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Decentralized traffic signal control methods, such as max-pressure (MP) control or back-pressure (BP) control, have gained increasing attention in recent years. MP control, in particular, boasts mathematically-proven network throughput properties, enabling it to optimize network throughput and stabilize vehicle queue lengths whenever possible. Urban traffic volume is dynamic and features a non-uniform distribution throughout the network. Specifically, heavier traffic is often observed along arterial corridors or major origin–destination streams, such as those in central business districts (CBD), while less traffic is found on sub-arterial roads. To address these issues, many existing signal plans incorporate coordinated signal timing. Numerous previous studies have formulated signal coordination optimization as mixed-integer programming problems, with most belonging to centralized traffic signal controller categories. However, centralized approaches do not scale well to larger city networks. In this paper, we introduce a novel max-pressure signal control approach called Smoothing-MP, which considers signal coordination in urban networks to achieve both maximum vehicle stability and reduced travel time and delay along specific urban corridors, without altering the original stable region proposed by Varaiya (2013). This study represents a pioneering effort in modifying max-pressure control to incorporate signal coordination. Crucially, this policy retains the decentralized characteristic of the original max-pressure control, relying exclusively on local information sourced from upstream and downstream intersections. To evaluate the proposed Smoothing-MP control, we executed simulation studies on two different types of networks, the Downtown Austin Network and a Grid Network. The results unequivocally show that Smoothing-MP matches the maximum throughput of the original MP control. Moreover, it significantly reduces both travel time and delay along coordinated corridors. This dual accomplishment underscores the efficacy and potential advantages of the Smoothing-MP control approach. • For the first time, this research integrates max-pressure control with signal coordination, marking a significant advancement in traffic management strategies. • It enhances the practical applicability of max-pressure control, making it more viable for real-world traffic systems. • The newly proposed max-pressure signal control modification retains the ability to achieve maximum network throughput while preserving the original max-pressure control's stable region. • Simulations were conducted using the Downtown Austin Network and a Grid Network, focusing on selected corridors to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• A virtual risk computation based on matching degree was introduced. • A universal planning method for pre-merging in mixed traffic and regular car-following has been proposed. • A virtual risk-based longitudinal planning model has been formulated. • Enhanced stability and improvements in safety are characteristics of the proposed planning approach. Ramp-merging zones have perennially served as bottlenecks for both safety and efficiency within road traffic. Addressing the inherent uncertainties and anomalous behaviors potentially exhibited by human-driven vehicles (HDVs) in mixed traffic flow, this study introduced a distributed merging control algorithm based on virtual risk assessment. Initially, we proposed a progressive vehicle projection method based on matching degree, formulated to engender smooth longitudinal accelerations. Building upon this foundation, a virtual risk-based longitudinal planning model named V-RQM was established, furnishing longitudinal accelerations with robust stability and safety to Connected and Autonomous Vehicles (CAVs). Results from four diverse simulation experiments corroborate that V-RQM is aptly versatile, suited for conventional one-dimensional car-following environments as well as pre-merging control in heterogeneous traffic flow. The model not only mitigates oscillatory amplitudes while sustaining high traffic efficiency but also exhibits commendable resilience against uncertainties and anomalies indigenous to mixed traffic flow. This research pioneers a novel model-driven approach to ramp-merging control, setting the stage for the full exploitation of CAV capabilities. [ABSTRACT FROM AUTHOR]

In the last decade, pressure-based schemes such as Back Pressure and Max Weight algorithms have been widely researched and applied for traffic signal control due to their simplicity and proven throughput maximization. In such algorithms, the next chosen signal phase at an intersection in a road network is the one with the highest measured weight, representing the pressure of traffic movements at the intersection, determined based on a single characteristic of the traffic flow or vehicles' state at that intersection. This paper develops a new optimal Max Weight control mechanism to enhance the network throughput and reduce vehicle delays in a network using a concept of platooning enabled by Connected Vehicles (CVs). To this end, we propose a new proven optimal Max Weight control scheme where the weight consists of several features including the platoon delay, as well as the speed and position of vehicles within the platoon. To the best of our knowledge, this work is the first to propose a platoon pressure-based concept considering multiple configurable attributes in formulating the pressure. Furthermore, we provide a rigorous stability proof that ensures the throughput optimality of the proposed control scheme. In addition, we also develop a machine learning procedure in this paper to optimize the weighting parameter of each attribute contributing to the total pressure enabling its seamless deployment in practice. A number of simulation results demonstrate the feasibility of the learning procedure and show that our Max Weight platoon pressure-based scheme outperforms the state-of-the-art and well-known existing pressure-based algorithms. • Develop a novel MaxWeight decentralized signal control scheme considering vehicle platooning. • Propose the weight (or pressure) based on the platoons' features or characteristics. • Develop a cross entropy method as the learning algorithm to optimize the parameters of the control algorithm. • Provide theoretical proof for the stability property of the proposed MaxWeight scheme. • Numerically show significant improvements over other well-known existing pressure-based schemes. [ABSTRACT FROM AUTHOR]

A distributed method for concurrent traffic signal and routing control of traffic networks is proposed. The method is based on the multi-commodity store-and-forward model, in which the destinations are the commodities. The system benefits from the communication between vehicles and infrastructure, providing optimal signal timings to intersections and routes to vehicles on a link-by-link basis. Using the augmented Lagrangian to model the constraints into the objective, the baseline centralized problem is decomposed into a set of objective-coupled subproblems, one for each intersection, enabling the solution to be computed by a distributed-gradient projection algorithm. The intersection agents only need to communicate and coordinate with neighboring intersections to ensure convergence to the optimal solution while tolerating suboptimal iterations that offer more flexibility, unlike other distributed approaches. Through microsimulation, we demonstrate the effectiveness of the proposed algorithm in traffic networks with time-varying demand. Computational analysis shows that the distributed problem is suitable for real-time applications. A robustness analysis show that the distributed formulation enables a graceful degradation of the system in case of failure. • A multi-commodity traffic flow model cast into a linear dynamic network. • A distributed algorithm for linear dynamic networks and in particular applicable to multi-commodity urban signal traffic control and routing in a connected vehicle environment. • Demonstration of the real-time applicability and robustness to failure of the proposed algorithm and model. • Comparison through microscopic simulation with the corresponding centralized approach, with a decentralized backpressure algorithm, and with fixed-time. [ABSTRACT FROM AUTHOR]

In urban traffic management, the primary challenge of dynamically and efficiently monitoring traffic conditions is compounded by the insufficient utilization of thousands of surveillance cameras along the intelligent transportation system. This paper introduces the multi-level Traffic-responsive Tilt Camera surveillance system (TTC-X), a novel framework designed for dynamic and efficient monitoring and management of traffic in urban networks. By leveraging widely deployed pan–tilt-cameras (PTCs), TTC-X overcomes the limitations of a fixed field of view in traditional surveillance systems by providing mobilized and 360-degree coverage. The innovation of TTC-X lies in the integration of advanced machine learning modules, including a detector–predictor–controller structure, with a novel Predictive Correlated Online Learning (PiCOL) methodology and the Spatial–Temporal Graph Predictor (STGP) for real-time traffic estimation and PTC control. The TTC-X is tested and evaluated under three experimental scenarios (e.g., maximum traffic flow capture, dynamic route planning, traffic state estimation) based on a simulation environment calibrated using real-world traffic data in Brooklyn, New York. The experimental results showed that TTC-X captured over 60% total number of vehicles at the network level, dynamically adjusted its route recommendation in reaction to unexpected full-lane closure events, and reconstructed link-level traffic states with best MAE less than 1.25 vehicle/hour. Demonstrating scalability, cost-efficiency, and adaptability, TTC-X emerges as a powerful solution for urban traffic management in both cyber–physical and real-world environments. • This study proposes a traffic-responsive tilt camera system to dynamically monitor traffic. • A Predictive Correlated Online Learning for real-time traffic data decisions. • A Spatial-temporal learning system integrates road structure and traffic dynamics. • Three scenarios are tested for max flow capture, route planning, and traffic state estimation. [ABSTRACT FROM AUTHOR]

