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Adaptive cruise control (ACC) is the core building block of future full autonomous driving. Numerous recent research demonstrated that Autonomous Vehicles (AVs) adopting shorter headways generally increase road capacity and may relieve congestion at bottlenecks for moderate demand scenarios. However, with high demand scenarios, bottlenecks can still be activated causing capacity breakdown. Therefore, extra control measures as dynamic traffic control near bottlenecks is necessary. The challenge is harder on urban freeways with consecutive bottlenecks which affect each other. This paper aims to improve the performance of ACC systems in a high demand scenario. A multi‐bottleneck dynamic headway control strategy based on deep reinforcement learning (DRL) that adapts headways to optimize traffic flow and minimize delay is proposed. The controller dynamically assigns an optimal headway for each controlled section, based on state measurement representing the current traffic conditions. The case study is a freeway stretch with three consecutive bottlenecks which is then extended to include eight bottlenecks. Three different RL agent configurations are presented and compared. It is quantitatively demonstrated that the proposed control strategy improves traffic and enhances the system delay by up to 22.30%, and 18.87% compared to shortest headway setting for the three‐bottleneck and the eight‐bottleneck networks, respectively. [ABSTRACT FROM AUTHOR]

With the introduction of connected and automated vehicles (CAVs), the integrated control of traffic signals, lane assignments, and vehicle trajectories becomes feasible, offering notable benefits for enhancing intersection operations. However, during the prolonged transition to an entirely CAV environment, how to fully leverage the advantage of CAVs while considering the characteristics of human‐driven vehicles remains a huge challenge. To address this challenge, this paper proposes a joint optimization method for spatiotemporal resources at isolated intersections under mixed‐autonomy traffic conditions. Initially, the lane assignment optimization problem is modeled as a mixed integer linear program model to maximize the reserve capacity. Subsequently, the signal‐vehicle coupled control is formulated as a dynamic programming model with the objective of reducing vehicle travel time. Additionally, criteria are established to assess the need for re‐optimizing lane assignments. Simulations validate the superiority of the proposed control method over adaptive control in terms of traffic efficiency and intersection capacity amid substantial traffic demand fluctuations. Sensitivity analyses reveal that the proposed control method can yield higher benefits under medium traffic demand levels. Furthermore, the proposed algorithm exhibits no significant sensitivity to the CAV market adoption rate, suggesting its applicability throughout the CAV adoption process. [ABSTRACT FROM AUTHOR]

Adaptive signal control technology (ASCT) is an advanced traffic control system that optimizes signal timing based on real-time traffic demand. ASCT can potentially improve the operation and safety of intersections by establishing dynamic coordination among signalized intersections in real-time. This study used a binary Bayesian logit model with random effects, which accounts for unobserved heterogeneity, to explore the impacts of ASCT on the severity of intersection-related crashes in Florida. Two distinct ASCT types (Type I and II) were analyzed to assess their impacts on crash severity. The analysis revealed that ASCT reduced the likelihood of a fatal plus injury (FI) crash by 14.6%. This reduction was significant at a 90% Bayesian credible interval (BCI). Also, each ASCT type (Type I and II) showed a potential reduction in the likelihood of a FI crash, although the decrease was not significant at a 90% BCI. Other factors such as driving under the influence, angle crashes, dark lighting conditions, posted speed limit, and median along a minor approach, were associated with a higher risk of a FI crash. Transportation agencies could use the study results to justify the deployment and expansion of ASCT at signalized intersections with a high frequency of severe crashes. [ABSTRACT FROM AUTHOR]

