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Traditional traffic signal control is one of the most widely used urban traffic control methods, due to its high efficiency, robustness, and simplicity. However, in the era of connected and automated vehicles (CAVs), it is prospective to enhance traffic control precision and achieve vehicle coordination in a more efficient manner. Focusing on an isolated intersection, this study proposes an innovative concept of Fine-grained Phase (FP) for CAV-enabled traffic control, which realizes more refined right-of-way allocation than traditional signal phases while preserving the simplicity and scalability. The development of FP jointly optimizes traffic control strategy and intersection layout design to fully utilize time-space resources, and multiple forms of FP could be generated in response to various traffic demands; meanwhile, vehicle coordination is achieved in an offline–online manner to balance solution quality and efficiency. Under the FP-based control, each lane is assigned with periodical entry time points using FP design results, then vehicles are supposed to catch up with the appropriate time points to fulfill their trips. Theoretical analyses are conducted under stationary vehicle arrival patterns, where the average delay and the intersection capacity associated with different forms of FP can be analytically acquired. For the implementation of FP-based control, we construct a general offline–online framework, consisting of an offline model that optimizes the geometry of the conflict zone at intersections and the control strategy of vehicles simultaneously, which is formulated as a mixed-integer linear programming problem, and an online FP adoption mechanism that can adapt to varying demand patterns. Under the framework, limited online computation is required, facilitating its practical real-time implementation. Simulation studies under a variety of demand settings show that FP-based control is advantageous over benchmark methods in reducing vehicle delay and also improving intersection capacity in most cases; even for cases with extremely imbalanced demands, FP-based control can admit comparable throughput, while the average delay is much smaller. In addition, results of computation time reveal that FP-based control could realize real-time implementation with negligible computation burden. • An innovative concept called FP for intersection traffic control with CAVs. • Joint optimization of traffic control and intersection layout design. • Offline–online implementation framework to balance solution quality and efficiency. • Theoretical analysis for average vehicle delay and admissible traffic capacity. • Improvement over signalized control and the state-of-art reservation strategy. [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]

• 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]

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]

Traffic congestion in urban environment is one of the most critical issue for drivers and city planners for both environment and efficiency reasons. Traffic lights are one of the main tools used to regulate traffic by diverting the drivers between different paths. Rational drivers, in turn, react to the traffic light duration by evaluating their options and, if necessary, by changing direction in order to reach their destination quicker. In this paper, we introduce a macroscopic traffic model for urban intersections that incorporates this rational behavior of the drivers. Then, we exploit it to show that, by providing additional information about the expected red-time duration to the drivers, one can decrease the amount of congestion in the network and the overall length of the queues at the intersections. Additionally, we develop a control policy for the traffic lights that exploits the reaction of the drivers in order to divert them to a different route to further increase the performances. These claims are supported by extensive numerical simulations. • A traffic-flow macroscopic model of the drivers' rational decision-making. • It considers origin-destination pairs and the ability to show the red time duration. • Numerical simulations illustrate the effectiveness of showing the red time duration. • A novel control policy that exploit the drivers' reaction to the red time duration. • The increased efficiency of the controller is validated with extensive simulations. [ABSTRACT FROM AUTHOR]

• This work focuses on macroscopic traffic flow model calibration and validation of large freeway networks. • An intensive literature review is presented on the subject to determine the strengths and weaknesses of various technical paths, and figure out a viable roadmap for future studies. • The paper proposes a benchmarking framework concerning some of the key factors about macroscopic traffic flow model calibration and validation, which including congestion tracking, traffic flow inhomogeneity, adverse weather conditions and accidents, capacity drop, scattering, hysteresis, stop-and-go waves, and traffic heterogeneity. • The paper presents comprehensive results of model calibration and validation concerning key factors included in the benchmarking framework as stated above. • Works of the same focus were not reported before. Macroscopic traffic flow models are of paramount importance to traffic surveillance and control. Before their employments in applications, the models need to be calibrated and validated against real traffic data. The model calibration determines an optimal set of model parameters that minimizes the discrepancy between the modeling results and real traffic data. The model validation is furthermore performed to corroborate the accuracy of a calibrated model using data other than used for calibration. The model calibration aims to reflect traffic reality, while model validation focuses on the prediction of future traffic using calibrated models. This paper delivers a comprehensive review of state-of-the-art works on macroscopic model calibration and validation, proposes a benchmarking framework on traffic flow modeling, and has conducted a large number of case studies based on the framework using macroscopic traffic flow model METANET with respect to the urban expressway network in Shanghai. In comparison to previous works, quite more comprehensive results on model calibration have been presented in this paper, in consideration of congestion tracking, traffic flow inhomogeneity, capacity drop, stop-and-go waves, scattering, adverse weather conditions, and accidents. The paper has also reported many results of model validation with respect to the same field examples. The results demonstrate that METANET is able to model complex traffic flow dynamics in large-scale freeway networks with sufficient accuracy. The paper is closed with discussion on limitations and future works. [ABSTRACT FROM AUTHOR]

