Residual Queues Research Articles

Privacy-preserving adaptive traffic signal control in a connected vehicle environment

Although Connected Vehicles (CVs) have demonstrated tremendous potential to enhance traffic operations, they can impose privacy risks on individual travelers, e.g., leaking sensitive information about their frequently visited places, routing behavior, etc. Despite the large body of literature that devises various algorithms to exploit CV information, research on privacy-preserving traffic control is still in its infancy. In this paper, we aim to fill this research gap and propose a privacy-preserving adaptive traffic signal control method using partially connected vehicle data. Specifically, we proposed a privacy-preserving mechanism to protect CV data against three types of attacks: CV collusion attacks, database attacks, and inference attacks. The mechanism leverages secure Multi-Party Computation and differential privacy to aggregate individual-level CV data to calculate key traffic parameters without compromising the privacy of CV users. For seamless integration with the privacy-preserving mechanism, we develop a traffic signal optimization model and an arrival rate estimator relying only on aggregated CV data, being applied to both undersaturated and oversaturated traffic conditions. The optimization model is further extended to a stochastic programming problem to explicitly handle the noises added by the privacy-preserving mechanism. Evaluation results show that the linear optimization model preserves privacy with a marginal impact on control performance, and the stochastic programming model can significantly reduce residual queues compared to the linear programming model, with almost no increase in vehicle delay. Overall, our methods demonstrate the feasibility of incorporating privacy-preserving mechanisms in CV-based traffic modeling and control, which guarantees both utility and privacy.

Simple abstract models to study stability of urban networks with decentralized signal control

Traffic Signal Controllers (TSCs) used to manage intersections can influence the residual queues at intersections. These residual queues can lead to an irreversible state of network gridlock. This paper discovers the advantages of locally adaptive TSCs utilizing traffic information on both upstream and downstream approaches of an intersection (e.g., back-pressure or BP control) over employing only upstream approaches information (e.g., proportional control) in avoiding gridlock. Although BP algorithms are mathematically proven to be throughput optimal as they bound network queues for all feasible demands, the proofs exist only for networks with infinite link capacities, fixed-route choices, or for TSCs with no minimum green-time requirement.We propose a simple asymmetric two-bin model to study the influence of the BP control on network behavior (e.g., gridlock probability, average time to gridlock and maximum throughput) and compare it with the other control policy. Theoretical insights from the asymmetric two-bin model are verified using microsimulation results of the asymmetric two-ring model. Both analytical and simulation results imply that BP-based TSCs have an increased capability of delaying or avoiding gridlock in heavy traffic with a larger time step at the cost of potentially lower throughput in undersaturated networks. The paper provides a framework to evaluate control policies in inhomogeneous networks and shows the superiority of BP-based TSCs against gridlock in grid networks with finite capacities of links.

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.

Travel demand matrix estimation for strategic road traffic assignment models with strict capacity constraints and residual queues

This paper presents an efficient solution method for the matrix estimation problem using a static capacity constrained traffic assignment (SCCTA) model with residual queues. The solution method allows for inclusion of route queuing delays and congestion patterns besides the traditional link flows and prior demand matrix whilst the tractability of the SCCTA model avoids the need for tedious tuning of application specific algorithmic parameters.The proposed solution method solves a series of simplified optimization problems, thereby avoiding costly additional assignment model runs. Link state constraints are used to prevent usage of approximations outside their valid range as well as to include observed congestion patterns. The proposed solution method is designed to be fast, scalable, robust, tractable and reliable because conditions under which a solution to the simplified optimization problem exist are known and because the problem is convex and has a smooth objective function.Four test case applications on the small Sioux Falls model are presented, each consisting of 100 runs with varied input for robustness. The applications demonstrate the added value of inclusion of observed congestion patterns and route queuing delays within the solution method. In addition, application on the large scale BBMB model demonstrates that the proposed solution method is indeed scalable to large scale applications and clearly outperforms the method mostly used in current practice.

Method of Bidirectional Green Wave Coordinated Control for Arterials under Asymmetric Release Mode

The existing coordinated control methods of green wave are complicated, difficult to operate and mainly applicable to intersection groups with symmetrical arriving upstream flows. Based on engineering practice, a new method of bidirectional progression green wave coordination control was presented by designing particular overlapping phases on the basis of NEMA dual-ring phasing configuration. Applying the characteristics of asymmetric release mode and the requirements of green wave coordinated control, the overall optimization designs of phase sequence combination and offset were carried out, and the influences of cruising speed and residual queues at red light on offset were considered, and then the classical bidirectional green wave graphic method was optimized. Based on the investigation data of the intersections group of Ziwu Road in Qujing City, bidirectional green wave designs were conducted under both symmetric and asymmetric release mode. The results show that the latter approach not only improved the bandwidth of bidirectional green wave band effectively, but also reduced the average delay and the average number of stops on the main road.

