Queue Discharge Rate Research Articles

Study of medium and long-term free flow capacity and queue discharge rates on roads.

With the rise in vehicle ownership, traffic congestion has emerged as a major barrier to urban progress, making the study and optimization of urban road capacity exceedingly crucial. The research on the medium and long-term free-flowing capacity and queue emission rate of roads takes an in-depth exploration of this issue from a cutting-edge perspective, aiming to find solutions adaptable to the progression of the times. The purpose of this study is to understand and predict the road capacity and queue emission rate more accurately, thus improving the urban traffic condition. Existing literature primarily focuses on short-term forecasts of road capacity, leaving a notable void in the research of medium and long-term road capacity and queue emission rate. This gap often results in a lack of sufficient foresight when urban traffic planning faces practical issues. To fill this void, this study undertook an in-depth examination of the road capacity and queue emission rate over the medium and long term (10 years) based on big data analysis and artificial intelligence theories. This paper employs a Radial Basis Function (RBF) neural network, combined with twelve other parameters that could potentially impact road capacity, such as traffic volume, road width, number of lanes, traffic signal control methods, etc., to analyze the relationship between each parameter and free-flow traffic and queue emission rate. These analyses are grounded in extensive road data, encompassing not only the city's main roads but also secondary roads and community roads. The study results show a continuous downward trend in the free-flowing capacity of roads and a slight upward trend in the queue emission rate over the past decade. Further analysis reveals the extent of impact each factor has on the free-flow traffic and queue emission rate, providing a scientific basis for future urban traffic planning.

Empirical Proof of the Characteristics of the Queue Discharge Rate under Different Rainfall Conditions on an Active On-Ramp Bottleneck

Empirical studies show that queue discharge rate is lower than pre-queue capacity in congestion. This is the the capacity drop phenomenon. All previous research about this event used data during clear weather conditions. This is the first time that empirical relationships between queue discharge rate and weather conditions have been studied. Previous studies show that the capacity drop is triggered by a critical density. Once this density is reached, a drop in the discharge rate is expected. We show that this critical density decreases during any weather condition. Previous studies also prove that the capacity drop is related to speed in congestion but that this might not be true during inclement weather. We show that queue discharge rate is correlated to the speed of congestion in any weather condition. We have also shown for the first time that the speed in congestion and the percentage of the capacity drop have a negative linear relationship.

Free Flow Capacity and Queue Discharge Rate: Long-Term Changes

Traffic jams are caused by a traffic demand that exceeds road capacity. Road capacity, therefore, is an important road feature. This capacity might change as function of time, even for the same road stretch, owing to changing driving behaviors or vehicle characteristics. In this study, we empirically analyzed the changes in road capacity over a 5- to 10-year period. The study differentiated between free flow capacity and queue discharge rate. We used three road stretches that remained unchanged to study free flow capacity. For 143 other locations that experienced changing properties over time, we analyzed queue discharge rates and corrected for external changes. We found that free flow capacity decreased, and queue discharge rates (slightly) increased over time. It is remarkable that one decreased, whereas the other increased. These results could be used in policies for road planning and design. Moreover, they provide an interesting background for further studies analyzing the effects of particular behavioral changes or driver assistance systems.

Estimating key traffic state parameters through parsimonious spatial queue models

As an active performance evaluation method, the fluid-based queueing model plays an important role in traffic flow modeling and traffic state estimation problems. A critical challenge in the application of traffic state estimation is how to utilize heterogeneous data sources in identifying key interpretable model parameters of freeway bottlenecks, such as queue discharge rates, system-level bottleneck-oriented arrival rates, and congestion duration. Inspired by Newell’s deterministic fluid approximation model, this paper proposes a spatial queue model for oversaturated traffic systems with time-dependent arrival rates. The oversaturated system dynamics can be described by parsimonious analytical formulations based on polynomial functional approximation for virtual arrival flow rates. With available flow, density and end-to-end travel time data along traffic bottlenecks, the proposed modeling framework for estimating the key traffic queueing state parameters is able to systematically map various measurements to the bottleneck-level dynamics and queue evolution process. The effectiveness of the developed method is demonstrated based on three case studies with empirical data in different metropolitan areas, including New York, Los Angeles, and Beijing.

