Search

Flight delay prediction has recently gained growing popularity due to the significant role it plays in efficient airline and airport operation. Most of the previous prediction works consider the single-airport scenario, which overlooks the time-varying spatial interactions hidden in airport networks. In this paper, the flight delay prediction problem is investigated from a network perspective (i.e., multi-airport scenario). To model the time-evolving and periodic graph-structured information in the airport network, a flight delay prediction approach based on the graph convolutional neural network (GCN) is developed in this paper. More specifically, regarding that GCN cannot take both delay time-series and time-evolving graph structures as inputs, a temporal convolutional block based on the Markov property is employed to mine the time-varying patterns of flight delays through a sequence of graph snapshots. Moreover, considering that unknown occasional air routes under emergency may result in incomplete graph-structured inputs for GCN, an adaptive graph convolutional block is embedded into the proposed method to expose spatial interactions hidden in airport networks. Through extensive experiments, it has been shown that the proposed approach outperforms benchmark methods with a satisfying accuracy improvement at the cost of acceptable execution time. The obtained results reveal that deep learning approach based on graph-structured inputs have great potentials in the flight delay prediction problem. [ABSTRACT FROM AUTHOR]

Connected automated driving technologies have shown substantial benefits to improve the safety and efficiency of traffic. However, connected mixed traffic, which involves both connected automated vehicles and connected human-driven vehicles, is more foreseen for the realistic case in the near future. This brings new challenges because of the complexity of human elements in the system. In addition, the communication constraints in realistic connectivity such as random delays and packet losses bring even more challenges to the system. Therefore, this paper proposes a new anticipative and predictive automated vehicle control approach in connected mixed traffic. The approach first anticipates the states of surrounding vehicles including human-driven vehicles, and then integrates the anticipation into the predictive control of automated vehicles, which can help improve the control performance and also handle the communication constraints. An inverse model predictive control (IMPC) based anticipation approach has been proposed. The proposed approach, together with constant speed (CS), intelligent driver model (IDM) and artificial neural network (ANN) based anticipation methods are integrated with model predictive control (MPC) for automated vehicle control. The approaches have been tested in human-in-the-loop experiments and the results show that the integration with a newly proposed IMPC based anticipation has shown the best performance in terms of accuracy, efficiency and scalability in connected mixed traffic with both ideal and constrained communications. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) provides a novel computing paradigm to satisfy the increasing computation requirements of mobile applications. In MEC-enabled intelligent transportation systems (ITS), the latency-sensitive computing tasks are offloaded to RSUs for execution, reducing the transmission latency compared with the cloud solutions. However, the repetitive executions of the same tasks whose outputs are dependent on the inputs lead to the extra system latency, an alternative is to cache the required services on RSUs in advance. The service requirements of latency-sensitive computing tasks are satisfied by jointly considering computation offloading and service caching. Besides, the digital twin (DT) is utilized to construct the virtual world reflecting the physical world in real-time to efficiently make offloading strategies. In this paper, a computation offloading and service caching method using decision theory in ITS with DT, named CODT, is proposed. Specifically, the computation offloading and service caching in ITS is modeled first with DT. Then, a mixed-integer nonlinear programming (MINLP) problem is formulated to minimize the system latency. Afterward, the decision theory is used to analyze the utilities of offloading strategies in different states of RSUs and make the optimal strategy. Finally, extensive simulations based on the real-world datasets demonstrate that the proposed CODT outperforms other baselines. [ABSTRACT FROM AUTHOR]

This paper presents a novel embedding protocol that allows for safe and efficient operation of the Hybrid Autonomous Intersection Management (H-AIM) protocol concurrently with actuated and adaptive signal controllers. The proposed protocol extends H-AIM to allow it to cope with some operational uncertainty that is common in actuated signal controllers. A novel approach for computing safety bounds on signal timing is presented as a way of insuring safety in the face of demand uncertainty. Experimental results show the feasibility and effectiveness of combining H-AIM with actuated controllers for various levels of connected and autonomous vehicle (CAV) market penetration and different combinations of common signal control schemes, namely, adaptive signal timing, fixed signal timing, and signal actuation. The benefits are presented in terms of delay improvement when common actuation protocols are used in conjunction with the H-AIM protocol. In contrast to previous reports, the results presented in this paper suggest that mixtures of turning movement assignments that are more permissive for CAVs and less permissive for human operated vehicles are often detrimental in terms of traffic delay. Nonetheless, when implemented on top of an actuated and adaptive controller, the extended H-AIM protocol is shown to never be detrimental while presenting statistically significant reductions in total delay when more than 15% of the traffic is composed of CAVs. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the PID controller design problem of path following for an autonomous ground vehicle (AGV). Firstly, a bicycle model is adopted and a vehicle offset model from the target path is integrated to the bicycle model. The PID controller considers the vehicle longitudinal speed variation for adaption to the road curvature and the stochastic delay induced by the network communication. This is realized by transforming the tuning problem of proportional-integral-derivative (PID) gains for path following into a design problem of a static-output-feedback (SOF) controller for a time-delayed linear parameter varying (LPV) model form. A sufficient condition is adopted to guarantee the stability of the closed-loop system. In order to achieve better tracking performance, we propose a strategy in which the PID gains are piecewise constant and are dependent on the road-curvature ranges. The stability of the switched system is guaranteed via the common Lyapunov function method. Grey wolf optimizer (GWO) is employed to solve the optimization problem with maximum absolute tracking error as the optimization objective and stability condition and actuator dynamics as constraints. Both simulation results based on the CarSim-Simulink joint platform and hardware-in-loop experiment results are used to verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Most existing studies on autonomous intersection management (AIM) often focus on algorithms to accommodate conflicts among vehicles by assuming that the entrance lane and the exit lane of vehicles are exogenous inputs. This paper shows that allowing entrance lanes and exit lanes to be optimized can significantly improve traffic efficiency. In particular, this paper proposes “all-direction” lanes, where left-turn, through, and right-turn traffic is all allowed at the same lane. We develop two methods for optimizing entering time (i.e., when to enter the intersection) and route choice decisions (i.e., entrance lane and exit lane), including the sliding-time-window-based global optimum (GO-STW) and the first-come-first-served method with optimal route choices (FCFS-R). The developed lane-based methods can be formulated as mixed integer linear programming (MILP) problems, which can be solved using the CPLEX solver. A heuristic is further adopted to solve the MILP model in a timely manner, which illustrates the potential real-time applicability of the proposed method. Numerical analysis is conducted to examine performance and effectiveness of the proposed methods and heuristic. We found that the optimization of lane/route choices is often more critical than entering time. [ABSTRACT FROM AUTHOR]