Traffic Volume Estimation for both Undersaturated and Oversaturated Signalized Intersections With Stopbar Location Estimation Using Trajectory Data

We proposed an estimator for traffic volumes at signalized intersections using only sparse trajectory data. Each pair of observed trajectories defined a statistical event, from which traffic volume was inferred. The interaction between trajectory stop distances, arrival speeds, and signal plan defined different classes of statistical events, with distinct likelihood expressions. Contrary to recent approaches found in the literature, our method addressed oversaturation and residual queues. We also proposed a method to estimate stopbar location, crucial to properly estimating stop distances and queue lengths. Arrivals were assumed to follow a negative exponential distribution., and the method was compatible with any kind of control. The signal plan was assumed to be known, but an estimated signal plan could also be used. The estimation was formulated as a maximum marginal likelihood problem. We showed the problem to be globally concave and, thus, optimally solvable by standard gradient-based methods. The estimator was first validated by an event-based Lighthill–Whitham–Richards simulation, suppressing any measurement errors from trajectories and uncertainty from driver behavior. The estimator was found to consistently show bias below 10% in low-penetration-rate situations, when the [Formula: see text] ratio was medium to high. Finally, the estimator was tested at a real-world intersection set by the authors in Beijing, China, which obtained a bias of similar of magnitude.

Modelling the road network capacity considering residual queues and connected automated vehicles

This paper investigates the network capacity considering residual queues and connected automated vehicles. First, the weighted network outflow is introduced to measure the network capacity. Then, two bilevel models are built to calculate the network capacity with residual queues under the user equilibrium (UE) principle and the system optimum (SO) principle, respectively. The UE-based network capacity model is employed to estimate the capacity of the dedicated road network for human-driven vehicles (HVs), while the SO-based network capacity model is employed to estimate the capacity of the dedicated road network for connected automated vehicles (CAVs). Furthermore, by combining the two proposed network capacity models, a model to allocate link capacity between CAV lanes and HV lanes to maximise the whole network capacity is presented, in which the CAV travellers’choice behaviour of driving modes is considered. Finally, through two numerical examples, the characteristics of the proposed models are demonstrated and their potential applications are discussed. The results show that the proposed models are capable of 1) identifying critical links and potential critical links of networks, 2) guiding the land-use development and macroscopic travel demand management, 3) evaluating network optimisation schemes, and 4) allocating the road supply to CAVs and HVs efficiently.

A CO emission-based adaptive signal control for isolated intersections

ABSTRACT Rapid economic growth in recent decades has led to a rising motorization and congestion in urban areas. Congestion at intersections is considered as the prime source of emissions. This study presents a CO emission-based adaptive signal control for isolated intersections. Traffic dynamics at signalized intersections are modeled on the time--space diagrams using the shockwave theory and information from loop detectors installed upstream of intersections. Emissions are estimated from the sum of the product of emission rates and times spent by vehicles in each operating mode. With the assumption that traffic demand remains the same in the next cycle, a split adjustment policy is established by incrementally adjusting splits so that the total intersection emissions gradually reduce. Cycle length is adjusted in the next cycle by evaluating the residual queues. Efficiency of the proposed control algorithm is investigated via simulation. It is found that when the sum of flow ratios of the critical movements is between 0.6 and 1.0, the proposed adaptive control produces smaller CO emissions, delay, and stop than the Webster fixed-time control does with the 0.025 level of significance. On average, the proposed control algorithm can reduce CO emissions by 7.67% when compared to the Webster fixed-time signal settings. However, the reduction depends on the sum of flow ratios. Implications: Congestion at intersections is considered as the prime source of emissions. This study presents an emission-based adaptive signal control for isolated intersections with an aim at reducing CO emissions in urban areas. The proposed control method uses occupancy information obtained from loop detectors to construct the time-space diagrams using the shockwave theory while most existing signal control algorithms rely on flow information to calculate signal timing. A simple feedback control policy for signal timing adjustment is presented. It is found that the proposed algorithm can help reducing emissions at signalized intersections in urban areas.