Queue Analysis at Work Zones from Field-Collected Data

Estimation of the capacity of work zones is vital to manage the possibility of traffic flows exceeding capacity and resulting in unbearable queues during work zone lane closures. A plethora of research papers have studied several ways to estimate work zone capacity, with the Highway Capacity Manual (HCM) having its own methodology to estimate capacity based on various site characteristics. However, HCM always recommends validating its model with local data to reflect the actual driving behavior of the region. This study considered work zone capacity as a function of queue discharge rate (QDR), defined as the 15-min average flow rate immediately after breakdown, also known as postbreakdown flow rate. By collecting data from 10 different work zones within the state of Louisiana, the study estimated QDR and its corresponding duration at breakdowns. An average QDR of 1,664 pcphpl and an associated queue of 120 min average duration was found. Analysis of variance showed that average QDRs across all sites were not significantly different. The QDR prediction model revealed that a closed right lane and a work zone on linear roadways significantly increased the discharge rate. However, the presence of nearby exit ramps, daytime scenarios, and an increase in the speed ratio and truck percentages were found to decrease the discharge rate. A separate model for the duration of queue or breakdown found the time of day, change in the speed ratio, presence of entry ramp, location of work zones, and annual average daily traffic of the roadway to be significant variables.

Influencing the queue configuration to increase bicycle jam density and discharge rate: An experimental study on a single path

Congestion in bicycle traffic is a daily occurrence at many urban intersections. It is known that a higher density in the queue leads to a higher discharge rate. In theory, higher jam densities than those currently observed in practice are feasible. This leads to our hypothesis that the delay at intersections can be further reduced when cyclists are encouraged to queue up closer together. To explore this option, we carried out an experiment in which the queue configuration was influenced to increase the jam density. This paper presents ways to increase the queuing density, up to twice the density found without instructions. Results show that increasing the jam density does indeed increase the queue discharge rate; this also holds for jam density values that exceed those observed in normal queuing conditions. The efficiency of the queue discharge process, captured by the discharge rate, was found to increase by 40% when cyclists queue up closely together. Qualitative comparison of the queuing positions and discharge patterns showed that the discharge sequence is largely determined by the queuing position, and that cyclists keep a distance from each other in both time and space during the queue discharge phase. When applied in practice, these findings can be used to update the signal length and green phases for all traffic, thereby reducing congestion in urban areas.

Development of Breakdown Probability Models and Heavy Vehicle Passenger Car Equivalents for Rural Freeway Work Zones

With nearly nine million lane-miles of public roadway and an economy driven by the automobile, interruptions to normal traffic operations for construction and maintenance are inevitable in the U.S.A., but the substantial safety and mobility impacts associated with queueing at freeway lane closures are mitigable. The current freeway work zone capacity methodology in the 6th edition of the Highway Capacity Manual is a vast improvement over historical guidance but still approaches the issue differently than research suggests agencies and practitioners should. Namely, a capacity defined by the mean queue discharge rate is deterministic and fails to account for the stochastic nature of traffic flow and breakdown. These core issues were addressed in this research by developing a methodology for obtaining probabilistic estimates of rural freeway work zone capacity from simulated data in PTV Vissim. Results for a two-to-one lane closure were presented as a series of breakdown probability distributions to demonstrate the viability of this methodology. The data indicated that the impact of trucks on freeway capacity is exacerbated in the presence of lane closures and led to the development of work zone capacity-based passenger car equivalents. Such a procedure may be extended to freeway facilities exhibiting different geometric, traffic, and environmental characteristics and utilized by agencies to make data-driven, risk tolerance-based planning, design, and operations decisions at freeway work zones.

Improving Traffic Flow Efficiency at Motorway Lane Drops by Influencing Lateral Flows

Lane drops are a common bottleneck source on motorway networks. Congestion sets in upstream of a lane drop as a result of the lane changing activity of merging vehicles. This causes the queue discharge rate at the bottleneck to decrease and drop below the capacity, leading to capacity drop and further congestion. The objective of this study is to minimize the total travel time of the system by controlling lateral flows upstream of the lane drop. This is equivalent to maximizing the exit flows at the bottleneck. An optimization problem is formulated for a 3–2 lane drop section with high inflow. The problem is solved for different test cases where the direction of lateral flows being controlled is varied. An incentive based macroscopic model representing the natural lane changing scenario is used as a benchmark for comparison. The results showed that by influencing the lateral flows upstream of the bottleneck, the queue discharge rate increased by more than 4.5%. The total travel time of the system was consequently found to be reduced. The improvements in performance were primarily a result of the distribution of lane changing activity over space and the balancing of flow among the lanes which lead to the decrease in the severity of congestion. The findings reveal a potentially effective way to reduce the severity of congestion upstream of lane drop bottlenecks during high demand which could be implemented using roadside and in-car advisory systems.