Improper handling of on-ramp merging may cause severe decrease of traffic efficiency and contribute to lower fuel economy, even increasing the collision risk. Cooperative control for connected and automated vehicles (CAVs) has the potential to significantly reduce the negative impact and improve safety and traffic efficiency. Implementation of cooperative on-ramp merging requires the assistance of the vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication, wherein the communication delay may cause negative impact on CAV cooperative control. In this paper, scenario of on-ramp merging for CAVs considering the V2I communication delay are studied. Statistical characteristics of the V2I communication delay are explored from both literature and real field test, and a communication delay estimation model based on statistical techniques are proposed. Specifically, we firstly model the CAV on-ramp merging scenario using optimal control in ideal situation. Then, several statistical characteristics of the V2I communication are investigated especially the probability density function of the V2I communication delay in several application scenarios. Further, we proposed a communication delay estimation model and used the modified vehicle state to compute the corresponding control law. Real field test of V2I communication delay indicated that distribution of V2I communication delay could correlate with the application scenario and normal distribution can be generally adopted to approximate the probability density function (PDF) when the number of samples is large enough. Numerical simulation of the CAV on-ramp merging scenario considering the V2I communication delay revealed that dynamic performance of the control process would be deteriorated impacted by the V2I communication delay and it might further impact the final control effect and lead to potential lateral collision in the merging area. [ABSTRACT FROM AUTHOR]

With the emerging connected-vehicle technologies and smart roadways, the need for intelligent adaptive traffic signal controls (ATSC) is more than ever before. This paper first proposes an Accumulated Exponentially Weighted Waiting Time-based Adaptive Traffic Signal Control (AEWWT-ATSC) model to calculate priorities of roadways for signal scheduling. As the size of the traffic network grows, it adds great complexities and challenges to computational efficiencies. Considering this, we propose a novel Distributed Multi-agent Reinforcement Learning (DMARL) with a graph decomposition approach for large-scale ATSC problems. The decomposition clusters intersections by the level of connectivity (LoC), defined by the average residual capacities (ARC) between connected intersections, enabling us to train subgraphs instead of the entire network in a synchronized way. The problem is formulated as a Markov Decision Process (MDP), and the Double Dueling Deep Q Network with Prioritized Experience Replay is utilized to solve it. Under the optimal policy, the agents can select the optimal signal durations to minimize the waiting time and queue size. In evaluation, we show the superiority of the AEWWT-ATSC based RL methods in different densities and demonstrate the DMARL with a graph decomposition approach on a large graph in Manhattan, NYC. The approach is generic and can be extended to various types of use cases. [ABSTRACT FROM AUTHOR]

In this paper, we study how to learn a harmonious deep reinforcement learning (DRL) based lane-changing strategy for autonomous vehicles without Vehicle-to-Everything (V2X) communication support. The basic framework of this paper can be viewed as a multi-agent reinforcement learning in which different agents will exchange their strategies after each round of learning to reach a zero-sum game state. Unlike cooperation driving, harmonious driving only relies on individual vehicles’ limited sensing results to balance overall and individual efficiency. Specifically, we propose a well-designed reward that combines individual efficiency with overall efficiency for harmony, instead of only emphasizing individual interests like competitive strategy. Testing results show that competitive strategy often leads to selfish lane change behaviors, anarchy of crowd, and thus the degeneration of traffic efficiency. In contrast, the proposed harmonious strategy can promote traffic efficiency in both free flow and traffic jam than the competitive strategy. This interesting finding indicates that we should take care of the reward setting for reinforcement learning-based AI robots (e.g., automated vehicles) design, when the utilities of these robots are not strictly in alignment. [ABSTRACT FROM AUTHOR]

Perimeter control based on Macroscopic Fundamental Diagram (MFD) aims to meter the number of transferring vehicles at the periphery of the protected urban region in order to obtain the desired number of vehicles in that region. The advantage of perimeter control is less computational effort, while its drawback is that it may create long queues and delays at the perimeter of the controlled area. For capturing boundary queue dynamics, an enhanced accumulation-based MFD model is proposed using colored Petri Nets by considering transfer flows, boundary queues and travel delays simultaneously. The gated intersections and related road segments on the border of a protected region are modeled as so-called boundary buffers. Based on the enhanced MFD model, anintegrated perimeter control framework is proposed with consideration of travel time and queuing time in buffers. In this framework, the controllers between peripheral and protected region are optimized using model predictive control theory. Then, internal flow controllers are adopted to homogenize traffic density among subregions, and route guidance is also used to balance the number of queuing vehicles among boundary buffers. Simulation results verify the effectiveness of the proposed integrated perimeter control. Furthermore, the impacts of buffer storage capacity on region heterogeneity and trip completion rates are also investigated in this paper. Note to Practitioners—It is challenging to manage traffic congestion in large-scale urban network. Perimeter control provides an accumulation-based methodology with consideration of the existing correlation between traffic density and flow, which is known as MFD. For practical application, the efficiency as well asweakness of potential perimeter control strategies need to be evaluated and improved using customized traffic simulations. Accumulation-based traffic model using Petri Nets is introduced to serve for perimeter control, in which the intersections and road segments on the boundary of each pair of adjacent subregions are modeled as a boundary buffer. Both perimeter control and route guidance are integrated in the proposed control framework considering the queuing vehicles in the boundary buffers. Moreover, the effect of buffer storage capacity on network performances is tested, which is the essential for traffic engineers to design and implement management measures in practice. [ABSTRACT FROM AUTHOR]

This paper is about obtaining stable vehicle platooning by using Cooperative Adaptive Cruise Control when the communication is unreliable and suffers from message losses. We model communication losses as independent random events and we propose an original design for the cooperative controller, which effectively mitigates the effects of the losses. Our design explicitly takes into account the stochastic nature of the losses by considering both the average evolution of the system and the stochastic variations around it. The control design promotes both plant stability and string stability of the average error dynamics by an $\mathscr {H}_\infty $ approach, while minimizing the variance of the trajectories around their average. We show by simulations that the proposed controller is able to compensate losses even for high loss probabilities. [ABSTRACT FROM AUTHOR]

Infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications are often combined to extend the connectivity and coverage in the Intelligent Transportation System (ITS) and its applications, e.g., augmented reality, real-time parking management and online shopping. Through multi-hop I2V and V2V communications, requesting vehicles are always connected to road side units (RSUs) even when they do not reside within the RSUs’ coverage range. However, there may be not adequate network resource for several I2V and V2V links when multiple vehicles request services simultaneously. In this paper, we propose a joint frequency scheduling and power control scheme to enhance connectivity in multi-hop I2V/V2V networks. We associate I2V and V2V links with tuple-links, then formulate an NP-hard problem in which a frequency scheduler and a power controller are jointly designed for the tuple-links. The NP-hard problem is decomposed into two separate subproblems by employing the delayed column generation technique. Then, we employ a method for linear programming and a greedy algorithm to address these subproblems. Through numerical experiments with practical parameter settings, we demonstrate the proposed scheme outperforms several existing ones in terms of connectivity enhancement, measured by the service resumption number and average achieved throughput. Furthermore, the efficiency of our scheme is further enhanced when the number of available channels is high, and buffer size equipped to the requesting vehicles is large. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) have attracted increasing attention in cross-regional communication. The interconnection between VANETs and satellite networks expands their communication range. To improve the transmission performance of heterogeneous networks composed of VANETs and satellite networks, this paper proposes an end-to-end transmission control scheme for satellite network-assisted VANETs, analyzes the bandwidth delay product of multihop VANETs through heterogeneous networks, and establishes a transmission control model applicable to cross-regional environments. To address the long delay and high bit error rate in heterogeneous networks, the model designs the congestion window in the slow start and congestion avoidance of the transmission scheme. The data transmission volume is increased in the slow start, and various packet losses are distinguished in congestion avoidance. The experimental results show that the scheme can achieve good transmission performance in heterogeneous networks composed of VANETs and satellite networks. The results show that VANETs data can be effectively transmitted in heterogeneous networks. Compared with the traditional transmission scheme, this simple and practical end-to-end transmission control scheme can improve speed by 83.49 kbps and reduce transmission duration time of FTP server by $4.72\times 10^{3}$ s. The results of this study provide a reference for massive data transmission in heterogeneous networks of VANETs and satellite networks. [ABSTRACT FROM AUTHOR]

