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Single occupancy vehicles are the most attractive transportation alternative for many commuters, leading to increased traffic congestion and air pollution. Advancements in information technologies create opportunities for smart solutions that incentivize ridesharing and mode shift to higher occupancy vehicles (HOVs) to achieve the car lighter vision of cities. In this study, we present HumanLight, a novel decentralized adaptive traffic signal control algorithm designed to optimize people throughput at intersections. Our proposed controller is founded on reinforcement learning with the reward function embedding the transportation-inspired concept of pressure at the person-level. By rewarding HOV commuters with travel time savings for their efforts to merge into a single ride, HumanLight achieves equitable allocation of green times. Apart from adopting FRAP, a state-of-the-art (SOTA) base model, HumanLight introduces the concept of active vehicles, loosely defined as vehicles in proximity to the intersection within the action interval window. The proposed algorithm showcases significant headroom and scalability in different network configurations considering multimodal vehicle splits at various scenarios of HOV adoption. Improvements in person delays and queues range from 15% to over 55% compared to vehicle-level SOTA controllers. We quantify the impact of incorporating active vehicles in the formulation of our RL model for different network structures. HumanLight also enables regulation of the aggressiveness of the HOV prioritization. The impact of parameter setting on the generated phase profile is investigated as a key component of acyclic signal controllers affecting pedestrian waiting times. HumanLight's scalable, decentralized design can reshape the resolution of traffic management to be more human-centric and empower policies that incentivize ridesharing and public transit systems. [Display omitted] • HumanLight is the first scalable human-centric RL-based adaptive signal controller. • Scalability is enabled from the decentralized design and algorithmic formulation. • HumanLight can democratize urban traffic by equitably allocating green times. • Active vehicles are proposed to handle the variance in occupancy of multimodal traffic. • HumanLight offers policymakers control of the aggressiveness in HOV prioritization. [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]

Signal control has been effective to alleviate urban traffic congestion. Massive related works about signal timing optimization have been proposed and led to many signal control methods and systems. In recent years, reinforcement learning (RL) algorithms have attracted the increasing attention of researchers in the area of signal control optimization, since they can learn the optimal timing policy themselves by analyzing changing patterns between the traffic condition and signal timing plans. Multi-intersection traffic signal control problems face more challenges than classical single intersection problems such as the dimensionality issue as the joint action space of multiple agents grows exponentially with the number of intersections. The existing state-of-art multi-agent reinforcement learning algorithms developed for other areas may not suit well with traffic signal control given the complex spatial–temporal nature of traffic flows. In this paper, we propose a novel multi-agent counterfactual actor–critic with scheduler (MACS) framework for multiple interactions. In this method, decentralized actors control the traffic signals and the centralized critic combines recurrent policies with feed-forward critics. Additionally, a scheduler module that exchanges information among agents helps the individual agent better understand the entire environment. The proposed method was evaluated using simulation experiments based on a real-world urban street network in Shenzhen, China. Results showed that the proposed method outperforms the classic model-based method and several existing RL-based methods according to different measures such as queue length, average travel time delay, and throughput. • Develop an AI-based signal control framework to collaborate multiple intersections. • The counterfactual baseline can well assign the credit among multiple intersections. • The scheduler module helps agents better understand the entire environment. [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]

• An internal model-Kalman filtering-based optimal hybrid feedforward/feedback control strategy is developed. • A practically decoupled platoon regulation around an equilibrium state is established. • String stability of the control system thus obtained is analyzed. • Numerical simulations verified the theoretical analysis in decoupling between successive vehicles. This paper presents an internal model-Kalman filtering-based optimal hybrid feedforward/feedback control strategy for traffic platoon control coordination enabled by SAE Automation Level 2 or Level 3 vehicles, i.e., partially automated vehicles (PAVs). Based on the Helly linear car-following model, a PAV platoon is established. Taking each vehicle's characteristic polynomial as the dominant internal model polynomial, an augmented system state model with filtered inputs is formed, and then both the system states and the external disturbance/internal perturbation from previous vehicle can be estimated by a delicately designed Kalman filter. By using a linear quadratic regulation approach, a distributed hybrid optimal feedforward/feedback controller utilizing the local estimated states is constructed to achieve a practically decoupled platoon regulation around an equilibrium state. String stability of the control system thus obtained is further analyzed. Finally, extensive numerical simulations verified the theoretical analysis in decoupling between successive vehicles, in suppressing the influence of various external disturbances, and in maintaining string stability. [ABSTRACT FROM AUTHOR]

Effective traffic control methods have great potential in alleviating network congestion. Particularly, in an urban network consisting of heterogeneous roads (e.g., freeways and urban roads), how to integrate and coordinate control policies on different roads is a critical issue in large-scale networks. This study addresses this question from two aspects: modeling and control. From the modeling aspect, we formulate the hybrid traffic modeling in heterogeneous networks with the Asymmetric Cell Transmission Model (ACTM) for freeways and the generalized bathtub model for urban roads. For the control aspect, this study considers two representative control approaches: ramp metering for freeways and perimeter control for urban roads, and we aim to develop a deep reinforcement learning (DRL)-based coordinated control framework for large-scale networks. However, there are two significant challenges in the coordinated control in large-scale networks with DRL methods: non-stationary environment and large search space. To address both issues, we incorporate the demonstration to guide the DRL method for better convergence by introducing the concept of "teacher" and "student" models. The teacher models are traditional controllers that provide control demonstrations. For instance, ALINEA and Gating are two representative feedback controllers for ramp metering and perimeter control which can be "teacher" models. The student models are DRL methods, which learn from teachers and aim to surpass the teachers' performance. Additionally, we develop a parallel training scheme to accelerate the proposed DRL method. To validate the proposed framework, we conduct two case studies in a small-scale network and a real-world large-scale traffic network in Hong Kong. Numerical results show that the proposed DRL method outperforms demonstrators as well as DRL methods, and the coordinated control is more effective than just controlling ramps or perimeters respectively. The research outcome reveals the great potential of combining traditional controllers with DRL for coordinated control in large-scale networks. • A generalized hybrid traffic model supporting simulation in large-scale networks. • A novel demonstration-guided DRL method for coordinated traffic control. • A parallel training scheme for DRL methods. • The proposed DRL method is examined in real networks in Hong Kong SAR. [ABSTRACT FROM AUTHOR]

