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Inference of detailed vehicle trajectories is crucial for applications such as traffic flow modeling, energy consumption estimation, and traffic flow optimization. Static sensors can provide only aggregated information, posing challenges in reconstructing individual vehicle trajectories. Shockwave theory is used to reproduce oscillations that occur between sensors. However, as the emerging of connected vehicles grows, probe data offers significant opportunities for more precise trajectory reconstruction. Existing methods rely on Eulerian observations (e.g., data from static sensors) and Lagrangian observations (e.g., data from connected vehicles) incorporating shockwave theory and car-following modeling. Despite these advancements, a prevalent issue lies in the static assignment of shockwave speed, which may not be able to reflect the traffic oscillations in a short time period caused by varying response times and vehicle dynamics. Moreover, driver dynamics while reconstructing the trajectories are ignored. In response, this paper proposes a novel framework that integrates Eulerian and Lagrangian observations for trajectory reconstruction on freeways. The approach introduces a calibration algorithm for time-varying shockwave speed. The shockwave speed calibrated by the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory. Additionally, vehicle and driver dynamics are introduced to optimize the trajectory and estimate energy consumption by applying a vehicle movement model. The proposed method is evaluated using real-world datasets, demonstrating superior performance in terms of trajectory accuracy, reproducing traffic oscillations, and estimating energy consumption. • Integrate Lagrangian and Eulerian observations to reconstruct trajectories. • Calibrate time-varying short-term shockwave speeds using the two types of data. • Reconstruct trajectories for non-connected vehicles based on shockwave theory. • Optimize trajectories by adding driver dynamics for better energy estimation. • Evaluation on real-world datasets shows excellent performances from several aspects. [ABSTRACT FROM AUTHOR]

Traffic-responsive signal control is a cost-effective, easy-to-implement, network management strategy, bearing high potential to improve performance in heavily congested networks with dynamic traffic characteristics. Max Pressure (MP) distributed control gained significant popularity due to its theoretically proven ability of throughput maximization under specific assumptions. However, its effectiveness is questionable in over-saturated conditions, while network-scale implementation is often practically limited due to high instrumentation cost, which increases proportionally to the number of controlled intersections. Perimeter control (PC) based on the concept of Macroscopic Fundamental Diagram (MFD) is a state-of-the-art aggregated control strategy that regulates exchange flows between homogeneously congested regions, with the objective of maximizing traffic system performance and prevent over-saturation. However, homogeneity assumption is hardly realistic under congested conditions, which can compromise PC effectiveness. In this paper, network-wide parallel application of PC and MP strategies embedded in a two-layer control framework is evaluated in a macroscopic simulation environment. With the aim of reducing implementation cost of network-wide MP without significant performance drop, we propose a critical node identification algorithm that is based on node traffic characteristics and assess partial MP deployment to the most critical nodes. An enhanced version of Store-and-forward dynamic traffic paradigm incorporating finite queues and spill-back consideration is used to test different configurations of the two-layer framework, as well as each layer individually, for a real large-scale network, in moderate and highly congested conditions. Results show that: (i) combination of MP and PC outperforms individual layer application in almost all cases for both demand scenarios tested; (ii) MP control in critical node sets formed by the proposed strategy leads to similar or even better performance compared to full-network implementation, thus allowing for significant cost reduction; iii) the proposed control schemes improve system performance even under stochastic demand fluctuations of up to 20% of mean. • Development of a joint Max Pressure and perimeter control framework in large-scale network • Design of critical node selection algorithm for partial Max Pressure control • Incorporation of queue spill-backs and dynamic rerouting in simulation experiments • Sensitivity analysis of partial Max Pressure schemes under demand stochasticity [ABSTRACT FROM AUTHOR]

Urban mobility is facing a paradigm shift towards providing more convenient, environmentally friendly, and on-demand services. Satisfying customer needs in a cost-efficient way has been the goal of many ridesharing systems. Capacitated ridesharing is considered as an effective service for reducing traffic congestion and pollution nowadays. Providing more operational strategies that can optimize on-demand ridesharing needs further investigation. In the current work, we focus on developing a matching algorithm for solving the on-demand ridesharing operation task in a real-time setting. We develop a simulation framework that can be used to propose a real-time shuttle ridesharing search algorithm. We propose a novel, computationally efficient, real-time ridesharing algorithm. We formulate the ridesharing assignment algorithm as a combinatorial optimization problem. The computational complexity of the proposed algorithm is reduced from exponential to linear, and the search space of the optimization problem is reduced by introducing heuristics. Our approach implements dynamic congestion by regularly updating the network's road segments' travel time during the simulation horizon to have more realistic results. We demonstrate how our algorithm, when applied to the New York City taxi dataset, provides a clear advantage over the current taxi fleet in terms of service rate. Furthermore, the developed simulation framework can provide valuable insights regarding cost functions and operational policies. • Development of a modular real-time simulation framework for the capacitated ridesharing problem. • Formulation of the ridesharing problem as a dynamic deterministic ondemand matching problem with tolerance times. • Implementation of dynamic congestion by regularly updating link travel times during the simulation horizon. • Solving the optimization problem in an online manner using both heuristics and commercial solvers. • Modeling of stakeholders' multiple objectives and design of policies that lead to efficient and mutually beneficial solutions. [ABSTRACT FROM AUTHOR]