Conventional platooning control algorithms that improve traffic flow require complex computations and are not well-suited for real-time operations. Moreover, prior research has primarily concentrated on idealized settings, such as isolated intersections or corridors that solely consist of fully connected automated vehicles (CAVs), making it challenging to be applied to real-world traffic systems. To overcome these challenges, this paper aims to design an innovative learning framework based on arrival timing vectors (ATVs) and traffic signal synchronization for platooning control that can improve traffic throughput and reduce fuel consumption through four basic platoon maneuvers: split, acceleration, deceleration, and no-op. We integrate reinforcement learning (RL) with neural networks (NNs) to model non-linear relationships between inputs and outputs for a complex application. The utilization of ATVs as inputs in our proposed framework furnishes an agent with ample information to facilitate a better comprehension of the actions that need to be taken for particular states. Additionally, our proposed DRL-based platooning control collaborates with traffic signal synchronization across adjacent intersections to optimize traffic efficiency in a further step. Numerical experiments have been conducted on both corridor and network scenarios, demonstrating the positive effects of synchronization and DRL-based platooning control on average delay and fuel consumption. Various DRL-based methods have been compared to demonstrate the fast convergence and improved mobility of our proposed algorithm. Furthermore, our algorithm shows robust performance in mixed-traffic environments of CAV and human-driven vehicles. • A new DRL framework using deep learning to manipulate vehicle platoon behaviors. • The approach integrates platooning control with traffic signal synchronization. • A comparison shows the 2DSARSA algorithm outperforms other popular DRL methods. • Case studies showed DRL-based control reduce delay and fuel use in different networks. [ABSTRACT FROM AUTHOR]

Accurately monitoring road traffic state is crucial for various applications, including travel time prediction, traffic control, and traffic safety. However, the lack of sensors often results in incomplete traffic state data, making it challenging to obtain reliable information for decision-making. This paper proposes a novel method for imputing traffic state data using Gaussian processes (GP) to address this issue. We propose a kernel rotation re-parametrization scheme that transforms a standard isotropic GP kernel into an anisotropic kernel, which can better model the congestion propagation in traffic flow data. The model parameters can be estimated by statistical inference using data from sparse probe vehicles or loop detectors. Moreover, the rotated GP method provides statistical uncertainty quantification for the imputed traffic state, making it more reliable. We also extend our approach to a multi-output GP, which allows for simultaneously estimating the traffic state for multiple lanes. We evaluate our method using real-world traffic data from the Next Generation simulation (NGSIM) and HighD programs, along with simulated data representing a traffic bottleneck scenario. Considering current and future mixed traffic of connected vehicles (CVs) and human-driven vehicles (HVs), we experiment with the traffic state estimation (TSE) scheme from 5% to 50% available trajectories, mimicking different CV penetration rates in a mixed traffic environment. We also test the traffic state estimation when traffic flow information is obtained from loop detectors. The results demonstrate the adaptability of our TSE method across different CV penetration rates and types of detectors, achieving state-of-the-art accuracy in scenarios with sparse observations. • Using Gaussian process (GP) for Traffic State Estimation (TSE). • Capturing congestion propagation in traffic wave with a rotation kernel. • Leveraging inter-lane correlation to improve TSE accuracy. • Model works well on small datasets and can quantify the TSE uncertainty. • Experiments on real-world and simulated data show GP-TSE accuracy and robustness. [ABSTRACT FROM AUTHOR]

• Introduction of a generalizable Bayesian recurrent neural network framework for uncertainty quantification in traffic flow prediction. • Utilization of spectral normalization in hidden layers of neural network to enhance generalizability and improve uncertainty estimates. • Demonstration of how normalization alters the training process, controlling model complexity and reducing overfitting. • Spectral normalization improves generalization performance of deep learning models on out-of-distribution datasets, which is crucial for traffic management applications. • Ability of spectral normalization to localize latent feature space under data perturbations, leading to improved generalizability. • Significant outperformance of spectral normalization over layer normalization and models without normalization in both single and multistep prediction horizons. Deep-learning models for traffic data prediction can have superior performance in modeling complex functions using a multi-layer architecture. However, a major drawback of these approaches is that most of these approaches do not offer forecasts with uncertainty estimates, which are essential for traffic operations and control. Without uncertainty estimates, it is difficult to place any level of confidence in the model predictions, and operational strategies relying on overconfident predictions can lead to worsening traffic conditions. In this study, we propose a Bayesian recurrent neural network framework for uncertainty quantification in traffic prediction with higher generalizability by introducing spectral normalization to its hidden layers. In our paper, we have shown that normalization alters the training process of deep neural networks by controlling the model's complexity and reducing the risk of overfitting the training data. This, in turn, helps improve the generalization performance of the model on out-of-distribution datasets. Results demonstrate that spectral normalization improves uncertainty estimates and significantly outperforms both the layer normalization and model without normalization in both single and multistep prediction horizons. This improved performance can be attributed to the ability of spectral normalization to better localize the latent feature space of the data under perturbations. Our findings are especially relevant to traffic management applications, where predicting traffic conditions across multiple locations is the goal, but the availability of training data from multiple locations is limited. Spectral normalization, therefore, provides a more generalizable approach that can effectively capture the underlying patterns in traffic data without requiring location-specific models. [ABSTRACT FROM AUTHOR]

• A Multi-layer Graph Mask Q-Learning algorithm for traffic signals is proposed. • This study constructs a new upper and lower graph structure to extract geometric and spatial features of intersections. • This article introduces an action masking mechanism, enabling the model to be applicable to intersections of arbitrary structures when utilizing any phase as the action. • Model tests were conducted on synthetic road networks with different traffic volumes and two real urban road networks, and the test results showed that the algorithm has strong generalization ability and scalability. Due to its capability in handling complex urban intersection environments, deep reinforcement learning (DRL) has been widely applied in Adaptive Traffic Signal Control (ATSC). However, most existing algorithms are designed for specific road networks or traffic conditions, making it difficult to transfer them to new environments. Moreover, current graph-based algorithms do not fully capture the geometric and spatial features of intersections, leading to incomplete embedding of the agent's environment depiction. Additionally, the actions adopted by these algorithms are inherently based on fixed-cycle phases, limiting the flexibility of traffic signal control. To address the aforementioned issues, this paper proposes a Multi-layer Graph Mask Q-Learning (MGMQ) algorithm for multi-intersection ATSC to optimize traffic and reduce delay. Unlike previous graph-based algorithms, this paper introduces a method for computing multi-layer graphs, dividing the traffic environment into upper-level traffic network-layer graphs and lower-level intersection-layer graphs, and employs the graph attention algorithm and an improved GraphSAGE algorithm for computation. This method not only enables the generation of embedded state for intersections that include geometric and spatial features, but also allows the algorithm to adapt to different traffic conditions and road networks. Additionally, we introduce an action masking mechanism, allowing this algorithm can be adapted to different action spaces. As a result, the algorithm uses arbitrary signal phases as actions to achieve flexible traffic flow control, and can be directly applied to intersections with arbitrary geometry. The final test results demonstrate that a model trained solely on synthetic road networks can be directly transferred to other synthetic network configurations or real-world urban road networks, outperforming current state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Monitoring traffic dynamics at both link and network levels is vital for effective traffic management and control in urban road networks. Traffic monitoring at the network level, though, is an extremely challenging task due to the amount of data (and consequently sensors) needed to represent a good coverage of the network traffic dynamics. This has motivated researchers to investigate where to locate sensors (e.g. loop detectors) within a network to capture the so-called Reduced Network Macroscopic Fundamental Diagram (NMFD). The existing approaches for reduced NMFD estimation have presented limitations, because of the reliance on solely traffic flow characteristics or single topological features. In response, in this paper, a framework has been proposed to take into account the simultaneous impact of topological and traffic flow features for the estimation of reduced NMFD. Contrary to existing works in literature, which have mainly focused on the loading period, this work considers both loading and unloading periods. The presented framework deploys regression models to identify the set of critical links within the network. The efficiency of the presented method has been validated under both single and multi-reservoir settings and has been compared to two methods that solely consider traffic characteristics. Our findings reveal that the proposed method demonstrates superior performance, particularly when dealing with low coverage rates of loop detectors (e.g. 1%). Moreover, results show that during the recovery period, traffic characteristics (e.g. queue distribution) play a more important role in estimating the NMFD. On the other side, a more significant impact has been observed for the topological features (e.g. speed limit distribution) during the loading period. • Critical links identification through historical FCD and network topological info. • Reduced operational network MFD estimation during loading and unloading periods. • Feature importance analysis using Random Forest Regression (RFR). • Investigating the combined impact of topological and traffic characteristics on NMFD. [ABSTRACT FROM AUTHOR]