The Influence of Jam Density and Merging Cyclists on the Queue Discharge Rate

An increasing number of people use the bicycle for urban trips resulting in local congestion at intersections, especially during peak hours. Understanding the queue dynamics is key to find the correct measures that can reduce the delays for cyclists without affecting other traffic modes. To this end, the discharge process of bicycle queues is studied, focusing on the impact of jam density on the queue discharge rate and how this process is affected by cyclists that merge into the queue during the discharge phase. The impact of merging cyclists is captured by a newly introduced bicycle equivalent (BE) value. This direction-specific BE value is used to convert a merging cyclist into a cyclist that is waiting in the original queue. Results show that the queue discharge rate increases with increasing density of the queue. Furthermore, cyclists that merge by overtaking contribute to the queue discharge rate, while cyclists who merge from a perpendicular direction hinder the discharge process, thereby decreasing the bicycle flow at the intersection. The insights can be used to develop measures which minimise delay at intersections and to design efficient infrastructure for bicyclists.

Calibration of Vissim Models for Rural Freeway Lane Closures: Novel Approach to the Modification of Key Parameters

The definition of freeway work zone capacity has been a topic of debate for several decades, leaving agencies with limited guidance on predicting the behavior of traffic flow at given volumes for various work zone configurations. The methodology presented in the recently published 6th edition of the Highway Capacity Manual (HCM) is a substantial improvement over historical guidance and provides estimates of the mean queue discharge rate under a variety of prevailing site conditions. However, it is limited by the fact that its outputs are deterministic, while traffic flow and breakdown are stochastic phenomena. Recently, well-calibrated microsimulation models have shown promise as a freeway work zone traffic analysis tool, but most guidance is focused on site-specific modeling. This research aimed to address these shortcomings by presenting a novel approach to developing and calibrating generalizable microsimulation models for rural freeway lane closures in Vissim, a traffic simulation software package developed by the PTV Group. Specifically, it was determined that such models may best replicate field conditions at rural freeway work zones when time headway is described by a field-measured distribution and truck characteristics are representative of the United States (U.S.) fleet. The results suggested that the default desired acceleration for heavy trucks should be set between 2 and 3 ft/s2 and that separate time headway distributions should be constructed for passenger cars and trucks. The methodology presented herein may be extended to obtain stochastic estimates of capacity for sites exhibiting a variety of geometric, traffic, and environmental characteristics.

Uncertainty-based prediction of work zone capacity using a Bayesian approach

The uncertainty-based work zone capacity model is formulated as a mixed linear regression function of explanatory variables where capacity is a random variable following a lognormal distribution. Model parameters are calibrated using a Bayesian approach. Results show that the proposed work zone capacity distribution model can accurately predict the mean and prediction interval of work zone capacity at any given confidence level. It is found that work zones located in urban roads, with a larger number of open lanes or with long-term work duration have a larger mean work zone capacity. A short prediction interval (i.e. low uncertainty) for work zone capacity is found to be associated with the following situations: (a) a bigger number of open lanes; (b) rural work zone; (c) short-term work duration; (d) left lane closure; (e) daytime work and (f) a smaller percentage of heavy vehicles. The predicted interval length for the medium level of work intensity is increased, although the corresponding mean work zone capacity is reduced, as compared with the light level of work intensity. There will be a bigger measured work zone capacity on average if the measurement method is adopted in which work zone capacity is taken as the mean queue-discharge rate.

