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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]

• 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]

• 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]

• 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]

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]

• 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]

• 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]

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 • Apply CTM to urban traffic including traffic lights, queue discharge, merge/diverge. • Implement and validate proposed model by comparing to SUMO results, and explain differences. • Develop local MPC selecting switching times of traffic lights, at time resolution of a few seconds. • Evaluate performance of traffic system using local MPC control for traffic lights, for small urban region. Abstract This paper is the first in a series of reports presenting a framework for the hierarchical design of feedback controllers for traffic lights in urban networks. The goal of the research is to develop an easy to understand methodology for designing model based feedback controllers that use the current state estimate in order to select the next switching times of traffic lights. In this paper we introduce an extension of the cell transmission model that describes with sufficient accuracy the major causes of delay for urban traffic. We show that this model is computationally fast enough such that it can be used in a model predictive controller that decides for each intersection, taking into account the vehicle density as estimated along all links connected to the intersection, what switching time minimizes the local delay for all vehicles over a prediction horizon of a few minutes. The implementation of this local MPC only requires local online measurements and local model information (unlike the coordinated MPC, to be introduced in the next paper in this series, that takes into account interactions between neighbouring intersections). We study the performance of the proposed local MPC via simulation on a simple 4 by 4 Manhattan grid, comparing its delay with an efficiently tuned pretimed control for the traffic lights, and with traffic lights controlled according to the max pressure rule. These simulations show that the proposed local MPC controller achieves a significant reduction in delay for various traffic conditions. [ABSTRACT FROM AUTHOR]

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

Highlights • Guidelines for evaluating traffic prediction systems are proposed. • A methodology complying with these guidelines is described. • The approach is tested on two traffic prediction systems. Abstract Model-based traffic prediction systems (mbTPS) are a central component of the decision support and ICM (integrated corridor management) systems currently used in several large urban traffic management centers. These models are intended to generate real-time predictions of the system's response to candidate operational interventions. They must therefore be kept calibrated and trustworthy. The methodologies currently available for tracking the validity of a mbTPS have been adapted from approaches originally designed for off-line operational planning models. These approaches are insensitive to the complexity of the network and to the amount and quality of the data available. They also require significant human intervention and are therefore not suitable for real-time monitoring. This paper outlines a set of criteria for designing tests that are appropriate for the mbTPS task. It also proposes a test that meets the criteria. The test compares the predictions of the mbTPS in question to those of a model-less alternative. A t-test is used to determine whether the predictions of the mbTPS are superior to those of the model-less predictor. The approach is applied to two different systems using data from the I-210 freeway in Southern California. [ABSTRACT FROM AUTHOR]

Highlights • Novel visualization methods applied to multi-objective signal timing optimization problems. • Metrics developed to quantify level of tradeoff between objectives. • Methods proposed to identify and remove redundant objectives. • Dimensionless variables proposed to combine objectives for final solution ranking. Abstract Adjusting traffic signal timings is a practical way for agencies to manage urban traffic without the need for significant infrastructure investments. Signal timings are generally selected to minimize the total control delay vehicles experience at an intersection, particularly when the intersection is isolated or undersaturated. However, in practice, there are many other potential objectives that might be considered in signal timing design, including: total passenger delay, pedestrian delays, delay inequity among competing movements, total number of stopping maneuvers, among others. These objectives do not tend to share the same relationships with signal timing plans and some of these objectives may be in direct conflict. The research proposes the use of a new multi-objective optimization (MOO) visualization technique—the mosaic plot—to easily quantify and identify significant tradeoffs between competing objectives using the set of Pareto optimal solutions that are normally provided by MOO algorithms. Using this tool, methods are also proposed to identify and remove potentially redundant or unnecessary objectives that do not have any significant tradeoffs with others in an effort to reduce problem dimensionality. Since MOO procedures will still be needed if more than one objective remains and MOO algorithms generally provide a set of candidate solutions instead of a single final solution, two methods are proposed to rank the set of Pareto optimal solutions based on how well they balance between the competing objectives to provide a final recommendation. These methods rely on converting the objectives to dimensionless values based on the optimal value for each specific objectives, which allows for direct comparison between and weighting of each. The proposed methods are demonstrated using a simple numerical example of an undersaturated intersection where all objectives can be analytically obtained. However, they can be readily applied to other signal timing problems where objectives can be obtained using simulation outputs to help identify the signal timing plan that provides the most reasonable tradeoff between competing objectives. [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]