Stationary condition based performance analysis of the contraflow left-turn lane design considering the influence of the upstream intersection

The present study conducted an operational performance analysis of the contraflow left-turn lane (CLL) design considering the influence of the upstream signalized intersection. The arrival distribution was generated using a platoon dispersion model. A stationary condition was defined, in which the performance of the CLL design remained stable in any stationary cycle. It has proved that the CLL system always converges to the stationary condition after a few cycles if the arrival distribution is fixed. In stationary cycles, the CLL design generates either recurrent and constant residual queues or no queues, depending on the arrival distributions of left-turning vehicles. Based on the stationary condition, analytical models were developed to estimate the operational performance for left-turns at signalized intersections with the CLL design. The results show that both the arrival pattern and the length of the contraflow lane can significantly influence the operational performance of the CLL design. The residual queues in the stationary condition could increase control delay significantly, leading to an overlong delay of the left-turning vehicles if the contraflow lane was not carefully designed. To this end, an empirical optimization method was proposed to minimize the control delay by optimizing the length of contraflow lanes and the offset between adjacent intersections. The research results can be directly employed by traffic engineers to optimize the CLL design and to estimate the operational performance of the signalized intersections with the CLL design.

Lane-Based Traffic Signal Simulation and Optimization for Preventing Overflow

In the lane-based signal optimization model, permitted turn directions in the form of lane markings that guide road users to turn at an intersection are optimized with traffic signal settings. The spatial queue requirements of approach lanes should be considered to avoid the overdesigning of the cycle, effective red, and effective green durations. The point-queue system employed in the conventional modeling approach is unrealistic in many practical situations. Overflow conditions cannot be modeled accurately, while vehicle queues are accumulated that block back upstream intersections. In a previous study, a method was developed to manually refine the traffic signal settings by using the results of lane-based optimization. However, the method was inefficient. In the present study, new design constraint sets are proposed to control the effective red and effective green durations, such that traffic enters the road lanes without overflow. The reduced cycle times discharge the accumulated vehicles more frequently. Moreover, queue spillback and residual queues can be avoided. One of the most complicated four-arm intersections in Hong Kong is considered as a case study for demonstration. The existing traffic signal settings are ineffective for controlling the observed traffic demand, and overflow occurs in short lanes. The optimized traffic signal settings applied to the proposed optimization algorithm effectively avoided traffic overflow. The resultant queuing dynamics are simulated using TRANSYT 15 Cell Transmission Model (CTM) to verify the proposed model. The model application is extended to handle the difficult residual queue scenario. It is found that the proposed model can optimize the traffic signal settings in cases where there are short initial residual queues.

Static traffic assignment with residual queues and spillback

In this paper we propose a novel static traffic assignment model where the network loading component accounts for non-stationary residual queues as well as spillback effects by explicitly adding capacity and storage constraints. This is achieved by deriving a static version of the dynamic general link transmission model that describes average link inflow and outflow rates during a given time period under simplified temporal assumptions. The resulting model adopts the same concave fundamental diagram and first order node model used in state-of-the-art macroscopic dynamic network loading without the need to explicitly describe time. We show that our mathematical problem formulation is an extension of several other models described in the literature, including the traditional capacity restrained static modelling paradigm. The equilibrium problem is formulated as a variational inequality problem while the network loading problem can be formulated as a fixed point problem. We prove that a solution exists to each problem as long as all traffic is loaded onto the network (i.e., no queue spillback into an origin). The model is illustrated via numerical examples on several hypothetical transport networks.

Static traffic assignment with residual queues and spillback

Abstract In this paper we propose a novel static traffic assignment model where the network loading component accounts for non-stationary residual queues as well as spillback effects by explicitly adding capacity and storage constraints. This is achieved by deriving a static version of the dynamic general link transmission model that describes average link inflow and outflow rates during a given time period under simplified temporal assumptions. The resulting model adopts the same concave fundamental diagram and first order node model used in state-of-the-art macroscopic dynamic network loading without the need to explicitly describe time. We show that our mathematical problem formulation is an extension of several other models described in the literature, including the traditional capacity restrained static modelling paradigm. The equilibrium problem is formulated as a variational inequality problem while the network loading problem can be formulated as a fixed point problem. We prove that a solution exists to each problem as long as all traffic is loaded onto the network (i.e., no queue spillback into an origin). The model is illustrated via numerical examples on several hypothetical transport networks.