In recent years, developments in vehicle-to-everything communication (V2X) have steadily increased in applications such as platooning and collision avoidance. V2X provides vehicles with long-range information regarding traffic congestion and routing, but also short and mid-range information allowing cooperative adaptive cruise control, automatic collision warnings, and others. Despite being potentially beneficial in several aspects, such interdependence poses a set of specific challenges from safety and reliability standpoint due to the possibility of cyber-attacks aimed at influencing the behavior of the vehicles. In this paper, a Cloud-based approach, a sandboxing framework is presented to detect the false data injection attack on connected and automated vehicles (CAVs). The sandboxing concept comes from computer security and it is adopted in a control framework as a way to isolate and evaluate the data exchanged by the CAVs affecting the vehicle control system. Numerical experiments are conducted to show the effectiveness of the approach using microscopic traffic simulation. Simulation results show that our proposed methodology is able to detect the false data injection attack existing in the V2X communication and identify the attacker in real-time less than 0.2 s. [ABSTRACT FROM AUTHOR]

Recent advances in vehicle connectivity have allowed formation of autonomous vehicle platoons for improved mobility and traffic throughput. In order to avoid a pile-up in such platoons, it is important to ensure platoon (string) stability, which is the focus of this work. As per conventional definition of string stability, the power (2-norm) of the spacing error signals should not amplify downstream in a platoon. But in practice, it is the infinity-norm of the spacing error signal that dictates whether a collision occurs. We address this discrepancy in the first part of our work, where we reconsider string stability from a safety perspective and develop an upper limit on the maximum spacing error in a homogeneous platoon as a function of the acceleration maneuver of the lead vehicle. In the second part of this paper, we extend our previous results by providing the minimum achievable time headway for platoons with two-predecessor lookup schemes experiencing burst-noise packet losses. Finally, we utilize throttle and brake maps to develop a longitudinal vehicle model and validate it against a Lincoln MKZ which is then used for numerical corroboration of the proposed time headway selection algorithms. [ABSTRACT FROM AUTHOR]

The coordinated control problem for a multiple high-speed train (HST) system subject to unknown communication delays is systematically investigated in this paper. Taking into consideration the inertial lag of the servo motor, a third-order nonlinear control model is constructed to capture the dynamics of a train in real-world operations. By virtue of the backstepping linearization technique, the coordinated control of trains is formulated as a stabilization problem for a linear multiple-input multiple-output system with an unknown input delay. Distributed control laws with a time-varying low gain parameter are designed, besides solving the stabilization problem, to guarantee a fast convergency rate during the train status adjustment process. Numerical examples are provided to illustrate that the time-varying low gain parameter design achieves better control performance compared with the traditional constant low gain feedback design in terms of the convergency rate and the system overshot, and that the proposed control method is effective in train tracking distance adjustment. [ABSTRACT FROM AUTHOR]

This paper presents the design of a robust distributed state-feedback controller in the discrete-time domain for homogeneous vehicle platoons with undirected topologies, whose dynamics are subjected to external disturbances and under random single packet drop scenario. A linear matrix inequality (LMI) approach is used for devising the control gains such that a bounded $H_{\infty }$ norm is guaranteed. Furthermore, a lower bound of the robustness measure, denoted as $\gamma $ gain, is derived analytically for two platoon communication topologies, i.e., the bidirectional predecessor following (BPF) and the bidirectional predecessor leader following (BPLF). It is shown that the $\gamma $ gain is highly affected by the communication topology and drastically reduces when the information of the leader is sent to all followers. Finally, numerical results demonstrate the ability of the proposed methodology to impose the platoon control objective for the BPF and BPLF topology under random single packet drop. [ABSTRACT FROM AUTHOR]

In battery swapping environments, electric vehicles (EVs) can play roles as battery providers as well as consumers. In this paper, we propose an optimal battery charging algorithm (OBCA) where a battery swapping station (BSS) charges batteries in its storage with the consideration of the profile of the electricity price and the arrival rates of EVs. To maximize the net profit of BSS while maintaining the battery changing probability above a certain level (i.e., maintaining high quality of service (QoS) of BSS), we formulate a constraint Markov decision process (CMDP) problem and the optimal charging schedule for batteries in BSS is obtained by a linear programming (LP). Evaluation results demonstrate that OBCA with the optimal policy can improve the net profit of BSS up to 418% compared to an electric price-aware scheme while maintaining high QoS of BSS. [ABSTRACT FROM AUTHOR]

Train delay prediction can improve the quality of train dispatching, which helps the dispatcher to estimate the running state of the train more accurately and make reasonable dispatching decision. The delay of one train is affected by many factors, such as passenger flow, fault, extreme weather, dispatching strategy. The departure time of one train is generally determined by dispatchers, which is limited by their strategy and knowledge. The existing train delay prediction methods cannot comprehensively consider the temporal and spatial dependence between the multiple trains and routes. In this paper, we don’t try to predict the specific delay time of one train, but predict the collective cumulative effect of train delay over a certain period, which is represented by the total number of arrival delays in one station. We propose a deep learning framework, train spatio-temporal graph convolutional network (TSTGCN), to predict the collective cumulative effect of train delay in one station for train dispatching and emergency plans. The proposed model is mainly composed of the recent, daily and weekly components. Each component contains two parts: spatio-temporal attention mechanism and spatio-temporal convolution, which can effectively capture spatio-temporal characteristics. The weighted fusion of the three components produces the final prediction result. The experiments on the train operation data from China Railway Passenger Ticket System demonstrate that TSTGCN clearly outperforms the existing advanced baselines in train delay prediction. [ABSTRACT FROM AUTHOR]

Different from the existing train delay studies that had strived to explore sophisticated algorithms, this paper focuses on finding the bound of improvements on predicting multi-scenario train delays with different machine learning methods. Motivated by the observation of deep learning methods failing to improve the prediction performance if the delay occurs rarely, we present a novel augmented machine learning approach to improve the overall prediction accuracy further. Our solution proposes a rule-driven automation (RDA) method, including a delay status labeling (DSL) algorithm, and the resilience of section (RSE) and resilience of station (RST) indicators to generate the forecast for train delays. The experiment results demonstrate that the Random Forest based implementation of our RDA method (RF-RDA) can significantly improve the generalization ability of multivariate multi-step forecast models for multi-scenario train delay prediction. The proposed solution surpasses state-of-art baselines based on real-world traffic datasets, which treat various real-time delays differently. Even when the predictability of conventional deep learning methods decreases, the performance of our method is still acceptable for practical use to provide accurate forecasts. [ABSTRACT FROM AUTHOR]