Perimeter control based on the Macroscopic Fundamental Diagram (MFD) is widely developed for alleviating or postponing congestion in a protected region. Recent studies reveal that traffic conditions might not be improved if the perimeter control strategies are applied to unstable systems where high demand generates heavy and heterogeneously distributed traffic congestion. Therefore, considering stability of the targeted traffic system is essential, for the sake of developing a feasible and then optimal control strategy. This paper sheds light on this direction. It integrates a stability characterization algorithm of MFD system equations into the Model Predictive Control (MPC) scheme, and features respectively an upper and a lower bound of the feasible control inputs, to guarantee system stability. Firstly, the dynamics of traffic heterogeneity and its effect on the MFD are analyzed, using real data from Guangzhou in China. Piecewise affine functions of average flow are proposed to capture traffic heterogeneity in both regional and subregional MFDs. Secondly, stability of a three-state two-region system is investigated via stable equilibrium and surface boundaries analysis. Finally, a three-layer hierarchical control strategy is introduced for the studied two-region heterogeneous urban networks. The first layer of the controller calculates the stable surface boundaries for the given traffic demands and then determines the bounds of control input (split rate). An MPC approach in the second layer is used to solve an optimization problem with two objectives of minimizing total network delay and maximizing network throughput. Heterogeneity among the subregions is minimized in the last layer by implementing simultaneously a subregional perimeter flow control and an internal flow control. The effectiveness and stability of the proposed control approach are verified by comparison with four existing perimeter control strategies. [ABSTRACT FROM AUTHOR]

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

The paper describes a new method of optimizing traffic signal settings. The area-wide urban traffic control system developed in the paper is based on the Bee Colony Optimization (BCO) technique. The BCO method is based on the principles of the collective intelligence applied by the honeybees during the nectar collecting process. The optimal (or near-optimal) values of cycle length, offsets, and splits are discovered by minimizing the total travel time of all network users travelling through signalized intersections. The set of numerical experiments is performed on well-known traffic benchmark network. The results obtained by the BCO approach are compared with the results found by Simulated Annealing (SA). It has been shown that the suggested BCO approach outperformed the SA. [ABSTRACT FROM AUTHOR]

Traffic flow pattern identification, as well as anomaly detection, is an important component for traffic operations and control. To reveal the characteristics of regional traffic flow patterns in large road networks, this paper employs dictionary-based compression theory to identify the features of both spatial and temporal patterns by analyzing the multi-dimensional traffic-related data. An anomaly index is derived to quantify the network traffic in both spatial and temporal perspectives. Both pattern identifications are conducted in three different geographic levels: detector, intersection, and sub-region. From different geographic levels, this study finds several important features of traffic flow patterns, including the geographic distribution of traffic flow patterns, pattern shifts at different times-of-day, pattern fluctuations over different days, etc. Both spatial and temporal traffic flow patterns defined in this study can jointly characterize pattern changes and provide a good performance measure of traffic operations and management. The proposed method is further implemented in a case study for the impact of a newly constructed subway line. The before-and-after study identifies the major changes of surrounding road traffic near the subway stations. It is found that new metro stations attract more commute traffic in weekdays as well as entertaining traffic during weekends. [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]

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices. [ABSTRACT FROM AUTHOR]