• The effect of "backpressure" on responsive signal control performance in one simple network is studied. • Only a simple network is considered and travellers are supposed to choose their own best routes. • It is shown that on the simple network having backpressure increases equilibrium delays. • It is shown that on the simple network having backpressure reduces network capacity. Many responsive traffic signal control strategies are "pressure-driven". These strategies move green-time from stages with a lower pressure to stages with a higher pressure, at each junction; often aiming to approximately equalise stage pressures at each junction. In some of these pressure-driven strategies each stage pressure at a junction includes a "backpressure" term, which depends on downstream links. Such backpressure terms reduce the pressure on stages and reduce pressure most for those stages leading toward the most highly congested downstream road links. The expectation is that there will then be a reduction in traffic flow exiting the junction toward already highly congested road links, and that this will reduce queue lengths and network delays. This paper shows that this is not always the case by giving two examples where having backpressure within the control policy on a network with two signal controlled junctions (1) increases both equilibrium delays at one of the junctions for certain demands, and (2) never reduces any of the equilibrium delays at either junction. A simple dynamic evolution with a steady reduction in demand is also shown in which two of the considered policies, one with backpressure, give rise to equilibrium delays and queues which tend to infinity, and reveal a gap; in this gap feasible demands have no equilibrium consistent with the two control policies and in a day-to-day dynamical context delays and queues are unbounded. This paper considers only the simple network shown in figure 1 under certain specified conditions. One main avenue for further research might be to consider whether the results shown here also hold in different networks under different conditions. [ABSTRACT FROM AUTHOR]

• Proposes a macro–micro approach for vehicle motion inference using velocity contour. • Macro: tailors an enhanced traffic state estimation method to address data sparsity. • Micro: infers car-following and lane-changing behaviors using a joint optimization framework. • Achieves higher accuracy and smoothness compared to two SOTA methods. Vehicle trajectories can offer the most precise and detailed depiction of traffic flow and serve as a critical component in traffic management and control applications. Various technologies have been applied to reconstruct vehicle trajectories from sparse fixed and mobile detection data. However, existing methods predominantly concentrate on single-lane scenarios and neglect lane-changing (LC) behaviors that occur across multiple lanes, which limit their applicability in practical traffic systems. To address this research gap, we propose a macro–micro approach for reconstructing complete vehicle trajectories on multi-lane freeways, wherein the macro traffic state information and micro driving models are integrated to overcome the restrictions imposed by lane boundary. Particularly, the macroscopic velocity contour maps are established for each lane to regulate the movement of vehicle platoons, meanwhile the velocity difference between adjacent lanes provide valuable criteria for guiding LC behaviors. Simultaneously, the car-following models are extended from micro perspective to supply lane-based candidate trajectories and define the plausible range for LC positions. Later, a two-stage trajectory fusion algorithm is proposed to jointly infer both the car-following and LC behaviors, in which the optimal LC positions is identified and candidate trajectories are adjusted according to their weights. The proposed framework was evaluated using NGSIM and HighD datasets, and the results indicated a remarkable enhancement in both the accuracy and smoothness of reconstructed trajectories, with performance indicators reduced by over 30% compared to two representative reconstruction methods. Furthermore, the reconstruction process effectively reproduced LC behaviors across contiguous lanes, adding to the framework's comprehensiveness and realism. [ABSTRACT FROM AUTHOR]

To improve the performance of systems, optimization has been the prevailing approach in the past. However, the approach faces challenges when multiple goals shall be simultaneously achieved. For illustration, we study a multi-agent system, where agents have a plurality of different, and mutually inconsistent goals. We then allow agents in the system to vote on which traffic signal controllers, which were trained on different goals using deep reinforcement learning, would control the intersection. Taking decisions based on suitable voting procedures turns out to lead to favorable solutions, which perform highly for several goals rather than optimally for one goal and poorly for others. This opens up new opportunities for the management or even self-governance of complex systems that require the consideration and achievement of multiple goals, such as many systems involving humans. Here, we present results for traffic flows in urban street networks, which suggest that "democratizing traffic" would be a promising alternative to centralized control of traffic flows. • We combine voting and traffic signal control. • We use votes of agents as weights for aggregating Q-values of multiple Deep Q-Networks trained on different goals. • Voting allows users to express their preferences and choose how/which algorithm governs them. • We demonstrate the performance of different voting schemes across different voting scenarios. [ABSTRACT FROM AUTHOR]

• Develop a framework including both stability analysis and implementation constraints for traffic platoon control via sparse autonomous vehicles. • Identify the number of human-driven vehicles that can be stabilized by one single autonomous vehicle. • Establish error dynamics in traffic platoon considering multiple disturbances and error composition. • Propose a novel ring string stability that is flexible and applicable to mixed ring traffic. The emergence of autonomous vehicles (AVs) is expected to revolutionize road transportation and it has been demonstrated that AVs can be employed as mobile actuators to effectively dissipate stop and go waves. This paper proposes a comprehensive theoretical framework including stability and implementation considerations for analyzing traffic platoon control via sparse AVs and discussing the number of HDVs that can be stabilized by one AV. Considering multiple disturbance sources, we first discuss the output error composition and analyze traffic dynamics in a platoon system. Then we discuss the Lyapunov asymptotic stability (LAS) of the system based on eigenvalue analysis and propose a novel ring string stability (RSS), which ensures the boundedness of disturbance error and guarantees that the effect of disturbance will not be amplified over a lap. The proposed RSS is flexible and applicable to mixed traffic, since the string instability for predecessor-follower pairs are allowed, provided they can be compensated by AVs. In addition, the practical constraints including driving safety and vehicle physical limitations are modeled. The worst-case evaluation of constraints is conducted to reproduce the critical scenarios in real life. Finally, we conduct numerical experiments and analyze the traffic platoon control from the aspects of both stability and practical implementations. New findings include: (1) The introduction of AVs is beneficial to dampen the waves and stabilize the HDV flow. (2) Considering the implementation issues, there is an upper bound of HDVs that can be stabilized by one single AV. (3) As the number and amplitudes of exogenous disturbances increase, it will be more challenging for sparse AVs to ensure stability and meanwhile satisfy the practical constraints. [ABSTRACT FROM AUTHOR]

• Centralised and decentralised algorithms with optimal control formulation. • Sensitivity based decentralised signal optimisation algorithms. • Comparison with distributed backpressure control policy. • Investigation of different network decomposition schemes. • Case study of optimisation algorithms on Central London networks. This study develops and analyses the centralised and decentralised solution procedures for urban network traffic management through an optimal control framework. The optimal control is formulated based upon the Hamilton-Jacobi formulation of kinematic wave model. Following the problem decomposition under the decentralised scheme, we introduce the use of semi-analytical performance derivatives when developing the decentralised solution algorithm. The proposed control strategies are applied to a set of test scenarios constructed from a real road network in Central London in the UK. Specific interests in the investigation include comparison of the performance gain and computational effort of the two strategies under different circumstances. We also investigate effect of network decomposition strategies on the performance of the solution algorithm. This study generates insight on urban traffic management with use of traffic flow theory, decentralised optimisation, and network decomposition techniques. [ABSTRACT FROM AUTHOR]

• Propose a state transfer risk decision (STRD) method for a large-scale traffic network. • This method is based on the conditional risk decision model. • This method provides an acceptable traffic flow range at the sub-region boundary by risk interval. • Proposed method improves the state stability compared to PI and NSTRD. Network state degradation probabilities increase with traffic density. Specifically, at critical or congested densities, any over-input traffic flow may cause degradation, local traffic jams, and even hysteresis of the road network. Existing macroscopic traffic control methods usually target the maximum capacity or minimum delay to improve the road network efficiency and do not consider the negative effect of perimeter control on the state transfer of the road network. This study provides a method to prevent the transfer of the network state when implementing boundary control. First, according to the data detected for the road network, the concept of a conditional value at risk is used to establish a risk decision model that considers the influence of the boundary input flow rate on sub-region state degradation. Then, based on the risk decision model, we propose a state transfer risk decision (STRD) perimeter control method for a large-scale traffic network with multiple sub-regions. This perimeter control method predicts the traffic demand of every sub-region, selects an acceptable traffic flow range at the sub-region boundary by risk interval, regards the maximum trip completion flow and the minimum total travel time as the decision-making objectives, and controls multiple sub-region boundaries. The simulation results show that compared with proportional integral (PI) control and no STRD (NSTRD) control, the STRD control scheme can effectively prevent the state transfer of all sub-regions, improve the trip completion flow, and decrease the travel delay of the network. [ABSTRACT FROM AUTHOR]