Unravelling effects of cooperative adaptive cruise control deactivation on traffic flow characteristics at merging bottlenecks

Cooperative Adaptive Cruise Control (CACC) systems have the potential to increase roadway capacity and mitigate traffic congestion thanks to the short following distance enabled by inter-vehicle communication. However, due to limitations in acceleration and deceleration capabilities of CACC systems, deactivation and switch to ACC or human-driven mode will take place when conditions are outside the operational design domain. Given the lack of elaborate models on this interaction, existing CACC traffic flow models have not yet been able to reproduce realistic CACC vehicle behaviour and pay little attention to the influence of system deactivation on traffic flow at bottlenecks. This study aims to gain insights into the influence of CACC on highway operations at merging bottlenecks by using a realistic CACC model that captures driver-system interactions and string length limits. We conduct systematic traffic simulations for various CACC market penetration rates (MPR) to derive free-flow capacity and queue discharge rate of the merging section and compare these to the capacity of a homogeneous pipeline section. The results show that an increased CACC MPR can indeed increase the roadway capacity. However, the resulting capacity in the merging bottleneck is much lower than the pipeline capacity and capacity drop persists in bottleneck scenarios at all CACC MPR levels. It is also found that CACC increases flow heterogeneity due to the switch among different operation modes. A microscopic investigation of the CACC operational mode and trajectories reveals a close relation between CACC deactivation, traffic congestion and flow heterogeneity.

A geometric Brownian motion car-following model: towards a better understanding of capacity drop

ABSTRACTTraffic flow downstream of the congestion is generally lower than the pre-queue capacity. This phenomenon is called the capacity drop. Recent empirical observations show a positive relationship between the speed in congestion and the queue discharge rate. Literature indicates that variations in driver behaviors can account for the capacity drop. However, to the best of authors' knowledge, there is no solid understanding of what and how this variation in driver behaviors lead to the capacity drop, especially without lane changing. Hence, this paper fills this gap. We incorporate the empirically observed desired acceleration stochasticity into a car-following model. The extended parsimonious car-following model shows different capacity drop magnitudes in different traffic situations, consistent with empirical observations. All results indicate that the stochasticity of desired accelerations is a significant reason for the capacity drop. The new insights can be used to develop and test new measures in traffic control.

A Microscopic Investigation Into the Capacity Drop: Impacts of Longitudinal Behavior on the Queue Discharge Rate

The capacity drop indicates that the queue discharge rate is lower than the free-flow capacity. Studies show that the queue discharge rate varies under different traffic conditions. Empirical data show that the queue discharge rate increases as the speed in congestion increases. Insights into the underlying behavioral mechanisms that cause these variable queue discharge rates can help minimize traffic delays and eliminate congestion. However, to our knowledge, few efforts have been devoted to testing impacts of traffic behaviors on the queue discharge rate. This paper tries to fill this gap. We investigate to what extent the acceleration spread and reaction time can influence the queue discharge rate. It is found that the (interdriver) acceleration spread does not reduce the queue discharge rates as much as found empirically. Modeling reaction time might be more important than modeling acceleration for capacity drop in car-following models. A speed-dependent reaction time mechanism for giving variable queue discharge rates is proposed. That is, decreasing reaction time as the speed in congestion increases can give the same queue discharge rate as found empirically. This research suggests that motivating drivers to speed up earlier could increase the queue discharge rate and thereby minimize delays.

Driving Characteristics and Adaptive Cruise Control ? A Naturalistic Driving Study

With the increasing number of vehicles equipped with Adaptive Cruise Control (ACC), it becomes important to assess its impact on traffic flow efficiency, in particular with respect to capacity and queue discharge rate. Simulation studies and surveys suggest that ACC has both positive and negative effects on traffic flow, but empirical evidence on this topic is scarce. A naturalistic driving study has been conducted with 8 participants who drove their own ACC-equipped vehicle during their regular trips on freeways for a period of 4 to 5 weeks. We measured spacing, headway, speed, acceleration, lane use, and the number of lane changes, and compared these between ACC On and ACC Off in different traffic states, for a total of 48 hours of driving data. Results show that with ACC On, average spacing and headways were larger, whereas standard deviations were smaller. Larger headways can be assumed to reduce capacity, whereas more constant spacing, headway, speed, and acceleration indicate more stable traffic. With ACC On, drivers performed 36% fewer lane changes in saturated traffic, resulting in increased use of either the faster or the slower lane, depending on the driver. Furthermore we found that headways were smaller with ACC On than ACC Off when only selecting accelerations below -0.5m/s2 and above 0.5m/s2, which is the opposite of the overall finding. The latter result suggests that ACC has an important limitation: a lack of anticipation. On the other hand, the smaller headways with ACC On during acceleration indicate an increased queue discharge rate.