Previous studies have shown that the max pressure control is a throughput-optimal policy that can stabilize the store-and-forward traffic network when the demand is within the network capacity. Most of the existing studies on the max pressure control do not consider the loss of capacity associated with phase switching, which will undermine the stability of the network. This work proposes a novel framework that utilizes reinforcement learning algorithms to optimize a max pressure controller considering the phase switching loss. We extend the max pressure control by introducing a switching curve and prove that the proposed control method is throughput-optimal in a store-and-forward network. Then the theoretical control policy is extended by using a distributed approximation and position-weighted pressure so that the policy-gradient reinforcement learning algorithms can be utilized to optimize the parameters in the policy network including the switching curve and the weight curve. Simulation results show that the proposed control method greatly outperforms the conventional max pressure control. The proposed framework combines the strengths of the data-driven method and the theoretical control model by utilizing reinforcement learning algorithm to optimize the max pressure controller, which is also of great significance for real-world implementations because the proposed control policy can be generated in a distributed fashion based on local observations. • Extend the original max pressure control by adding the phase switching loss. • Propose a Switching-Curve based Max Pressure (SCMP) control framework with a stability proof. • Propose a novel framework utilizing the policy gradient RL algorithms to improve the max pressure control. [ABSTRACT FROM AUTHOR]

Guaranteeing the quality of transit service is of great importance to promote the attractiveness of buses and alleviate urban traffic issues such as congestion and pollution. Emerging technologies of automated driving and V2X communication have the potential to enable the accurate control of vehicles and the efficient organization of traffic to enhance both the schedule adherence of buses and the overall network mobility. This study proposes a novel network-level control scheme for heterogeneous automated traffic composed of buses and private cars under a full connected and automated environment. Inheriting the idea of network-level rhythmic control proposed by Lin et al. (2021b), an innovative rhythmic control scheme for heterogeneous traffic, i.e., RC-H, is established to organize the mixed traffic in a rhythmic manner. Realized virtual platoons are designed for accommodating vehicles to pass through the network, including dedicated virtual platoons for buses to provide exclusive right-of-ways (ROWs) on their trips and regular virtual platoons for private cars along with an optimal assignment plan to minimize the total travel cost. A mixed-integer linear program (MILP) is formulated to optimize the RC-H scheme with the objective of minimizing the weighted travel costs of both buses and private cars. A bilevel heuristic solution method is designed to relieve the computational burden of MILP. Numerical examples and simulation experiments are conducted to evaluate the performance of the RC-H scheme under different scenarios. The results show that the bus punctuality can be guaranteed and the traveler delay can be minimized under various demand levels with transit priority. Moreover, compared with traffic signal control (TSC) strategies, including those with fixed timing and those with bus priority, the RC-H scheme shows significant advantages in handling heavy traffic demand, in terms of both reducing vehicle delay and enhancing network throughput. • An innovative network-level control scheme for heterogeneous automated traffic. • Different realized virtual platoons for accommodating vehicles to pass through the network. • An optimal design model of RC-H scheme considering bus priority. • A bilevel heuristic solution method to relieve the computational burden. • Simulation experiments to test the performance of RC-H scheme and compare with TSC. [ABSTRACT FROM AUTHOR]