A real-time signal control strategy for mitigating the impact of bus stops at urban signalized intersections

ABSTRACTBuses stopping at transit stops reduce the capacity of signalized intersections, which can lead to excessive delays for all users. In order to avoid such phenomena, signal control strategies can be utilized. This article presents a real-time signal control strategy to mitigate the impact of bus stop operations on traffic operations along an undersaturated approach. The objective of the proposed strategy is to ensure that any residual queues due to a bus stopping are fully dissipated as soon as possible by increasing the green time for the bus stop approach as soon as the bus departs. In addition, this strategy ensures that the cross-street approaches do not become oversaturated. Kinematic wave theory is utilized to track the formation and dissipation of queues and determine the green extension (or red truncation) for the subject approach. The benefits achieved from the proposed strategy are illustrated through simulation tests at a single intersection for a variety of bus stop and bus operation characteristics. Average delay and average queue length for the bus stop and cross-street approaches, as well as for the whole intersection, are used to assess the performance of the strategy. The tests performed indicate that the signal control strategy can achieve substantial reductions in delay for the bus stop approaches and the intersection as a whole without adversely affecting the cross street operations, when the demand at those cross streets is low.

Quasi-dynamic traffic assignment with residual point queues incorporating a first order node model

Static traffic assignment models are still widely applied for strategic transport planning purposes in spite of the fact that such models produce implausible traffic flows that exceed link capacities and predict incorrect congestion locations. There have been numerous attempts to constrain link flows to capacity. Capacity constrained models with residual queues are often referred to as quasi-dynamic traffic assignment models. After reviewing the literature, we come to the conclusion that an important piece of the puzzle has been missing so far, namely the inclusion of a first order node model. In this paper we propose a novel path-based static traffic assignment model for finding a stochastic user equilibrium in general transportation networks. This model includes a first order (steady-state) node model that yields more realistic turn capacities, which are then used to determine consistent capacity constrained traffic flows, residual point (vertical) queues (upstream bottleneck links), and path travel times consistent with queuing theory. The route choice part of the model is specified as a variational inequality problem, while the network loading part is formulated as a fixed point problem. Both problems are solved using existing techniques to find a solution. We illustrate the model using hypothetical examples, and also demonstrate feasibility on large-scale networks.

Issues Affecting Performance of Adaptive Traffic Control Systems in Oversaturated Conditions

This paper identifies three broad issues that may negatively affect the performance of an adaptive traffic control system operating a coordinated arterial when oversaturated conditions arise on at least one intersection approach. The first issue is underallocating green time to the critical (oversaturated) approach at the bottleneck intersection. Second, more green time than necessary may be given to the peak (critical) direction at intersections upstream of the bottleneck, and offsets that favor progression in the critical direction may be used inappropriately when the active bottleneck counters any benefits associated with green waves in the peak direction. Finally, offsets at intersections downstream of the bottleneck may be set inefficiently and result in additional delays for traffic departing the bottleneck and creating the potential for queue spillbacks to the bottleneck itself. Possible remedial actions involve reallocating green time to the critical approach until residual queues form on conflicting approaches (Issue 1), reducing green time at signals upstream of the bottleneck in the peak direction so that signal capacities are equivalent to the bottleneck capacity (Issue 2), and manually designing an offset plan that gives priority to traffic departing the bottleneck and protects the traffic's full green bandwidth (Issue 3). These problems are validated and analyzed on a 5-mi adaptively controlled segment of Pacific Coast Highway in Los Angeles, California, with field data from March through June 2013. The Los Angeles adaptive traffic control system is used for the validation and analysis.

Managing oversaturated signalized arterials: A maximum flow based approach

Under oversaturated traffic conditions, signal timings need to be adjusted accordingly in order to alleviate the detrimental impacts caused by oversaturation. In this research, our focus is to mitigate two types of detrimental effects, signal phase failure with residual queue and downstream queue spillover. Building upon our previous work on the oversaturation severity indices, a maximum-flow based approach to manage oversaturated intersections is developed. The proposed model maximizes the discharging capacity along oversaturated routes, while satisfying the constraints on available green times. We show that a simple Forward–Backward Procedure (FBP) can be used to obtain the optimal solution to the maximum flow model. The forward process aims to increase green time to mitigate oversaturation, therefore improve the throughput for the oversaturated approach; and the backward process aims to gate the traffic at some intersections to prevent residual queues and downstream queue spill-back when the available green time is insufficient. The algorithm is tested using a microscopic traffic simulation model for an arterial network in the City of Pasadena, CA. The results indicate the model can effectively and efficiently reduce oversaturation and improve system performance.