• A series of methods are proposed for queue length and traffic volume estimation. • Only a few assumpitions are imposed and only probe vehicle data are needed. • The methods work well even when the penetration rate of probe vehicles is low. • Test results show the methods are useful for signal control and performance measures. The rapid development of connected vehicle technology and the emergence of ride-hailing services have enabled the collection of a tremendous amount of probe vehicle trajectory data. Due to the large scale, the trajectory data have become a potential substitute for the widely used fixed-location sensors in terms of the performance measures of transportation networks. Specifically, for traffic volume and queue length estimation, most of the trajectory data based methods in the existing literature either require high market penetration of the probe vehicles to identify the shockwave or require the prior information about the queue length distribution and the penetration rate, which may not be feasible in the real world. To overcome the limitations of the existing methods, this paper proposes a series of novel methods based on probability theory. By exploiting the stopping positions of the probe vehicles in the queues, the proposed methods try to establish and solve a single-variable equation for the penetration rate of the probe vehicles. Once the penetration rate is obtained, it can be used to project the total queue length and the total traffic volume. The validation results using both simulation data and real-world data show that the methods would be accurate enough for assistance in performance measures and traffic signal control at intersections, even when the penetration rate of the probe vehicles is very low. [ABSTRACT FROM AUTHOR]

Traffic volume is an indispensable ingredient to provide fine-grained information for traffic management and control. However, due to the limited deployment of traffic sensors, obtaining full-scale volume information is far from easy. Existing work on this topic focuses primarily on improving the overall estimation accuracy of a particular method and ignores the underlying challenges of volume estimation, thereby having inferior performance in some critical tasks. This paper studies two key problems with regard to traffic volume estimation: (1) underdetermined traffic flows caused by fully undetected paths that can allow arbitrary volume values without violating the conservation law, where using local side information is insufficient to tackle, and (2) nonequilibrium traffic flows arise when traffic flows vary in density over space and time due to congestion propagation delay, which produce time-shifted volume readings to varying degrees. Here we demonstrate a graph-based deep learning method that offers a data-driven, equation-free, and correlation-adaptive approach to address the above issues and perform accurate network-wide traffic volume estimation. Particularly, in order to quantify the dynamic and nonlinear speed-volume relationships for the estimation of underdetermined flows, a speed pattern-adaptive adjacency matrix based on graph attention is developed and integrated into the graph convolution process, to capture nonlocal correlations between volumes. To measure the impacts of nonequilibrium flows, a temporal masked and clipped attention combined with a gated temporal convolution layer is customized to capture time-asynchronous correlations between upstream and downstream sensors under varying impacts of congestion delay. We then evaluate our model on a real-world highway traffic volume dataset and compare it with several benchmark models. It is demonstrated that the proposed model achieves high estimation accuracy even under 20% sensor coverage rate and outperforms other baselines significantly, especially on underdetermined and nonequilibrium flow locations. Furthermore, comprehensive quantitative model analysis is also carried out to justify the model designs. The source code and datasets are publicly available at:. • Identifies underdetermined and non-equilibrium problems for network-wide traffic volume estimation. • Tailors a spatiotemporal correlation adaptive graph convolution network (STCAGCN) for volume estimation. • Exploits global speed-volume relationship as a remedy to underdetermined flows. • Captures time-asynchronous correlations in non-equilibrium flows with masked and clipped attention. • STCAGCN achieves state-of-the-art performances on all tasks. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) has been widely used for traffic management, such as for minimizing the total time spent or the total emissions of vehicles. When long-term green urban mobility is considered including e.g. a constraint on the total yearly emissions, the optimization horizon of the MPC problem is significantly larger than the control sampling time, and thus the number of the variables that should be optimized per control time step becomes very large. For systems with dynamics that involve nonlinear, non-convex, and non-smooth functions, including urban traffic networks, this results in optimization problems that are computationally intractable in real time. In this paper, we propose a novel bi-level temporal distribution of such complex MPC optimization problems, and we develop two mathematically linked short-term and long-term MPC formulations with small and large control sampling times that will be solved together instead of the original complex optimization problem. The resulting bi-level control architecture is used to solve the two MPC formulations online for real-time control of urban traffic networks with the objective of long-term green mobility. In order to assess the performance of the bi-level control architecture, we perform a case study where a rough version of the model of the urban traffic flow, S-model, is used by the long-term MPC level to estimate the states of the urban traffic networks, and a detailed version of the model is used by the short-term MPC level. The results of the simulations prove the effectiveness (with respect to the objective of control, as well as computational efficiency) of the proposed bi-level MPC approach, compared to state-of-the-art control approaches. • A bi-level MPC-based architecture is implemented for green mobility. • The link of the control levels is based on the long and short-term allowed emissions. • This framework can limit the emissions with an affordable online computation time. [ABSTRACT FROM AUTHOR]

• We propose an integrated simulation framework to assess traffic safety performance. • Evaluating traffic safety of a moving vehicle as a part of traffic flow. • Simultaneous consideration of complex geometric and other environmental conditions. • Introduction of an overall safety performance index of the whole traffic flow. Vehicles are extremely vulnerable to single-vehicle accidents under some hazardous driving conditions (i.e. strong wind, icy or snowy road surface). An integrated framework is proposed to assess single-vehicle traffic safety performance of stochastic traffic flow under hazardous driving conditions. Different from most existing studies focusing on a single vehicle moving at a constant speed, for the first time, the proposed work evaluates individual vehicle safety performance based on the time-dependent simulation results of stochastic traffic flow, including instantaneous speeds and positions of each vehicle as a part of simulated traffic flow. Simultaneously, complex geometric and other environmental conditions of the highway system are also considered realistically, not only during the safety assessment process, but also in quantifying the wind loads applied on the vehicles. Finally, with the safety information of each individual vehicle, an overall safety performance index of the whole traffic flow on the highway system is further introduced, which serves as a potential traffic safety performance measure and resilience indicator of transportation infrastructure systems under various hazards. This study has potential applications to not only regular vehicles, but also advanced traffic management and control algorithms for connected and autonomous vehicles in hazardous driving environments. [ABSTRACT FROM AUTHOR]

Highlights • Provide a systematic review of CAV based UTC studies in the last few decades. • Highlight the change of design philosophy for UTC. • Summarize the benefits and drawbacks of current CAV-based UTC methods. • Predict the future study directions. Abstract Inefficient traffic control is pervasive in modern urban areas, which would exaggerate traffic congestion as well as deteriorate mobility, fuel economy and safety. In this paper, we systematically review the potential solutions that take advantage of connected and automated vehicles (CAVs) to improve the control performances of urban signalized intersections. We review the methods and models to estimate traffic flow states and optimize traffic signal timing plans based on CAVs. We summarize six types of CAV-based traffic control methods and propose a conceptual mathematical framework that can be specified to each of six three types of methods by selecting different state variables, control inputs, and environment inputs. The benefits and drawbacks of various CAV-based control methods are explained, and future research directions are discussed. We hope that this review could provide readers with a helpful roadmap for future research on CAV-based urban traffic control and draw their attention to the most challenging problems in this important and promising field. [ABSTRACT FROM AUTHOR]

Highlights • Model heterogeneous flows in the interdependent infrastructure system. • Consider partial capacity losses under facility disruption risks. • Formulate a non-linear integer programming model for the studied problem. • Draw insights into the features of the proposed model with numerical examples. • Reveal managerial insights into optimal location design and facility improvement. Abstract Due to the interdependency between multiple infrastructure systems, the performance of a facility may depend on the resources or supplies received from other facilities. However, cross-system interdependence has seldom been studied in the location design context, probably due to the lack of a concise model describing interdependence across heterogeneous systems. This paper proposes a new heterogeneous flow scheme to describe cross-system interdependence. This scheme has two features distinguished from existing models in describing an interdependent facility location problem. First, it is a simple linear model upon which a compact facility location model can be built. Secondly, it relaxes the need to maintain flow conservation between different systems and is suitable in describing heterogeneous systems that take in and output different resources or services. Built on this scheme, this paper proposes a reliable location design model for a nexus of interdependent infrastructure systems. This model aims to locate the optimal facility locations in multiple heterogeneous systems to balance the tradeoff between the facility investment and the expected nexus operation performance. Different from other reliable facility location models, this expected performance captures interdependence among heterogeneous systems due to the resource input-output relationships. The consideration of continuous partial capacity losses complements the reliable location literature that mainly focuses on binary disruptions. Two numerical examples are conducted for investigating features and applications of the proposed model. The results indicate that with a standard off-the-shelf integer programming solver, the proposed model is able to solve optimal facility location design for problem instances of realistic scales to the near-optimum solutions with optimality gap assurance. Sensitivity analyses of key parameters indicate that improving facility capacity and reducing interdependency between systems can mitigate impacts of facility capacity losses and reduce the overall system cost. [ABSTRACT FROM AUTHOR]