This paper analyses how train delays propagate in a metro network due to disturbances and disruptions when different recovery strategies are implemented. Metro regulators use traffic management policies to recover from delays as fast as possible, return to a predefined schedule, or achieve an expected regularity of train arrivals and departures. We use as a metro traffic simulator SIMSTORS, which is based on a Stochastic Petri Net variant and simulates a physical system controlled by traffic management algorithms. To model existing metro networks, SIMSTORS has been mainly used with rule-based traffic management algorithms. In this work, we enhance traffic management strategies. We integrate SIMSTORS and the AGLIBRARY optimization solver in a closed-loop framework. AGLIBRARY is a deterministic solver for managing complex scheduling and routing problems. We formulate the real-time train rescheduling problem by means of alternative graphs, and use the decision procedures of AGLIBRARY to obtain rescheduling solutions. Several operational issues have been investigated throughout the use of the proposed simulation–optimization framework, among which how to design suitable periodic or event-based rescheduling strategies, how to setup the traffic prediction horizon, how to decide the frequency and the length of the optimization process. The Santiago Metro Line 1, in Chile, is used as a practical case study. Experiments with this framework in various settings show that integrating the optimization algorithms provided by AGLIBRARY to the rule-based traffic management embedded in SIMSTORS optimizes performance of the network, both in terms of train delay minimization and service regularity. • We propose a closed-loop framework for traffic disturbance recovery in metro systems. • We analyse deterministic optimization techniques in a stochastic environment. • We study when and how to use automatic rescheduling tools in dynamic traffic situation. • We test the simulation–optimization framework on real-world data. • Results show that even-based calls to the optimizer return the best performance. [ABSTRACT FROM AUTHOR]

• Propose a control framework for two-region bi-modal traffic networks with 3D-MFD. • Integrate perimeter control with regional bus service frequency setting. • Formulate dynamics of regional bus systems with regional bus frequencies. • Develop a modified GA for optimizing control inputs. Public buses and private vehicles are two main transportation modes for urban road traffic. It has strong flow interactions between the two traffic modes. From data analysis, the vehicle density of a traffic mode not only determine its flow, but also affect the flow of the other mode. With such mixed vehicle flows, the application of traditional traffic management that controls each vehicle flow system separately is limited. Thus, it is beneficial to control both bus and private vehicle flows in an integrated way. Under such bi-modal traffic management, the network efficiency cannot be reflected by vehicle flows since the occupancies of the traffic modes are greatly different. It leads to that the control objective should be necessarily based on passengers. To do so, we adopt bi-modal passenger-based macroscopic fundamental diagram (3D-pMFD) to capture the relations among vehicle accumulations and the total passenger flow in a network. From this aggregate perspective, a passenger-oriented traffic control strategy is proposed by integrating the perimeter control and the regional bus service frequency setting. The characteristics of both traffic modes, especially the operation strategy of buses, are taken into account in the model to describe the bi-modal traffic flow dynamics. A control framework comprised of optimization and simulation is established using model predictive control. The corresponding passenger-based optimization problem is solved by a modified genetic algorithm. With multiple data collected from the network of Shenzhen City in China, numerical experiments are designed to verify the validity and efficiency of the proposed integrated control strategy. Results show that: (1) the proposed strategy can maintain a high level of passenger flow during peak hour with a relatively high demand; (2) the interest of private vehicles is guaranteed even if buses are more recommended in the control strategy; (3) the crowding degree in a bus is reduced properly, which is able to improve the attraction of buses during peak hours; and (4) the proposed passenger-oriented objective results in a comprehensively good performance of the bi-modal network system. [ABSTRACT FROM AUTHOR]

