Comparison of Delay on Arterials by Simulation and Highway Capacity Manual Computations

2002 ◽  
Vol 1802 (1) ◽  
pp. 69-76
Author(s):  
Elena S. Prassas
Keyword(s):  
Model Building ◽  
Delay Estimation ◽  
Delay Model ◽  
Microscopic Simulation ◽  
Highway Capacity ◽  
Basic Principles ◽  
State Delay ◽  
Highway Capacity Manual ◽  
Flow Speeds ◽  
Progression Factor

A comparison of delay on arterials was made using simulation and Highway Capacity Manual (HCM) computations. An extensive set of cases was defined over a range of flow rates, free-flow speeds, and signals per mile. Delay was calculated on the basis of the HCM computation for the specified signal progressions. The same cases were run using a specially revised version of the NETSIM microscopic simulation model. Detailed analysis showed that the discrepancies were traceable to the progression factor (PF). Corrections can be made (and are outlined) but must be done with care. There are a number of complex mechanisms at work. The primary result of this study may be clarifying the insufficiency in the current PF algorithm. The expectation that a single multiplicative factor could provide the needed correction to a long-link random-arrival steady-state delay model (that is, the existing HCM delay model), so that it could be a network-based model for delay estimation, appears to be simply unrealistic. For these and other reasons, research is focusing on the creation of a time-varying intersection delay model, building from basic principles.

Validation of Generalized Delay Model for Oversaturated Conditions

1997 ◽  
Vol 1572 (1) ◽  
pp. 122-130 ◽  
Author(s):  
Roelof J. Engelbrecht ◽  
Daniel B. Fambro ◽  
Nagui M. Rouphail ◽  
Aladdin A. Barkawi
Keyword(s):  
Simulation Model ◽  
Close Agreement ◽  
Signalized Intersections ◽  
Delay Model ◽  
Traffic Operations ◽  
Microscopic Simulation ◽  
Highway Capacity ◽  
Traffic Demand ◽  
Highway Capacity Manual ◽  
The U.S

With today’s ever-increasing traffic demand, more and more signalized intersections are experiencing congestion for longer periods of time. To better quantify oversaturated conditions, it is necessary to accurately estimate oversaturation delay. The generalized delay model, proposed for inclusion in the next update of the U.S. Highway Capacity Manual (HCM), is introduced here. The generalized delay model differs from the model in the 1994 edition of the HCM as it is sensitive to the duration of the analysis period and is not restricted to degrees of saturation less than 1.2. The TRAF-NETSIM microscopic simulation model was used to verify the generalized delay equation for oversaturated conditions. A simulation model was used, because it is extremely difficult to measure oversaturated delay in the field. The study was designed to cover as much of the domain of oversaturated traffic operations as possible. The variability in simulated delays was investigated, and an equation was developed to predict the standard deviation of oversaturated delay estimates. It was found that delays estimated by the proposed generalized delay model are in close agreement with those simulated by TRAF-NETSIM. On average, simulated delays are overestimated slightly, but the error is small compared with actual delays. The proposed generalized delay model is expected to provide a good estimate of actual oversaturation delays that occur in the field.

Estimating the Effects of Urban Street Incidents on Capacity

2017 ◽  
Vol 2615 (1) ◽  
pp. 55-61 ◽  
Author(s):  
Aidin Massahi ◽  
Mohammed Hadi ◽  
Maria Adriana Cutillo ◽  
Yan Xiao
Keyword(s):  
Network Performance ◽  
Field Measurements ◽  
Signalized Intersection ◽  
Limited Information ◽  
Microscopic Simulation ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Urban Streets ◽  
Urban Street ◽  
Incident Duration

The effect of incidents on capacity is the most critical parameter in estimating the influence of incidents on network performance. The Highway Capacity Manual 2010 (HCM 2010) provides estimates of the drop in capacity resulting from incidents as a function of the number of blocked lanes and the total number of lanes in the freeway section. However, there is limited information on the effects of incidents on the capacity of urban streets. This study investigated the effects on capacity of the interaction between the drop in capacity below demand at a midblock urban street segment location and upstream and downstream of signalized intersection operations. A model was developed to estimate the drop in capacity at the incident location as a function of the number of blocked lanes, the distance from the downstream intersection, and the green time–to–cycle length (g:C) ratio of the downstream signal. A second model was developed to estimate the reduction in the upstream intersection capacity resulting from the drop in capacity at the midblock incident location as estimated by the first model. The second model estimated the drop in capacity of the upstream links feeding the incident locations as a function of incident duration time, the volume-to-capacity (V/C) ratio at the incident location, and distance from an upstream signalized intersection. The models were developed on the basis of data generated with the use of a microscopic simulation model calibrated by comparison with parameters suggested in HCM 2010 for incident and no-incident conditions and by comparison with field measurements.