In this paper, we present FedServ, a federated task service system for fog-enabled Internet of vehicles (IoV), using which we aim to minimize the delay of vehicle task service. FedServ considers different task service requirements such as delay, computation-intensiveness, processing units required, and energy consumption. The existing literature mainly focuses on the delay requirement of tasks and considers the energy consumption for task processing, whereas FedServ aims to achieve the Quality of Service (QoS) of the task while optimizing different resource requirements for task service. FedServ provisions the federated task service by fragmenting the tasks, which minimizes the task complexity while preserving task fragment dependency. To determine the suitable fog nodes for task fragment service, we formulate the problem as a coalition graph game. Analytical results depict that the proposed scheme minimizes the delay of the task service and the energy consumption while reducing the number of QoS violated tasks by 34.32%, 25.32%, and 22.78% compared to the existing schemes. [ABSTRACT FROM AUTHOR]

The precise operation control of high-speed trains is pivotal to maintain the safety and efficiency of trains, while the inevitable state delays will seriously attenuate the performance of control system. In this paper, an adaptive iterative learning control (ILC) approach for high-speed trains is presented in the presence of the nonlinearly parameterized uncertainties and multiple unknown state delays, aiming to drive that the displacements and velocities of trains can track the desired reference trajectories. To describe the operational dynamics of trains more realistically, the multi-particle model of trains involving multiple time-varying delays is established by analyzing the aerodynamic resistance, mechanical resistance, and coupler force acting on different cars. The proposed adaptive ILC scheme fully leverages various techniques, e.g., the hyperbolic tangent function, the parameter separation, to cope with the inherent nonlinearities, uncertainties and couplings of system. Specially, to eliminate the negative influence of unknown delays, an appropriate Krasovskii function is integrated into the Lyapunov criterion to devise the learning controller and check the stability of control systems. The novelties of our work lie in that the refinement model and periodical characteristic are simultaneously utilized to improve the practicability and performance of control scheme for the high-speed trains with multiple state delays. [ABSTRACT FROM AUTHOR]

Rapid development and deployment of vehicular ad-hoc networks (VANETs) require an efficient and scalable media access control (MAC) protocol to support high-priority safety applications and infotainment requirements. This paper proposes SATMAC, a self-adaptive time division multiple access (TDMA)-based MAC protocol for VANETs. In order to improve the stability of the time slot scheduling in VANETs, a slot status updating strategy is carefully designed, which utilizes accurate information of the two-hop neighbors and the rough information of the three-hop neighbors to detect the potential packet collisions and avoids potential collisions by adjusting the occupied time slot. Besides, an adaptive frame length (the number of time slots contained in a frame) approach is proposed on the basis of the slot adjustment to support various densities of vehicles, where the frame length between neighbors can be inconsistent. We conduct theoretical analysis and extensive simulations in a realistic VANET environment to evaluate SATMAC. Simulation results show that compared with IEEE 802.11p and LTE-V2X PC5 Mode 4, SATMAC significantly improves PDR of beacons over 60%. Moreover, our SATMAC design is further implemented and validated on our FPGA-based testbed. [ABSTRACT FROM AUTHOR]

Despite the constant rise in global urban populations and subsequent rise in transportation demand, significant expansion of infrastructure has been hampered by the constraints of space, cost, and environmental concerns. Therefore, optimizing the efficiency of existing infrastructure is becoming increasingly important. Adaptive traffic signal controllers aim to provide demand-responsive strategies to minimize motorists’ delay and achieve higher throughput at signalized intersections. With the advent of new sensory technologies and more intelligent control methods, the contribution of this paper is an adaptive traffic signal controller able to receive un-preprocessed high-dimensional sensory information such as GPS traces from connected vehicles and self-learn to minimize intersection delays. We use deep neural networks to operate directly on detailed sensory inputs and feed them into a reinforcement learning-based optimal control agent. The integration of these two components is known as deep learning. Using deep learning, we achieve two goals: (1) We eliminate the need for handcrafting a feature extraction process such as determining queue lengths, which is challenging and location-specific, and (2) we achieve better performance and faster training times compared to conventional tabular reinforcement learning approaches. We test our proposed controller against a tabular reinforcement learning agent, a reinforcement learning agent with a fully-connected Neural Network as a function approximator, and a state-of-practice, actuated traffic signal controller. [ABSTRACT FROM AUTHOR]

Perimeter control is to manipulate traffic flows in different regions through adjusting traffic signals at the border of the regions. Traditionally, the complete trips in a region are assumed to be dependent on the current accumulated vehicles in this region. This assumption is invalid because the vehicle needs time to complete its trip in a region. In order to solve this shortcoming, some recent studies have introduced a time-delay dynamical system to describe the dynamical behaviour of the accumulated vehicles in a region. However, in these studies, the perimeter control problem is formulated as a tracking problem with a given optimal reference point. In reality, such an optimal reference point is unavailable in advance. This paper will fill this gap through formulating perimeter control as an optimal control problem governed by a state-dependent time delay system. The control parametrization technique and an exact penalty method are introduced to solve such a challenging optimal control problem. Model predictive control is applied to obtain close-loop solution through solving a series of online optimal control problems. Some experiments are performed to demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

In order to mitigate the performance degradation of intermittent vehicular network caused by traffic mobility and sporadic connectivity issues, Vehicular Content Centric Network (VCCN) has been proposed to apply many technologies in Content Centric Network (CCN) into vehicular ad hoc networks. The open in-network caching strategy of CCN enables sharing and coordination of the cached data among multiple nodes as an efficient data access without relying on remote fetching. Nonetheless, few studies have considered the effective use of overall cache capacity with these cooperative nodes, especially the issues of cache duplication. Furthermore, most content replacement polices have ignored the needs of cooperative contents when making cache decisions. In this paper, we design a novel Cooperative Caching scheme by using Mobility Prediction and Consistent Hash for VCCN (called CCMPCH). Specifically, based on the observation that vehicles with the same trajectory are more likely to maintain stable communication links, we adopt Prediction by Partial Matching (PPM) to forecast each vehicle’s path and cluster the vehicles with similar future path, moving direction, and moving speed into one group. In each cluster, the consistent hash algorithm is used to allocate contents among cooperative nodes, thereby reducing unnecessary cache duplication while maintaining strong content availability. A popularity-based cache replacement policy is also developed to prioritize cooperative contents. We evaluate CCMPCH via extensive simulations, which demonstrates its higher cache hit ratio, shorter content access delay, and lower hop count compared to other state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

