Queue Spillback Research Articles

Passenger-oriented Distributed Traffic Signal Control Using Dynamic Programming with Vehicle Queue Spillback and Waiting Time Constraints

Passenger-oriented Distributed Traffic Signal Control Using Dynamic Programming with Vehicle Queue Spillback and Waiting Time Constraints

An emergency vehicle traffic signal preemption system considering queue spillbacks along routes and negative impacts on non‐priority traffic

AbstractIn urban areas, emergency vehicles (EVs) need efficient traffic signal preemption to ensure timely responses during peak hours. While emergency vehicle traffic signal preemption (EVTSP) has garnered significant attention in the literature, the issues of queue spillbacks and negative impacts on non‐priority traffic have been relatively underreported. These issues are particularly critical during peak hours, notably in densely populated urban areas. This study presents an EVTSP system that considers queue spillbacks on approaches along the routes of EVs and the negative impacts of signal preemption on non‐priority traffic. The proposed control system has four key components: (1) a queue length management algorithm to ensure that an EV will not be impeded by excessive queues, particularly on its initial approaches along its route; (2) a signal preemption algorithm to guarantee uninterrupted passage of an EV even for approaches experiencing queue spillbacks; (3) a traffic status recovery algorithm to alleviate the extra waiting time for non‐priority vehicles after an EV crosses each intersection; and (4) a signal plan recovery algorithm to smoothly transit traffic signals to normal operation. The experimental results confirm that the proposed system considerably improves the travel time of an EV and mitigates the negative impacts on non‐priority traffic.

Left-turn queue spillback identification based on single-section license plate recognition data

Left-turn queue spillback occurs when the queue of left-turn or adjacent through traffic exceeds the channelized section's capacity, resulting in discharge flow breakdown and inefficient use of green time. License plate recognition (LPR) systems, which offer lane-based data on individual vehicle discharging, present an ideal data source for the identification of queue spillback. Therefore, this paper proposes a method for left-turn queue spillback identification using single-section LPR data. The proposed method involves defining 10 types of left-turn spillbacks based on the queuing status of the left-turn and adjacent through lanes. Then, a lightweight algorithm is proposed to estimate lane-based queue lengths. At last, a rule-based identification algorithm is developed to specify spillback types by considering the left-turn phasing, estimated queue lengths, and lane capacity. The empirical experiment demonstrates a promising accuracy of 87.28%. Additionally, simulations with five left-turn phasing schemes further validate the proposed method, averaging nearly 90% accuracy in identifying spillbacks.

POLARIS-LC: A Multi-Class traffic Flow Model in Lagrangian Coordinates for Large-Scale Simulation

Agent-based modeling has become increasingly prevalent in transportation systems simulation as the scenarios around new technologies and policies become increasingly complex. This increased complexity, in terms of traveler behavior, traffic flow, modal operations, system management, and so on, requires increasingly sensitive and detailed representation of the core components of the simulation, especially as it relates to traffic flow. Many future mobility solutions rely on connectivity, communication, advanced sensing, and detailed information flows, while the simulation also needs to remain computationally tractable. In this paper, we propose a new Lagrangian Coordinate within the POLARIS Agent-Based-Modeling Framework (LC-POLARIS) of traffic flow, which combines computational efficiency while adding microscopic features. This model allows multi-class traffic flow by setting different speed-spacing relationships. In this modeling, automated vehicles have lower reaction time, while light-duty trucks have lower free-flow speed and higher jam spacing. The model captures the reduction in capacity and higher queue spillback because of higher jam spacing of trucks. Also, the model yields higher capacity for both passenger and light duty automated vehicles on expressways. The LC-ABM traffic flow model is validated in Bloomington, IL, USA, in a scenario with different rates of four vehicle types.