Most feedback perimeter control approaches that are based on the Macroscopic Fundamental Diagram (MFD) and are tested in detailed network structures restrict inflow from the external boundary of the network. Although such a measure is beneficial for the network performance, it creates virtual queues that do not interact with the rest of the traffic and assumes small unrestricted flow (i.e. almost zero disturbance). In reality, these queues can have a negative impact to traffic conditions upstream of the protected network that is not modelled. In this work an adaptive optimization scheme for perimeter control of heterogeneous transportation networks is developed and the aforementioned boundary control limitation is dropped. A nonlinear model is introduced that describes the evolution of the multi-region system over time, assuming the existence of well-defined MFDs. Multiple linear approximations of the model (for different set-points) are used for designing optimal multivariable integral feedback regulators. Since the resulting regulators are derived from approximations of the nonlinear dynamics, they are further enhanced in real-time with online learning/adaptive optimization, according to performance measurements. An iterative data-driven technique is integrated with the model-based design and its objective is to optimize the gain matrices and set-points of the multivariable perimeter controller based on real-time observations. The efficiency of the derived multi-boundary control scheme is tested in microsimulation for a large urban network with more than 1500 roads that is partitioned in multiple regions. The proposed control scheme is demonstrated to achieve a better distribution of congestion (by creating “artificial” inter-regional queues), thus preventing the network degradation and improving total delay and outflow. [ABSTRACT FROM AUTHOR]

Advancements in sensor technologies, vehicle automation, communication, and intelligent transportation systems create unforeseen possibilities for the development of novel traffic management approaches in road transport systems. Furthermore, data observations with different accuracy and noise levels are fused towards advanced traffic state estimators. This work builds on the existing family of data assimilation techniques in the literature and proposes an online adaptive framework, fusing observations from static and moving sensors, along with estimations inferred from a traffic flow model and performing real-time traffic estimation in the presence of Connected and Automated Vehicles (CAVs). A real-world case study was used for validation and assessment of the proposed framework against well-known methodologies in the literature. The benefits and downsides of each approach for different scenarios are discussed, as well as the performance of each framework for different traffic models and penetration rates of CAVs. • Propose an Adaptive Unscented Kalman Filter for traffic state estimation. • Dynamic update of noise error covariance and readjustment of sigma points. • Compare results for AUKF, EnKF, EKF and UKF frameworks for TSE. • The role of reporting instantaneous speeds from moving vehicles for TSE. • Comparing CTM and METANET performance for TSE on a large case study. [ABSTRACT FROM AUTHOR]

In this paper, we study a mixed traffic system moving along a single-lane open-road. This platoon includes a number of human-driven vehicles (HDVs) together with one connected and automated vehicle (CAV). The dynamics of HDVs are assumed to follow the optimal velocity model (OVM), and the acceleration of the single CAV is directly controlled by a static output-feedback controller. Due to different traffic conditions, the desired velocity of the platoon can change over time. Moreover, there are multiple system parameters that are uncertain. The ultimate goal of this work is to present a gain-scheduled robust control strategy that, with a varying desired speed, smooths the traffic flow in the presence of undesired disturbances and parametric uncertainties. In this direction, a gain-scheduled H ∞ static output-feedback controller is designed, and its efficiency is illustrated through numerical simulations. [ABSTRACT FROM AUTHOR]

Abstract: Traffic signal control for urban road networks has been an area of intensive research efforts for several decades, and various algorithms and tools have been developed and implemented to increase the network traffic flow efficiency. Despite the continuous advances in the field of traffic control under saturated conditions, novel and promising developments of simple concepts in this area remains a significant objective, because some proposed approaches that are based on various meta-heuristic optimization algorithms can hardly be used in a real-time environment. To address this problem, the recently developed notion of network fundamental diagram for urban networks is exploited to improve mobility in saturated traffic conditions via application of gating measures, based on an appropriate simple feedback control structure. As a case study, the proposed methodology is applied to the urban network of Chania, Greece, using microscopic simulation. The results show that the total delay in the network decreases significantly and the mean speed increases accordingly. [Copyright &y& Elsevier]