For enhancing the operation efficiency of the fully automatic operation (FAO) system in the urban rail transit (URT), this paper investigates the robust dynamic train regulation problem with respect to frequent disruptions and imperfect wireless transmissions. To better express the characteristic of the arriving passengers, the fuzzy passenger arrival rate is adopted to address the uncertainty of the passenger flow, and a T-S fuzzy state-space model is established to express the periodical movement of the train traffic in an URT loop line. By considering the possible packet dropout phenomenon during the wireless data transmissions, which may lead to the instability of the train traffic system and degrade the performance of the regulation strategy, a robust real-time train regulation strategy is developed based on the fuzzy predictive control theory, which distinguishes existing studies in that the uncertainty dropout rate is contemplated to address the complexity of the actual operation environment. A sufficient condition for the proposed control law is presented to guarantee that the nominal train schedule is recovered from disturbed situations with a given attenuation level by means of the $H_{\infty }$ performance index, and meanwhile the optimization of the upper bound on the objective function balancing the service efficiency and control cost is achieved. Numerical simulations based on the Beijing subway loop line 2 are presented for demonstration of the effectiveness of the introduced strategy. [ABSTRACT FROM AUTHOR]

This paper studies the distributed consensus protocol for the connected vehicle platoon with heterogeneous time-varying delays and switching topologies. A third-order dynamics model with powertrain inertial lag is proposed to characterize the node longitudinal dynamics of vehicles in platoon. A novel distributed adaptive consensus protocol considering the time-varying delays and the random switched inter-vehicular communication topologies is designed to stabilize the heterogeneous vehicle platoon in the presence of external disturbance. The delay-range-dependent approach is used to deal with the system heterogeneous time-varying delays by considering the characteristics of the heterogeneous platoon. Directed graphs are adopted to describe the accessible information flow among vehicles. The necessary and sufficient conditions for the unified closed-loop vehicle platoon system are derived by using matrix analysis and Lyapunov-Krasovskii approach. Numerical simulations demonstrate the proposed method is effective. [ABSTRACT FROM AUTHOR]

This paper analyzes trajectory observations from vehicles driving in platoon formation and they are equipped with Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) systems; aiming to quantify response delays. When a preceding vehicle induces a perturbation, the delay until the reaction of the following vehicle, often quoted as observable response time, can have negative implication to the traffic flow and other factors such as energy consumption, stability and safety. Quantifying such delays can help towards more realistic traffic simulation modeling. The analysis is performed based on empirical observations from three well-known experimental campaigns in the literature with data from ACC-driven and CACC-driven vehicle platoons. Three state-of-the-art techniques were implemented to provide quantitative results for the observed response times. The benefits and downsides of each technique are discussed as well. The results show that ACC systems do not exhibit a significant improvement compared to human drivers, yet, it can be concluded that the additional vehicle-to-vehicle communication incorporated in the CACC systems allows for a substantially higher traffic flow and possibly other benefits. [ABSTRACT FROM AUTHOR]

This paper aims at the problem of intersection traffic oversaturation and the spillover of queuing vehicles caused by random fluctuations of traffic flow. A self-organizing control model with road capacity constraints and taking minimum expected traffic flow delay as the goal is established to realize real-time adaptive control of traffic signal phase duration. Taking the local intersection and its adjacent intersections as the basic unit of traffic signal control, defining the constraints of road space in queuing state and the equations of traffic signal control basic unit are established. According to the equations, an online method for solving the optimal phase duration of traffic signal is established to efficiently allocate intersection space resources, by which a rule-based real-time traffic signal control model is established. Through the interaction between adjacent intersections, the traffic inflow rate of oversaturated phase is automatically restricted to alleviate the queuing spillover problem. Computer simulation shows that the control effect is significantly better than that of SCATS under the same boundary inflow conditions. Compared with the traditional centralized coordinated control, the rule-based real-time traffic signal control model can better adapt to the fluctuation of traffic flow and effectively relief the problem of vehicle queuing spillover caused by fluctuations of traffic flow. [ABSTRACT FROM AUTHOR]

With the rapid advancement of the Internet of Vehicles and artificial intelligence (AI) technologies, the cooperative intelligent transportation system (C-ITS) has drawn great attention in recent years. To provide an ultra-reliable, low-latency computation experience of C-ITS, computation offloading is deemed indispensable by working with edge-cloud servers. In this paper, we first investigate a distributed dynamic computation offloading model for multi-access edge computing (MEC) enabled C-ITS under a heterogeneous road network, in which the multiple and heterogeneous computing power sources cooperatively provide computation offloading services for vehicles. Considering the autonomous offloading manner of the vehicles, we formulate the task offloading and computing power allocation as a distributed Stackelberg game, where the MEC servers as the leader to allocate computing resources and manage local energy, and the vehicles as the followers to offload local computation task. Since the observable states in the game is incomplete, the problem of resolving the optimal strategies for each game player is modeled as a partially observable Markov decision process (POMDP) to maximize the long-term cumulative reward. Then we develop a computation offloading algorithm using Stackelberg game-based multi-agent deep deterministic policy gradient (SG-MADDPG), which uses a centralized training and decentralized execution method to learn the optimal computing power allocation and computation offloading policies. Finally, extensive simulations are carried out and show the rationality and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Providing transport users and operators with accurate forecasts on travel times is challenging due to a highly stochastic traffic environment. Public transport users are particularly sensitive to unexpected waiting times, which negatively affect their perception on the system’s reliability. In this paper we develop a robust model for real-time bus travel time prediction that departs from Gaussian assumptions by using Student-t errors. The proposed approach uses spatiotemporal characteristics from the route and previous bus trips to model short-term effects, and date/time variables and Gaussian processes for long-run forecasts. The model allows for flexible modeling of mean, variance and kurtosis spaces. We propose algorithms for Bayesian inference and for computing probabilistic forecast distributions. Experiments are performed using data from high-frequency buses in Stockholm, Sweden. Results show that Student-t models outperform Gaussian ones in terms of log-posterior predictive power to forecast bus delays at specific stops, which reveals the importance of accounting for predictive uncertainty in model selection. Estimated Student-t regressions capture typical temporal variability between within-day hours and different weekdays. Strong spatiotemporal effects are detected for incoming buses from immediately previous stops, which is in line with many recently developed models. We finally show how Bayesian inference naturally allows for predictive uncertainty quantification, e.g. by returning the predictive probability that the delay of an incoming bus exceeds a given threshold. [ABSTRACT FROM AUTHOR]

In advanced industrial applications, computational visual attention models (CVAMs) could predict visual attention very similarly to actual human attention allocation. This has been used as a very important component of technology in advanced driver assistance systems (ADAS). Given that the biological inspiration of the driving-related CVAMs could be obtained from skilled drivers in complex driving conditions, in which the driver’s attention is constantly directed at various salient and informative visual stimuli by alternating the eye fixations via saccades to drive safely, this paper proposes a saccade recommendation strategy to enhance the driving safety under urban road environment, particularly when the driver’s vision is often impaired by the visual crowding. The altered and directed saccades are collected and optimized by extracting four innate features from human dynamic vision. A neural network isdesigned to classify preferable saccades to reduce perceptual blindness due to visual crowding under urban scenes. A state-of-the-art CVAM is firstly adopted to localize the predicted eye fixation locations (EFLs) in driving video clips. Besides, human subjects’ gaze at the recommended EFLs is measured via an eye-tracker. The time delays between the predicted EFLs and drivers’ EFLs are analyzed under different driving conditions, followed by the time delays between the predicted EFLs and the driver’s hand control. The visually safe margin is then measured by mediating the driving speed and the total delay. Experimental results demonstrate that the recommended saccades can effectively reduce the amount of perceptual blindness, which is known to be of help to further improve road driving safety. [ABSTRACT FROM AUTHOR]

