Queue Formation Research Articles

Analysis of queue estimation process at signalized intersections under low connected vehicle penetration rates

This study investigates the factors affecting estimation accuracy of queue length at signalized intersections under low penetration of connected vehicles. A shockwave-based algorithm is proposed to estimate the maximum queue length and residual queue on a cycle-by-cycle basis. Simulation data collected from three consecutive signalized intersections were used to extract trajectories of connected vehicles under five different market penetration rates and two different traffic conditions (under-saturated and moderate). The results confirm that the queue length estimation process is probabilistic and affected by the stochastic changes in traffic conditions. This probabilistic nature is defined by a queue formation coverage index (QI) that proved to significantly affect the queue length estimation accuracy. Overall, the results show that the accuracy of the queue estimates is acceptable when a QI value of at least 50% is achieved. In such limited data environments, the QI showed potential to help as an assessment tool to evaluate the obtained queue estimates.

Modeling multi-class disordered traffic flow subject to varying vehicle composition using the concept of traversable distance

Disordered traffic stream at the microscopic level can be described as a permeable medium. Each vehicle is considered to traverse through a series of lateral gaps created by other vehicles. We develop a multi-class traffic flow model that considers such viable and accessible gaps for individual vehicle classes to traverse downstream. The model accounts for the varying shares of different vehicle classes. The concept of traversable distance and modified equilibrium speed functions are used to model the interplay among multiple vehicle classes. Using a higher order finite volume approximation method, evolution for a two-class traffic stream is shown. This model replicates prominent empirical characteristics exhibited by multi-class disordered traffic such as overtaking and creeping. Varying shares of smaller vehicles affect queue formation and discharge characteristics, and have significant impacts on roadway capacity. The model also could compute dynamic class-specific travel times at different vehicular compositions more realistically.

Effect of Distance between Ramp and Upstream Signal on Ramp Meter Operation

Ramp metering is typically proposed as a responsive strategy that takes freeway traffic parameters as control inputs to the ramp control logic. Such a strategy can be implemented in two ways; isolated ramp control or coordinated ramp control. Coordinated ramp control typically involves the cooperation between several ramp meters connected to a freeway segment to manage traffic on the freeway and traffic all ramps. Few studies also proposed the coordination between the on-ramp and the upstream traffic signal. Such coordination can help to mitigate congestion on the freeway and to avoid queue formation at the on-ramp. In this study, the authors' previous work on ramp metering and upstream signal coordination was extended to further evaluate the performance of such schemes by considering the impact of the distance between the upstream traffic signal and freeway. Extensive simulations in SUMO were performed to evaluate the benefit of the proposed coordinated strategy and the impact of ramp distance on the effectiveness of such coordination.

Bottleneck Based Gridlock Prediction in an Urban Road Network Using Long Short-Term Memory

The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck indicates the congestion evolution and queue formation, which consequently disturb travel delay and degrade the urban traffic environment and safety. For urban road networks, sensors are needed to cover a wide range of areas, especially for bottleneck and gridlock analysis, requiring high installation and maintenance costs. The emerging widespread availability of GPS vehicles significantly helps to overcome the geographic coverage and spacing limitations of traditional fixed-location detector data. Therefore, this study investigated GPS vehicles that have passed through the links in the simulated gridlock-looped intersection area. The sample size estimation is fundamental to any traffic engineering analysis. Therefore, this study tried a different number of sample sizes to analyze the severe congestion state of gridlock. Traffic condition prediction is one of the primary components of intelligent transportation systems. In this study, the Long Short-Term Memory (LSTM) neural network was applied to predict gridlock based on bottleneck states of intersections in the simulated urban road network. This study chose to work on the Chula-Sathorn SUMO Simulator (Chula-SSS) dataset. It was calibrated with the past actual traffic data collection by using the Simulation of Urban MObility (SUMO) software. The experiments show that LSTM provides satisfactory results for gridlock prediction with temporal dependencies. The reported prediction error is based on long-range time dependencies on the respective sample sizes using the calibrated Chula-SSS dataset. On the other hand, the low sampling rate of GPS trajectories gives high RMSE and MAE error, but with reduced computation time. Analyzing the percentage of simulated GPS data with different random seed numbers suggests the possibility of gridlock identification and reports satisfying prediction errors.