Highlights • A data-driven approach for airport arrival rate prediction/planning is shown. • The framework accounts for metroplex operational interdependency aspects. • Probabilistic capacity forecasts are created with Gaussian Process regression. • An optimization approach prescribes capacity allocation and ground delays. • We discuss how incorporating robustness affects Ground Delay Program planning. Abstract Efficient planning of Airport Acceptance Rates (AARs) is key for the overall efficiency of Traffic Management Initiatives such as Ground Delay Programs (GDPs). Yet, precisely estimating future flow rates is a challenge for traffic managers during daily operations as capacity depends on a number of factors/decisions with very dynamic and uncertain profiles. This paper presents a data-driven framework for AAR prediction and planning towards improved traffic flow management decision support. A unique feature of this framework is to account for operational interdependency aspects that exist in metroplex systems and affect throughput performance. Gaussian Process regression is used to create an airport capacity prediction model capable of translating weather and metroplex configuration forecasts into probabilistic arrival capacity forecasts for strategic time horizons. To process the capacity forecasts and assist the design of traffic flow management strategies, an optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal AARs in GDPs, an overall delay reduction of up to 9.7% is achieved. The results also reveal that incorporating robustness in the design of the traffic flow management plan can contribute to decrease delay costs while increasing predictability. [ABSTRACT FROM AUTHOR]

Highlights • Estimating historical traffic volumes between sparsely-located traffic sensors is addressed. • Neural network yields 24% more accurate results than the state of the practice approach. • Value of vehicle probe data in estimating historical traffic volumes is quantified. Abstract This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural network models to estimate hourly volumes in the Maryland highway network, and proposes a novel approach that combines neural networks with an existing profiling method. On average, the proposed approach yields 24% more accurate estimates than volume profiles, which are currently used by transportation agencies across the US to compute statewide performance measures. The paper also quantifies the value of using vehicle probe data in estimating hourly traffic volumes, which provides important managerial insights to transportation agencies interested in acquiring this type of data. For example, results show that volumes can be estimated with a mean absolute percent error of about 21% at locations where average number of observed probes is between 30 and 47 vehicles/h, which provides a useful guideline for assessing the value of probe vehicle data from different vendors. [ABSTRACT FROM AUTHOR]

Highlights • Propose a multimodal dynamic user equilibrium model with MFD traffic dynamics. • Derive the optimal bus fare and service frequency for given space allocation in steady state. • Discover the counter-intuitive properties of traffic equilibrium and optimal bus service. • Examine the interaction between managing authority and service operators under responsive bus service. • Explore the optimal space allocation strategies in dynamic cases with different economic objectives. Abstract A smart design of transport systems involves efficient use and allocation of the limited urban road capacity in the multimodal environment. This paper intends to understand the system-wide effect of dividing the road space to the private and public transport modes and how the public transport service provider responds to the space changes. To this end, the bimodal dynamic user equilibrium is formulated for separated road space. The Macroscopic Fundamental Diagram (MFD) model is employed to depict the dynamics of the automobile traffic for its state-dependent feature, its inclusion of hypercongestion, and its advantage of capturing network topology. The delay of a bus trip depends on the running speed which is in turn affected by bus lane capacity and ridership. Within the proposed bimodal framework, the steady-state equilibrium traffic characteristics and the optimal bus fare and service frequency are analytically derived. The counter-intuitive properties of traffic condition, modal split, and behavior of bus operator in the hypercongestion are identified. To understand the interaction between the transport authority (for system benefit maximization) and the bus operator (for its own benefit maximization), we examine how the bus operator responds to space changes and how the system benefit is influenced with the road space allocation. With responsive bus service, the condition, under which expanding bus lane capacity is beneficial to the system as a whole, has been analytically established. Then the model is applied to the dynamic framework where the space allocation changes with varying demand and demand-responsive bus service. We compare the optimal bus services under different economic objectives, evaluate the system performance of the bimodal network, and explore the dynamic space allocation strategy for the sake of social welfare maximization. [ABSTRACT FROM AUTHOR]

Highlights • Microscopic model with lane change and car-following for CACC. • Representation of operational design domain and authority transition. • New insights into capacity and flow heterogeneity of CACC traffic. • Strong correlation between congestion and authority transition. Abstract 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. [ABSTRACT FROM AUTHOR]

Highlights • A new method of constructing traffic state vectors is proposed. • Variables are selected using spatio-temporal correlations based on Mutual Information. • Weighted distance is used in selecting K nearest neighbors. • The proposed method can improve the prediction accuracy of data-driven models. Abstract Short-term traffic flow prediction is an integral part in most of Intelligent Transportation Systems (ITS) research and applications. Many researchers have already developed various methods that predict the future traffic condition from the historical database. Nevertheless, there has not been sufficient effort made to study how to identify and utilize the different factors that affect the traffic flow. In order to improve the performance of short-term traffic flow prediction, it is necessary to consider sufficient information related to the road section to be predicted. In this paper, we propose a method of constructing traffic state vectors by using mutual information (MI). First, the variables with different time delays are generated from the historical traffic time series, and the spatio-temporal correlations between the road sections in urban road network are evaluated by the MI. Then, the variables with the highest correlation related to the target traffic flow are selected by using a greedy search algorithm to construct the traffic state vector. The K-Nearest Neighbor (KNN) model is adapted for the application of the proposed state vector. Experimental results on real-world traffic data show that the proposed method of constructing traffic state vector provides good prediction accuracy in short-term traffic prediction. [ABSTRACT FROM AUTHOR]

Highlights • A Hierarchical Flow Network is proposed to formulate the traffic demand flow estimation model. • Multiple data sources are fused by mapping them to different variables in the hierarchical network. • Computational graph framework is used to propagate different layers of gradients sequentially. • Back propagation algorithm is utilized to aggregate loss errors and update the traffic variables. Abstract Aiming to develop a theoretically consistent framework to estimate travel demand using multiple data sources, this paper first proposes a multi-layered Hierarchical Flow Network (HFN) representation to structurally model different levels of travel demand variables including trip generation, origin/destination matrices, path/link flows, and individual behavior parameters. Different data channels from household travel surveys, smartphone type devices, global position systems, and sensors can be mapped to different layers of the proposed network structure. We introduce Big data-driven Transportation Computational Graph (BTCG), alternatively Beijing Transportation Computational Graph, as the underlying mathematical modeling tool to perform automatic differentiation on layers of composition functions. A feedforward passing on the HFN sequentially implements 3 steps of the traditional 4-step process: trip generation, spatial distribution estimation, and path flow-based traffic assignment, respectively. BTCG can aggregate different layers of partial first-order gradients and use the back-propagation of "loss errors" to update estimated demand variables. A comparative analysis indicates that the proposed methods can effectively integrate different data sources and offer a consistent representation of demand. The proposed methodology is also evaluated under a demonstration network in a Beijing subnetwork. [ABSTRACT FROM AUTHOR]

Highlights • A new mobility model combines the advantages of mobile phone and transportation data. • The model can capture ordinary and anomalous human mobility in a real-time manner. • We apply the model to predict crowd gatherings that usually cause traffic jams. Abstract Even though a variety of human mobility models have been recently developed, models that can capture real-time human mobility of urban populations in a sustainable and economical manner are still lacking. Here, we propose a novel human mobility model that combines the advantages of mobile phone signaling data (i.e., comprehensive penetration in a population) and urban transportation data (i.e., continuous collection and high accuracy). Using the proposed human mobility model, travel demands during each 1-h time window were estimated for the city of Shenzhen, China. Significantly, the estimated travel demands not only preserved the distribution of travel demands, but also captured real-time bursts of mobility fluxes during large crowding events. Finally, based on the proposed human mobility model, a predictive model is deployed to predict crowd gatherings that usually cause severe traffic jams. [ABSTRACT FROM AUTHOR]

