Capacity Analysis for Fixed-Time Signalized Intersection for Non-Lane Based Traffic Condition

2009 ◽  
Vol 83-86 ◽  
pp. 904-913
Author(s):  
M. Hadiuzzaman ◽  
M. Mizanur Rahman
Keyword(s):  
Fixed Time ◽  
Signalized Intersections ◽  
Degree Of Saturation ◽  
Capacity Analysis ◽  
Delay Model ◽  
Traffic Condition ◽  
Site Specific ◽  
Study Approach ◽  
Saturation Flow ◽  
Lane Based

Capacity analysis of signalized intersections basically consists of estimating saturation flow and delay. Pre-timed signals are most commonly used in developing countries. This research deals with development of saturation flow and delay models for pre-timed signalized intersections with reference to non-lane based traffic condition prevailing in Bangladesh. In order to account non-uniformity in the static and dynamic characteristics of the vehicles passenger car unit (PCU) values for each vehicle is found out using synchronous regression technique and a range of site-specific PCU values were obtained. From this study, it has been observed that unified PCU concept does not hold good for non-lane based traffic condition and it has been recommended that the analysis should be site specific for non-lane based traffic condition. The saturation flow for each study approach was calculated using the average PCU values and multiple linear regression techniques were then used to derive predictive saturation flow models. Field delay for each approach is calculated based on HCM 2000 guidelines. It has been observed that HCM 2000 delay model consistently over estimate delay at degree of saturation more than 1.0. It has been suggested from the analysis that theoretical incremental delay (due to random arrival and over saturated queues) in HCM 2000 delay model be reduced by 70 % to better reflect field conditions in capacity analysis for non lane based traffic condition.

scholarly journals Effect of increasing rate of tricycles on saturation flows at signalized intersections in Uyo, Akwa Ibom State, Nigeria

2021 ◽  
Vol 40 (2) ◽  
pp. 191-198
Author(s):  
I.N. Usanga ◽  
R.K. Etim
Keyword(s):  
Field Measurement ◽  
Signalized Intersections ◽  
P Value ◽  
Highway Capacity ◽  
Traffic Condition ◽  
Transit System ◽  
Highway Capacity Manual ◽  
Study Approach ◽  
Departure Time ◽  
Saturation Flow

This study involves understanding the effect of tricycles on saturation flow rate at signalized intersections. The goal is to show that intersection dominated by tricycle experience congestion especially at peak periods (morning and evening). This was done by collecting vehicular traffic data, signal timing and geometric data from five (5) signalized intersections at ten (10) cycles. The period covered October, 2015 to June, 2016 for four working days of the week (Mondays, Tuesdays, Wednesdays, and Fridays), between the hours of 7:30 am–9:30 am and 4:30 pm– 6:30 pm. The duration of data collected covered both rainy and dry seasons. Average vehicular departure time during green time was determined and saturation flow obtained through field measurement by the ratio of average vehicular departure time to green time. Highway Capacity Manual method was also used to obtain saturation flow at each study approach. Saturation flow obtained through field measurement and Highway Capacity Manual were compared using independent t-test having t-value of 4.239 and P-value of 0.000 at 20 degree of freedom were obtained. The analysis indicated that P-value is less than 0.05, hence the mean of Highway Capacity Manual 2000 Model (5918.60) was significantly higher than the field measurement (4687.50). The result indicated that the increasing rate of tricycle with non-lane discipline causes congestion at signalized intersection. The findings suggest that the widely used Highway Capacity Manual is not appropriate for determining saturation flow for a mixed traffic with increasing rate of tricycle coupled with non-lane discipline traffic condition. From the analysis, it is recommended that Government should give priority to use of buses as a means of mass transit system so that it can accommodate more commuters than tricycle.

Accounting for Nonrandom Arrivals in Estimate of Delay at Signalized Intersections

1996 ◽  
Vol 1555 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Janice Daniel ◽  
Daniel B. Fambro ◽  
Nagui M. Rouphail
Keyword(s):  
Field Data ◽  
Empirical Studies ◽  
Primary Objective ◽  
Signalized Intersections ◽  
Degree Of Saturation ◽  
Delay Model ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Research Show