• Develop a three-level model to integrate signal timing and CAV trajectory planning. • Left-turn and through CAVs share CAV-dedicated lanes with trajectory planning. • CAVs cross the intersection during shared phases with HVs. • Propose a repeated-barrier-group cycle structure in signal timing. • Design a rolling-horizon scheme to dynamically apply the model with varying traffic. Connected and automated vehicles (CAVs) and human-driven vehicles (HVs) are expected to coexist in the near future. CAV-dedicated lanes and phases have been explored to handle the uncertainty in the driving behavior of HVs in the mixed traffic environment. However, CAV-dedicated phases could significantly sacrifice HV benefits. This study proposes a shared-phase-dedicated-lane (SPDL)-based traffic control model at an isolated intersection with CAV dedicated lanes in the mixed traffic environment. In the proposed model, left-turn and through CAVs share CAV-dedicated lanes and cross the intersection during the shared phases with HVs. A three-level optimization model is developed. At the upper level, a standard NEMA (National Electrical Manufacturers Association) ring barrier structure is used for the signal optimization and barrier durations are optimized by dynamic programming to minimize the total vehicle delay. At the middle level, phase sequences and phase durations are optimized by enumeration for the given barrier from the upper level and the minimum vehicle delay is fed to the upper level. At the lower level, CAV platooning in the buffer zone and trajectory planning in the passing zone are conducted based on the signal timings of the barrier from the middle level and the travel time of CAVs is fed to the middle level. A rolling-horizon scheme is designed for the dynamical implementation of the proposed model with time-varying traffic conditions. The proposed model is applicable to intersections with more than two lanes in each approach and without shared lanes by left-turn and through HVs, which are commonly seen critical intersections in urban traffic control. Numerical studies validate the advantages of the SPDL-based control over the blue-phase-based control in previous studies in terms of average vehicle delay and intersection capacity. Further, the SPDL-based model is extended to serve as an alternative approach without the buffer zone. [ABSTRACT FROM AUTHOR]

• This paper investigates the key ways for automated vehicles (AVs) to improve the traffic efficiency of two-dimensional urban network traffic. • Different cooperative driving strategies are investigated in a systematic and comprehensive way, and we find that the passing order has a dominant impact on the network traffic efficiency. • This paper compares the differences and characteristics of the impact of the passing orders and the car-following gaps on the urban network traffic. • By using the macroscopic fundamental diagram (MFD) as the performance metric, we find that a better passing order can broaden the free flow region and improve the maximum flow, which is of great benefit to the network traffic efficiency. Cooperative driving, especially the passing order, is the critical link to improve the traffic efficiency of the road network by using automated vehicles (AVs). However, most studies have only considered the performance of the passing order at the isolated intersection and have not yet investigated its impact on the road network. In this paper, we will focus on the performance of the passing orders derived from different cooperative driving strategies on the network traffic through a series of simulation experiments. Meanwhile, we will compare the impacts of the passing order at intersections and the car-following gap in straight links on the network traffic efficiency. The experiments results show that the passing order has a dominant impact on the network traffic efficiency, and a better order can significantly raise the curve of the macroscopic fundamental diagram (MFD); due to the inevitable conflicts in the two-dimensional traffic, the choice of the car-following gap within a reasonable range has a relatively small improvement on the network traffic efficiency. The findings in this paper have instructive significance for the rising research on network-wide cooperative driving and provide a systematical perspective for network traffic control. [ABSTRACT FROM AUTHOR]

Real-time information about the dynamic variation of turning rates at intersections is essential for achieving a more efficient traffic control and management system. Limitations in obtaining measurements of turning movements have drawn attention to the need for alternative methods for estimation of turning rates. In a large-scale urban network, characteristics such as disparate signal plans and various intersection layouts make defining a generic model for the entire network challenging. For this reason, most of the proposed methods are impractical for real-field implementation. The application of existing optimization-based methods is also limited by computational complexity, the need for pre-defined assumptions and insufficient stationary traffic measurements (e.g. loop detectors and plate recognition cameras) in urban networks. In this study, we take advantage of probe vehicles to propose a hybrid method that noticeably reduces the need for stationary measurements in the network. This hybrid method consists of two core stages: signal processing, and physical data-driven modeling to consecutively estimate turning rates. In the signal processing phase, we use wavelet transformation to extract fundamental components of inferred floating car turning rates. These components are then used as inputs to a turning rate estimation model. Using these decomposed components of floating car turning rates instead of raw input helps to balance the effect of low and high-frequency signal components and leads to a more accurate estimation. Investigation of the proposed method in a microscopic simulation environment reveals the ability of the hybrid model to detect turning rate variations even when a severe incident occurs. • A hybrid data-driven method is used for network-wide turning rate estimation. • The method makes use of floating car data for turning rate estimation. • A small proportion of stationary measurements is needed for the training purpose. • Deployment of wavelet transformation allows access to more accurate information. • The method makes a balance between the effect of low and high-frequency changes. [ABSTRACT FROM AUTHOR]