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

• Traffic signal optimization is integrated with a comprehensive and vehicle-level trajectory planning model for mixed traffic conditions. • The low-level vehicle trajectory planning model integrates both car following and lane changing maneuvers. • The high-level model optimizes traffic signal parameters, and arrival time and lane assignment of CAVs. • A customized solution algorithm is designed to solve the complex problem. • The proposed model shows great mobility and energy benefits over fixed-time and adaptive signal control strategies. Infrastructure-assisted cooperative driving, which consists of connected and automated vehicle (CAV) trajectory planning and traffic signal control, can greatly improve the efficiency of signalized intersection operations. In existing studies, the CAV trajectory planning problem is simplified to only modeling longitudinal vehicle behaviors or assuming the lane changing can be executed instantaneously. The generated CAV trajectories are not realistic and can't be implemented at the vehicle level. Instantaneous lane changing process may also lead to safety issues or uncomfortable driving behavior of following vehicles in a mixed traffic environment. To fill this research gap, we propose an optimization framework that integrates a full CAV trajectory planning model with traffic signal control in a cooperative driving environment with both CAVs and human-driven vehicles (HDVs). A two-level optimization model is formulated based on discrete time. The high-level model optimizes traffic signal parameters, CAV arrival time, and lane assignment at the stop bar, to minimize total vehicle delay. Given arrival time and lane assignment, the low-level model generates trajectories with integrated car-following and lane-changing maneuvers to mimic a human driving policy based on imitation learning. Numerical experiments from a real-world intersection show that the proposed model outperforms adaptive and fixed time signal control by as much as 63.06% in terms of reduction of average vehicle delay, due to better utilization of lane capacity and reduced lost time. In addition to mobility benefits, fuel consumption is also significantly reduced under the proposed infrastructure-assisted cooperative driving framework. [ABSTRACT FROM AUTHOR]

The traffic capacity of signalized intersections can be increased using vehicle to infrastructure (V2I) communication by broadcasting traffic control signals to connected autonomous vehicles (CAVs). However, the potential improvement in traffic capacity with only CAVs can be substantially reduced under mixed traffic environments that include autonomous vehicles (AVs) without V2I communication. The main contribution of this work is to increase the traffic capacity (close to that achieved by CAVs with V2I communication) when there is a substantial number of AVs without V2I communication — by using a delayed-self-reinforcement (DSR) approach for vehicle spacing control with already available history of the AVs own control inputs and sensed information. Moreover, analytical estimates are established to predict the improvements in traffic capacity achieved for AVs with the DSR approach compared to the case without the DSR approach. Simulation results match the analytical predictions and show that AVs using the DSR approach (without V2I communication) are able to recover 88% of intersection capacity achieved with CAVs (with V2I communication) and result in 2.7 times the intersection capacity achieved by AVs without DSR on one way traffic. Moreover, similar improvements with DSR are shown for a real-world intersection using the Simulation of Urban MObility (SUMO) platform. AVs with DSR approach are able to recover 96% of intersection capacity achieved with CAVs (with V2I communication) and result in 1.4 times the intersection capacity achieved by AVs without DSR. Improvements using DSR are also seen with mixed traffic that includes human-driven vehicles (HDVs). [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]

Controlling traffic in large-scale urban road networks is a challenging problem. Aggregated dynamical models, based on the macroscopic fundamental diagram (MFD) of urban traffic, enable model-based control design. As an alternative to perimeter control actuation commonly used in MFD-based control, in this paper, we propose actuation over regional space-mean speeds, which we name average speed control. The method involves manipulation of regional speeds via instrumentation similar to variable speed limits in freeways, or using vehicle-to-infrastructure communication. We develop nonlinear model predictive control schemes considering actuation over average speed and perimeter control. Their performances are compared using simulations on congested scenarios, the results of which suggest potential of the method as an alternative or complementary actuation to perimeter control. • A novel speed control strategy for urban networks. • Manipulation of regional average speeds to optimize traffic conditions in the whole network. • An MPC scheme combining perimeter control and average speed control. [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]

• A decentralized DRL signal control for multi-modal traffic networks is introduced based on a microscopic environment. • Car traffic and bus performances are improved simultaneously and traded off. • The proposed method is applicable to different bus lane layouts and multiple bus lines. • The scalability and transferability of the model are verified. Managing traffic flow at intersections in a large-scale network remains challenging. Multi-modal signalized intersections integrate various objectives, including minimizing the queue length and maintaining constant bus headway. Inefficient traffic signals and bus headway control strategies may cause severe traffic jams, high delays for bus passengers, and bus bunching that harms bus line operations. To simultaneously improve the level of service for car traffic and the bus system in a multi-modal network, this paper integrates bus priority and holding with traffic signal control via decentralized controllers based on Reinforcement Learning (RL). The controller agents act and learn from a synthetic traffic environment built with the microscopic traffic simulator SUMO. Action information is shared among agents to achieve cooperation, forming a Multi-Agent Reinforcement Learning (MARL) framework. The agents simultaneously aim to minimize vehicles' total stopping time and homogenize the forward and backward space headways for buses approaching intersections at each decision step. The Deep Q-Network (DQN) algorithm is applied to manage the continuity of the state space. The tradeoff between the bus transit and car traffic objectives is discussed using various numerical experiments. The introduced method is tested in scenarios with distinct bus lane layouts and bus line deployments. The proposed controller outperforms model-based adaptive control methods and the centralized RL method regarding global traffic efficiency and bus transit stability. Furthermore, the remarkable scalability and transferability of trained models are demonstrated by applying them to several different test networks without retraining. [ABSTRACT FROM AUTHOR]

Ramp Metering (RM) is one of the most effective control techniques to alleviate freeway congestion. We consider RM at the microscopic level subject to vehicle following safety constraints for a freeway with arbitrary number of on- and off-ramps. The arrival times of vehicles to the on-ramps, as well as their destinations are modeled by exogenous stochastic processes. Once a vehicle is released from an on-ramp, it accelerates towards the free flow speed if it is not obstructed by another vehicle; once it gets close to another vehicle, it adopts a safe gap vehicle following behavior. The vehicle exits the freeway once it reaches its destination off-ramp. We design traffic-responsive RM policies that maximize the throughput. For a given routing matrix, the throughput of a RM policy is characterized by the set of on-ramp arrival rates for which the queue sizes at all the on-ramps remain bounded in expectation. The proposed RM policies operate under vehicle following safety constraints, where new vehicles are released only if there is sufficient gap between vehicles on the mainline at the moment of release. Furthermore, the proposed policies work in synchronous cycles during which an on-ramp does not release more vehicles than the number of vehicles waiting in its queue at the start of the cycle. All the proposed policies are reactive , meaning that they only require real-time traffic measurements without the need for demand prediction. However, they differ in how they use the traffic measurements. In particular, we provide three different mechanisms under which each on-ramp either: (i) pauses release for a time interval before the next cycle begins, or (ii) adjusts the release rate during a cycle, or (iii) adopts a conservative safe gap criterion for release during a cycle. The throughput of the proposed policies is characterized by studying stochastic stability of the induced Markov chains, and is proven to be maximized when the merging speed at all the on-ramps equals the free flow speed. Simulations are provided to illustrate the performance of our policies and compare with a well-known RM policy from the literature which relies on local traffic measurements. • Designing traffic-responsive Ramp Metering (RM) policies at the microscopic level. • Analyzing the interplay of safety, connectivity, automation protocols, and RM throughput. • Designing RM policies that reduce the risk of collision at merging bottlenecks without compromising the throughput. [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]