Effect of Manual Traffic Control on Evacuation Time Estimates

Manual traffic control (MTC) is a common intersection control strategy where trained personnel, typically police law enforcement officers, allocate intersection right-of-way to approaching vehicles. MTC is frequently used for special events and during emergencies. However, the current state-of-the-practice has shown little research that quantifies or assesses the benefits of MTC during evacuations. This paper describes research to develop microscopic traffic simulation models that were used to assess traffic processes of emergency evacuations of nuclear power plant sites involving MTC. A recently developed model to represent MTC was integrated into the simulation to quantify its impact on clearance time and other operational parameters. The results of this effort showed that in rural or less congested urban areas clearance times experienced no significant benefit from the application of MTC. Conversely, in densely populated regions clearance times increased significantly when MTC was deployed. This suggested that for congested urban areas, with closely spaced intersections, the characteristically long cycle lengths associated with MTC resulted in significant queues. These queues propagated upstream, interfering with traffic operations at neighboring intersections and bottleneck points within the network. This, in turn, triggered even more queues and, ultimately, led to localized gridlock. From the perspective of police officers, longer cycle lengths result in fewer phase changes and less lost-time at the intersection. As such, the officer is incentivized to extend green times and cycle lengths as long as possible. What the officer cannot see, however, is the effect of such actions on queue formation at upstream intersections.

BBR Advanced (BBR-A) — Reduced retransmissions with improved fairness

Abstract In 2016, Google proposed Bottleneck Bandwidth and Round-trip-time (BBR), a new TCP congestion control algorithm to avoid bottleneck queue formation and utilize underlying bandwidth in full. However, recent reports have found significant flaws in BBR, especially a high packet retransmissions problem, and throughput unfairness issue. We propose BBR Advanced (BBR-A) that resolves these issues by halving the congestion window and slowing down the pacing gain for actual congestion events, which it decides based on packet losses and RTT. Mininet emulation experiments confirm that BBR-A reduces retransmissions up to 60% and increases fairness index up to 10% in comparison with BBR.

Review and performance evaluation of FIFO, PQ, CQ, FQ, and WFQ algorithms in multimedia wireless sensor networks

The best service mechanism in multimedia wireless sensor networks can be achieved based on the multimedia traffic flow by developing a proper simulation algorithm process model, to be a trustable indication for real implementations, which is proposed in this article, together with the algorithm model outcome analysis. The quality estimation of the proposed mechanism is investigated by simulating real data transmission and obtaining the integral criterion of the processed mechanism, to determine the queue formation and loading control. Accordingly, it was proved that the first-in first-out algorithm is not useful as a flow algorithm; however, it is regarded as suitable to be considered as the benchmark algorithm when compared with the other algorithms such as priority queue, custom queue, fair queuing, and weighted fair queuing algorithms. Finally, each algorithm’s advantages and disadvantages were verified and the best usage conditions according to certain parameters, such as packet loss probability, average time delay, and jitter, were declared.

An empirical test of hypercongestion in highway bottlenecks

There is a widely-held view that as demand for travel goes up, this decreases not only speed but also the capacity of the road system, a phenomenon known as hypercongestion. We revisit this idea in the context of highway bottlenecks. We propose an empirical test using an event study design to measure changes in highway capacity at the onset of queue formation. We apply this test to three highway bottlenecks in California for which detailed data on traffic flows and vehicles speeds are available. We find no evidence of a reduction in highway capacity at any of the three sites during periods of high demand. Across sites and specifications we have sufficient statistical power to rule out even small reductions in highway capacity. This lack of evidence of hypercongestion stands in sharp contrast to most previous studies and informs core models in urban and transportation economics.

Capacity-Constrained Bus Bridging Optimization Framework

Urban rail systems frequently suffer from unexpected service disruptions, which can result in severe delays and user dissatisfaction. “Bus bridging” is the strategy most commonly applied in responding to rail service interruptions in North America and Europe. Buses are pulled from regular routes and dispatched to serve as shuttles along the disrupted rail segment until regular train service is restored. In determining the required number of buses and source routes, most transit agencies rely on ad hoc approaches based on operational experience and constraints, which do not necessarily alleviate the extensive delays and queue build-ups at affected stations, nor do they minimize system-wide impacts in an optimal manner. This paper proposes a genetic algorithm-based optimization model to determine the optimal number of shuttle buses and route allocation to minimize overall subway- and bus rider delay for any given rail disruption incident. The generated optimal solutions were sensitive to bus-bay capacity constraints along the shuttle service corridor of any given disrupted subway segment, utilizing methods found in the Transit Capacity and Quality of Service Manual. The model was used in an analysis of real-world incident data obtained from the Toronto Transit Commission and supplemented by other passenger and travel time data. The bus bridging toolkit showed strong potential to produce efficient shuttle response plans that reduced the transit user delays by more than 50% while ensuring minimum queue formation at disrupted stations and maximizing the utilization of shuttle buses.