The primary objective of this research was to determine the effect of nonrandom or platoon arrivals on the estimate of delay at signalized intersections. The delay model used in the 1994 Highway Capacity Manual (HCM) accounts for nonrandom arrivals through the variable m, which can be shown to be equal to 8kI, where k describes the arrival and service distributions at the intersection and I describes the variation in arrivals due to the upstream intersection. The 1994 HCM delay model m-values are a function of the arrival type, where the arrival type describes the quality of progression at the intersection. Although an improvement to the fixed k I-value used in the 1985 delay model, the 1994 m values are based on empirical studies from limited field data and do not account for the decrease in random arrivals as the volume approaches capacity at the downstream intersection. This research provides an estimate of the variable kI for arterial conditions. An analytical equation was developed as a function of the degree of saturation, and a separate equation was developed for each signal controller type. The results from this research show that the proposed kI's provide delay estimates closer to the measured delay compared with the delay estimates using the kI-values in the 1994 HCM delay model.

Development of Driver Population Factors for Capacity Analysis of Signalized Intersections

2000 ◽  
Vol 1710 (1) ◽  
pp. 239-245 ◽  
Author(s):  
Yanhu Zhou ◽  
Jian John Lu ◽  
Edward A. Mierzejewski ◽  
Xuewen Le
Keyword(s):  
Flow Rate ◽  
Signalized Intersections ◽  
Capacity Analysis ◽  
Study Results ◽  
Adjustment Factors ◽  
Saturation Flow Rate ◽  
High Level ◽  
Saturation Flow ◽  
The Impact ◽  
Population Adjustment

In the Highway Capacity Manual (HCM), driver population factors are included to adjust for the impact of nonlocal drivers on freeway capacity. There are no such factors in the HCM to account for the possible change of capacity at signalized intersections caused by unfamiliar drivers in the traffic stream. The results obtained from a research study to develop driver population adjustment factors for capacity analysis of signalized intersections are summarized. Detailed procedures for quantifying driver population adjustment factors are presented. The factors were derived on the basis of data collected in Hillsborough, Pinellas, Orange, and Osceola counties in Florida. Study results indicated that nonlocal drivers had a significant impact on the saturation flow rate. When a signalized intersection was identified with a high level of nonlocal drivers, the saturation flow rate as well as the capacity could be reduced by 19 percent, which corresponded to a driver population factor as low as 0.81.

Impact of Geometry and Operations on Left-Turn Gap Acceptance at Signalized Intersections with Permissive Indication

2021 ◽  
pp. 036119812110114
Author(s):  
Boris Claros ◽  
Madhav Chitturi ◽  
Andrea Bill ◽  
David A. Noyce
Keyword(s):  
The United States ◽  
Signalized Intersections ◽  
Video Data ◽  
Likelihood Method ◽  
Speed Limit ◽  
Gap Acceptance ◽  
Site Specific ◽  
The Mean ◽  
Saturation Flow

Critical and follow-up headways are the foundation for estimating the saturation flow of permissive left-turns at signalized intersections. Current critical and follow-up headways recommended in the 2016 Highway Capacity Manual (HCM) are based on limited data collected from five intersections in Texas in the 1970s. This study analyzed over 2,500 left-turning vehicles at 45 intersection approaches, provides insights into gap acceptance parameters, and evaluates the effect of different site-specific factors. Video data were collected and processed from different geographical regions in the United States—Arizona, Florida, North Carolina, Virginia, and Wisconsin. Using the maximum likelihood method to estimate gap acceptance parameters, the mean critical headway was 4.87 s and the mean follow-up headway was 2.73 s. To account for site-specific characteristics, the effect of several geometric and operational variables on critical and follow-up headway were explored. Through a meta-regression analysis, the posted speed limit and width of opposing travel lanes were found to have a significant effect on gap acceptance parameters. Results showed that with decreasing posted speed limit and width of opposing lanes, critical and follow-up headways also decreased, resulting in greater saturation flows. When site-specific saturation flow estimates were compared with HCM saturation flow estimates, the differences ranged from −30% to +23%. This paper quantifies and illustrates the impact of site-specific characteristics on gap acceptance parameters and saturation flow.