Operational Analysis to Establish Guidelines for Designing Auxiliary Lanes on Frontage Roads

Auxiliary lanes (ALs) provide significant operational benefits by spreading merging or weaving movements over a long distance or by providing an extra lane of capacity through a freeway bottleneck or signalized intersection. However, if ALs are not long enough, they may produce merge bottlenecks and queue spillbacks onto the main lanes of the freeway or arterial, creating operational and safety concerns. While there is literature or guidance for ALs on freeways, there is minimal design guidance for ALs on frontage roads. This study systematically examines various design elements of ALs on frontage roads and the design parameters that influence these design elements by leveraging microsimulation. The key purpose of this study is to develop recommendations for the length of ALs in six common frontage road design configurations based on the selected nine design parameters. More than 3,000 simulation runs were performed to develop and compare guidance with the existing design manuals. The results reveal new insights that would complement existing design guidance.

The potential of signalized offset T-intersections to accommodate new developments

An offset T-intersection splits a conventional four leg intersection into two three-leg T-intersections to reduce the number of conflicts. While the safety benefits of offset T-intersections have been widely documented, the effects on operations are not well understood. To fix that, this paper employed microsimulation modeling to investigate the differences in operational performance between offset T-intersections and four-leg standard intersections under various traffic demands, intersection spacings, and signal timing schemes for three development types: superstore, hybrid gas station, and residential area. Queue length and delay were employed as measurements of effectiveness. Based on microsimulation modeling, we found that under most of the tested scenarios, offset T-intersections were superior to four-leg intersections in terms of reducing delay for the main street traffic. In addition, we found that the left–right (L-R) offset T-intersection configuration outperformed the right-left (R-L) offset configuration in terms of preventing main-street left turn queue spillback. Based on the simulation results, the paper provided practice-ready guidelines on selecting an optimum intersection configuration for each specific development type given the volume demands and known geometric constraints for a given site.

General solution scheme for the static link transmission model

Most static traffic assignment models, both in the literature and in practice, are neither capacity constrained nor storage constrained. They allow flows to exceed the link capacity and/or queues to exceed the link length. Recent studies in this area have resulted in novel approaches that do consider capacity constraints, resulting in residual queues, and sometimes even storage constraints, resulting in possible queue spillback. We build upon the results of these works and in particular on the model formulated in Bliemer and Raadsen (2020) that introduced a static network loading model formulation that is both capacity constrained as well as storage constrained. Their static network loading model formulation is derived from – and consistent with - the link transmission model, a well-established dynamic network loading model. It is referred to as the static link transmission model (sLTM) in this paper. This model considers a general concave fundamental diagram for each link and a general first order node model. It is well known that ignoring queue spillback can result in significant underestimation of path travel times. This is especially true for paths that do not traverse any of the bottleneck(s) directly, but that are affected by space occupied by queues that are spilling back. The prospect of being able to capture queuing and spillback effects in static assignment provides new opportunities for improving the modelling capabilities of this paradigm. In this paper, we propose a solution scheme to sLTM capable of finding a solution on large scale networks. This is the first time that an algorithm is proposed for solving an analytical static model with queue spillback. The inclusion of a node model in a static context - while enhancing the model's capabilities – generally results in the absence of a guaranteed convergent algorithm, and introducing spillback exacerbates the issue. Given the challenges of finding a stable solution, we discuss a base solution scheme and three (configurable) extensions. Further, we investigate algorithmic settings with respect to convergence and its impact on computational cost. A large-scale case study demonstrates the feasibility of the proposed scheme by finding solutions under the most challenging of conditions in a real-world setting. We show that improving convergence capabilities negatively affects computational efficiency. To this end, several potential improvements based on our findings are discussed. Lastly, we discuss the potential of this line of research in more general terms, highlighting strengths and weaknesses following our experiences.