Well-defined relationships between flow and density averaged spatially across urban traffic networks, more commonly known as Macroscopic Fundamental Diagrams (MFDs), have been recently verified to exist in reality. Researchers have proposed using MFDs to monitor the status of urban traffic networks and to inform the design of network-wide traffic control strategies. However, it is also well known that empirical MFDs are not easy to estimate in practice due to difficulties in obtaining the requisite data needed to construct them. Recent works have devised ways to estimate a network’s MFD using limited trajectory data that can be obtained from GPS-equipped mobile probe vehicles. These methods assume that the market penetration level of mobile probe vehicles is uniform across the entire set of OD pairs in the network; however, in reality the probe vehicle market penetration rate varies regionally within a network. When this variation is combined with the imbalance of probe trip lengths and travel times, the compound effects will further complicate the estimation of the MFD. To overcome this deficit, we propose a method to estimate a network’s MFD using mobile probe data when the market penetration rates are not necessarily the same across an entire network. This method relies on the determination of appropriate average probe penetration rates, which are weighted harmonic means using individual probe vehicle travel times and distances as the weights. The accuracy of this method is tested using synthetic data generated in the INTEGRATION micro-simulation environment by comparing the estimated MFDs to the ground truth MFD obtained using a 100% market penetration of probe vehicles. The results show that the weighted harmonic mean probe penetration rates outperform simple (arithmetic) average probe penetration rates, as expected. This especially holds true as the imbalance of demand and penetration level increases. Furthermore, as the probe penetration rates are generally not known, an algorithm to estimate the probe penetration rates of regional OD pairs is proposed. This algorithm links count data from sporadic fixed detectors in the network to information from probe vehicles that pass the detectors. The simulation results indicate that the proposed algorithm is very effective. Since the data needed to apply this algorithm are readily available and easy to collect, the proposed algorithm is practically feasible and offers a better approach for the estimation of the MFD using mobile probe data, which are becoming increasingly available in urban environments. [ABSTRACT FROM AUTHOR]

The design and deployment of the majority of Management and Control Systems (MCS) for ITS involves a tedious, effort- and time-consuming manual tuning and calibration procedure not only during the initial design and deployment of the ITS but, in most cases, during its whole lifetime. Recently, we have developed and evaluated, both by means of theoretical analysis and extensive simulation experiments, a new methodology which fully automatically takes over the manual tuning and calibration procedure. Most importantly, this new methodology, called Adaptive Fine-Tuning (AFT), achieves to improve the performance of the system and compensate the effect of the continuous changes of its behavior that may be due to either internal or external factors. In this paper, we report results of implementing AFT to a real-life ITS MCS. More precisely, this paper reports and analyzes the results from implementing AFT to an urban traffic signal control application. The results from AFT real-life application demonstrate that it is capable of significantly improving the performance of the system in a safe and robust manner. Moreover, the real-life results exhibit the capability of AFT to efficiently adapt and compensate in cases of changes in the system behavior, even if these changes are significant. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated that Macroscopic Fundamental Diagram (MFD), which provides an aggregated model of urban traffic dynamics linking network production and density, offers a new generation of real-time traffic management strategies to improve the network performance. However, the effect of route choice behavior on MFD modeling in case of heterogeneous urban networks is still unexplored. The paper advances in this direction by firstly extending two MFD-based traffic models with different granularity of vehicle accumulation state and route choice behavior aggregation. This configuration enables us to address limited traffic state observability and to scrutinize implications of drivers’ route choice in MFD modeling. We consider a city that is partitioned in a small number of large-size regions (aggregated model) where each region consists of medium-size sub-regions (more detailed model) exhibiting a well-defined MFD. This paper proposes a route guidance advisory control system based on the aggregated model as a large-scale traffic management strategy that utilizes aggregated traffic states while sub-regional information is partially known. In addition, we investigate the effect of equilibrium conditions (i.e. user equilibrium and system optimum) on the overall network performance, in particular MFD functions. [ABSTRACT FROM AUTHOR]