Investigation of saturation flow rate using video camera at signalized intersections in Jordan

2020 ◽  
Vol 11 (1) ◽  
pp. 216-226
Author(s):  
Bara’ W. Al-Mistarehi ◽  
Ahmad H. Alomari ◽  
Mohamad S. Al Zoubi
Keyword(s):  
Flow Rate ◽  
Standard Procedure ◽  
Video Camera ◽  
Signalized Intersections ◽  
Speed Limit ◽  
Local Conditions ◽  
Highway Capacity Manual ◽  
Saturation Flow Rate ◽  
Turning Radius ◽  
Saturation Flow

AbstractThis study aimed to investigate a potential list of variables that may have an impact on the saturation flow rate (SFR) associated with different turning movements at signalized intersections in Jordan. Direct visits to locations were conducted, and a video camera was used. Highway capacity manual standard procedure was followed to collect the necessary traffic data. Multiple linear regression was performed to classify the factors that impact the SFR and to find the optimal model to foretell the SFR. Results showed that turning radius, presence of camera enforcement, and the speed limit are the significant factors that influence SFR for shared left- and U-turning movements (LUTM) with R2 = 76.9%. Furthermore, the presence of camera enforcement, number of lanes, speed limit, city, traffic volume, and area type are the factors that impact SFR for through movements only (THMO) with R2 = 69.6%. Also, it was found that the SFR for LUTM is 1611 vehicles per hour per lane (VPHPL),which is less than the SFR for THMO that equals to 1840 VPHPL. Calibration and validation of SFR based on local conditions can improve the efficiency of infrastructure operation and planning activities because vehicles’ characteristics and drivers’ behavior change over time.

scholarly journals PENGARUH KEMACETAN DI SIMPANG SABILILLAH TERHADAP IMPLEMENTASI CITRA LALU LINTAS DI SIMPANG PROGRESIF

2015 ◽  
Vol 9 (2) ◽  
pp. 114
Author(s):  
Supiyono, Dwi Ratnaningsih, Rudy Ariyanto
Keyword(s):  
Road Traffic ◽  
Flame Length ◽  
Traffic Volume ◽  
Signalized Intersections ◽  
Degree Of Saturation ◽  
The Road ◽  
Road Section ◽  
On The Road ◽  
High Traffic Volume

Progress of a country in line with the progress of traffic (transport). Fluency in traffic is determined by the smoothness of traffic on the road. Problems often arise on the highway is congestion at the intersection. Neither was signalized intersections and signalized intersections. Problems at the intersection is less accuracy green flame at the intersection with the number of vehicles in a segment. A road with high traffic volume vehicle green flame low while other road traffic volume small green flame length. So in a long queue roads, while other roads are deserted while still green flame.     This study aims to minimize the occurrence of conflic at the intersection of green flame. Research will make iterations in the intersection, where a road section which will be nominated densely green flame, the flame of the green according to the volume of traffic on these roads. Each road will be a green flame in accordance with the volume of traffic, without having to change any program there is a change in traffic volume.The degree of saturation of the calculation obtained by ....Keywords: roads, hight traffic, progressive intersection, degree saturation

Validation of Generalized Delay Model for Vehicle-Actuated Traffic Signals

1997 ◽  
Vol 1572 (1) ◽  
pp. 105-111 ◽  
Author(s):  
Nagui M. Rouphail ◽  
Mohammad Anwar ◽  
Daniel B. Fambro ◽  
Paul Sloup ◽  
Cesar E. Perez
Keyword(s):  
North Carolina ◽  
Field Data ◽  
Traffic Signals ◽  
Signalized Intersections ◽  
Delay Model ◽  
Highway Capacity ◽  
Generalized Model ◽  
Highway Capacity Manual ◽  
Traffic Volumes ◽  
Isolated Intersection

One limitation of the Highway Capacity Manual (HCM) model for estimating delay at signalized intersections is its inadequate treatment of vehicle-actuated traffic signals. For example, the current delay model uses a single adjustment for all types of actuated control and is not sensitive to changes in actuated controller settings. The objective in this paper was to use TRAF-NETSIM and field data to evaluate a generalized delay model developed to overcome some of these deficiencies. NETSIM was used to estimate delay at an isolated intersection under actuated control, and the delay values obtained from NETSIM were then compared with those estimated by the generalized delay model. In addition, field data were collected from sites in North Carolina, and delays observed in the field were compared with those estimated by the generalized delay model. The delays estimated by the generalized model were comparable with the delays estimated by NETSIM. The data compared favorably for degrees of saturation of less than 0.8. However, at higher degrees of saturation, the generalized model produced delays that were higher than NETSIM’s. Some possible explanations for this discrepancy are discussed. The delays estimated by the generalized model were comparable with delays observed in the field. Researchers have concluded that the generalized delay model is sensitive to changes in traffic volumes and vehicle-actuated controller settings and that the generalized delay model is much improved over the current HCM model in estimating delay at vehicle-actuated traffic signals.

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