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.

Lane-by-Lane Analysis Framework for Conducting Highway Capacity Analyses at Freeway Segments

2019 ◽  
Vol 2673 (8) ◽  
pp. 523-535
Author(s):  
Fabio Sasahara ◽  
Lily Elefteriadou ◽  
Shen Dong
Keyword(s):  
Flow Distribution ◽  
Time Of Day ◽  
Heavy Vehicles ◽  
Highway Capacity ◽  
Operational Conditions ◽  
Highway Capacity Manual ◽  
Loop Detectors ◽  
Speed Flow ◽  
Flow Speeds ◽  
The Usa

The Highway Capacity Manual (HCM) methodology for freeway systems yields average speed values for each segment and does not consider lane-by-lane flow and operational conditions. However, flows are not equally distributed between lanes. In congested conditions and particularly when spillback occurs, flows and traffic conditions vary widely. For example, the rightmost lane may be blocked while the leftmost lane is free-flowing. The purpose of this research is to develop a model for estimating lane-by-lane speeds and flows under various freeway designs and demands. Speed and flow data from loop detectors at several locations around the USA were collected, totaling 531,000 observations aggregated in 15-min intervals. The results show that lane flow distribution is highly dependent on the segment total flow, with different patterns for 4-, 6-, and 8-lane segments. The percentage of heavy vehicles, presence of nearby ramps, day of week, and time of day also affect the distribution of flow among freeway lanes. Theoretical lane-by-lane speed-flow curves were developed and the results were compared with field data. Results showed that lane-by-lane speeds can be estimated accurately, as long as inputs for capacity and free-flow speeds can be provided for each lane in the segment.

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.

Arrival-Based Uniform Delay Model for Oversaturated Signalized Intersections with Poor Progression

2005 ◽  
Vol 1920 (1) ◽  
pp. 86-94
Author(s):  
Rahim F. Benekohal ◽  
Sang-Ock Kim
Keyword(s):  
Signalized Intersections ◽  
Delay Model ◽  
Adjustment Factor ◽  
Highway Capacity ◽  
New Approach ◽  
Highway Capacity Manual ◽  
Traffic Conditions ◽  
Proposed Model ◽  
Uniform Delay ◽  
Control Delays

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.

scholarly journals Capacity and Delay Estimation for Roundabouts Using Conflict Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Zhaowei Qu ◽  
Yuzhou Duan ◽  
Hongyu Hu ◽  
Xianmin Song
Keyword(s):  
Queuing Theory ◽  
High Volume ◽  
Conflict Theory ◽  
Model Parameters ◽  
Delay Model ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Traffic Characteristics ◽  
Capacity Model ◽  
Proposed Model

To estimate the capacity of roundabouts more accurately, the priority rank of each stream is determined through the classification technique given in the Highway Capacity Manual 2010 (HCM2010), which is based on macroscopical analysis of the relationship between entry flow and circulating flow. Then a conflict matrix is established using the additive conflict flow method and by considering the impacts of traffic characteristics and limited priority with high volume. Correspondingly, the conflict relationships of streams are built using probability theory. Furthermore, the entry capacity model of roundabouts is built, and sensitivity analysis is conducted on the model parameters. Finally, the entrance delay model is derived using queuing theory, and the proposed capacity model is compared with the model proposed by Wu and that in the HCM2010. The results show that the capacity calculated by the proposed model is lower than the others for an A-type roundabout, while it is basically consistent with the estimated values from HCM2010 for a B-type roundabout.

scholarly journals Self-Organizing Tree Algorithm (SOTA) Clustering for Defining Level of Service (LOS) Criteria of Urban Streets