Network-level signal predictive control with real-time routing information

This paper presents a framework for signalized road network predictive optimization using real-time routing information from connected vehicles (CVs). An important feature of the real-time routing information is the ability of CVs to broadcast the target routes they expect to travel through to the infrastructure in real time while assuming that a majority of the CVs can provide their target routes. A fully movement-level network representation model is proposed to easily describe the traffic state and demand of the signalized network. The problem is formulated as a mixed integer linear programming model to predict the movement-level network dynamics, which is solved in real time to optimize phase-free movement signal timings. The objective is to maximize the network throughput within the prediction horizon while avoiding queue spillbacks. A decentralized solution algorithm is developed to decompose the network-level problem into intersection-level subproblems, thereby reducing computational complexity. Simulation experiments validate the advantages of the proposed framework over TRANSYT schemes and max pressure-based control strategy in various scenarios. Sensitivity analysis shows the control performance under different traffic demand levels and penetration rates of the target route information. Comparisons in prediction accuracy, control performance, and computational efficiency between centralized and decentralized solutions of the proposed model are also conducted. This study explores the application of real-time routing information as a potential type of data for the network-level predictive signal optimization in the future CV environment.

Developing Commercial Motor Vehicle Crash-Specific Safety Performance Functions at Interchange Ramp Terminals in Kentucky

Commercial motor vehicle (CMV) drivers often experience difficulties in turning and crossing at ramp terminals. CMV-involved crashes could cause queue spillback at ramp terminals and possibly nearby freeway mainline and crossroads. The safety and mobility of ramp terminals for accommodating CMVs is thus of significant importance. However, interchange ramp terminals have rarely been investigated in previous safety studies, especially with regard to CMV-involved crashes. This study aimed at examining CMV-related crashes that occurred at ramp terminals. Heterogeneous negative binomial (HTNB) and traditional negative binomial (NB) models were fitted and compared while developing a safety performance function (SPF) for predicting CMV crashes and identifying high-risk ramp terminals. Information on crash history and site-specific characteristics was collected at 285 ramp terminals in the state of Kentucky between 2015 and 2019. The results showed that the HTNB model outperformed the NB model in relation to various goodness-of-fit measures (e.g., likelihood ratio test “LRT”, Akaike information criterion “AIC” and McFadden Pseudo R-squared statistic). The predicted crash frequencies while applying the HTNB model were then used to identify and rank high-risk ramp terminals. Ramp terminals with signalized traffic control type, with greater average daily traffic on the exit ramp, with two or more traffic lanes on the exit ramp, and adjacent to commercial or industrial areas were found to experience more CMV crashes. Several safety countermeasures were proposed to alleviate CMV-involved crashes at ramp terminals. One example is ensuring the presence of physical medians on crossroads (or major roads) ahead of exits ramps.

Ramp Metering Strategies: A Literature Review

Ramp metering (RM) is a control tool that allows keeping the flow in freeways main stream stable (occupancy below critical values), in order to delay the onset of congestion. A proper application guarantees a lower Total Travel time Spent in the network (TTS) (travel time in the main stream and waiting time at on-ramps), to varying degrees depending on the application. The benefits of RM arise from preventing congestion and/or from preventing queue spillback. In this paper, the state of the art is surveyed, ultimately selecting twenty-one relevant documents reporting case studies that compare the performance of two or more RM strategies. These documents are classified by the strategy used and reported gains (reduction in TTS). Regardless of the strategy, the authors recommend applying RM to highly congested freeways with similar TTS among ramps. Bottlenecks should be clearly identified to establish the place where the control must be carried out. Control parameters should be set using historical and real-time data and updated according to a receding horizon. Detrimental factors that could exacerbate initial conditions of congestion and spillback include: incorrect input parameters, reactive instead of proactive approaches, and queue formation only on some coordinated ramps.