• Systematic review on urban traffic control under mixed traffic with CAVs. • Cover signal timing, CAV trajectory/route planning, and their cooperative control. • Summarize mixed traffic control methods at intersections with CAV-dedicated facilities. • Outline promising research directions and challenges. Efficient traffic control can alleviate traffic congestion, reduce fuel consumption, and improve traffic safety. With the development of communication and automation technologies, regular vehicles (RVs), connected vehicles (CVs), and connected and automated vehicles (CAVs) will coexist on urban roads in the near future. In this paper, we systematically review the studies on urban traffic control under the mixed traffic environment, which utilize CVs and CAVs to improve the performance of urban traffic control. We first give a glance at the evolution of urban traffic control with the deployment of CVs and CAVs to identify the important and promising research fields in the mixed traffic control. We then review the recent advances in traffic signal timing with CAV/CV trajectory data, CAV trajectory/route planning, and the cooperative control of CAV trajectories and traffic signals from the intersection level to the network level under the mixed traffic environment. Besides, we summarize two types of studies on mixed traffic control at intersections with CAV-dedicated facilities: 1) traffic signal timing and vehicle trajectory planning at intersections with CAV-dedicated lanes, and 2) deployment of CAV-dedicated facilities including CAV-dedicated lanes, links, and zones. Finally, the future research directions and challenges are discussed. We hope that this review could provide researchers with a helpful roadmap for future research on urban traffic control under the mixed traffic environment. [ABSTRACT FROM AUTHOR]

Connected automated vehicles (CAVs) have shown great potential in improving traffic throughput and stability. Although various longitudinal control strategies have been developed for CAVs to achieve string stability in mixed-autonomy traffic systems, the potential impact of these controllers on safety has not yet been fully addressed. This paper proposes safety-critical traffic control (STC) by CAVs—a strategy that allows a CAV to maintain safety relative to both the preceding vehicle and the following human-driven vehicles (HVs). Specifically, we utilize control barrier functions (CBFs) to impart collision-free behavior with formal safety guarantees to the closed-loop system. The safety of both the CAV and HVs is incorporated into the framework through a quadratic program-based controller that minimizes deviation from a nominal stabilizing traffic controller subject to CBF-based safety constraints. Considering that some state information of the following HVs may be unavailable to the CAV, we employ state observer-based CBFs for STC. Finally, we conduct extensive numerical simulations – that include vehicle trajectories from real data – to demonstrate the efficacy of the proposed approach in achieving provably safe traffic. [ABSTRACT FROM AUTHOR]

• Dynamic feedback control for a class of day-to-day traffic adjustment processes. • Demand management and supply regulations are considered. • Only two adjustment parameters with well-identified physical meanings are involved. • The requirement on adjustment parameters for convergence and stability is mild. • Novel output stability notion. In the literature, networks are usually assumed to operate at their steady states in static traffic assignment models. Given a set of demand management and supply regulation strategies, whether the vehicular traffic on a network would approach or not to a steady state under certain assumptions of behavioral realism is of importance and great interest in practice. Moreover, one primary reason for understanding the long-term traffic evolution dynamics is to control or influence the system trajectory in an efficient manner. To address these problems, we focus on a dynamic feedback control design and its subsequent steady-state analysis for a class of day-to-day (DTD) disequilibrium process so that the network traffic would evolve towards the desired stable traffic equilibrium. We propose an interconnected dynamic system framework for the stability and convergence analysis of DTD traffic adjustment process under the dynamic feedback control. Distinguished from the existing studies, we introduce a notion of output stability as well as the Lyapunov-like conditions to carry out the investigation noting that the steady states of the dynamic tolls that correspond to the Lagrange multipliers of the traffic equilibrium problem can be non-unique. The proposed method gives rise to a new approach to selecting pricing strategy. The proposed method involves only two adjustment parameters with well-identified physical meanings while the requirement on adjustment parameters for convergence and stability is mild. Finally, several numerical examples are presented to illustrate the insightful findings. It is found that, with dynamic feedback control, the network traffic state can converge to the desired equilibrium in a much faster manner than the case without control. The dynamic feedback controller can be implemented as demand management and supply regulation strategies such as road pricing and signal control. [ABSTRACT FROM AUTHOR]

• Neighborhood MFDs are re-scaled to account for effects of cordon queues. • Simulation experiments reveal that re-scaled MFDs better predict traffic performance. • Cordon-metering plans are found to be more effective as well. Optimal cordon-metering rates are obtained using Macroscopic Fundamental Diagrams in combination with flow conservation laws. A model-predictive control algorithm is also used so that time-varying metering rates are generated based on their forecasted impacts. Our scalable algorithm can do this for an arbitrary number of cordoned neighborhoods within a city. Unlike its predecessors, the proposed model accounts for the time-varying constraining effects that cordon queues impose on a neighborhood's circulating traffic, as those queues expand and recede over time. The model does so at every time step by approximating a neighborhood's street space occupied by cordon queues, and re-scaling the MFD to describe the state of circulating traffic that results. The model also differentiates between saturated and under-saturated cordon-metering operations. Computer simulations of an idealized network show that these enhancements can substantially improve the predictions of both, the trip completion rates in a neighborhood and the rates that vehicles cross metered cordons. Optimal metering policies generated as a result are similarly shown to do a better job in reducing the Vehicle Hours Traveled on the network. The VHT reductions stemming from the proposed model and from its predecessors differed by as much as 14%. [ABSTRACT FROM AUTHOR]