BBR-ACD: BBR with Advanced Congestion Detection

With the aim of improved throughput with reduced delay, Google proposed the bottleneck bandwidth and round-trip time (BBR) congestion control algorithm in 2016. Contrasting with the traditional loss-based congestion control algorithms, it operates without bottleneck queue formation and packet losses. However, we find unexpected behaviour in BBR during testbed experiments and network simulator 3 (NS-3) simulations. We observe huge packet losses, retransmissions, and large queue formation in the bottleneck in a congested network scenario. We believe this is because of BBR’s nature of sending extra data during the bandwidth probing without considering the network conditions, and the lack of a proper recovery mechanism. In a congested network, the sent extra data creates a large queue in the bottleneck, which is sustained due to insufficient drain time. BBR lacks a proper mechanism to detect such large bottleneck queues, cannot comply with the critical congestion situation properly, and results in excessive retransmission problems. Based on these observations, we propose a derivative of BBR, called “BBR with advanced congestion detection (BBR-ACD)”, that reduces the excessive retransmissions without losing the merits. We propose a novel method to determine an actual congestion situation by considering the packet loss and delay-gradient of round-trip time, and implement a proper recovery mechanism to handle such a congestion situation. Through extensive test and NS-3 simulations, we confirmed that the proposed BBR-ACD could reduce the retransmissions by about 50% while improving the total goodput of the network.

Quality of floating car data (FCD) as a surrogate measure for urban arterial speed

Commercial floating car data (FCD) is being increasingly used as a traffic data source due to its lower cost despite concerns about its reliability. This paper focuses on the evaluation of FCD speed quality as a surrogate measure for arterial speed from different aspects. First, FCD speed is compared to video-based traffic data, collected from a specific urban road segment and assumed as ground truth in (a) descriptive evaluations, (b) speed estimation, and (c) level of service estimation. Regression analysis carried out to derive transformation function between two datasets showed a nonlinear relation with a high correlation coefficient of 0.82. Working with data along an urban corridor of 3.6 km also showed that despite some outliers, FCD was capable of detecting peak-hour queue formations as well as incident related ones. Use of transformation function on FCD speeds helped to increase its potential in urban traffic monitoring.

Modelling crowd movements in domains with boundaries

Abstract This paper contributes to the macroscopic modelling of crowd movements. The presented model is ‘non-local’; i.e. it takes into account interactions among pedestrians at different distances. Particular care is given to how non-local interactions are influenced by walls, obstacles and exits. The resulting dynamics captures various well-known patterns of crowd movements, such as the clogging of exits and the spontaneous formation of queues. The careful choice of obstacles near an exit is shown to be able to reduce evacuation times. An ad hoc numerical algorithm is detailed, some of its properties discussed and the convergence of the corresponding approximate solutions is investigated.

The Degree of Parallelism in Generalized Stochastic Network

For the generalized stochastic network the concept of degree of parallelism is entered. The method of determination of this value is offered. It makes the choice of the minimum number of performers of the network at which there is no formation of queues for passing of arcs.

Modelling cyclist queue formation using a two-layer framework for operational cycling behaviour

Operational cycling behaviour is greatly understudied, even lacking a definition of what this behavioural level actually entails in terms of decision making. In this paper, we define the cyclist operational level and argue that it consists of two intertwined processes, a mental and a physical process. The mental process refers to path choices made within a route and the physical process refers to the bicycle control dynamics through pedalling and steering. We propose a novel two-layer framework, where each layer captures the tasks of one of the processes within the operational level. Discrete choice theory is proposed to model each layer. The plausibility of the framework is demonstrated through an application focusing on the queue formation process upstream of a red traffic light, including selecting a queuing position and cycling towards it. Models are estimated for the two layers using cyclist trajectory data collected at a signalised intersection in Amsterdam, the Netherlands. The models reveal the attributes that influence the decisions made in each layer and are face validated using simulation. The proposed framework and the (behavioural) findings of its application are the main scientific contributions of this paper, which pave the way for future research.

Bid-Based Priority Signal Control in a Connected Environment: Concept

Though the loss of time is considered equivalent to opportunity loss, little research has been conducted in the field of signal control that accounts for individual differences in subjective opportunity loss. Because bids reflect the subjective valuation of opportunity loss, this paper introduces the concept of a bid-based priority signal control that accommodates indeterministic characteristics of queue formation in a connected environment and addresses several key elements of such a concept. Within this conception, drivers can bid for their desired signal indication. Based on these bids, an algorithm extends a green interval as long as the cumulative opportunity loss observed in stopped movements remains less than the value that would be lost through the termination of that green interval. The effects of this new type of control on user benefit and queueing delay were assessed using the asymmetric simple exclusion process. Simulation results showed that the bid-based priority signal control produced a greater subjective user benefit when measured in relation to a pre-timed control with a similar green interval. In addition, the bid-based priority signal kept the average maximum queue length relatively equal to its pre-timed equivalent. The bid-based priority signal control also balanced out the expected values of user benefit in conflicting movements. Bid-based signal priority control was recommended for further study to investigate the effects of bidding distributions on effectiveness and queue length in high-fidelity microsimulations.