The Macroscopic Model for Signalized Intersections to Consider Progression in relation to Delay

A performance index of singalized intersections is a standard to optimize signal control variables and to manage traffic flow. Traffic delays is generally used to minimize the average delay time on intersections or networks, progression efficiency is used to improve travel speed of main cooridors or to provide transit signal priority. We manage traffic flows with only selecting one index between delays and progression according to the objective of traffic management and field characteristics. In real field, the driver`s satisfaction is high in any performance criteria when the waiting time is shorter and the unnecessary stop in front of traffic is smaller. This paper aims to develop simulation model to represent real progression with concurrently considering delays and progression. In order to reflect an effect of level of traffic volumes and residual queues which don`t be considered in prior progression model, we apply shockwave model with flow-density diagram. We derive Cell Transmission Model of Daganzo in order to develop the delay index and the progression index for the macroscopic simulation model. In order to validate the effect, we analysis traffic delays and progression efficiency with comparing this model to Transyt-7F and PASSER V.

Modeling Delay during Heavy Traffic for Signalized Intersections with Short Left-Turn Bays

This paper develops theoretical delay models for protected left-turn operations at a pretimed signalized intersection during heavy traffic. When the through traffic demand is heavy, residual queues from the previous cycle may occur and elevate the probability of blockage to the left-turn bay, leading to increased delay for left turns. A probabilistic left-turn delay model based on the queuing diagram is proposed for a leading left-turn operation; the influence of residual queues and blockage by the through traffic are taken into account. When the left-turn demand becomes heavy, the left turns may spill back to block the through traffic, resulting in through traffic delays. Through traffic delay is modeled probabilistically on the basis of the analysis of left-turn bay spillback for a lagging protected left-turn operation. The left-turn delay models are validated through carefully designed simulation studies using VISSIM and the results are compared with those from the Highway Capacity Manual (HCM) delay model, which does not consider blockage or spillback situations. The proposed delay models can be used to replace the uniform delay term in the HCM model for high-demand situations when the left-turn operation is affected by spillback and blockage.

A Self-tuning Signal Control Algorithm for Isolated Intersections Based on Time-space Diagrams

In this study, a self-tuning signal control algorithm is developed for isolated intersections. Traffic dynamics at signalized intersections are represented on time-space diagrams using the shockwave theory and information from detectors installed upstream of intersections. Splits are incrementally adjusted so that the delay per cycle is gradually diminished. Cycles are modified to have an efficient use of the provided green times without causing the residual queues. Unlike most algorithms, the proposed method uses occupancy information and can manage traffic even when queues extend beyond detector locations. Simulation experiments on a four-phase intersection with different demand scenarios are performed to demonstrate efficiency of the developed algorithm. Hypothesis tests are conducted to statistically verify the efficiency comparison between the proposed method and the Webster formula. It is found that in case of fixed demand, the proposed method can optimize splits and cycle lengths with no worse performance measures than the optimal fixed-time signal settings. For the variable demand case, the result indicates that the algorithm can adjust splits and cycle lengths in response to the change of demand and provides better performance measures than the Webster formula. The proposed algorithm has demonstrated itself to be a potential split and cycle optimization for an adaptive signal control system.

Impact of Transit Signal Priority on Level of Service at Signalized Intersections

The assessment of Transit Signal Priority (TSP) impacts at traffic signals is typically based on simulation and field studies. There is a need for macroscopic procedures for analysis of TSP as part of the Highway Capacity Manual (HCM) analysis methodology for signalized intersections. This capability will allow prediction of TSP impacts (and related control strategies) at a planning and operations level without the complexity of simulation modeling. The paper presents a technique of estimating the average green times for each lane group, and modifications to the HCM formula for estimating control delay in order to estimate the impact of TSP on the Level of Service (LOS) at each approach and the whole intersection. The technique uses readily available information on the frequency of the transit vehicles, TSP features (e.g., green extension, or red truncation), and also takes into consideration the additional delays because of the residual queues that are likely to occur on non priority approaches operating close to saturation. Application of the method at a signalized intersection with signal priority in the San Francisco Bay Area, and comparisons with simulated data show that the proposed methodology provides reasonable estimates of the TSP impacts, and it can be incorporated into the HCM analysis procedures for signalized intersections. © 2011 Published by Elsevier Ltd.