• New data driven framework for OD matrix estimation. • No equilibrium assignment nor network loading model needed. • Scalable for large networks using dimensionality reduction techniques. • Supervised learning to estimate production and attraction from 3D supply patterns. • Results on small and large network promising. The fundamental challenge of the origin-destination (OD) matrix estimation problem is that it is severely under-determined. In this paper we propose a new data driven OD estimation method for cases where a supply pattern in the form of speeds and flows is available. We show that with these input data, we do not require an iterative dynamic network loading procedure that results in an equilibrium assignment, nor do we need an assumption on the kind of equilibrium that emerges from this process. The minimal number of ingredients which are needed are (a) a method to estimate/predict production and attraction time series; (b) a method to compute the N shortest paths from each OD zone to the next; and (c) two—possibly OD-specific—assumptions on the magnitude of N ; and on the proportionality of path flows between these origins and destinations, respectively. The latter constitutes the most important behavioral assumption in our method, which relates to how we assume travelers have chosen their routes between OD pairs. We choose a proportionality factor that is inversely proportional to realized travel time, where we incorporate a penalty for path overlap. For large networks, these ingredients may be insufficient to solve the resulting system of equations. We show how additional constraints can be derived directly from the data by using principal component analysis, with which we exploit the fact that temporal patterns of production and attraction are similar across the network. Experimental results on a toy network and a large city network (Santander, Spain) show that our OD estimation method works satisfactorily, given a reasonable choice of N , and the use of so-called 3D supply patterns, which provide a compact representation of the supply dynamics over the entire network. Inclusion of topological information makes the method scalable both in terms of network size and for different topologies. Although we use a neural network to predict production and attraction in our experiments (which implies ground-truth OD data were needed), there are straight-forward paths to improve the method using additional data, such as demographic data, household survey data, social media and or movement traces, which could support estimating such ground-truth baseline production and attraction patterns. The proposed framework would fit very nicely in an online traffic modeling and control framework, and we see many paths to further refine and improve the method. [ABSTRACT FROM AUTHOR]

• Development of an adaptive control system to manage railway traffic in real-time. • Application of approximate dynamic programming to railway traffic management. • Application of reinforcement learning to approximate railway traffic states. • A computationally efficient approach to manage the stochastic nature of railways. This study presents an adaptive railway traffic controller for real-time operations based on approximate dynamic programming (ADP). By assessing requirements and opportunities, the controller aims to limit consecutive delays resulting from trains that entered a control area behind schedule by sequencing them at a critical location in a timely manner, thus representing the practical requirements of railway operations. This approach depends on an approximation to the value function of dynamic programming after optimisation from a specified state, which is estimated dynamically from operational experience using reinforcement learning techniques. By using this approximation, the ADP avoids extensive explicit evaluation of performance and so reduces the computational burden substantially. In this investigation, we explore formulations of the approximation function and variants of the learning techniques used to estimate it. Evaluation of the ADP methods in a stochastic simulation environment shows considerable improvements in consecutive delays by comparison with the current industry practice of First-Come-First-Served sequencing. We also found that estimates of parameters of the approximate value function are similar across a range of test scenarios with different mean train entry delays. [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]

• Designing a macroscopic modeling framework for perimeter gating control and dynamic user equilibrium. • Designing a routing scheme with instantaneous dynamic user equilibrium and predictive travel time. • Application of Proportional-Integral (PI) controller for traffic control. • Investigating the impact of perimeter gating control on routing inside-and-outside the reservoir. • Analysis of emissions in the traffic network. In recent years, several perimeter control strategies have been proposed for traffic management in cities. The common factor found in these works is the use of Macroscopic Fundamental Diagram (MFD) models to describe the dynamics of the network and optimize traffic inside the perimeter by manipulating perimeter inflows. Perimeter gating control strategies are attractive for traffic management inside the inner city. However, it inevitably creates a negative impact on the traffic outside. Most of the works in this research area have neglected vehicle re-routing outside the controlled perimeter, i.e., they do not consider demand elasticity to the central region resulting from gating and the related queues. In this paper, we propose a global modeling framework capable of assessing the effect of perimeter gating control (in terms of queue, emission, and total time spent) on the full network, considering demand elasticity resulting from Dynamic User Equilibrium (DUE). Classical Proportional-Integral (PI) control scheme is used to control traffic congestion inside a central region (reservoir). The modeling framework is comprised of: (i) an accumulation-based MFD model to reproduce traffic dynamics inside the reservoir, (ii) point-queue model to represent queuing vehicles on inbound links to the gating points, and (iii) a time-dependent travel time profile based on a steady-state approximation of MFD dynamics to characterize the alternative road network (bypass). DUE is then implemented, considering instantaneous predicted travel time. This determines how the demand to the inner region is affected by the gating. The functioning of the global system is assessed by total time spent and NOx and CO 2 emissions inside the reservoir and for the full network. The presented simulation results show that the perimeter gating control helps to maintain congestion at the desired level with significant improvements in the total time spent and the mean speed in the network. However, it shows a slight increase in the queues. As expected, deviation to the bypass alternative is significant and should not be neglected when carrying out a global assessment of gating system performance. [ABSTRACT FROM AUTHOR]

• We design an optimal lane management strategy for a heterogeneous traffic condition. • We dynamically control the network inflow by optimizing the size of the CAV platoons. • The multiple lane type corridors are modeled as sets of parallel bottlenecks. • The dynamics of the congestion in the network is modeled using the MNFD. • We solve the optimization problem for the city of San Francisco, CA. The advent of autonomous driving technologies has created a crucial need for upgrading conventional traffic control and lane management strategies in large cities. In this research, we design an optimal lane management strategy for corridors with a heterogeneous demand of human-driven, autonomous, and communicant autonomous vehicles (HVs, AVs, and CAVs). In a monocentric city setting, we dynamically control the inflow of the network by optimizing the size of CAV platoons in the corridors based on the instantaneous condition of the integrated system. These corridors can potentially have three types of lanes for vehicles with different levels of automation technology. We model the multiple lane type corridors as sets of parallel bottlenecks with general distributions of multiclass demand. The dynamics of the congestion in the network is also modeled using the macroscopic network fundamental diagram (MNFD). To study the impacts of the rise in the penetration rate of AVs and CAVs on the performance of the system, we derive a closed-form representation of the model. We show that the increase of the delay in the network with the rise in the penetration rate of AVs and CAVs can have a stable, an unstable, or a hybrid pattern. To optimize the system, we minimize a weighted summation of the experienced delay in the corridors and the total travel time in the urban network by optimizing the number of lanes of each type and the dynamic size of the CAV platoons. The results of the San Francisco case study show that implementing an optimal lane management strategy can reduce the experienced delay in the corridors up to 78% with a rise in the AV/CAV penetration rate. By dynamically controlling the size of the CAV platoons in the automated highway of the Bay Bridge, we limit the increase of the travel time in the downtown network as low as 5%. [ABSTRACT FROM AUTHOR]