Real-Time Arterial-Friendly Ramp Metering System

To balance the benefits between the freeway and arterial users and also to prevent on-ramp queue spillbacks, the authors have developed an arterial-friendly local ramp metering control (AF-ramp) system for time-of-day operations during recurrent congestion. This study presents the real-time version of the AF-ramp (named RAF-ramp) system, with a lane-group-based macroscopic traffic module for predicting traffic state and for executing control strategies, aiming at maximizing the total throughput from the control area, comprising the ramp meter and nearby local intersection signals. Recognizing the discrepancy in the dynamic nature between ramp traffic and arterial flows, the RAF-ramp system with its embedded traffic state prediction and monitoring mechanism can trigger the concurrent optimization of both controls when justified to do so, or only dynamically adjust the ramp metering rate under the pre-optimized local signal environment. The results of extensive simulation experiments have confirmed that the proposed system outperforms the widely-applied real-time ramp control model, ALINEA/Q, under various experimental traffic scenarios, because the produced control strategies can effectively utilize the freeway’s weaving capacity and also best coordinate neighboring intersections’ signals to maximize the entire network’s performance. Such a real-time arterial-friendly ramp metering system, addressing both the time-varying freeway dynamics and the concerns of local traffic users, may well serve as an effective tool for contending with bottlenecks at freeway interchanges.

CALIBRATION AND VALIDATION OF A MACROSCOPIC TRAFFIC FLOW MODEL BASED ON PLATOON DISPERSION AND QUEUE PROPAGATION

This paper proposes a preliminary calibration and validation of a macroscopic traffic flow model for signalised junctions. In fact, on the network signal setting design problem, a reliable modelling approach must be adopted to acknowledge the traffic flow effects, considering two phenomena: queue dispersion and spillback. The proposed model is an extension of the space-time discrete Cell Transmission Model (CTM), which can simulate dispersion and horizontal queue. This preliminary calibration and validation use real-world data collected on an arterial of the city of Salerno (south of Italy). Results showed that the estimated parameters are consistent with the literature.

Real-time decentralized traffic signal control for congested urban networks considering queue spillbacks

This paper proposes a decentralized network-level traffic signal control method addressing the effects of queue spillbacks. The method is traffic-responsive, does not require data communication between intersections’ controllers, uses lane-based queue measurements, and is acyclic. Each traffic controller operating at an intersection aims at maximizing the effective outflow rate locally and independently with the goal of maximizing global throughput of the entire network. At each intersection, the signal control method estimates and adopts the maximum possible phase time in which all active movements discharge at their full capacity. This is modeled using a shockwave based queue length estimation model while capturing the spillback at the downstream links. The method demands real-time data including, the queue lengths, the arrival flows, and the downstream queue lengths in all the lanes at the control decision times. The proposed method results in a feasible solution in all conditions in the entire network with any scale within a short amount of time. A stability concept for the traffic network is defined, and asymptotic stability of the controlled traffic network are verified. Moreover, a sufficient condition for the optimality of the proposed control algorithm for maximizing the instantaneous total throughput of the network intersections is demonstrated. Numerical results show that the proposed method outperforms benchmark methods in both isolated intersection and network configurations.

Towards improving sustainability of rail transport by reducing traffic delays at level crossings: A case study for the State of Florida

Rail transport makes a significant contribution to the economy of many geographical locations around the globe. Along with numerous merits, rail transport has some demerits as well, such as traffic delays and safety issues at level crossings. Traffic delays at level crossings have various negative impacts, including economic losses, increasing number of violations because of delays, queue spillbacks at the adjacent intersections, and environmental impacts. This study reviews a number of level crossing delay estimation approaches found in the literature. Based on the state-of-the-art review and practical considerations, a new approach for estimation of the overall traffic delay per day at level crossings with the existing warning devices is proposed to improve the sustainability of rail transport. Moreover, in order to assist with the selection of countermeasures for level crossing upgrades, the proposed approach can be also used to estimate the overall traffic delay per day at level crossings after implementation of countermeasures. A case study is conducted for the public and private level crossings in the State of Florida, considering the countermeasures commonly used in the United States. It was revealed that traffic delays were higher at the public level crossings than at the private level crossings, both before and after implementation of the candidate countermeasures. The case study also indicated that low-cost countermeasures could be more advantageous in terms of safety effectiveness and traffic delays as compared to many high-cost conventional countermeasures. Furthermore, certain attributes of the level crossings in Florida with the highest traffic delays were investigated.