We present a compact characterization of network-level traffic processes for a dense urban area operating under an adaptive traffic control system. The characterization is based on a state classification scheme that is employed at a detector level, and a state transition model that works with combinations of detectors that are topologically dependent. Jointly, the two models provide a concise but rich representation of traffic processes at the network level. The key insight is the identification of transient states, termed under-utilized (U) states, where network effects such as insufficient downstream capacity are captured. In such states the green time is not fully used. The approach provides the space–time evolution of states across the network, conditional probabilities of upstream traffic states that drive state propagation in the near term, and probabilistic information on congested paths on the network, where paths are described as a sequence of detectors. The paper presents empirical evidence based on the SCATS adaptive control system in Dublin, the insights provided by the proposed approach, and the importance of under-utilized states, which represent as much as 20% of unused capacity along certain corridors in peak periods. The results provide a basis for future network control procedures. [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]

The optimization of traffic signalization in urban areas is formulated as a problem of finding the cycle length, the green times and the offset of traffic signals that minimize an objective function of performance indices. Typical approaches to this optimization problem include the maximization of traffic throughput or the minimization of vehicles’ delays, number of stops, fuel consumption, etc. Dynamic Traffic Assignment (DTA) models are widely used for online and offline applications for efficient deployment of traffic control strategies and the evaluation of traffic management schemes and policies. We propose an optimization method for combining dynamic traffic assignment and network control by minimizing the risk of potential loss induced to travelers by exceeding their budgeted travel time as a result of deployed traffic signal settings, using the Conditional Value-at-Risk model. The proposed methodology can be easily implemented by researchers or practitioners to evaluate their alternative strategies and aid them to choose the alternative with less potential risk. The traffic signal optimization procedure is implemented in TRANSYT-7F and the dynamic propagation and route choice of vehicles is simulated with a mesoscopic dynamic traffic assignment tool (DTALite) with fixed temporal demand and network characteristics. The proposed approach is applied to a reference test network used by many researchers for verification purposes. Numerical experiments provide evidence of the advantages of this optimization method with respect to conventional optimization techniques. The overall benefit to the performance of the network is evaluated with a Conditional Value-at-Risk Analysis where the optimal solution is the one presenting the least risk for ‘guaranteed’ total travel times. [ABSTRACT FROM AUTHOR]

This paper proposes a bi-level model for traffic network signal control, which is formulated as a dynamic Stackelberg game and solved as a mathematical program with equilibrium constraints (MPEC). The lower-level problem is a dynamic user equilibrium (DUE) with embedded dynamic network loading (DNL) sub-problem based on the LWR model (Lighthill and Whitham, 1955; Richards, 1956). The upper-level decision variables are (time-varying) signal green splits with the objective of minimizing network-wide travel cost. Unlike most existing literature which mainly use an on-and-off (binary) representation of the signal controls, we employ a continuum signal model recently proposed and analyzed in Han et al. (2014), which aims at describing and predicting the aggregate behavior that exists at signalized intersections without relying on distinct signal phases. Advantages of this continuum signal model include fewer integer variables, less restrictive constraints on the time steps, and higher decision resolution. It simplifies the modeling representation of large-scale urban traffic networks with the benefit of improved computational efficiency in simulation or optimization. We present, for the LWR-based DNL model that explicitly captures vehicle spillback, an in-depth study on the implementation of the continuum signal model, as its approximation accuracy depends on a number of factors and may deteriorate greatly under certain conditions. The proposed MPEC is solved on two test networks with three metaheuristic methods. Parallel computing is employed to significantly accelerate the solution procedure. [ABSTRACT FROM AUTHOR]