• Real-time, Connected Vehicle-based traffic signal control algorithm. • Algorithm seeks to balance between efficiency and equity. • Algorithm minimizes average delay while limiting the maximum individual delay. • Equity at intersections can be improved with minimum loss of efficiency. This paper extends a real-time, Connected Vehicle-based traffic signal control algorithm at isolated intersections to balance between two competing intersection objectives: efficiency and equity. In this approach, a central controller is used to collect real-time locations of connected vehicles at regular intervals, which can then be used to also identify the existence of some of the non-connected vehicles. The control algorithm then aims to optimize the discharge sequence of naturally occurring platoons of vehicles based on their proximity. Specifically, the strategy selects the platoon discharge sequence—and associated signal phase and timing plan—that minimizes average vehicle delay (measure of efficiency) while limiting the maximum delay any individual vehicle may experience (measure of equity). The latter objective is only possible with detailed vehicle-level information available from connected vehicles. The results show that without the threshold on maximum individual vehicle delay, average delay is often minimized at the expense of very large delays imposed onto some vehicles. By implementing a threshold, both the maximum vehicle delay and the distribution of individual vehicle delays—as measured by the standard deviation and a common measure of population inequity, the Gini coefficient—can be improved, often with only negligible impacts to intersection efficiency. However, the tradeoff between equity and efficiency becomes more significant as the maximum vehicle delay threshold decreases. Sensitivity tests show that this control algorithm works well for different total traffic demands and different demand patterns. The proposed algorithm is also effective under imperfect connected vehicle penetration rates when the connected vehicles make up more than 40% of the traffic stream. The results suggest that the proposed strategy can help significantly reduce long delays and inequitable treatment of vehicles at an intersection when vehicle-level information is available to a signal controller. [ABSTRACT FROM AUTHOR]

• Developing a max pressure traffic controller based on travel times. • Investigating work-conservative property in the developed max pressure traffic controller. • Comparison analysis with other traffic controllers via microsimulation test study. • Real implementation of the travel time max pressure controller to a signalized intersection. The traffic control of an arbitrary network of signalized intersections is considered. This work presents a new version of the recently proposed max-pressure controller, also known as back-pressure. The most remarkable features of the max-pressure algorithm for traffic signal control are: scalability, stability, and distribution. The modified version presented in this paper improves the practical applicability of the max-pressure controller by considering as input travel times instead of queue lengths. The two main practical advantages of this new version are: (i) travel times are easier to estimate than queue lengths, and (ii) max-pressure controller based on travel times has an inherent capacity-aware property, i.e., it takes into account the finite capacity of each link. Travel time tends to diverge when the queue length is close to its capacity. It should be noted that previous max-pressure algorithms rely exclusively on queue length measurements, which may be difficult to accomplish in practice. Moreover, these previous algorithms generally assume queues with unbounded capacity. This may be problematic because a model with unbounded capacity links is not able to reproduce spillbacks, which are one of the most critical phenomena that a traffic signal controller should avoid. After presenting the new version of the max-pressure controller, it is analyzed and compared with existing control policies in a microscopic traffic simulator. Moreover, results of a real implementation of the developed algorithm to a signalized intersection, located at an urban arterial in Jerusalem, are shown and analyzed. To the best of the authors' knowledge, this experiment is the first real implementation of a max-pressure controller at a signalized intersection. [ABSTRACT FROM AUTHOR]

• This paper proposes a network-wide method to identify intersection traffic congestion in a road network. • The proposed method is solely based on low-frequency probe vehicle data, i.e., map-independent. • It is able to rapidly and approximately detect intersection congestion along all turning directions. • It is a computer-aided method in the era of big data can greatly decrease traffic engineers' workload. Locating the bottlenecks in cities where traffic congestion usually occurs is essential prior to solving congestion problems. Therefore, this paper proposes a low-frequency probe vehicle data (PVD)-based method to identify turn-level intersection traffic congestion in an urban road network. This method initially divides an urban area into meter-scale square cells and maps PVD into those cells and then identifies the cells that correspond to road intersections by taking advantage of the fixed-location stop-and-go characteristics of traffic passing through intersections. With those rasterized road intersections, the proposed method recognizes probe vehicles' turning directions and provides preliminary analysis of traffic conditions at all turning directions. The proposed method is map-independent (i.e., no digital map is needed) and computationally efficient and is able to rapidly screen most of the intersections for turn-level congestion in a road network. Thereby, this method is expected to greatly decrease traffic engineers' workloads by providing information regarding where and when to investigate and solve traffic congestion problems. [ABSTRACT FROM AUTHOR]

• This paper presents a data-driven hybrid control (DDHC) framework. • DDHC framework composed of data-driven control module, performance module, and optimization module. • Control strategies including acceleration strategy and deceleration strategy. • The effectiveness of the proposed DDHC framework is evaluated with the data of a transit route in Urumqi, China. This paper presents a data-driven hybrid control (DDHC) framework that can arrange adaptive control strategies for vehicles to effectively improve the transit performance of the public transport system. The framework depicts a powerful combination of a data-driven control method that is used to imitate the control behaviour of dispatchers and a mathematical optimization method. Three components comprise the DDHC framework: a data-driven control module, a performance module, and an optimization module. The data-driven control module contains a random forest model which is adopted to justify whether to intervene in the operation of a bus line, and if so, which vehicles should be controlled and what type of control strategy should be taken – an acceleration strategy or deceleration strategy. The performance module including vehicle operation state models is used to describe the system evolution. The last component optimizes the specific control actions – which type of acceleration or deceleration strategy should be adopted – by minimizing total passenger travel time. The effectiveness of the proposed DDHC framework is evaluated with the data of a transit route in Urumqi, China. The results show that the DDHC framework with reasonable parameters can suit the needs of real-time control in complex traffic environments. [ABSTRACT FROM AUTHOR]