Highlights • Trajectories play a critical role in CF model calibration and validation. • Developed a pattern recognition algorithm to differentiate driving regimes. • Developed a method to distinguish between free flow and CF sections. • Applied the algorithm to US-101 and reconstructed I-80 data. • Most trajectories in these datasets belong to acceleration, deceleration, and following. Abstract Vehicular trajectories are widely used for car-following (CF) model calibration and validation, as they embody characteristics of individual driving behaviour (each trajectory reflects an individual driver). Previous studies have highlighted that the trajectories should contain all the major vehicular interactions (driving regimes) between the leader and the follower for reliable CF model calibration and validation. Based on Dynamic Time Warping and Bottom-Up algorithms, this paper develops a pattern recognition algorithm for vehicle trajectories (PRAVT) to objectively, accurately, and automatically differentiate different driving regimes in a trajectory and then select the most complete trajectories (i.e. trajectories containing a maximum number of regimes). PRAVT is rigorously tested using synthetic data and then applied to the NGSIM data. We have observed that the NGSIM data are dominated by the trajectories which contain only three regimes, namely acceleration, deceleration, and following, 77% of the trajectories lack the standstill regime, and no trajectory in the NGSIM data is complete. These findings' impact on how to properly utilize NGSIM data can be profound. Given the extensive use of the NGSIM data in the traffic flow community, this paper also provides insights about the types of regimes contained in each trajectory of the NGSIM data. [ABSTRACT FROM AUTHOR]

It has been well documented that the mixed traffic flow of human-driven vehicles (HDVs) and connected and automated vehicles (CAVs) causes an unsound communication environment and weakens the beneficial effect of CAVs. However, the communication heterogeneity in the mixed traffic, which leads to incomplete information flow topology (IFT) and further affects the processes of vehicle control and platoon operation, did not receive its deserved attention. In contrast to previous research related to the IFT and platoons in static size, this study introduced the multi-anticipation and dynamic platoons into a more complex mixture of CAVs and HDVs and jointly considered the mode transition and driver uncertainties, e.g., estimation error and driving compliance. Specifically, we proposed multi-class generic controls to represent the basic longitudinal maneuver for each type of vehicle and developed customized car-following dynamics for each controller. In order to better adapt to the communication heterogeneity in the mixed traffic, we defined a novel controller for CAVs, namely mixed AV-CAV vehicle (MACV) controller and developed the corresponding control algorithm, namely AV-CAV integrated control (ACIC) with multi-source information from both sensors system and inter-vehicle communication. Besides, another important point worth considering in mixed traffic is the spatial distribution of CAVs in platoons. As such, three platoon policies, i.e., the cross policy, the continuum policy, and the random policy, were investigated. To our best knowledge, this is a new attempt to discuss the impact of platoon policies on mixed traffic, with a combination of different IFTs. Finally, multiple traffic flow characteristics, covering robustness, efficiency, safety, and emission, were assessed by calculating their corresponding surrogate measures. Results showed that the proposal of MACV controller and ACIC was expected to efficiently prevent the deleterious degradation from the CAV controller to AV controller and construct a more stable transition for changes in vehicle controls. Furthermore, a high proportion of communicable vehicles could significantly reduce the heterogeneity of mixed traffic and improve traffic flow characteristics. Besides, this study demonstrated the outstanding performance of multi-anticipative IFTs compared to the single-anticipative IFT and revealed the advantage of cross policy in traffic robustness and safety. These findings can help provide feasible solutions for IFTs, platoon operation, and vehicle control in the mixed traffic flow and identify their respective benefits in terms of traffic flow characteristics. • Incorporate multi-anticipation and driver uncertainties into the mixed traffic. • Propose a novel controller for CAVs and develop an integrated control algorithm. • Address dynamic mode transition and platooning in the heterogeneous network. • Construct a framework for comprehensive assessment of traffic flow characteristics. [ABSTRACT FROM AUTHOR]

A promising framework for understanding flow-density relationship in traffic flow theory is the Fundamental Diagram, originally developed for uninterrupted traffic flow facilities. The concept has been extended to the Arterial Fundamental Diagram (AFD), which has shown that the same relationship holds on arterial streets. However, constructing an AFD is subject to considerable variability in the measured quantities, due to the highly cyclical nature of signalized intersections. In most cases, these diagrams are based on the data from upstream detectors, located away from traffic signals. Recent scientific literature has shown a value of using stop-line detection data to develop AFDs, opening a plethora of opportunities to further investigate traffic dynamics utilizing the data from adaptive traffic control systems (ATCSs). This can, however, be problematic for two major reasons. First, the data may come from detectors unfit to provide good-quality inputs to develop an AFD. Second, such ATCSs may use their own surrogate measures of density and traffic flow, primarily developed for the purpose of controlling traffic, which may be inappropriate for developing fundamental relationships. This study aims to address these issues by investigating appropriateness of using Degree of Saturation (DS), a density-like measure from Sydney Coordinated Adaptive Traffic System (SCATS), to develop an AFD. Empirical SCATS data shows an interesting pattern of the AFD, which cannot be explained by the data itself. Hence, we derive a new analytical model of DS based on the high-resolution signal and detection data, which reveals parameters that drive its behavior. Additionally, we develop the Cyclical Vehicle Arrival and Discharge Model to simulate SCATS-like operations and derive causal relationships between traffic flow variables and density-like performance measures in a controlled environment. The findings show that DS does not have to be a poor estimator of traffic conditions, but when it is combined with SCATS-measured traffic flows it gives a false representation of near-capacity and over-saturated conditions. [ABSTRACT FROM AUTHOR]

This article proposes a macroscopic traffic control strategy to reduce fuel consumption of vehicles on highways. By implementing Greenshields fundamental diagram, the solution to Moskowitz equations is expressed as linear functions with respect to vehicle inflow and outflow, which leads to generation of a linear traffic flow model. In addition, we build a quadratic cost function in terms of vehicle volume to estimate fuel consumption rate based on COPERT model. A convex quadratic optimization problem is then formulated to generate energy-efficient traffic control decisions in real-time. Simulation results demonstrate significant reduction of fuel consumption on testing highway sections under peak traffic demands of busy hours. [ABSTRACT FROM AUTHOR]

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation. [ABSTRACT FROM AUTHOR]

Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities. [ABSTRACT FROM AUTHOR]

This study presents a thorough microscopic simulation investigation of a recently developed model-based approach for per-lane density estimation, as well as on-ramp and off-ramp flow estimation, for highways in the presence of connected vehicles. The estimation methodology is mainly based on the assumption that a certain percentage of vehicles is equipped with Vehicle Automation and Communication Systems (VACS), which provide the necessary measurements used by the estimator, namely vehicle speed and position measurements. In addition, a minimum number of conventional flow detectors is needed. In the investigation, a calibrated and validated, with real data, microscopic multi-lane model is employed, which concerns a stretch of motorway A20 from Rotterdam to Gouda in the Netherlands. It is demonstrated that the proposed methodology provides satisfactory estimation performance even for low penetration rates of connected vehicles, while it is also shown that the method is little sensitive to the parameters (two in total) of the model utilized by the estimator. [ABSTRACT FROM AUTHOR]

In this work, moving synchronized flow patterns (MSP) in empirical city traffic at signals have been observed which was predicted by Kerner (2013, 2014). The single vehicle data has been obtained through aerial observations. Based on this microscopic data, we have found trajectories for all vehicles together with vehicle speeds as functions of time and space. This has allowed us to find empirical MSPs in oversaturated city traffic as well as to perform microscopic analysis, that revealed the following empirical spatiotemporal features of MSPs: (i) MSPs emerge upstream of the queue at the signal and then they can propagate through the complete road section, (ii) merging of several MSPs into a single MSP, (iii) dissolving MSPs and (iv) spontaneous emerging of MSPs faraway upstream of the signal, (v) spontaneous emerging of moving queue within an MSP that is far away upstream of the signal. As a base for this work, we used an unmanned aerial vehicle (UAV) to record videos of inner city traffic situations and supervised tracking methods to gather the vehicle trajectories. [ABSTRACT FROM AUTHOR]