Loop detectors are the oldest and widely used traffic data source. On urban arterials, they are mainly installed for signal control. Recently state-of-the art Bluetooth MAC Scanners (BMS) has significantly captured the interest of stakeholders for exploiting it for area-wide traffic monitoring. Loop detectors provide flow – a fundamental traffic parameter; whereas BMS provides individual vehicle travel time between BMS stations. Hence, these two data sources complement each other, and if integrated should increase the accuracy and reliability of the traffic state estimation. This paper proposed a model that integrates loops and BMS data for seamless travel time and density estimation for urban signalised network. The proposed model is validated using both real and simulated data and the results indicate that the accuracy of the proposed model is over 90%. [ABSTRACT FROM AUTHOR]

In the urban setting, the roadway and railway modes of mass transit are basically purported to carry large flows of passengers. Thus the issue of flowing capacity is crucial in the design and planning of a transit network. As a transit system involves two types of traffic units, respectively passengers and vehicles, there is a broad range of capacity phenomena: (i) as a vehicle has given seat capacity, additional riders have to stand which is less comfortable and more exposed to in-vehicle crowding, (ii) the total capacity in a vehicle, including sitting and standing places, influences the wait time on platform if it is exceeded by the number of candidate riders, (iii) the exchange capacity at vehicle doors influences the vehicle dwell time at a station, (iv) from the station dwell times stems the run time of vehicles – hence of passengers – and in turn the service frequency, (v) vehicle traffic is constrained by dwell time and operating margins, which may reduce the frequency delivered, etc. The paper provides a static, macroscopic model of traffic assignment to a transit network, in which these capacity phenomena are captured. A key feature is the line sub-model that deals with a line of operations, comprised of one or several service routes, by using the topological order of stations. From a matrix of flows by pair of access-egress stations, the sub-model derives the matrix of average passenger costs by access-egress pair, as well as local passenger wait time and the apparent frequency of each leg. At the network level, passenger route choice is modeled by optimal hyperpaths that are route-based (as in De Cea and Fernandez, 1989 ). It is shown that there exists a state of traffic equilibrium. A Method of Successive Averages is put forward to compute equilibrium. A large scale application to the whole transit network of greater Paris is presented, with focus on capacity issues. [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]

Highlights: [•] The two-fluid model was derived in an expected utility framework. [•] The theory predictions were tested using historical two-fluid model data from various cities. [•] Contrary to traditional assumptions the two parameters of the two-fluid model were shown to be correlated. [•] Impact of network features were estimated on perception of: likelihood and severity of the crash. [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]

Abstract: Traffic congestion has become a stringent issue in urban areas. Traffic control systems are designed to make a better use of the existing traffic infrastructures in order to improve traffic conditions. Along with the fast development of the transportation infrastructures, traffic networks become larger and more complex. Therefore, network-wide traffic control systems that can coordinate the whole network and improve the utilization of the entire traffic infrastructure, are highly required. In this paper, a structured network-wide traffic controller is presented based on Model Predictive Control (MPC) theory. Two macroscopic models are proposed to be the prediction model of the MPC controller. One is more accurate, but correspondingly requires more computation time; the other sacrifices a certain amount of accuracy, but is computationally much more efficient. Based on these two models, MPC controllers are developed. Simulation results show that the MPC controllers are capable of coordinating an urban traffic network, especially in the situations that the traffic flow is not spread evenly through the network. Through reducing the prediction model, the corresponding MPC controller exhibits less on-line computational burden, and thus becomes more applicable in practice. Therefore, it becomes possible for the control system to deal with complex urban road networks more efficiently. [Copyright &y& Elsevier]