• In 2 simple networks P0 and MP do not maximise throughput if demand exceeds capacity. • In these 2 networks modified P0 and MP maximise throughput if demand exceeds capacity. • In a simple network with route choice, P0 maximises throughput for all demands. • In this simple network MP does not maximise long run throughput for all demands. This paper seeks traffic signal control policies which maximise long-run network throughput when demand is within network capacity and also when demand is beyond network capacity. The paper considers smooth versions of two well-known responsive traffic signal control policies: the P 0 policy (see Smith, 1979b, 1980 or Smith et al., 2015) and the maxpressure or MP policy (see Varaiya, 2013a). The paper gives two simple example networks where both of these smooth responsive traffic control policies give rise to decreasing throughput for increasing demands; when these demands are beyond the network capacity. The paper then suggests a simple modification of the two responsive traffic control policies and shows that the two modified policies do maximise the long run throughput of the two signal-controlled networks; both when a steady input demand D is within network capacity and also when a steady demand exceeds network capacity. The modified version of P 0 is as follows: for a certain positive number K , for D ⩽ K use responsive policy P 0 , and for D > K use the green-times generated by P 0 when D = K. Thus in this modification the green-time proportions generated by responsive P 0 are frozen as demand passes through K ; and remain fixed for all demands D which exceed K. The paper shows that the above modification when applied to a continuous version of MP also maximises throughput in both networks for all values of the steady demand D. Finally the paper considers a signal controlled network with route choice. It is shown that if routes are chosen by travellers in their own best interests then the continuous version of MP does not always maximise throughput, even if the constant input demand D is within the network capacity, but P 0 does always maximise throughput, even when the constant input demand D is beyond the network capacity. [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]

• Propose priority-based infrastructure and control aware user heterogeneity, i.e. value-of-time. • Develop a joint optimization framework for traffic signal control and priority-based pricing. • Utilize connected vehicles to inform traffic flow and value-of-time information. • Test the proposed joint framework in real-city settings that resemble Zurich network. • Demonstrate traffic performance can be significantly improved while equitable savings achieved. Real-time control of large-scale urban networks has been attracting significant research attention. This paper, using the information provided by connected vehicles, proposes a novel control approach based on the concept of perimeter control to maximize the social welfare of all vehicles. The contributions of this paper are threefold. First, we consider vehicle heterogeneity (i.e. corresponding to different transportation modes, or with different occupancies, values of time, priority levels, etc.) and integrate a priority scheme into perimeter control to improve both the traffic performance and the social welfare. This is achieved by installing priority lanes at some of the perimeter intersections. Unlike the existing research works that provide priority to certain traffic modes, we dynamically identify the groups of vehicles that we should prioritize, in order to maximize the social welfare. Second, we develop a model predictive control approach to simultaneously optimize the toll for using the priority lanes and the traffic signal timings at the perimeter intersections. This approach can explicitly handle the constraint of the storage capacity of each intersection link. Third, we propose a recursive estimation algorithm to update our knowledge on the distribution of the value of times (VOTs), using the lane choice information of connected vehicles. The proposed approach is tested in a simulated network which resembles the main features of the city center of Zurich, Switzerland. By using the proposed strategy, the traffic accumulation inside the network is still stabilized, and the monetary costs due to delay are significantly reduced at the entire network (up to 25.8%) compared to the strategy without priority. The distribution of the combined cost (including cost due to delay and tolls) is more uniform across VOT groups than that resulting from the strategy without priority. It is also shown that the proposed recursive estimation algorithm quickly converges and further improves the social welfare. [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]

• Cooperative traffic signal control algorithm for enhancing the CACC vehicle string operation. • A robust system that operates under various CACC market penetrations and vehicle connectivity levels. • An algorithm that brings significant improvement of average vehicle speed and fuel efficiency at congested intersections. Vehicles equipped with Cooperative Adaptive Cruise Control (CACC) have the capability to broadcast their real-time speed and location information via wireless communications. They can also safely operate in multi-vehicle strings while keeping shorter than normal gaps among adjacent vehicles in the high-speed traffic stream. Such capabilities can greatly benefit the management of urban signalized intersections. In this study, we have developed a cooperative signal control algorithm that adopts the CACC datasets and the datasets collected by the traditional fixed traffic sensors to predict the future traffic conditions. The prediction allows the signal controller to assign signal priority to the intersection approach that accommodates the most CACC strings. Such a control strategy can significantly enhance the CACC string operation, which ultimately improves the overall intersection performance. The effectiveness of the algorithm has been tested in a simulated 4-way signalized intersection. The algorithm substantially outperforms the traditional actuated controller as it perceives the traffic flow more comprehensively and assigns the green time resource more efficiently than the traditional controller. Particularly, the average vehicle speed and the average vehicle miles travelled per gallon fuel consumed (MPG) can be increased by more than 10% when the CACC market penetration is 100%. In mixed traffic where CACC fleets frequently interact with manually driven vehicles, the algorithm is found to be more beneficial. The speed and MPG improvement exceed 30% when the CACC market penetration is 40%. The signal control algorithm can bring about significant benefit even when the CACC market penetration is 0%. In this case, it completely relies on the datasets obtained from the traditional traffic sensors. This finding demonstrates the robustness of the algorithm. It makes the proposed algorithm suitable to implement in real-world intersections under various CACC market penetrations and different levels of vehicle connectivity. [ABSTRACT FROM AUTHOR]

• Development of an integrated optimization program for cooperative signal timing and perimeter control. • Development of a distributed methodology to find solutions to the cooperative problem in real-time. • Increasing network throughput by 6.0–12.8% compared to separate signal and perimeter control. This paper presents an integrated formulation and a distributed solution technique for cooperative signal control and perimeter traffic metering in urban street networks with various market penetration rates of connected vehicles. The problem is formulated as a mixed integer nonlinear program thus, does not scale well with the size of the network in a centralized optimization framework due to the presence of many mixed integer decision variables and nonlinear constraints. To address this limitation, we will develop a distributed model predictive control that distributes the network-level cooperative problem into several intersection-level sub-problems and coordinates their decisions. Our numerical analyses show that the proposed distributed methodology finds solutions to the problem in real-time with the optimality gap of at most 3.6% in our case studies. We have implemented the distributed methodology in Vissim and observed that cooperative signal timing and perimeter control yielded significant improvements in traffic operations. Our case study results show that the cooperative approach increases the number of completed trips by 6.0–12.8% and 10.9–11.0% and reduces the total travel times by 8.1–9.0% and 23.6–24.2% compared to independent signal control and independent perimeter control, respectively. [ABSTRACT FROM AUTHOR]