2018 ◽  
Vol 47 (4) ◽  
pp. 309-317
Author(s):  
Amit Kumar Das ◽  
Prasanta Kumar Bhuyan
Keyword(s):  
Travel Speed ◽  
Flow Speed ◽  
Free Flow ◽  
Level Of Service ◽  
Highway Capacity ◽  
Tree Algorithm ◽  
Highway Capacity Manual ◽  
Urban Streets ◽  
Flow Speeds ◽  
Self Organizing

This study is intended to define the Free Flow Speed (FFS) ranges of urban street classes and speed ranges of Level of Service (LOS) categories. In order to accomplish the study FFS data and average travel speed data were collected on five urban road corridors in the city of Mumbai, India. Mid-sized vehicle (car) mounted with Global Positioning System (GPS) device was used for the collection of large number of speed data. Self-Organizing Tree Algorithm (SOTA) clustering method and five cluster validation measures were used to classify the urban streets and LOS categories. The above study divulges that the speed ranges for different LOS categories are lower than that suggested by Highway Capacity Manual (HCM) 2000. Also it has been observed that average travel speed of LOS categories expressed in percentage of free flow speeds closely resembles the percentages mentioned in HCM 2010.

Why Field Measurements Differ from Model Estimates: Analysis Framework for Capacity and Level-of-Service Analysis of Unsignalized Intersections

2003 ◽  
Vol 1852 (1) ◽  
pp. 32-39 ◽  
Author(s):  
Michael Kyte ◽  
Michael Dixon ◽  
Purushotham Murali Basavaraju
Keyword(s):  
Field Measurements ◽  
Level Of Service ◽  
End User ◽  
Microscopic Simulation ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Unsignalized Intersections ◽  
User Expectations ◽  
Using Data ◽  
Moderate User

Several questions are considered relating to the variability between field measurements and model forecasts, with a focus on the need to moderate user expectations about this variability. Considered first are the degree of variability observed in field measurements of delay and the stochastic effects in delay estimates produced by microscopic simulation. Examined next are the structure of the models of two-way stop-controlled (TWSC) intersection capacity and delay and how this structure might cause differences between field measurements and model estimates. How much the end user can moderate these differences using backcalculations, observation, and calibration and a “correct” perspective for the end user regarding these differences and variability are also discussed. These questions are considered using data collected as part of the NCHRP project used to develop the TWSC intersection capacity and level-of-service procedures contained in the Highway Capacity Manual models.

Predicting Lane-by-Lane Flows and Speeds for Freeway Segments

2020 ◽  
Vol 2674 (9) ◽  
pp. 1052-1068
Author(s):  
Fabio Sasahara ◽  
Luan Guilherme Staichak Carvalho ◽  
Tanay Datta Chowdhury ◽  
Zachary Jerome ◽  
Lily Elefteriadou ◽  
...  
Keyword(s):  
Field Data ◽  
Performance Estimation ◽  
Free Flow ◽  
Highway Capacity ◽  
Distribution Models ◽  
Highway Capacity Manual ◽  
Speed Flow ◽  
Flow Speeds ◽  
The Relationship

The Highway Capacity Manual is a major reference for evaluating the capacity and quality of service of road facilities. However, it holds the assumption that lanes perform equally, which can result in inaccuracies in performance estimation. The main purpose of this research is to develop a series of models for estimating flows and speeds by lane for various types of freeway segments, including basic, merge, and diverge types. These models consider the demand-to-capacity ratio, the presence of trucks, grade, and the presence of upstream and downstream ramps. To predict lane performance effectively, it is critical that capacity and free-flow speeds are also determined for individual lanes. Therefore, this study also investigates the relationship between segment average values and lane values for free-flow speeds and capacities, and proposes a method to estimate these parameters for each lane as a function of the segment average. Observed field data has shown that free-flow speeds and capacities have lowest values on the shoulder lanes and highest values on the median lanes. Speed and flow data were collected from 48 segments throughout the U.S.A., including basic, merge, and diverge segments, to develop flow and speed distribution models. A case example is provided to illustrate the application of the developed model and the predicted speed–flow relationship is compared with field data, with satisfactory results.

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