The optimization and control of traffic signals is very important for logistics transportation. It not only improves the operational efficiency and safety of road traffic, but also conforms to the direction of the intelligent, green, and sustainable development of modern cities. In order to improve the optimization effect of traffic signal control, this paper proposes a traffic signal optimization method based on deep reinforcement learning and Simulation of Urban Mobility (SUMO) software for urban traffic scenarios. The intersection training scenario was established using SUMO micro traffic simulation software, and the maximum vehicle queue length and vehicle queue time were selected as performance evaluation indicators. In order to be more relevant to the real environment, the experiment uses Weibull distribution to simulate vehicle generation. Since deep reinforcement learning takes into account both perceptual and decision-making capabilities, this study proposes a traffic signal optimization control model based on the deep reinforcement learning Deep Q Network (DQN) algorithm by considering the realism and complexity of traffic intersections, and first uses the DQN algorithm to train the model in a training scenario. After that, the G-DQN (Grouping-DQN) algorithm is proposed to address the problems that the definition of states in existing studies cannot accurately represent the traffic states and the slow convergence of neural networks. Finally, the performance of the G-DQN algorithm model was compared with the original DQN algorithm model and Advantage Actor-Critic (A2C) algorithm model. The experimental results show that the improved algorithm increased the main indicators in all aspects. [ABSTRACT FROM AUTHOR]

Cooperative adaptive cruise control (CACC) realizes efficient, intelligent control of vehicle acceleration, deceleration, and steering, through inter‐vehicle communication and cooperative control. However, the close combination of the platoon makes it difficult for other vehicles to cut‐in, which can lead to severe traffic jams on certain sections of the road. The control effect of the CACC depends on the platoon penetration rate, which is the percentage of connected and autonomous vehicles (CAVs) in the total number of platoon members. There is no quantitative control method for different penetration rates, and it is difficult to quantify the impact of CACC vehicles on traffic. Therefore, this paper proposes an innovative CACC control method based on deep reinforcement learning (DRL). First, the altruism control and the quantitative control of the car‐following strategy are realized by the virtual car‐following distance method to reduce the exclusivity of the CACC platoon or improve the road utilization efficiency. Second, a more appropriate platoon reward function and collision avoidance method are proposed. Finally, the Car Learning to Act (CARLA) simulator is used. The obtained results confirm that the CACC control of CAVs based on DRL can absorb speed oscillation and improve fuel economy. [ABSTRACT FROM AUTHOR]

The paper proposes a traffic responsive control framework based on a Model Predictive Control (MPC) approach. The framework focuses on a centralized method, which can simultaneously compute the network decision variables (i.e., the green timings at each junction and the offset of the traffic light plans of the network). Furthermore, the framework is based on a hybrid traffic flow model operating as a prediction model and plant model in the control procedure. The hybrid traffic flow model combines two sub-models: an aggregate model (i.e., the Cell Transmission Model; CTM) and a disaggregate model (i.e., the Cellular Automata model; CA), using a transition cell to connect them. The whole framework is tested on a signalized arterial, performing several analyses to calibrate the MPC strategy and evaluate the traffic control approach using fixed and adaptive control strategies. All analyses are made in terms of total time spent, network total delay, queue lengths and degree of saturation. [ABSTRACT FROM AUTHOR]

The emerging technologies of connected and automated vehicles (CAVs) and deep reinforcement learning (DRL) provide innovative methods and have a great potential for developing new solutions to improve the efficiency of several intersection systems. Based on the multisource data collected from the transportation environments, CAVs with the cooperative adaptive cruise control (CACC) system could merge into platoons and traverse the intersection quickly and smoothly. Meanwhile, the traffic information about the CAVs enables intelligent traffic signal controls with the help of DRL technologies. This research investigates the performance of a state-shared multiagent deep reinforcement learning (MADRL) controlled signal corridor with platooning-based CAVs. A corridor with seven intersections from the Ingolstadt Traffic Scenario (InTAS) in Germany is selected as a case study. The state information is shared between neighboring intersections to overcome the partial information observation of the decentralized agents in the MADRL framework. A platooning framework with specific CACC systems for the leading and following vehicles is proposed. Results indicate that the state-shared MADRL with CAV platoons could significantly decrease the total waiting time, average queue length, and total CO2 emission of the corridor by 80%, 73%, and 54%, respectively, which could be beneficial in further improving the intersection efficiency, designing future intersections, and cooperating signals and CAVs platoons. [ABSTRACT FROM AUTHOR]