Abstract: Model-based intersection optimization strategies have been widely investigated for distributed traffic signal control in road networks. Due to the form of “black-box” optimization that is typically assumed, a basic challenge faced by these strategies is the combinatorial nature of the problem that must be solved. The underlying state space is exponential in the number of time steps in the look-ahead optimization horizon at a given time resolution. In this paper, we present a schedule-driven intersection control strategy, called SchIC, which addresses this challenge by exploiting the structural information in non-uniformly distributed traffic flow. Central to our method is an alternative formulation of intersection control optimization as a scheduling problem, which effectively reduces the state space through use of an aggregate representation on traffic flow data in the prediction horizon. A forward recursive algorithm is proposed for solving the scheduling problem, which makes use of a dominance condition to efficiently eliminate most states at early stages. SchIC thus achieves near optimal solutions with a polynomial complexity in the prediction horizon, and is insensitive to the granularity of time resolution that is assumed. The performance of SchIC with respect to both intersection control and implicit coordination between intersections is evaluated empirically on two ideal scenarios and a real-world urban traffic network. Some characteristics and possible real-world extensions of SchIC are also discussed. [Copyright &y& Elsevier]

Abstract: Vehicles in VANET can form wireless ad hoc mesh networks (VMeshes) that are mobile, constantly changing both in size and the geographic area they cover. We focus on how the VMeshes can be used to capture and retain certain transient information on the road within a given region of interest for a certain period of time, without any infrastructure support. In this paper, we study this “storage capability” of VANET for highway and city traffic. Particularly, we studied different properties (average life time and deletion time) of the VANET storage for both 1-D highway traffic and 2-D city wide traffic. Through theoretical analysis and detailed simulation validated by the realistic mobility trace observed in San Francisco Yellow Cabs, we found that the same system parameters (e.g., transmission range and the size of the region of interest) can have different impact on VANET storage properties under different mobility patterns. Particularly, the size of the region of interest where the information needs to be stored has a large impact on the lifetime of the storage for the 2-D city-wide traffic, while the transmission range of wireless communication has large impact on the lifetime of the storage for 1-D highway traffic, and also on the success rate of deleting the information and the mean time to delete the information. Furthermore, the simulation analysis using SF Cab trace shows that even with low density of cabs, the average life time of VANET storage can be in order of minutes, demonstrating that it is possible to build realistic applications using VANET storage. [Copyright &y& Elsevier]

Abstract: During the past few years, researchers have found evidence indicating that various time series representing daily cycles of traffic, such as volumes, speeds and occupancies, may be nonlinear. In this paper it is shown that such nonlinearities can be adequately described by smooth-transition regression (STR) models which may characterize distinct regimes for free flow, congestion, and asymmetric behavior in the transition phases from free flow to congestion and vice versa. STR models are advantageous compared to the – frequently adopted in traffic modeling – Artificial Neural Networks models because their parameters are interpretable. An exposition of smooth transition models is presented with a focus on logistic multi-regime models that are deemed to be most appropriate for modeling traffic variables. The methodology is illustrated by an application to data on speeds volumes and occupancies, obtained from two loop detectors located in a major arterial of Athens, Greece. Tests on nonlinearity provided ample evidence of regime-dependent dynamics for all traffic time series examined. The following research questions are examined using STR models: How many regimes, described by linear dynamics, characterize the cycle of traffic for a typical weekday? Which time interval corresponds to each regime and how fast is the transition from one regime to another? Are the estimated dynamics of daily cycles stable across weekdays? [Copyright &y& Elsevier]

Abstract: This paper introduces an online pedestrian crossing detection system that uses pre-existing traffic-oriented video-sensors which, at regular intervals, provide coarse spatial measurements on areas along a crosswalk. Pedestrian crossing detection is based on the recognition of occupancy patterns induced by pedestrians when they move on the crosswalk. In order to improve the ability of non-dedicated sensors to detect pedestrians, we introduce an evidential-based data fusion process that exploits redundant information coming from one or two sensors: intra-sensor fusion uses spatiotemporal characteristics of the measurements and inter-sensor fusion uses redundancy between the two sensors. As part of the EU funded TRACKSS project on cooperative advanced sensors for road traffic applications, real data have been collected on an urban intersection equipped with two cameras. The results obtained show that the data fusion process enhances the quality of occupancy patterns obtained and leads to high detection rates of pedestrian crossings with multi-purpose sensors in operational conditions, especially when a secondary sensor is available. [Copyright &y& Elsevier]