This paper addresses an interesting and practical bus headway adherence issue for a given public bus route with a number of bus stops. It first defines the random headway adherence ratio (HAR) at a particular bus stop of a specific bus route as the ratio of difference between actual bus headway and scheduled headway with respect to the scheduled headway. This study proceeds to customize a four-step procedure to estimate a probability distribution that can describe the random HAR at each bus stop of the bus route by using the automatic vehicle location (AVL) data. Our real case studies with 44,025 HAR data show that the 19 existing probability distributions including Lognormal, Gamma, Beta and Wakeby are unable to well fit these HAR data. This study thus proposes a tailored Wakeby-type distribution with five parameters. After deriving two fundamental propositions for the tailored Wakeby-type distribution, a tangible L -moment based method to estimate those parameters involved the tailored Wakeby distribution is presented. The tailored Wakeby-type distributions can meet our expectation via our real case studies. Finally, applications of the tailored Wakeby-type distribution derived for the random HAR are conducted. [ABSTRACT FROM AUTHOR]

Congestion at the downstream of an off-ramp often propagates the traffic queue to the freeway mainline, and thus reduces the freeway capacity at the interchange area. To prevent such potential queue spillover and improve traffic control efficiency over the entire corridor, this study develops an integrated control system which includes three primary functions: off-ramp queue estimation, arterial adaptive signal operations, and freeway off-ramp priority control. Using detected flow data, the system firstly estimates the queue length on the target off-ramp. If no potential queue spillover is predicted, the adaptive signal control function will then adjust the intersection signal timings and provide dynamic signal progression to critical path-flows. Otherwise, the off-ramp priority control function will be activated to clear the queuing vehicles at the off-ramp. To evaluate the effectiveness of the proposed system, this study has conducted numerical studies on a freeway interchange using a well-calibrated simulation platform. The experimental results reveal that the overall network performance can indeed be improved under the proposed control system, compared with other operational strategies. Further analyses of freeway time-dependent travel time distribution also evidence the effectiveness of the proposed system in preventing off-ramp queue spillover. [ABSTRACT FROM AUTHOR]

Network Fundamental Diagram (NFD) or Macroscopic Fundamental Diagram (MFD) represents dynamics of traffic flow at the network level. It is used to design various network-wide traffic control and pricing strategies to improve mobility and mitigate congestion. NFD is well defined when congestion distribution in the network is homogenous. However, in real world networks traffic is often heterogeneously distributed and initiated from an asymmetric and time-varying origin-destination (OD) demand matrix. In this paper, we formulate a resource allocation problem to find the optimal location of fixed measurement points and optimal sampling of probe trajectories to estimate NFD accounting for limited resources for data collection, network traffic heterogeneity and asymmetry in OD demand in a real-world network. Data from probe trajectories are used to estimate space-mean speed while data from fixed detectors are used to estimate traffic flow. Thus, the proposed model does not require an aggregate penetration rate of probe vehicles to be known a priori, which is one of the main contributions of this study. The proposed model is a mixed integer problem with non-linear constraints known to be NP-hard. A heuristic solution algorithm (Simulated Annealing) is implemented to solve the problem. Using a calibrated simulation-based dynamic traffic assignment model of Chicago downtown network, we present successful application of the proposed model and solution algorithm to estimate NFD. The results demonstrate sensitivity of the NFD estimation accuracy to the available budget, namely number of fixed measurement points and probe trajectories. We show that for a fixed proportion of OD trajectories, the increase in the proportion of fixed detection points increases the accuracy of NFD estimation as expected. However, when the proportion of fixed detection points is set to be constant, the increase in the proportion of OD trajectories does not necessarily improve the estimated NFD. Results hold true when varying demand is used to emulate variation in day-to-day traffic patterns. The robustness of the proposed methodology to the initial solution and trajectory availability for each OD pair is demonstrated in the numerical results section. We also found that a uniform distribution of selected links and ODs for NFD estimation across the network may not necessarily result in an optimal solution. Instead, distribution of links and OD pairs should follow the same distribution of links and OD pairs in the network. [ABSTRACT FROM AUTHOR]

Traffic congestion threats the growth and vitality of cities. Policy measures like punishments or rewards often fail to create a long term remedy. The rise of Information and Communication Technologies (ICT) enable provision of travel information through advanced traveler information systems (ATIS). Current ATIS based on shortest path routing might expedite traffic to converge towards the suboptimal User Equilibrium (UE) state. We consider that ATIS can persuade drivers to cooperate, pushing the road network in the long run towards the System Optimum (SO) instead. We develop an agent based model that simulates day-to-day evolution of road traffic on a simple binary road network, where the behavior of agents is reinforced by their previous experiences. Scenarios are generated based on various network designs, information recommendation allocations and incentive mechanisms and tested regarding efficiency, stability and equity criteria. Results show that agents learn to cooperate without incentives, but this is highly sensitive to the type of recommendation allocation and network-specific design. Punishment or rewards are useful incentives, especially when cooperation between agents requires them to change behavior against their natural tendencies. The resulting system optimal states are to most parts efficient, stable and not least equitable. The implications for future ATIS design and operations are further discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a recurrent neural network based microscopic car following model that is able to accurately capture and predict traffic oscillation. Neural network models have gained increasing popularity in many fields and have been applied in modelling microscopic traffic flow dynamics due to their parameter-free and data-driven nature. We investigate the existing neural network based microscopic car following models, and find out that they are generally accurate in predicting traffic flow dynamics under normal traffic operational conditions. However, they do not maintain accuracy under conditions of traffic oscillation. To bridge this research gap, we first propose four neural network based models and evaluate their applicability to predict traffic oscillation. It is found that, with an appropriate structure and objective function, the recurrent neural network based model has the capability of perfectly re-establishing traffic oscillations and distinguish drivers characteristics. We further compare the proposed model with a classical car following model (Intelligent Driver Model). Based on our case study, the proposed model outperforms the classical car following model in predicting traffic oscillations with different driver characteristics. [ABSTRACT FROM AUTHOR]

In this paper, a model-based perimeter control policy for large-scale urban vehicular networks is proposed. Assuming a homogeneously loaded vehicle network and the existence of a well-posed Network Fundamental Diagram (NFD), we describe a protected network throughout its aggregated dynamics including nonlinear exit flow characteristics. Within this framework of constrained optimal boundary flow gating, two main performance metrics are considered: (a) first, connected to the NFD, the concept of average network travel time and delay as a performance metric is defined; (b) second, at boundaries, we take into account additional external network queue dynamics governed by uncontrolled inflow demands. External queue capacities in terms of finite-link lengths are used as the second performance metric. Hence, the corresponding performance requirement is an upper bound of external queues. While external queues represent vehicles waiting to enter the protected network, internal queue describes the protected network’s aggregated behavior. By controlling the number of vehicles joining the internal queue from the external ones, herewith a network traffic flow maximization solution subject to the internal and external dynamics and their performance constraints is developed. The originally non-convex optimization problem is transformed to a numerically efficiently convex one by relaxing the performance constraints into time-dependent state boundaries. The control solution can be interpreted as a mechanism which transforms the unknown arrival process governing the number of vehicles entering the network to a regulated process, such that prescribed performance requirements on travel time in the network and upper bound on the external queue are satisfied. Comparative numerical simulation studies on a microscopic traffic simulator are carried out to show the benefits of the proposed method. [ABSTRACT FROM AUTHOR]

The emergence of automated vehicles may have significant impacts on traffic flow. While many studies suggest that fully automated vehicles can improve traffic flow by changing their macroscopic characteristics at different market penetration rates, recent studies show that partially automated vehicles (e.g., adaptive cruise control vehicles) commercially available on the market may negatively impact the traffic flow. With this in mind, existing traffic control strategies, such as ramp metering, may require further modification due to the change of macroscopic traffic flow characteristics in the presence of mixed autonomy traffic. However, the extent to which the ramp metering control algorithm will have to change in response to mixed autonomy traffic is still an open question. In this study, we first formulate the fundamental diagram analytically for mixed autonomy traffic, which is dependent on the market penetration rates of automated vehicles. Next, we model and simulate the composite traffic flow with a mixed autonomy macroscopic traffic flow model, and modify a standard ramp metering control strategy to optimize operations under the new flow conditions. The simulation results show that the total time spent is reduced by 4% compared to the scenario controlled with a standard ramp metering control strategy, suggesting a higher highway throughput is obtained at a merge section when leveraging the modified ramp metering controller. The results also show that the system-wide total delay is reduced by 11% compared to the scenario controlled with a standard ramp metering control strategy, implying the proposed ramp metering control strategy is efficient. • Traffic fundamental diagram depends on the fraction of automated vehicles. • Traffic makeup has potential to degrade traditional ramp metering control. • Ramp metering designed to consider automated vehicles in the flow. [ABSTRACT FROM AUTHOR]