Traffic management by applying congestion pricing is a measure for mitigating congestion in protected city corridors. As a promising tool, pricing improves the level of service in a network and reduces travel delays. However, previous advancements in pricing research that are responsive to the prevailing regional traffic conditions did not consider real-time applications and the effect on users' route choices. This work uses real-time dynamic pricing's influence and predicts pricing functions to aim for a system optimal traffic distribution. The framework models a large-scale network where every region is considered homogeneous, allowing for the Macroscopic Fundamental Diagram (MFD) application. We compute Dynamic System Optimum (DSO) and Dynamic Route Choice (DRC) of the macroscopic model by formulating a linear optimization problem and utilizing the Dijkstra algorithm and a Multinomial Logit model (MNL), respectively. The equilibria allow us to find an optimal pricing methodology by training Multi-Layer-Neural (MLN) network models. We test our framework on a case study in Zurich, Switzerland, and showcase that (a) our neural network model learns the complex user behavior and (b) allows predicting optimal pricing functions. Results show a significant performance improvement when operating a transportation network in the DSO and highlight how dynamic pricing influences the user's route choice behavior towards the system optimal equilibrium. • Effectiveness assessment of dynamic pricing in large-scale urban networks by computation of transportation equilibria. • Derivation of the Dynamic System Optimum by solving a linear optimal route guidance problem. • Training of neural network models to learn the users route choice behavior. • Utilization of neural network models to predict generalized costs and derive optimal pricing functions. [ABSTRACT FROM AUTHOR]

• Developing a traffic state estimation engine for large-scale urban networks modeled with MFD dynamics. • Tackling the real-time estimation problem when limited data is available for closed-loop perimeter control. • Stochastic modeling for the dynamics of the process (plant). • Aggregated dynamical model and real-time measurements for designing the EKF estimation scheme. • Application of the algorithm to both macro- and micro-simulation for evaluation. • Investigation of different measurements configurations (sparse data) and estimation efficiency. The problem of traffic state estimation for large-scale urban networks modeled with MFD dynamics is studied here. Given a network partitioned in a number of regions, aggregated traffic dynamics describe the vehicle accumulation in each region, as well as transfer flows to and from neighboring regions. Considering that MFD accumulation-based models have been integrated in perimeter control approaches, this work tackles the real-time estimation problem when limited data is available. An estimation engine is developed according to the Extended Kalman Filter (EKF) theory; it seeks to estimate the real state of the multi-region dynamic system based on traffic sensors' measurements. First, a stochastic model is presented for the dynamics of the process (plant). Then, the EKF estimation scheme is described based on a simpler aggregated model of dynamics and some real-time measurements. Estimation accuracy is investigated through detailed micro-simulation of downtown Barcelona by studying a realistic configuration of real-time measurement availability through loop detector data; however, the developed methodology is generic. The state vector we seek to estimate, as well as the available measurements configuration, can be altered according to the application. The proposed methodology is tested both in macro- and micro-simulation; resulting estimated traffic states (i.e., regional accumulations, demands, and distribution of outflows) are compared to actual ones obtained from the stochastic plant. The developed algorithm can be utilized by closed-loop online urban traffic management strategies to feed the estimated traffic state back to the controller. [ABSTRACT FROM AUTHOR]

• Formulating a combinatorial optimization problem for dedicated bus lane (DBL) allocation. • Integrating dynamic characteristics of congestion in the DBL problem. • Proposing a learning process to estimate link potential for hosting DBL. • Developing a Large Neighborhood Search with various types of destroy and repair methods. • Testing the methods and algorithms in a simulation of a large-scale network. Dedicated bus lanes provide a low cost and easily implementable strategy to improve transit service by minimizing congestion-related delays. Identifying the best spatial distribution of bus-only lanes in order to maximize traffic performance of an urban network while balancing the trade-off between bus priority and regular traffic disturbance is a challenging task. This paper studies the problem of optimal dedicated bus lane allocation and proposes a modeling framework based on a link-level dynamic traffic modeling paradigm, which is compatible with the dynamic characteristics of congestion propagation that can be correlated with bus lane relative positions. The problem is formulated as a non-linear combinatorial optimization problem with binary variables. An algorithmic scheme based on a problem-specific heuristic and Large Neighborhood Search metaheuristic, potentially combined with a network decomposition technique and a performance-based learning process for increased efficiency, is proposed for deriving good quality solutions for large-scale network instances. Numerical application results for a real city center demonstrate the efficiency of the proposed framework in finding effective bus lane network configurations; when compared to the initial network state they exhibit the potential of bus lanes to improve travel time for car and bus users. [ABSTRACT FROM AUTHOR]