• Sequential movements of CAVs are modelled as multi-agent Markov decision processes. • A decentralized coordination multi-agent learning approach (DCL-AIM) is proposed. • DCL-AIM explicitly identifies and dynamically adapts agent coordination needs. • Effectiveness of DCL-AIM is demonstrated through extensive simulation scenarios. • DCL-AIM outperforms the benchmarks: FCFS, LQF and fixed signal control policies. Conventional intersection managements, such as signalized intersections, may not necessarily be the optimal strategies when it comes to connected and automated vehicles (CAVs) environment. Autonomous intersection management (AIM) is tailored for CAVs aiming at replacing the conventional traffic control strategies. In this work, using the communication and computation technologies of CAVs, the sequential movements of vehicles through intersections are modelled as multi-agent Markov decision processes (MAMDPs) in which vehicle agents cooperate to minimize intersection delay with collision-free constraints. To handle the huge dimension scale incurred by the nature of multi-agent decision making problems, the state space of CAVs are decomposed into independent part and coordinated part by exploiting the structural properties of the AIM problem, and a decentralized coordination multi-agent learning approach (DCL-AIM) is proposed to solve the problem efficiently by exploiting both global and localized agent coordination needs in AIM. The main feature of the proposed approach is to explicitly identify and dynamically adapt agent coordination needs during the learning process so that the curse of dimensionality and environment nonstationarity problems in multi-agent learning can be alleviated. The effectiveness of the proposed method is demonstrated under a variety of traffic conditions. The comparison analysis is performed between DCL-AIM and the First-Come-First-Serve based AIM (FCFS-AIM), with Longest-Queue-First (LQF-AIM) policy and the signal control based on the Webster's method (Signal) as benchmarks. Experimental results show that the sequential decisions from DCL-AIM outperform the other control policies. [ABSTRACT FROM AUTHOR]

• A lane-level VSL (LVSL) approach with actor-critic framework is proposed to provide differential VSL control. • A hybrid reward function is presented to adjust relationship between the reducing crash risk and improving traffic efficiency. • A twin delayed deep deterministic policy gradient (TD3)-based LVSL approach is established for the first time. • The TD3LVSL shows better performance than baseline models in terms of improving traffic safety and traffic efficiency. Variable speed limit (VSL) control plays a vital role in the emerging connected automated vehicle highway (CAVH) system, which can alleviate recurrent traffic congestion caused by capacity drop and accidents. However, the effect of the VSL on improving traffic efficiency is still controversial. It is necessary to study how to explore the potential benefits of the VSL by balancing its influence on reducing crash risk and enhancing traffic efficiency. To fill the technological gap above, this paper proposes a reinforcement learning-based lane-level VSL (LVSL) control approach to conduct refined traffic control on the mainlines. Firstly, an actor-critic framework is developed to generate and evaluate the discrete speed limits of each lane in continuous action space. To optimize traffic control performance, a hybrid reward function is developed by synchronously considering traffic safety and traffic efficiency of the bottleneck area. Then, to solve the overestimation bias problem of the actor-critic methods caused by function approximation error, a twin delayed deep deterministic policy gradient (TD3) method is introduced to train the framework of the LVSL method. Finally, a real-world recurrent bottleneck of the State Route 91 highway in California is simulated with consideration of the connected automated vehicles to examine the performance of the TD3-based LVSL (TD3LVSL) controller. The simulation results reveal that the proposed method is capable of reducing crash risk and improving traffic efficiency synchronously. Compared with the LVSL controller based on the deep deterministic policy gradient approach, the TD3LVSL controller shows better performance in terms of traffic safety and efficiency. These findings indicate that the proposed controller could contribute to future traffic control in a CAVH environment. [ABSTRACT FROM AUTHOR]

• An extensive sensitivity analysis shows the influence of prediction errors on the system performance of structure-free model-based predictive signal control in an urban corridor with spillback. • The sensitivity analysis provides new insights in the impact of model aggregation and model biases that are valuable for the development of future model-based predictive control systems. • Even under errors, longer prediction horizons lead to better performance, up to a certain optimal prediction horizon length. • More accurate and vehicle-based predictions will lead to a performance gain in future control applications. • Identifying the bias direction with the least performance loss indicates guidelines towards more robust control applications. Model-based predictive signal control is a popular method to pro-actively control traffic and to reduce the effects of congestion in urban networks. In combination with structure-free controllers, which adapt signal settings in arbitrary order and combination (no imposed cycles), these predictive control methods have a high potential to increase system performance by adapting to individual vehicle patterns, which are increasingly available due to new technology. However, most of these control methods assume perfect predictions, while in practice there are prediction errors due to various reasons. In this paper, the sensitivity of the system performance to these prediction errors is analyzed, for an urban corridor with spillback. In a microscopic simulator, first the ideal world is created for the structure-free model-based predictive signal controller, in which perfect predictions are made and the controller can reach its optimal performance. Then prediction errors are introduced in this perfect world, distinguished in aggregation errors that arise using a macroscopic prediction model and biases that represent structural errors in the prediction model or in its demand and state input. The effects of these prediction errors on the system performance are analyzed, as a function of the prediction horizon and update frequency of the control system. The results show that, even under errors, longer prediction horizons lead to better performance, up to a certain optimal prediction horizon length. A high update frequency dampens the influence of prediction errors, enabling the structure-free controller to correct mistakes faster. However, there remains a significant performance loss due to aggregation errors and biases in the prediction model, indicating a promising performance gain of more reliable predictions and the incorporation of information on individual vehicles in future control applications. Moreover, for all model quantities one direction of the bias has more impact on the system performance than the other direction, indicating guidelines towards a more robust control system that suffers less from erroneous predictions. [ABSTRACT FROM AUTHOR]

• The train operation control problem is modeled by graphs and solved from a data-driven perspective. • The model can analyze and exploit past realization data to provide decision support for traffic control. • The proposed control model is superior to other standard rules and methods. • The model has the advantage of explainability compared to existing traffic control models. Railway operations are subject to deviations from the planned schedule, i.e., delays. In those situations, high-quality traffic control actions are needed to reduce the delays. Existing studies mainly used prescriptive techniques (e.g., mathematical programming, heuristics) to identify the best control action. These methods have limitations in the firm reliance on deterministic parameters prescriptively or normatively determined beforehand, and little understandability by the practitioners. These drawbacks hinder their acceptance in practice. This study exploits instead past realization data to provide decision support for traffic control. The realized data describe the traffic control actions taken by human controllers, and their effects; those latter are more complex than a linear sum of predetermined parameters. We use decision graphs to identify which traffic control action leads to the best solution, in terms of reduction of delays, based on the past performance of the same action in similar conditions. We are also able to explain the reasons and the factors that lead to each suggested action. We focus on the relevant case of merging stations, where multiple lines merge as one line, deciding the relative order between two consecutive trains. The method determines the stochastic effects of the two possible decisions at merge points, which allows for choosing the best one. Compared within the framework of realized data, the action suggested is the best out of a series of benchmarks, including simple rules and optimization, improving (reducing delays) approximately 11.7% on the common benchmarks. The variables with the highest impact on the utility are the length of the planned dwell time and the planned presence of an overtaking. The variables influencing the utility most are the actual delays of trains, the train type, and the order actually implemented. [ABSTRACT FROM AUTHOR]

