatory variables. Very good fits to the data were obtained; the results also fit models developed in other countries. The Australian model resulted in slightly higher entry capacities for moderate to low circulating flows and lower entry capacities for high circulating flows. Very close proximity to the German model was obtained, although it does not depend on the geometric characteristics of the circle. The roundabout provides an advantage over a conventional unsignalized intersection. A faithful concurrence between the model developed and the latest Highway Capacity Manual model for right-turn capacity at an unsignalized intersection is obtained if the circulating flow is replaced by the conflicting flow. The advantage of entry capacities of the roundabout over the calculated capacities of the Highway Capacity Manual left-turn model is shown. Further research is proposed to study the effect on entry capacity of two circulating lanes rather than one and the effect of the increase in circulating flows on the gap-acceptance process, particularly the reduction in critical gap at high flows.


2003 ◽  
Vol 1852 (1) ◽  
pp. 246-255 ◽  
Author(s):  
Jin-Tae Kim ◽  
Kenneth G. Courage

A study is described that was conducted to develop an improved average green time estimation model for traffic-actuated control and to suggest a maximum green time design method that analytically minimizes intersection control delay. Improvements in the green time estimation model include revisions in the concept of additional queue service time, explicit treatment of right turns in lane groups containing both through and right-turning vehicles, and other improvements that include updates based on recent studies and modifications in the approaches taken for the modeling procedure. The proposed maximum green time design procedure consists of four components: ( a) estimation of the average green time of a traffic-actuated phase, ( b) performance evaluation of the system through the 2000 Highway Capacity Manual (HCM) procedure, ( c) formulation of an overall average control delay minimization problem, and ( d) a search process to find the most efficient set of maximum green time parameters that minimize the average control delay at an intersection. Using simulation as a surrogate for field data collection, it was demonstrated that the proposed average green time estimation models offer better results than the one in the 2000 HCM. In addition, it was suggested that on the basis of the improvements demonstrated in terms of design, the proposed maximum green parameter design procedure represents an advancement of the methodology for analysis of signalized intersections.


2021 ◽  
Vol 29 (3) ◽  
pp. 31-40
Author(s):  
R. Sushmitha ◽  
K. V. R. Ravishankar

Abstract Control delay is the key performance indicator of a signalized intersection that defines the level of service. Several models have been developed in previous research work for estimating control delays, but many of them were based on homogeneous traffic conditions. In the present study, an Open Street Map (OSM) tracker mobile application was used to measure control delays from the field. A non-linear model was developed in the present study for estimating control delays in mixed traffic conditions using a MATLAB fitting tool. The field delay is compared with the developed non-linear model delay along with the Indian Highway Capacity manual (INDO HCM) and Highway Capacity Manual (HCM) models. The control delay estimated using the model developed in the present study shows a close relation with the field delay obtained using an OSM tracker when compared to that obtained using the INDO HCM and HCM models. Therefore, the OSM tracker mobile application can be used as a field control delay measuring technique.


2020 ◽  
pp. 1-17
Author(s):  
Francisco Javier Balea-Fernandez ◽  
Beatriz Martinez-Vega ◽  
Samuel Ortega ◽  
Himar Fabelo ◽  
Raquel Leon ◽  
...  

Background: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer’s disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiable factors and on the other, modifiable. Objective: This study aims to develop a processing framework based on machine learning (ML) and optimization algorithms to study sociodemographic, clinical, and analytical variables, selecting the best combination among them for an accurate discrimination between controls and subjects with major neurocognitive disorder (MNCD). Methods: This research is based on an observational-analytical design. Two research groups were established: MNCD group (n = 46) and control group (n = 38). ML and optimization algorithms were employed to automatically diagnose MNCD. Results: Twelve out of 37 variables were identified in the validation set as the most relevant for MNCD diagnosis. Sensitivity of 100%and specificity of 71%were achieved using a Random Forest classifier. Conclusion: ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD.


Author(s):  
Rahim F. Benekohal ◽  
Sang-Ock Kim

For oversaturated traffic conditions, the Highway Capacity Manual (HCM) does not apply a progression adjustment factor to the delay model for signalized intersections when there is an initial queue. This causes counterintuitive results in the calculation of delay; for some cases, delay for a nonzero initial queue condition ends up being less than the delay with zero initial queue conditions. Also, for oversaturated traffic conditions, the delay model in the 2000 edition of HCM yields the same uniform delay values for all arrival types when there is an initial queue. This does not seem reasonable because it ignores the effect of platooning on delay. This paper introduces a new approach for computing uniform delay for oversaturated traffic conditions when progression is poor. This approach directly considers the platooning effects in delay and thus eliminates the need to apply a progression adjustment factor. The proposed model is applicable whether there is an initial queue or not. The approach was validated by a comparison of the control delays obtained from a CORSIM simulation to the delays from the proposed model. Validation procedures were conducted on the basis of zero and nonzero initial queue conditions. The proposed approach resulted in more accurate delay values than the HCM model.