In this paper, a control solution to reduce congestion in highway traffic systems is presented. The aim is to produce a control strategy characterized by low computational cost, so that real-time implementation can be attained. The adopted model to describe traffic dynamics is the METANET model. A particular spatio-temporal derivative relationship, describing how control signals (ramp metering and variable speed limits) and disturbances effects propagate along the highway system, is highlighted in the paper. This relationship is the basis of a proposition providing the essential tool for relative degree calculation in generic highway systems. Utilizing this proposition, a feedback linearization-based control law is developed. The control design is completed by employing a linear MPC, which allows for complying with the physical constraints. The performance of the proposed method is evaluated by conducting comprehensive simulation studies, also considering a real-world traffic system. The computational costs are analyzed by comparing the developed methodology with a nonlinear MPC-based approach. Simulation evidence confirms that the proposed method can provide satisfactory solutions for coordinating RM and VSL in highway systems. Such solutions are compatible with real-time implementation. [ABSTRACT FROM AUTHOR]

• Propose a calibration framework for autonomously-driven vehicles to capture the multi-behavioral patterns. • Employ two prevailing ACC datasets from OpenACC database. • Calibrate two state-of-the-art car-following models, i.e., IDM and AOVRVM, within the proposed framework. • Test six typical behavioral patterns, i.e., spacing, time gap, response time, string stability, energy consumption and traffic hysteresis. • Compare the performance of the proposed framework with the state-of-the-art framework, namely, calibration on spacing, speed and acceleration. Calibration of car-following (CF) models is considered a very important task towards reproduction of individual vehicle behaviors and collective traffic phenomena. In most works, calibration is performed on trajectories of leading and following vehicles either on speed or spacing quantities, with the combination of the two plus the acceleration quantity to be considered as the most appropriate according to the literature. With the advent of adaptive cruise control (ACC) technology, there are intrinsic behavioral differences between human- and ACC-driven vehicles that are visible in experimental observations. Some examples include constant headway for ACC-enabled vehicles, string instability, hysteretic behavior, human-like response time and others. Since calibration is performed only on basic vehicle dynamics, i.e., the spacing, speed and acceleration, even after proper parametrization we cannot be certain that CF models will be able to reproduce some or all of the above phenomena and to what extend. The aim of this work is to propose a multi-objective calibration framework validated on empirical observations of car platoons. Furthermore, it investigates if, and to what extent, the traffic dynamics and behavioral patterns of ACC-driven vehicles can be reproduced. Two state-of-the-art CF models are tested with the empirical dataset. The results indicate that the proposed framework leads to acceptable errors and comparable performance, facilitating the models to capture phenomena in a way that is not possible until now, using the calibration on basic vehicle dynamics. [ABSTRACT FROM AUTHOR]