Modeling the permissive-only left-turn queue at signals

The traffic performance of signalized intersections can be evaluated by the average queue length of vehicles. However, the queue formation and dissipation of vehicles at a signalized intersection is a very complicated stochastic process; thus, it can hardly be explicitly described by the average index. Instead, the dynamic behavior of vehicles queueing at signalized intersections tends to be covered up by the average value of queue length, especially when a permissive-only left-turn phasing is used. To this end, a stochastic queueing model is developed to analytically investigate the evolution of queue length at a shared lane under the permissive-only phasing. In this paper, the queue length distribution of vehicles on a shared lane with permissive-only left-turn phasing is obtained, which can be used to analytically describe the dynamic characteristics of queue length of permissive-only left-turn vehicles. In addition, the factors that could affect the dynamic behavior of permissive-only left-turn queue are also experimentally determined, which will help to determine the selection of left-turn operational phasing and the type of left-turn lane (i.e., shared or exclusive left-turn lane) for a specific traffic condition.

Empirical Observations and Formulations of Tri-Class Traffic Flow Properties for Design of Traffic Signals

Recognizing the popularity of scooters and buses in many developing countries and the significant impact of their physical maneuverability on the properties of mixed traffic flows, this study presents our findings from empirical observations of tri-class traffic flows, including the complex interactions among scooters, passenger cars, and buses from their discharging to the formation of stop queues at downstream intersections. Using camcorders mounted either on high-rise structures or drones, our empirical data include individual vehicles and their trajectories over time. Formulations based on empirical findings have also been proposed to model the behaviors of such tri-class flows on queue discharging, formation, lane choice in propagation, and filtering processes. Evaluations of field data, with respect to time-varying throughputs, queue clearance times, and queue length evolution, confirms the effectiveness of the proposed formulations. These can serve as the basis for developing mesoscopic tri-class flow simulation models or for designing signals for arterials plagued by such mixed flows.

Computing Dynamic User Equilibria on Large-Scale Networks with Software Implementation

Dynamic user equilibrium (DUE) is the most widely studied form of dynamic traffic assignment (DTA), in which road travelers engage in a non-cooperative Nash-like game with departure time and route choices. DUE models describe and predict the time-varying traffic flows on a network consistent with traffic flow theory and travel behavior. This paper documents theoretical and numerical advances in synthesizing traffic flow theory and DUE modeling, by presenting a holistic computational theory of DUE, which is numerically implemented in a MATLAB package. In particular, the dynamic network loading (DNL) sub-problem is formulated as a system of differential algebraic equations based on the Lighthill-Whitham-Richards fluid dynamic model, which captures the formation, propagation and dissipation of physical queues as well as vehicle spillback on networks. Then, the fixed-point algorithm is employed to solve the DUE problems with simultaneous route and departure time choices on several large-scale networks. We make openly available the MATLAB package, which can be used to solve DUE problems on user-defined networks, aiming to not only facilitate benchmarking a wide range of DUE algorithms and solutions, but also offer researchers a platform to further develop their own models and applications. The MATLAB package and computational examples are available at https://github.com/DrKeHan/DTA.

A study on application of queuing theory at petrol retail outlet

Waiting line is formed whenever the service rate is lower than the demand for the service. At many fuel stations there is always a queue to get the fuel due to which it may happen that some of the customers may move to competitor's retail outlet where the waiting time in queue is less. Formation of queue causes the increased waiting time for the customers, over-utilisation of the servers and loss of customer good will. Application of queuing theory determines the measure of the performance of the service facility and which in turn helps to design appropriate/optimised service facility. In this paper, a single channel multiple server model is used to analyse the characteristics of queuing model.

A STUDY ON APPLICATION OF QUEUING THEORY AT PETROL RETAIL OUTLET

Waiting line is formed whenever the service rate is lower than the demand for the service. At many fuel stations there is always a queue to get the fuel due to which it may happen that some of the customers may move to competitor's retail outlet where the waiting time in queue is less. Formation of queue causes the increased waiting time for the customers, over-utilisation of the servers and loss of customer good will. Application of queuing theory determines the measure of the performance of the service facility and which in turn helps to design appropriate/optimised service facility. In this paper, a single channel multiple server model is used to analyse the characteristics of queuing model.