2000 ◽  
Vol 1710 (1) ◽  
pp. 222-230 ◽  
Author(s):  
Fadhely Viloria ◽  
Kenneth Courage ◽  
Donald Avery

Several measures of effectiveness (MOEs) are associated with the queuing process at traffic signals, including delay, number of stops, fuel consumption, emissions, and queue length. The focus in this study is on queue length in general and on the storage requirements for left turns in particular. Queue length is an important MOE because queues that overflow the available storage space have an adverse effect on the overall operation of the intersection. Many traffic models now provide queue-length estimates, but the procedures used by these models are based on different queue definitions and have different computational approaches that lead to different results. A classification framework is developed for the existing models, their behavior is compared with that of the proposed Highway Capacity Manual (HCM) 2000 queue model, and queue conversion factors are provided for translating the various model outputs to their HCM 2000 equivalent. The proposed HCM 2000 model and its parent model from the Signalized and Unsignalized Intersection Design and Research Aid (SIDRA) provide a comprehensive treatment of the queuing process, accounting for control parameters such as controller type and progression quality as well as for the random and overflow effects associated with traffic flow. As such, the queue-length estimates from these models are more analytically defensible than those of the simpler theoretical models. The SIDRA and HCM 2000 queue estimates are generally higher than those of most other models and are somewhat higher than what conventional wisdom would suggest. It is suggested as a result of the comparisons presented that the queue estimates from some models are unduly optimistic when demand approaches capacity and that a goal of 90 percent confidence in the adequacy of left-turn storage lanes may be difficult to achieve under these conditions.


2000 ◽  
Vol 1710 (1) ◽  
pp. 199-204 ◽  
Author(s):  
Xuewen Le ◽  
Jian Lu ◽  
Edward A. Mierzejewski ◽  
Yanhu Zhou

The capacity analysis procedure for signalized intersections included in the Highway Capacity Manual (HCM) needs to consider the area type of a given intersection. The area-type adjustment factor used in the procedure is based on conclusions from a limited number of studies. In addition, the procedure for using an area-type adjustment factor is not well defined in the HCM. A study undertaken in central Florida to study the effects of four different area types on the capacity of signalized intersections is summarized. These four area types include recreational, business, residential, and shopping. Study results indicated that differences in saturation headways among different area types were significant. The saturation headways observed in recreational areas were significantly higher than those in other areas for both left-turn and through movements. The through-movement saturation headways obtained in residential, shopping, and business areas were not significantly different. This study resulted in a new area-type adjustment factor of 0.92 for recreational areas, whereas the factor is 1.00 for other areas. Results in this study also indicated that the differences in start-up lost time among different area types were not significantly different. In addition, according to the results of the analysis, 75 percent of the yellow interval in undersaturated conditions and 35 percent of the yellow interval in oversaturated conditions were found to be unused and considered clearance lost time.


1997 ◽  
Vol 1572 (1) ◽  
pp. 167-173 ◽  
Author(s):  
Jamie W. Hurley

The capacity of multiple through lanes at signalized intersections depends on the distribution of traffic within these lanes, with equal lane distribution corresponding to maximum capacity. However, traffic characteristics, land use, and geometric factors usually prohibit this from occurring. Although the 1994 update of the Highway Capacity Manual considers the case of continuous through lanes at signalized intersections, the default values provided do not address situations in which lane reduction takes place downstream of the intersection. Lane distribution data obtained in the field can remedy the situation but for existing conditions only. This research employed the concept of captive and choice lane users in modeling lane use for intersection configurations with a single continuous through lane and an “auxiliary” through lane, which is continuous upstream of the intersection but is dropped downstream of it. Stepwise multiple regression was performed on data collected at sites in Tennessee to ascertain those factors significantly affecting auxiliary lane use. These factors were found to be ( a) right turns off the facility at the intersection, ( b) total left turns off the facility downstream of the intersection, ( c) right turns onto the facility in the first 122 m (400 ft) upstream of the intersection, ( d) right turns off the facility in the last 152 m (500 ft) of the auxiliary lane, ( e) downstream auxiliary lane length, and ( f) the existence of left-turn bays or two-way continuous left-turn lanes downstream of the intersection. For the configuration studied, lane distribution data often differed considerably from the default values given in the Highway Capacity Manual.


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