When traffic density on urban roads is relatively large, it is difficult for a large number of O-vehicles (O-vehicles) in front of emergency vehicles (E-vehicles) to give way in a short time, resulting in a delay of E-vehicles. This study aimed to design an automatic way-giving system based on vehicle-to-vehicle communication (V2V) in the internet of vehicles (IoV). Also, a control algorithm was proposed for the platoon to give way to the E-vehicle in the minimum time, the factors such as the traffic density, number of vehicles in the platoon, the initial state, and speed limit were considered in this paper. The algorithm firstly calculated the dynamic adjustment trajectory for the platoon to complete the lane change, the adjustment time, and the minimum distance between the E-vehicle and the platoon at the beginning of lane change, to guide the platoon while changing the lane. A simulator utility was developed to interface with the microscopic traffic simulation package Simulation of Urban Mobility (SUMO) by using the Python program. Lastly, a two-lane road segment was simulated to evaluate the effectiveness of the control algorithm of automatic way-giving system, and its performance is compared with conventional vehicle operation. The results demonstrated that the operating efficiency of E-vehicles can be improved by 12.65% and the operating efficiency of O-vehicles can only be decreased by 3.25% by the automatic way-giving system. Besides, the notification distance is a key factor in affecting the operating efficiency of E-vehicles and O-vehicles. [ABSTRACT FROM AUTHOR]

Employing regenerative braking in trains contributes to reducing the amount of energy used, especially when applied to commuter trains and to those used on very dense suburban networks. This paper presents a method to fine-tune the periodic timetable to improve the utilization of regenerative energy and to shave power peaks while maintaining the structure and robustness of the original timetable. First, a mixed-integer linear programming model based on the periodic event scheduling framework is proposed. A set of feasible timetables is determined and optimized with the aim of increasing synchronized acceleration and braking events at the same station, and maintaining the timetable robustness at the specified level. Next, a local search algorithm is developed to optimize the timetable such that the power peak value is minimized. The max-plus system model is adopted to estimate the delay propagation. Monte Carlo simulation is used to evaluate the utilization of regenerative energy and power peaks in random delayed circumstances. The proposed method was adopted to fine-tune the 2019 timetable for a sub-network of the Dutch railway. In the case of on- time scenarios, the optimized timetable increases the regenerative energy usage by almost 290% and decreases the 15-minute power peaks by 8.5%. In the case of delay scenarios, the optimized timetable outperforms the original timetable in terms of using regenerative energy and shaving power peaks. [ABSTRACT FROM AUTHOR]

Network-wide traffic signal control (NTSG) is one of the most important factors that impact the transportation network efficiency. Nevertheless, the NTSG problem still faces some issues impeding its development: (1) the traditional flow-based data has poor applicability to the evaluation of the network-wide traffic state, and the identification of the most delayed bottleneck intersections; (2) the existing control methods are difficult to capture the global optimal solution, owing to the lack of collaboration among multiple controllers. In this paper, to overcome the aforementioned challenges, we collect the trip-based data to evaluate the network-wide traffic state, and identify the most delayed trip-based bottlenecks as the controllers. Then, a bi-hierarchical game-theoretic (BHGT) method is proposed to solve the NTSG problem. At the lower layer, the NTSG problem is decomposed into several sub-problems of bottleneck control. A coalition game of intersections is formulated to solve the optimal signal control strategy of each single bottleneck. Furthermore, at the upper layer, a potential game is formulated to collaborate the multiple bottlenecks’ strategies solved from the lower-layer. After several iterations between two layers, the BHGT method will converge to a solution which minimizes the total travel delay of the network. Experimental results on a real-world dataset in Xuancheng City prove that the BHGT method outperforms other baseline methods in reducing the network-wide travel delay, both in low-traffic and high-traffic scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a new car-following (CF) model to capture the realistic behaviors of connected and automated vehicles (CAVs), whose communication topology (CT) among vehicles is characterized by graph theory in the V2V communication environment. By considering the heterogeneous time delays under the fixed and switching CTs, a generalized CF model is proposed. Based on the Lyapunov–Krasovskii method, a convergence analysis has been implemented for this new CF model with multiple time delays to obtain the convergence condition. Meanwhile, provides an estimate of the time delay bound. Finally, numerical experiments are performed under three typical fixed CTs (i.e., PF topology, BDLF topology, and TPLF topology) and the corresponding switching topology. Results support that the proposed CF model is capable of accurately reproducing the velocity, acceleration, position, and space headway profiles of CAVs traffic flow. [ABSTRACT FROM AUTHOR]

Railway system performances depend on effective dynamic scheduling and train operation control. The fast expansion and increasing complexity of high-speed railway (HSR) networks raise new challenges in maintaining the punctuality and efficiency in daily operations, in particular, in the event of disruption. This paper aims to review the state-of-art in dynamic traffic scheduling, trains operation control, and their integration for safer, more punctuate, efficient, and resilient HSRs, whose origins may trace back to their counterparts in traditional railways. First, the existing two-tier hierarchy of scheduling and control in HSR’s daily operation is introduced. At the higher layer of scheduling, a general model of dynamic train scheduling is discussed, followed by reviewing the scheduling methodologies. At the lower layer of train operation control, recent progress in tracking control of high-speed trains is discussed, with focus on the latest advances in single train control and cooperative control for multiple trains. Then, as the trend of technological progress for future HSRs, the recent development of integrating dynamic scheduling and operation control is introduced, which is made possible by efficient information exchanges among the scheduling subsystem and the train control subsystem. A three-layer integration framework and associated co-optimization methodologies are presented by introducing a co-optimization layer that bridges the separated scheduling and control layers. Finally, this review is concluded with discussions on open questions and possible directions for future research. [ABSTRACT FROM AUTHOR]

Bus priority signal control can reduce bus delay at intersections and improve bus operation efficiency. The existing research focus mainly on bus priority control methods at isolated intersections, as well as urban arterial corridors. Rarely existing works consider a network-level bus priority control. Treating regional-wide intersections can be challenging, given that more traffic directions and large multi-modal traffic volume should be considered. The conventional transit signal priority (TSP) research focus on the delay of buses and social vehicles and neglect the delay of pedestrians. To fill these gaps, this paper proposes a regional coordinated bus priority signal control (RCBPSC) method, which is a network-level bus priority control method considering pedestrian and passenger delays. This method is divided into two stages. The first stage is the regional coordinated signal control to obtain the basic signal timing schemes. The second stage is the bus priority signal control. At this stage, the timing schemes at each intersection will be adjusted according to the bus arrival time and the delays. In order to verify the effectiveness of this method, we choose four intersections in Chengdu to study. The results show that the total delay of the proposed method at the first stage (control case 1) can be reduced by 602s (2.3%) and 606s (3.7%) comparing with the conventional timing method in the peak and non-peak period. At the second stage, the proposed method can reduce more pedestrian delay than the conventional TSP method in both and. [ABSTRACT FROM AUTHOR]