With rapid population growth and urban development, traffic congestion has become an inescapable issue, especially in large cities. Many congestion reduction strategies have been proposed in the past, ranging from roadway extension to transportation demand management. In particular, congestion pricing schemes have been used as negative reinforcements for traffic control. In this paper, we study an alternative approach of offering positive incentives to drivers to take different routes. More specifically, we propose an algorithm to reduce traffic congestion and improve routing efficiency via offering (personalized) incentives to drivers. We propose to exploit the wide accessibility of smart devices to communicate with drivers and develop an incentive offering mechanism using individuals' preferences and aggregate traffic information. The incentives are offered after solving a large-scale optimization problem in order to minimize the total travel time (or minimize any cost function of the network such as total carbon emission). Since this massive-size optimization problem needs to be solved continually in the network, we developed a distributed computational approach. The proposed distributed algorithm is guaranteed to converge under a mild set of assumptions that are verified with real data. We evaluated the performance of our algorithm using traffic data from the Los Angeles area. Our experiments show congestion reduction of up to 5% in arterial roads and highways. • Reducing congestion by improving routing efficiency. • Offering incentives to reduce congestion using individuals' preferences of drivers. • A distributed computational approach to incentive offering. • The methods are evaluated on a large-scale real-world dataset. [ABSTRACT FROM AUTHOR]

• This study defends against cyber-attacks by designing robust signal control plans. • Biobjective worst-case attack models are developed from the aspect of cyber-attacker. • Bi-level robust signal control SO models are developed under direct cyber-attacks. • An improved biobjective robust SO method is proposed to solve the bi-level models. In the cyber age, modern traffic signal control systems are inevitably exposed to a wide range of cyber-attacks, which would negatively impact traffic system performance. To defend against direct cyber-attacks that alter signal settings directly and maliciously by hacking into the wireless connected traffic control center or signal controllers, this study applies a precautionary and inexpensive measure of designing the robust signal control plan. This is realized by modelling the biobjective traffic signal control simulation-based optimization (SO) problem under cyber-attacks in a bi-level framework, with the aim of minimizing the negative impact of potential cyber-attacks on traffic efficiency and safety performance. Depending on the type of attacker with complete or incomplete knowledge on traffic signal control system, two typical bi-level models exist. Then, we propose an improved biobjective robust simulation-based optimization (IBORSO) algorithm to solve the two models, during which the sequential interaction between the attacker and the defender is seen as the Stackelberg game, and the resulting equilibrium solutions are the desired robust signal control plans to withstand cyber-attacks. An urban road network in Changsha, China, is modelled as the experimental site and numerical results show the effectiveness of IBORSO in solving the two bi-level models. Meanwhile, the solved robust signal control plans can defend against two typical attacks satisfactorily and achieve better or comparable biobjective performance compared with the field-implemented one and optimized ones by biobjective efficient global optimization (BOEGO). [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]

Highlights • Trajectory-based calibration of simulation model parameters for safety analysis is investigated. • Automated computer vision techniques are used to extract vehicle trajectories and traffic conflicts. • High correlation between simulated and field-measured conflicts was found after calibration. • The results show that using simulation to evaluate safety without proper calibration should be avoided. • The calibration parameters varied considerably among different traffic environments. Abstract The primary objective of this study is to investigate the relationship between field-measured conflicts and simulated conflicts estimated from the surrogate safety assessment model (SSAM) for two signalized intersections in Brisbane, Australia. Traffic video data collected from the two signalized intersections (left-hand traffic regulations) were used for analysis. Automated computer vision analysis techniques were used to extract vehicle trajectories and identify field-measured conflicts. Simulated conflicts were obtained using VISSIM and SSAM tool. A two-step calibration procedure was performed to test the correlation between simulated and field-measured conflicts. In addition, the spatial distributions of simulated and field-measured conflicts were compared. Finally, the calibration results for the left-hand traffic environment were compared with results for signalized intersections in Canada and China (right-hand traffic environment). The results indicated a good correlation between simulated and field-measured conflicts, especially at higher TTC thresholds. However, major differences between field-measured and simulated conflict spatial distributions indicate that despite the good correlation obtained via calibration, simulated conflicts do not adequately capture the actual conflict occurrence mechanism. It was also found that there were significant differences between the left-hand and right-hand traffic environments calibration results. [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 • Coordination heuristics reduce emissions more than single vehicle optimization. • CAV control strategies can increase non-CAV vehicle emissions. • CAV control strategies increase passing maneuvers in non-CAV vehicles. • CAV control strategies used at low penetration rates increase system emissions. • CAV control strategies used at high penetration rates reduce system emissions. Abstract With increasing number of vehicles registered on road the CO 2 emissions and the amount of fuel wasted because of congestion has been rising. V2X and Connected and Automated Vehicle (CAV) technology allows vehicles and traffic infrastructure to communicate with each other, and could facilitate better use of existing resources by providing vehicles information about their surroundings and traffic signals. The information regarding the phase of traffic signal, vehicles' position and vehicles' speed can be used by drivers and autonomous vehicle control algorithms to make informed decisions as they approach traffic signals. This research proposes a coordination heuristic to improve traffic flow of CAV vehicles through traffic signals and reduce system wide emissions. A simulation was used to compare the system level impacts to emissions, travel time, and wait time of the proposed coordination heuristic to a single vehicle optimization strategy and baseline driver behavior. The results of this research suggest that traffic density and CAV market penetration impact the potential benefits of CAV control strategies. At low CAV penetration rates both the single vehicle and coordination heuristic produced increased emissions primarily driven by driving behavior of non-V2X enabled vehicles. At higher CAV penetration rates the coordination heuristic significantly outperformed the single vehicle control strategy and baseline driver behavior with regards to emissions reduction. The analysis indicates that at 900 vehicles per hour for either of the two driving strategies: coordination heuristic or single-vehicle optimization, to be more preferred over baseline driver behavior, at least 50% of the vehicles should be CAV. Once a threshold penetration rate of CAV vehicles is achieved, vehicles following the coordination heuristic generate nearly 10% fewer CO 2 emissions than vehicles following baseline driver behavior, a 30% improvement over the reduction in CO 2 emissions obtained using single-vehicle optimization. The vehicles following the coordination heuristic also have less travel time than vehicles following single-vehicle optimization, and less wait times than vehicles following baseline driver behavior. [ABSTRACT FROM AUTHOR]