Optimizing the integrated off-ramp signal control to prevent queue spillback to the freeway mainline

This study presents an integrated off-ramp control model that incorporates the impact of ramp queue spillback on the freeway mainline in design of the off-ramp intersection’s signal plan. The proposed model consists of a mesoscopic traffic module to capture the complex interrelations between the freeway mainline’s traffic state and the lane-changing maneuvers triggered by off-ramp queue spillback, and a local signal optimization module to maximize the total benefit for both the freeway and intersection in the vicinity of an interchange. Using the simulation–optimization solution logic, the proposed model will first estimate the off-ramp queue length, produced from the signal module, to assess the resulting impacts on the freeway mainline, and then feed such impacts back to the control objective to iteratively search for the optimal cycle length and phase durations with the genetic algorithm. To assess the effectiveness of the proposed model, this study has conducted a two-stage evaluation, where the first stage uses the extensive field data to confirm the reliability of its freeway queue impact module and the second stage focuses on evaluating the benefits of accounting for the queue impacts on the freeway in design of the off-ramp signal plan under various traffic scenarios. The results from extensive tests have confirmed that the proposed model can effectively minimize the likelihood of causing off-ramp queues to spill back to the freeway mainline, and the failing to incorporate such impacts in design of the off-ramp signal will contribute significantly to the formation of freeway bottlenecks in the interchange area.

Arterial-Friendly Local Ramp Metering Control Strategy

To prevent local streets being blocked by overflowing on-ramp queues, a standard practice of ramp metering control is to restrain its function when a series of preset conditions are identified by on-ramp queue detectors. Such a trade-off between potential ramp queue spillback and the restraint resulting from the operation of metering control may often fail to either effectively mitigate bottlenecks caused by on-ramp waving or convince arterial users and local traffic agencies of the need for ramp metering operations. This study, therefore, presents an arterial-friendly local ramp metering system (named AF-ramp) that can achieve the target metering rate to produce optimal freeway conditions without ramp queues spilling back onto local streets. This is achieved by concurrently optimizing the signal plans for those intersections that send turning flows to the ramp. At this stage, this system has been developed for time-of-day control. It could also serve as the base module for extending to real-time control, or multi-ramp coordinated operations. The AF-ramp model, with its ability to optimize the arterial signals concurrently with the ramp metering rate, can ensure the best use of the capacity of local intersections and prevent any gridlock caused by overflows from on-ramp queue spillback or arterial turning traffic. With extensive simulation experiments, the evaluation results confirmed the AF-ramp model’s effectiveness in improving traffic conditions on both the freeway and its neighboring arterial links at the same time. This study has also introduced the real-time extension of the proposed model and a framework of a transition from the time-of-day control to fully responsive real-time operations.

Optimizing distribution of metered traffic flow in perimeter control: Queue and delay balancing approaches

Perimeter traffic flow control based on the macroscopic or network fundamental diagram provides the opportunity of operating an urban traffic network at its capacity. Because perimeter control operates on the basis of restricting inflow via reduced green times at selected entry (gated) links, vehicles on those links may be subject to queuing and delay. The experienced delay or resulting queue lengths depend on the adopted policy for the distribution of the inflows and corresponding green times at the gated links. The chosen policy may have a significant impact on the traffic system under control. For example, managing queue lengths may reduce the interference with upstream traffic whereas the management of delays may improve users’ perception with respect to equity and fairness. In this paper, an approach has been proposed to distribute the gated flow based on the queue lengths or experienced delay at the gated signalized junctions. This is in contrast to standard practice that distributes inflows proportionally to the gated links’ saturation flows. Perimeter control is then evaluated in a microscopic simulator for a realistic traffic network and compared in three configurations against fixed-time: perimeter control without queue or delay management; perimeter control with relative queue balancing; and perimeter control with delay balancing. It has been found that managing the queues at the gated links not only improves the overall network performance but also reduces the possibility of queue propagation to the upstream junctions. This improves traffic flow outside the protected network by managing the queue propagation at the gated links and reducing the possibility of queue spill-back to upstream intersections. In addition, the results indicate that perimeter control with delay balancing has a similar performance as the case without queue or delay management being a suitable approach for flow distribution among the gated links. In the scenarios with perimeter control with either queue or delay balancing the gap between the ordered flow by the controller and the actual flow crossing the stop-line at the gated links reduced remarkably.