In railway operation, train delays may occur due to various reasons (e.g. severe weather, infrastructure failure, human factors, etc.) and may change and spread rapidly to subsequent trains. Prediction of train delay changes and propagation is important to provide decision-making support for railway dispatchers to reschedule. In this paper, a multi-stage intelligent method is proposed for predicting the dynamic changes and propagation of train delays using random vector functional-link networks (RVFLNs) with improved transfer learning and ensemble learning. First, to improve the prediction performance of the single RVFLNs model, a novel improved Stacking ensemble learning RVFLNs (SRN) regression algorithm is proposed for prediction modeling of the delay changes. Then, to ensure the classification accuracy of unlabeled and class-imbalanced train data, an improved transfer learning RVFLNs (ITRN) classifier is proposed to decide whether the initial delay will lead to associated delays, with the aid of the improved SMOTE algorithm for processing imbalanced data. If associated delays are identified by the ITRN classifier, the proposed SRN algorithm will be further to predict the subsequent associated delays. By iterating the classification and prediction procedures, the propagation range and chain of train delays can be obtained. The effectiveness and practicability of the proposed method are verified by using two experiments against actual train graph data from different railway lines. [ABSTRACT FROM AUTHOR]

Fully automated vehicles deployed with high computational/perceptive capabilities will soon become a reality. Such capabilities enable the cooperation among vehicles and the realization of interacting autonomous driving systems. Edge computing has emerged to provide a plethora of computational services to reduce network latency. Applications at the edge that apply analytics on the sensory data are therefore indispensable for self-driving vehicles. We consider in this paper a network that interconnects vehicles to an edge server at a roadside unit. Each vehicle extracts multiple information by sampling multiple processes and sends them to the corresponding edge application. To make timely decisions, “fresh” information needs to be offloaded, processed, and delivered back to vehicles; in this context, we adopt a new metric called Age of Information (AoI) that has been lately used to measure the freshness of information. We seek to jointly schedule vehicles’ transmission of information and schedule information processing at the edge to minimize the AoI of all processes. We mathematically formulate the problem and prove its NP-Hardness. To overcome this hardness, we propose a logic-based Benders decomposition to divide the problem into a master and several subproblems. Then, we present an exact polynomial-time solution for the subproblems, a scalable heuristic for the master, and devise a valid yet efficient Benders cut. We implement the system simulation on the well-known traffic simulator SUMO and compare the decomposition with CPLEX branch-and-cut; Although the problem is highly intricate, our method finds a near-optimal solution (maximum deviation is 7% from optimal solution) with a speedup that reaches 95%. We study the system performance by varying different system parameters. [ABSTRACT FROM AUTHOR]

The continuing increase of the on-road private cars is contributing to a deterioration of the urban traffic system. Public transportation is widely used to tackle this issue due to its large ridership. In this paper, we propose a multi-bus dispatching strategy combined with the boarding and holding control (MBDBH) to improve bus utilization and further decrease the passenger excess delay. Dispatching adjustments and operation control are taken into account in the system. At the dispatching level, on the one hand, either a bus platoon or a single bus can be dispatched for each trip to provide adaptive bus capacity to match the highly-fluctuated stop demands, on the other hand, we adjust the bus dispatching time based on the existing timetable to minimize passenger excess waiting time to a large extent. Meanwhile, the operation level incorporates both holding strategy and boarding limit strategy to bring more flexible adjustments in improving bus service. Besides the efficiency, we also minimize the headway variation in order to maintain a high system reliability. The problem is formulated as a Mixed Integer Nonlinear Programming (MINP) problem, which is solved by the commercial solver Gurobi. With the computational complexity as a concern, we propose a distributed algorithm to implement dual decomposition based on the partial Lagrangian relaxation. Finally, numerical examples are investigated to illustrate the significant time reduction of distributed algorithm and the efficiency of our proposed strategy: The proposed MBDBH model can reduce roughly 50% and 30% of remaining passenger volumes when compared with the timetable-based fixed schedule and the optimized single-bus dispatching schedule, respectively. [ABSTRACT FROM AUTHOR]

The air ground integrated networks can leverage unmanned aerial vehicle (UAV) communications to tackle the ever-increasing and unbalanced traffic load in future communication systems. This paper investigates the UAV enabled traffic offloading problem in air ground integrated networks with mixed user traffic. The problem jointly maximizes the system load balance and the total UAV reward, which can be formulated under a two-layer network graph model. In the cellular network graph, the association between the delay-sensitive users and the access points (APs) as well as the association between the UAVs and the APs are formulated. In the UAV network graph, the association between the delay-insensitive users and the UAVs is formulated. By observing the coupling relationship of the decision variables, we decouple the problem into three sub-problems and solve the first two sub-problems with reduced complexity. Then, we devise a Deep Neural Network (DNN) empowered genetic algorithm to solve the last sub-problem. The DNN can be leveraged to filter out the non-optimal solutions in the initialization operator of the genetic algorithm for improving the efficiency. Performance comparisons are provided between the proposed traffic offloading scheme and the existing ones, which validate the advantages of the DNN empowered genetic algorithm regarding its convergence, accuracy, and robustness. [ABSTRACT FROM AUTHOR]

Connected Vehicles and Autonomous Vehicles (CAVs) provide various sources of vehicular related information to intersection infrastructure by integrating on-board sensors processing, wireless communication and other Vehicle-to-Infrastructure (V2I) technologies. Thus connected vehicle technologies can potentially remedy data collection limitations of existing urban intersection managements, enhancing the performances of intersection controls such as reducing vehicle delay, reducing vehicle number of stops and improving energy efficiency. This paper reviews optimization-based signal controls for different penetrations of connected vehicles and conventional vehicles environments, autonomous intersection management specific to completely 100% AVs road states, as well as signal-trajectory joint control for different adoptions of conventional vehicles, CVs and AVs mixture environments. Real time data processing, signal timing optimizations, vehicle trajectory motion planning and evaluation frameworks are summarized to highlight the advantages and limitations of respective intersection control paradigms. It is important to recognize that realistic scenarios in comparative assessments for proposed methods need to be achieved in future works. The effectiveness of different approaches is challenging to be compared without complete evaluation frameworks, and sensitivity analysis and hypothesis tests involving variety penetration rates and flow demands should be performed in order to test the stability of methods in different scenarios. [ABSTRACT FROM AUTHOR]