Abstract: The use of fossil fuels in transportation generates harmful emissions that accounts for nearly half of the total pollutants in urban areas. Dealing with this issue, local authorities are dedicating specific efforts to seize the opportunity offered by new fuels and technological innovations in achieving a cleaner urban mobility. In fact, authorities are improving environmental performances of their public transport fleet by procuring cleaner vehicles, usually called low and zero emission vehicles (LEV and ZEV, respectively). Nevertheless there seems to be a lack of methodologies for supporting stakeholders in decisions related to the introduction of green vehicles, whose allocation should be performed since the network design process in order to optimize their available green capacity. In this paper, the problem of clean vehicle allocation in an existing public fleet is faced by introducing a method for solving the transit network design problem in a multimodal, demand elastic urban context dealing with the impacts deriving from transportation emissions. The solving procedure consists of a set of heuristics which includes a routine for route generation and a genetic algorithm for finding a sub-optimal set of routes with the associated frequencies. [Copyright &y& Elsevier]

Abstract: Estimation of time-dependent arterial travel time is a challenging task because of the interrupted nature of urban traffic flows. Many research efforts have been devoted to this topic, but their successes are limited and most of them can only be used for offline purposes due to the limited availability of traffic data from signalized intersections. In this paper, we describe a real-time arterial data collection and archival system developed at the University of Minnesota, followed by an innovative algorithm for time-dependent arterial travel time estimation using the archived traffic data. The data collection system simultaneously collects high-resolution “event-based” traffic data including every vehicle actuations over loop detector and every signal phase changes from multiple intersections. Using the “event-based” data, we estimate time-dependent travel time along an arterial by tracing a virtual probe vehicle. At each time step, the virtual probe has three possible maneuvers: acceleration, deceleration and no-speed-change. The maneuver decision is determined by its own status and surrounding traffic conditions, which can be estimated based on the availability of traffic data at intersections. An interesting property of the proposed model is that travel time estimation errors can be self-corrected, because the trajectory differences between a virtual probe vehicle and a real one can be reduced when both vehicles meet a red signal phase and/or a vehicle queue. Field studies at a 11-intersection arterial corridor along France Avenue in Minneapolis, MN, demonstrate that the proposed model can generate accurate time-dependent travel times under various traffic conditions. [Copyright &y& Elsevier]

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]

Abstract: This paper presents an adaptive hybrid fuzzy rule-based system (FRBS) approach for the modeling and short-term forecasting of traffic flow in urban arterial networks. Such an approach possesses the advantage of suitably addressing data imprecision and uncertainty, and it enables the incorporation of expert’s knowledge on local traffic conditions within the model structure. The model employs univariate and multivariate data structures and uses a Genetic Algorithm for the offline and online tuning of the FRBS membership functions according to the prevailing traffic conditions. The results obtained from the online application of the proposed FRBS are found to overperform those of the offline application and conventional statistical techniques, when modeling both univariate and multivariate traffic data corresponding to a real signalized urban arterial corridor. [Copyright &y& Elsevier]

• 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]

Urban traffic congestion is one of the most severe problems of everyday life in Metropolitan areas. In an effort to deal with this problem, intelligent transportation systems (ITS) technologies have concentrated in recent years on dealing with urban congestion. One of the most critical aspects of ITS success is the provision of accurate real-time information and short-term predictions of traffic parameters such as traffic volumes, travel speeds and occupancies. The present paper concentrates on developing flexible and explicitly multivariate time-series state space models using core urban area loop detector data. Using 3-min volume measurements from urban arterial streets near downtown Athens, models were developed that feed on data from upstream detectors to improve on the predictions of downstream locations. The results clearly suggest that different model specifications are appropriate for different time periods of the day. Further, it also appears that the use of multivariate state space models improves on the prediction accuracy over univariate time series ones. [Copyright &y& Elsevier]

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]