Queue Intensity Adaptive Signal Control for Isolated Intersection Based on Vehicle Trajectory Data

With recent development of mobile Internet technology and connected vehicle technology, vehicle trajectory data are readily available and exhibit great potential to be used as an alternative data source for urban traffic signal control. In this study, a Queue Intensity Adaptive (QIA) algorithm is proposed, using vehicle trajectory data as the only input to perform adaptive signal control. First, a Kalman filter-based method is employed to estimate real-time queue state with vehicle trajectories. Then, based on queue intensity that quantifies queuing pressure, five control situations are defined, and different min-max optimization models are designed correspondingly. Last, a situation-aware signal control optimization procedure is developed to adapt intersection’s queue intensity. QIA algorithm optimizes phase sequence and green time simultaneously. One case study was conducted at a field intersection in Shenzhen, China. The results show that provided with 7.4% penetrated vehicle trajectories, QIA algorithm effectively prevented queue spillback by constraining temporal percentage of queue spillback under 2.4%. The performance of QIA was also compared with the algorithm in Synchro and Max Pressure (MP) method. It was found that compared with Synchro, the extreme queue intensity, temporal percentage of queue spillback, delay, and stops were decreased by 54.7%, 97%, 22.3%, and 45.1%, respectively, and compared with MP the above four indices were decreased by 16%, 61.5%, −1.8%, and 49.4%, respectively.

A Link Queue Model of Network Traffic Flow

Fundamental to many transportation network studies, traffic flow models can be used to describe traffic dynamics determined by drivers’ car-following, lane-changing, merging, and diverging behaviors. In this study, we develop a deterministic queueing model of network traffic flow, in which traffic on each link is considered as a queue. In the link queue model (LQM), the demand and supply of a link queue are defined in the queue size (number of vehicles), and its in- and out-flows are computed from junction flux functions corresponding to macroscopic merging and diverging rules. The new model is a system of ordinary differential equations that is mathematically tractable and computationally efficient and can capture queue spillbacks and interactions among links. We further demonstrate that the LQM is fundamentally different from the cell transmission model (CTM) and link transmission model (LTM) for a road segment, a signalized ring road, and a diverge-merge network, with respect to the shock and rarefaction waves, network fundamental diagram, and stability property. In a sense, the new model is a space-continuous approximation of the kinematic wave model and can be a useful addition to the multiscale modeling framework of network traffic flow. The model has been applied to formulate and solve network traffic control and observation problems.

Estimating incident impacts on urban street travel time in the case of super saturation in traffic lights

Dynamic signal control strategies are effective in relieving congestions during nontypical days, such as those with high demands, incidents with different attributes, and adverse weather conditions. This research recognizes the need to model the impacts of dynamic signal controls for different days representing, different demand and incident levels. Methods are identified to calibrate the utilized tools for the patterns during different days based on demands and incident conditions utilizing combinations of real-world data with different levels of details. A significant challenge addressed in this study is to ensure that the mesoscopic simulation-based dynamic traffic assignment (DTA) models produces turning movement volumes at signalized intersections with sufficient accuracy for the purpose of the analysis. A new model is developed to estimate the drop in capacity at the incident location by considering the downstream signal control queue spillback effects. The developed capacity reduction models were used to estimate delay due to an urban street incident. The delay was calculated as a combination of the delay due to queuing on the incident link and the increase in upstream intersection control delays due the reduction in maximum throughputs resulting from queue spillback to the upstream intersection The HCS-based method estimated a reduction in delay resulting from the new signal timing plan to be around 3,404 vehicle-hours, whereas the VISSIM shows that the new signal timing saving in delay is 4,008 vehicle-hours. This confirms that the developed method and VISSIM estimation of the benefits are consistent.