Macroscopic fundamental diagram (MFD) describes the macro relationship between a network vehicle density and a network space mean flow, without requiring the mastery of complex origin to destination data. Thus, MFD provides an opportunity for the macro control of urban road network. However, most of the existing MFD control methods ignore the active role of traffic guidance in solving congestion problems. This study presents a traffic guidance–perimeter control coupled (TGPCC) method to improve the performance of macroscopic traffic networks. The method considers the optimal cumulative volume of a network as the goal and establishes a programming function according to the network equilibrium rule of traffic flow amongst multiple MFD sub-regions, which regards the minimum delay of network, as the objective. The Logit model for the compliance rate of driver route guidance is established by the stated preference survey. Moreover, the perimeter control (PC) method is proposed for adjusting the phase split of intersections. Finally, three schemes, namely, the TGPCC, PC and the method without PC and guidance are tested on a network with four well-defined MFD sub-regions. Results show that the TGPCC addresses the issue of congestion and decreases the total delay accordingly. [ABSTRACT FROM AUTHOR]

This paper presents a study towards the development of a real-time taxi movement planning system that seeks to optimize the timed taxiing routes of all aircraft on an airport surface, by minimizing the emissions that result from taxiing aircraft operations. To resolve this online planning problem, one of the most commonly employed operations research methods for large-scale problems has been successfully used, viz., mixed-integer linear programming (MILP). The MILP formulation implemented herein permits the planning system to update the total taxi planning every 15 s, allowing to respond to unforeseen disturbances in the traffic flow. Extensive numerical experiments involving a realistic (hub) airport environment bear out that an estimated environmental benefit of 1–3 percent per emission product can be obtained. This research effort clearly demonstrates that a surface movement planning system capable of minimizing the emissions in conjunction with the total taxiing time can be beneficial for airports that face dense surface traffic and stringent environmental requirements. [ABSTRACT FROM AUTHOR]

Vehicle headway distribution models are widely used in traffic engineering fields, since they reflect the fundamental uncertainty in drivers' car-following maneuvers and meanwhile provide a concise way to describe the stochastic feature of traffic flows. This paper presents a systematic review of vehicle headway distribution studies in the last few decades. Since it is impossible to enumerate the merits and drawbacks of all of existing distribution models, we emphasize four advances of headway distribution modeling in this paper. First, we highlight the chronicle of key assumptions on the existing distribution models and explain why this evolution occurs. Second, we show that departure headways measured for interrupted flows on urban streets and headways measured for uninterrupted flows on freeways have common features and can be simulated by a unified microscopic car-following model. The interesting finding helps gather two kinds of headway distribution models under one umbrella. Third, we review different approaches that aim to link microscopic car-following models and mesoscopic vehicle headway distribution models. Fourth, we show that both the point scattering on the density-flow plot and the shape of traffic flow breakdown curve implicitly depend on the vehicular headway distribution. These findings reveal pervasive connections between macroscopic traffic flow models and mesoscopic headway distribution. All these new insights bring new vigor into vehicle headway studies and open research frontiers in this field. [ABSTRACT FROM AUTHOR]

Under the Connected Vehicle environment where vehicles and road-side infrastructure can communicate wirelessly, the Advanced Driver Assistance Systems (ADAS) can be adopted as an actuator for achieving traffic safety and mobility optimization at highway facilities. In this regard, the traffic management centers need to identify the optimal ADAS algorithm parameter set that leads to the optimization of the traffic safety and mobility performance, and broadcast the optimal parameter set wirelessly to individual ADAS-equipped vehicles. Once the ADAS-equipped drivers implement the optimal parameter set, they become active agents that work cooperatively to prevent traffic conflicts, and suppress the development of traffic oscillations into heavy traffic jams. Measuring systematic effectiveness of this traffic management requires am analytic capability to capture the quantified impact of the ADAS on individual drivers’ behaviors and the aggregated traffic safety and mobility improvement due to such an impact. To this end, this research proposes a synthetic methodology that incorporates the ADAS-affected driving behavior modeling and state-of-the-art microscopic traffic flow modeling into a virtually simulated environment. Building on such an environment, the optimal ADAS algorithm parameter set is identified through a multi-objective optimization approach that uses the Genetic Algorithm. The developed methodology is tested at a freeway facility under low, medium and high ADAS market penetration rate scenarios. The case study reveals that fine-tuning the ADAS algorithm parameter can significantly improve the throughput and reduce the traffic delay and conflicts at the study site in the medium and high penetration scenarios. In these scenarios, the ADAS algorithm parameter optimization is necessary. Otherwise the ADAS will intensify the behavior heterogeneity among drivers, resulting in little traffic safety improvement and negative mobility impact. In the high penetration rate scenario, the identified optimal ADAS algorithm parameter set can be used to support different control objectives (e.g., safety improvement has priority vs. mobility improvement has priority). [ABSTRACT FROM AUTHOR]

To connect microscopic driving behaviors with the macro-correspondence (i.e., the fundamental diagram), this study proposes a flexible traffic stream model, which is derived from a novel car-following model under steady-state conditions. Its four driving behavior-related parameters, i.e., reaction time, calmness parameter, speed- and spacing-related sensitivities, have an apparent effect in shaping the fundamental diagram. Its boundary conditions and homogenous case are also analyzed in detail and compared with other two models (i.e., Longitudinal Control Model and Intelligent Driver Model). Especially, these model formulations and properties under Lagrangian coordinates provide a new perspective to revisit the traffic flow and complement with those under Eulerian coordinate. One calibration methodology that incorporates the monkey algorithm with dynamic adaptation is employed to calibrate this model, based on real-field data from a wide range of locations. Results show that this model exhibits the well flexibility to fit these traffic data and performs better than other nine models. Finally, a concrete example of transportation application is designed, in which the impact of three critical parameters on vehicle trajectories and shock waves with three representations (i.e., respectively defined in x - t , n - t and x - n coordinates) is tested, and macro- and micro-solutions on shock waves well agree with each other. In summary, this traffic stream model with the advantages of flexibility and efficiency has the good potential in level of service analysis and transportation planning. [ABSTRACT FROM AUTHOR]

Stop-and-go waves are easily caused by unstable traffic due to the collective behavior of human drivers, resulting in higher vehicle fuel consumption and emissions. In this article, we develop a general approach to synthesizing feedback controllers of autonomous vehicles (AVs) for smoothing unstable nonlinear mixed traffic flow, consisting of AVs and human-driven vehicles (HVs). In the context of traffic smoothing and stabilization, prior studies have mainly focused on analyzing string stability of specific car-following models linearized at flow equilibrium points. By contrast, we are interested to analytically synthesize appropriate feedback controllers of AVs for smoothing nonlinear mixed traffic in its general functional forms, covering a broad class of deterministic car-following models commonly seen in the literature. Specifically, by leveraging feedback control theory AVs are controlled to operate in such a way that they closely track a virtual speed profile, i.e., a subtler version of the disturbance resulting from the immediate preceding vehicle. Thus, traffic waves are reduced when propagating backward across controlled AVs. Based on the general functional form of car-following dynamics, we derive a class of effective additive AV controllers that are proven to be able to ensure convergence in tracking desired speed profiles, leading to smoother traffic. In addition, a set of sufficient conditions is devised for guaranteeing car-following safety. More importantly, head-to-tail string stability of the mixed traffic is likely to be achieved with a sufficient market penetration rate (MPR) of AVs. Notably, unlike many existing studies the feedback controllers synthesized for AVs require only local traffic information without having to rely on high degrees of vehicle connectivity, and the rate of smoothing mixed traffic is readily tunable, which is useful for practical implementations. The proposed approach is further illustrated with a theoretical intelligent driver model (IDM) and commercially available adaptive cruise control (ACC) vehicles represented by a well calibrated IDM. Extensive numerical results are presented to show the effectiveness and robustness of the feedback controllers synthesized for AVs on smoothing nonlinear mixed traffic. • Virtual tracking for autonomous vehicles to smooth nonlinear traffic • Applicable to a broad class of deterministic car-following dynamics • Only local traffic information is required for controller synthesis of autonomous vehicles [ABSTRACT FROM AUTHOR]