• Developing a dynamic MFD M model with total remaining travel distance as a state variable. • Integrating boundary queue dynamics in the M model to properly perform MPC perimeter control. • Performing model-based parameter estimation (MBPE) for the developed multi-region M model. • Reconstructing traffic states from incomplete measurements through a Moving Horizon Estimation. • Improving system performance compared to classical perimeter control with accumulation-based models. City-scale control of urban road traffic poses a challenging problem. Dynamical models based on the macroscopic fundamental diagram (MFD) enable development of model predictive perimeter control methods for large-scale urban networks, representing an advanced control solution carrying substantial potential for field implementation. In this paper we develop a multi-region approximation of the trip-based model, which describes in more details the trip length characteristics compared to existing accumulation-based models. The proposed M-model includes effects of the total remaining travel distance on the transfer flows driving the vehicle accumulation dynamics, potentially yielding improved accuracy over the standard production-over-trip length approximation of the outflow MFD considered in many works on MFD-based modeling and control. We explain that to properly perform perimeter control, boundary queue dynamics have to be integrated. Furthermore, model-based parameter estimation (MBPE), nonlinear moving horizon observer (MHO), and model predictive control (MPC) formulations for the proposed models are presented, forming an integrated traffic control framework. Microsimulation-based case studies, considering an urban network with 1500 links, where the model parameters obtained by MBPE method are used in MHO and MPC design, demonstrate the efficient operation of the proposed framework. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 constitutes an unprecedented disruption globally, in which risk management framework is on top priority in many countries. Travel restriction and home/office quarantine are some frequently utilized non-pharmaceutical interventions, which bring the worst crisis of airline industry compared with other transport modes. Therefore, the post-recovery of global air transport is extremely important, which is full of uncertainty but rare to be studied. The explicit/implicit interacted factors generate difficulties in drawing insights into the complicated relationship and policy intervention assessment. In this paper, a Causal Bayesian Network (CBN) is utilized for the modelling of the post-recovery behaviour, in which parameters are synthesized from expert knowledge, open-source information and interviews from travellers. The tendency of public policy in reaction to COVID-19 is analyzed, whilst sensitivity analysis and forward/backward belief propagation analysis are conducted. Results show the feasibility and scalability of this model. On condition that no effective health intervention method (vaccine, medicine) will be available soon, it is predicted that nearly 120 days from May 22, 2020, would be spent for the number of commercial flights to recover back to 58.52%–60.39% on different interventions. This intervention analysis framework is of high potential in the decision making of recovery preparedness and risk management for building the new normal of global air transport. • Propose a Causal Bayesian Network (CBN) based non-pharmaceutical policy intervention framework. • This framework can integrate multiple data source (expert knowledge, open-source data and questionnaires) together. • Multi-factors (authorities, traveller, epidemiological) interacted uncertainties are considered. • This work can be of great potential in post-recovery preparedness of global aviation. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles (CAVs) have the potential to improve the operation of future road traffic systems. In this paper, we propose a control method that uses CAVs as dynamic actuators to improve the capacity at motorway bottlenecks robustly. The proposed approach has been designed for mixed traffic flow using the fundamental diagram model of mixed traffic flow as a control activation tool. In order to implement our approach, we assume that the availability of detectors at motorway are able to obtain the density in real-time. The idea is that assuming a certain percentage of CAVs presence on the road, such vehicles can be used as mobile actuators to perform speed coordination tasks. Furthermore, the aim is to transfer the delays observed at the bottlenecks upstream on the motorway, where the conditions are more homogeneous. The proposed approached can be generalized and used in bottleneck scenarios with or without additional inflow from an on-ramp. According to the designed control strategy, when the traffic density at the bottleneck satisfies the activation condition, the CAVs will shift to moving actuator mode and generate a new speed profile. The objective is to improve traffic flow at the downstream bottleneck and also smooth the upstream arrival vehicle speed, thus improving the overall throughput of the network. The method has been evaluated through microscopic simulation experiments conducted with scenarios on the real-case study, a motorway in Antwerp, Belgium. The results show significant improvements in reducing traffic density and improving travel speed both locally at the control area, as well as at network level. Comparative experiments under different penetration rates show that the proposed method remains robust to the percentage of CAVs on the road. Finally, it can significantly reduce vehicle delays and prevent over-congested conditions, and also improve the traffic flow rate, even for relatively low penetration rates of CAVs. • A lane-level motorway speed control method using CAVs as moving actuators is proposed. • The FD model is used as a control activation tool for any penetration rate of CAVs. • The method is validated in a micro-simulation on a complex network in Antwerp. • The proposed method improves traffic efficiency and harmonizes the traffic flow. [ABSTRACT FROM AUTHOR]

• Empirically investigate the stimulus–response behavior between leader and follower in car-following. • Propose a parsimonious enhanced Newell's car-following model for accurate reproduction of driver and traffic dynamics. • Calibrate and validate the proposed model with two field car-following experiments. • Test the model in the aspects of the spontaneous oscillation, the concave growth pattern of oscillation, the vehicle movement prediction and the linear speed-capacity relationship. • Compare the performance of the proposed model with a state-of-the-art model, namely, the improved Newell's car-following model with stochastic wave travel time. This paper investigates the stimulus–response behavior between leader and follower in car-following, based on vehicle trajectories in the prevailing field experiments. The analysis result indicates that the follower's reaction time is time-varying, which can change significantly or keep a constant value for some time; and the follower cannot accurately track the leader in car following, which results in a residual from the follower's speed. Inspired by the findings, this paper proposes a parsimonious enhanced Newell's car-following model incorporating the stochastic reaction time and the fluctuation around the vehicle's desired speed subject to the mean reversion process. The numerical experiment is carried out. It is shown that the proposed model can qualitatively and quantitatively reproduce the following important field observations: (i) the spontaneous formation and evolution of traffic oscillations, (ii) the concave growth pattern of traffic oscillations, (iii) the oscillations' amplitude and frequency, (iv) the stochastic reproduction of individual trajectories, and (v) the linear speed-capacity relationship. The robustness of the proposed model is demonstrated, compared with the state-of-the-art model. Finally, the sensitivity analysis is carried out to evaluate the effect of each parameter of the proposed model. [ABSTRACT FROM AUTHOR]