Automatic fitting procedure for the fundamental diagram

ABSTRACTThe fundamental diagram of a road, including free-flow capacity and queue discharge rate, is very important for traffic engineering purposes. In the real word, most traffic measurements come from stationary loop detectors. This paper proposes a method to fit Wu's fundamental diagram to loop detector data. Wu's fundamental diagram is characterised by five parameters, being free-flow speed, wave speed, free-flow capacity, queue discharge rate and jam density. The proposed method entails fixing the wave speed and the free-flow speed. The method consists of two steps. We first use a triangular fundamental diagram to separate the congested branch from the free-flow branch. Then, the remaining three parameters of Wu's fundamental diagram are fitted on each branch using a least-square fit. This method is shown to be robust for cases tested in real life, and hence very noisy, data.

A kinematic wave model in Lagrangian coordinates incorporating capacity drop: Application to homogeneous road stretches and discontinuities

On freeways, congestion always leads to capacity drop. This means the queue discharge rate is lower than the pre-queue capacity. Our recent research findings indicate that the queue discharge rate increases with the speed in congestion, that is the capacity drop is strongly correlated with the congestion state. Incorporating this varying capacity drop into a kinematic wave model is essential for assessing consequences of control strategies. However, to the best of authors’ knowledge, no such a model exists. This paper fills the research gap by presenting a Lagrangian kinematic wave model. “Lagrangian” denotes that the new model is solved in Lagrangian coordinates. The new model can give capacity drops accompanying both of stop-and-go waves (on homogeneous freeway section) and standing queues (at nodes) in a network. The new model can be applied in a network operation. In this Lagrangian kinematic wave model, the queue discharge rate (or the capacity drop) is a function of vehicular speed in traffic jams. Four case studies on links as well as at lane-drop and on-ramp nodes show that the Lagrangian kinematic wave model can give capacity drops well, consistent with empirical observations.

Traffic Signal Design with an Increasing Queue Discharge Rate

The traditional method of traffic signal design is based on the assumption of a constant queue discharge rate, also termed the saturation flow rate. However, a number of field studies conducted in Taiwan and the USA contradict this assumption because they reported a marginally increasing trend observed for the queue discharge rate toward the back of the queue. This paper reports on a field study conducted in Auckland, New Zealand at six signalized intersections. The field observations confirm the findings of previous research, namely that the queue discharge rate increases toward the back of the queue. Two empirical models are proposed to reflect the observed queue discharge behavior. One of them is implemented to calculate the capacity and signal timings. The proposed model shows the potential to overcome the shortcomings of the traditional method in practical applications.

Characterizing corridor-level travel time distributions based on stochastic flows and segment capacities

AbstractTrip travel time reliability is an important measure of transportation system performance and a key factor affecting travelers’ choices. This paper explores a method for estimating travel time distributions for corridors that contain multiple bottlenecks. A set of analytical equations are used to calculate the number of queued vehicles ahead of a probe vehicle and further capture many important factors affecting travel times: the prevailing congestion level, queue discharge rates at the bottlenecks, and flow rates associated with merges and diverges. Based on multiple random scenarios and a vector of arrival times, the lane-by-lane delay at each bottleneck along the corridor is recursively estimated to produce a route-level travel time distribution. The model incorporates stochastic variations of bottleneck capacity and demand and explains the travel time correlations between sequential links. Its data needs are the entering and exiting flow rates and a sense of the lane-by-lane distribution of tr...

Capacity Drop

It has been empirically observed for years that the queue discharge rate is lower than the prequeue capacity. This difference is called the capacity drop. The magnitude of capacity drop varies over a wide range, depending on the local traffic conditions. However, it is unknown what determines the capacity drop value. No thorough empirical analysis has yet revealed a reliable relationship between the capacity drop and the congestion level. This paper fills the gap by revealing, through empirical analysis, the relationship between vehicle speed in congestion and the queue discharge rate. The research studies congested states in which speed ranges from 6 to 60 km/h. The queue discharge rate is shown to increase considerably with increasing speed in the congestion. In contrast to previous research, this study bases the relationship on empirical data collected on freeways, and the data present a sufficiently large observation sample. A discussion about the influence of weather and study site characteristics on the discharge rate indicates that the relationship needs site-specific calibrations. This study provides a better prediction of capacity drop and a better theoretical understanding of the fluctuations in capacity drop.