Load balance in vehicular ad hoc networks (VANETs) is a challenge in vehicle-to-vehicle computation offloading, due to stochastic requests of users, heterogeneous service capabilities and high mobility of vehicles, etc. This paper aims to fill this gap by formulating a problem for load balance in a VANET, with the objective of minimizing the maximum load under transmit power, storage capacity, per task completion time and energy consumption constraints. The formulated problem is proved to be NP-hard, then it is investigated by decomposing it into two subproblems, i.e., how to offload tasks for the case of fixed transmit power and how to adjust transmit power for the given offloading decision. For the first subproblem, an approximation algorithm is proposed by offloading the tasks in the vehicle with the maximum load to the vehicle with minimum load. Meanwhile, a deep reinforcement learning algorithm is proposed, in order to focus on the network dynamics. A coalition based algorithm, a distributed coalition based algorithm, as well as an incentive algorithm based on deep reinforcement learning, are proposed to maximize the total payoff for the selfishness of vehicles. For the second subproblem, an adjustment strategy for transmit power is customized to further reduce the computing load. The algorithms are evaluated on an integrated simulation platform with open street map, SUMO, NS-3 and dataset of Google cluster-usage traces. Simulation results show that, the proposed algorithms outperform three state-of-the-art works for most cases, in terms of the maximum load. The proposed distributed algorithm can significantly accelerate the proposed centralized algorithm with acceptable increase in maximum load. Besides, the load can be further reduced by the proposed adjustment strategy. [ABSTRACT FROM AUTHOR]

This paper aims to design speed advisory profile for connected vehicles (CVs) at an isolated signalized intersection in a mixed traffic flow of CVs and human driven vehicles (HDVs). To consider the uncertainty of HDVs, we propose the concept of $\alpha $ -percentile trajectory of HDVs. We calculate the advisory speed of a CV that follows a HDV, assuming that the HDV moves along its $\alpha $ -percentile trajectory. We use dynamic programming to solve the optimization problem. Simulation experiments show that, roughly speaking, the benefits of fuel consumption and travel time would increase with the increase of inflow rate and market penetration rate (MPR) of CVs. The maximum benefits would be achieved at the smallest value of $\alpha $ when the traffic flow is undersaturated. However, when the traffic flow is oversaturated, the maximum benefits would be achieved at intermediate value of $\alpha $ provided the MPR of CVs is not large. With the further increase of the MPR of CVs, the benefits would become not so sensitive to $\alpha $ in the range $\alpha \le 40$ %, but significantly decrease with the increase of $\alpha $ when $\alpha >40$ %. Finally, the impact of driver heterogeneity, inflow heterogeneity, tracking error and delay has been investigated. [ABSTRACT FROM AUTHOR]

Although considerable methods have been developed for the performance evaluation of signalized intersections using sampled vehicle trajectories, most of them aim at estimating a single parameter and cannot describe the entire arrival–departure process of the traffic flow. This significantly constrains the application of these methods for a comprehensive evaluation and efficient optimization of signalized intersections. In this paper, we propose a cumulative flow diagram (CFD) estimation and prediction method using sampled vehicle trajectories. It can be used to calculate multiple performance measures—traffic volume, queue length, average delay, and total delay—based on the estimated CFD for the current signal timing plan. Concurrently, it can be further employed for signal control optimization based on the predicted CFDs for candidate signal timing plans. The core idea of the proposed method is to generate the cumulative arrival curve based on the arrival characteristics of the sampled vehicles, and then fit the queue leaving points to obtain the cumulative departure curve. Thereby, given the current or any candidate signal timing plan, we can estimate or predict the CFDs by updating the sampled vehicle arrivals. The proposed method is evaluated using both simulation and empirical data. The simulation results yield that the average estimation error of the four performance measures is 10.3% under a real-world level penetration rate of 10%. Meanwhile, similar accuracies are achieved for the CFD prediction. The empirical results show that under a penetration rate of 8.6%, the estimation errors of the traffic volume and queue length are 2.7% and 3.3%, respectively. [ABSTRACT FROM AUTHOR]

In big cities, the metro lines usually face great pressure caused by huge passengers demand, especially during peak hours. When disruptions occur, passengers accumulate quickly at stations. It is of great importance for dispatchers to take passenger flow control into consideration for the traffic management to ensure passengers’ safety and to maintain their satisfaction. This paper proposes an integrated disruption management model, which incorporates train rescheduling and passenger flow control. In this model, the train services can be short-turned, cancelled and rerouted, while the number of passengers entering a station is managed by controlling the station gates with consideration of the capacities of platforms and trains. Moreover, the number of passengers arriving at a station is calculated according to the origin-destination matrices. The objectives are to recover the train operation to the original timetable as soon as possible and to minimize the waiting time of passengers outside the stations. With the interaction between train services, passengers and station gates, an iterative metaheuristic approach is proposed to solve the integrated disruption management problem. Based on the data of a Beijing metro line, numerical experiments are conducted to test the proposed algorithm. The results demonstrate the importance of integrated disruption management and the effectiveness of our solution method. [ABSTRACT FROM AUTHOR]

In this paper, we study the deadline-aware task data offloading in edge-cloud computing systems. The hard-deadline tasks strictly demand to be processed within their delay deadline, whereas the deadline can be relaxed for the soft-deadline tasks. Generally, edge computing aims to shorten the transmission delay between the remote cloud and the end-user, however, at the cost of limited computing capability. Therefore, it is challenging to decide where to offload the hard- and soft-deadline tasks based on the average delay and the service price set by the edge and cloud servers. Both edge and cloud servers aim to maximize their revenue by selling the computational resources at the optimal price. Interestingly, a Wardrop equilibrium is reached, considering that each task is considered independently to be offloaded to a suitable location. The numerical results demonstrate that the proposed price- and deadline-sensitive task offloading policy reaches the equilibrium and finds the optimal location for processing while maximizing the revenue of both edge and cloud servers. [ABSTRACT FROM AUTHOR]

In this paper, we propose a microservices and deep learning-based scheme, termed as Micro-Safe, for provisioning Safety-as-a-Service (Safe-aaS) in a 6G environment. A Safe-aaS infrastructure provides customized safety-related decisions dynamically to the registered end-users. As the decisions are time-sensitive in nature, the generation of these decisions should incur minimum latency and high accuracy. Further, scalability and extension of the coverage of the entire Safe-aaS platform are also necessary. Considering road transportation as the application scenario, we propose Safe-aaS, which is a microservices- and deep learning-based platform for provisioning ultra-low latency safety services to the end-users in a 6G scenario. We design the proposed solution in two stages. In the first stage, we develop the microservices-enabled application layer to improve the scalability and adaptability of the traditional Safe-aaS platform. Moreover, we apply the state space model to represent the decision parameters requested and the decision delivered to the end-users. During the second stage, we use deep learning models to improve the accuracy in the decisions delivered to the end-users. Additionally, we apply an assortment of activation functions to analyze and compare the accuracy of the decisions generated in the proposed scheme. Extensive simulation of our proposed scheme, Micro-Safe, demonstrates that latency is improved by 26.1 – 31.2%, energy consumption is reduced by 22.1 – 29.9%, throughput is increased by 26.1 – 31.7%, compared to the existing schemes. [ABSTRACT FROM AUTHOR]