Signal control strategies for congested urban road networks designed in a centralized framework require many communication links, serious processing power, and infrastructure for the centralized coordination. As a result, strategies based on a centralized framework are not scalable. The use of decentralized signal control strategies for large-scale urban traffic networks is a solution to this problem, since it allows for the implementation of such strategies on networks whose centralized solution is not easily scalable. This paper addresses the problem of designing a decentralized traffic-responsive signal control solution, proposing two methods based on different formulations of the store-and-forward model: (i) the Decentralized Traffic-responsive Urban Control (DTUC) method; and (ii) the Decentralized Decoupled Traffic-responsive Urban Control (D2TUC). The decentralized configuration is such that each intersection is associated with one computational unit, with limited computational power and memory, which controls the traffic signals of the incoming links. Sufficient conditions for the controllability of the considered store-and-forward models are also presented. Both methods are validated resorting to numerical simulations of the urban traffic network of Chania, Greece, for two demand scenarios, and their performance is compared with the performance of the Traffic-responsive Urban Control (TUC) centralized strategy. One of the proposed decentralized methods, D2TUC, is shown to match the performance of TUC. • Design of decentralized signal control solutions based on the store-and-forward model. • Sufficient controllability conditions for the store-and-forward model are presented. • Two decentralized solutions are synthesized considering the network globally. • Validation resorting to numerical simulations of the traffic network of Chania. • One of the proposed decentralized solutions, D2TUC, matches the performance of TUC. [ABSTRACT FROM AUTHOR]

• Probabilistic breakdown in individual lanes, is embodied in the ramp metering method. • Stochasticity in demand and capacity is incorporated in the prediction and control. • The proposed bargaining game achieves a more equitable distribution of breakdown events, while seeking system-wide efficiency. • The performance of the scenarios are evaluated based on efficiency, effectiveness, and equity. • Effectiveness, as a novel performance measure, is evaluated based on the number of failed control actions. • A novel equity measure based on a more balanced distribution of the control failures is introduced. • A balanced trade off between equity and efficiency is achieved via proposed bargaining game. This paper presents a dynamic predictive and cooperative ramp metering approach that considers stochastic breakdowns at merging bottlenecks. A stochastic microscopic model is used to estimate traffic state parameters based on speed, location, and travel time information from connected vehicles. Traffic state predictions are obtained on a lane by lane basis using an adaptive Kalman filter (AKF) that fuses fixed detector measurements with the model; the AKF then produces multiple step ahead predictions. The ramp metering problem in this paper is modeled as a stochastic distributed model predictive control (SDMPC) approach. The SDMPC problem is solved based on a bargaining game approach where each controller, a player in the game, receives traffic state and control decision information from other controllers to solve the local optimization problem based on expected local costs and constraints. The performance of the proposed model is evaluated for three aspects of efficiency: short-term and long-term equity and effectiveness compared to multiple control scenarios. The outcomes indicate that the proposed cooperative model with stochastic capacity considerations outperforms the deterministic capacity-based models in regard to effectiveness and equity properties. However, the centralized approach performs slightly better in respect to system-wide efficiency. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive signal controller for managing traffic delays and urban bus service reliability with fully adaptable acyclic timing plans. The signal controller is built upon a reinforcement learning framework that consists of a model-based and a data-driven component. The model-based component is represented by a hybrid kinematic wave traffic model that integrates macroscopic flow-based and microscopic vehicle-based state variables subject to stochastic demands and bus service status. To cope with the high dimensional solution space, the data-driven component is incorporated as a multi-layer artificial neural network and is used to approximate future traffic states and system performances with respect to prevailing control settings. Before the controller can be used, the neural network is to be trained through a series of realised dynamic state transitions via an on-policy temporal difference learning algorithm. The proposed control framework is tested over a real world corridor scenario in London, UK. The proposed controller is able to reduce both traffic delays and bus service variabilities subject to stochastic demands with acyclic timing plans that can be derived in short computational time. This study contributes to the design of adaptive network traffic control for multi-modal networks with connected vehicle technology and advanced learning-based optimisation techniques. [ABSTRACT FROM AUTHOR]