Automatic control systems are a new advanced system uses computers and hardware controllers to act as a driving system. Increasing in cars numbers while we have the same infrastructure caused a large traffic problem so we need automatic adaptive controllers to control the open and close tasks of traffic light system. Fuzzy Logic is a conversation from classical logic with values of 0 for false and 1 for true to a range of values between 0 and 1. This study aims to create an adaptive traffic control system using fuzzy logic and image processing, we have a camera on each traffic light to calculate the number of cars there and then we feed the number of cars to a fuzzy logic system to decide which traffic light must become on. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning has found great successes in addressing many challenging control problems; however, real-world implementations are still scarce if not non-existent. This is primarily due to three main challenges pertaining to the implementation, the stability, the optimal settings, and a lack of knowledge on methods that can be applied to field settings. This research attempts to address these issues with an adaptive simulation-based control framework proposed specifically for the training and evaluation. The control framework has implemented simulation models to conduct an extensive sensitivity analysis on the effects of key design variables, including rewarding schemes, state spaces, and model training parameters. The feasibility of transfer learning as a training strategy is also studied on scenarios with different layouts and different driver behavior models. Complex scenarios are also evaluated and used as test cases, including multiphase ring-and-barrier control and multi-intersection control. The research has contributed a significant amount of evidence on several critical design and implementation-related questions such as input representation, data (technology) requirements, training methods, and model transferability. [ABSTRACT FROM AUTHOR]

Traffic accidents are a leading cause of premature death for citizens, with millions of injuries and fatalities occurring annually. Due to the fact that a large proportion of accidents are caused by red light running, reduction of the frequency of red light running (RLR) has been extensively researched in recent years. However, most of the previous studies have focused on reducing RLR frequency through driver education or warning sign design, with little attention paid to the relationship between RLR behavior and traffic signal control. Considering RLR is significantly affected by the number of vehicles arriving during yellow, it is possible to identify RLR behaviors in advance by analyzing data on yellow-arriving vehicles. Meanwhile, based on the strong correlation between yellow arriving and RLR frequency, it is possible to reduce RLR by traffic signal control. In this paper, we propose a quantitative model of correlation between RLR frequency and yellow light arrival based on high-resolution traffic and signal event data from Twin Cities, Minnesota. On this basis, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is implemented to find trade-offs between minimizing the RLR frequency and the traffic delay. A case study of a 6-intersection arterial road reveals that in unsaturation, saturation, and supersaturation flow, our approach can converge to a Pareto optimal front in 30–50 iterations, which shows that is possible to simultaneously reduce RLR frequency and enhance traffic efficiency safety, which is conducive to ensuring the life safety of traffic participants. [ABSTRACT FROM AUTHOR]

To contend traffic congestion on urban networks, existing studies have made great efforts to develop traffic-responsive signal timing algorithms in the last decade. More recently, as an alternative to conventional model-based algorithms, machine learning-based methods have been tested on traffic light timing problems and show promising potentials. However, many researchers and practitioners still questioned the feasibility and applicability of adopting machine learning techniques in the ATSC domain. One of the reasons is that these methods assumed flawless detectors and heavily relied on simulators for training and evaluations. To address such a critical concern, this article customizes a Deep Q-learning Learning (DQL) method to optimize traffic signal timings at urban intersections, where the partial observations from identity-based detectors are inputs, and the green splits are outputs. A simulation-free data-driven prediction model is also developed to train the DQL with reduced computational time. Then the machine learning-based methods are evaluated on a real-world case with Automatic Number-Plate Recognition (ANPR) data. Experiments show the proposed data-driven model can predict the traffic state in limited computational time, and the DQL algorithm is 3.9% better than the field experiment performance from the adaptive control system, SCOOT, and 22% better than the time-of-day plan by SYNCHRO. The results indicate the DQL methods can only yield marginal improvement with restrictive input and output settings in congested traffic flow in comparison to the conventional adaptive method. [ABSTRACT FROM AUTHOR]

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Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently been developed using advanced sensors such as cameras, radars, and LiDARs. Among these sensors, cameras can provide a cost-effective way to determine the number, location, type, and speed of the vehicles for better-informed decision-making at traffic intersections. In this research, a new approach for accurately determining vehicle locations near traffic intersections using a single camera is presented. For that purpose, a well-known object detection algorithm called YOLO is used to determine vehicle locations in video images captured by a traffic camera. YOLO draws a bounding box around each detected vehicle, and the vehicle location in the image coordinates is converted to the world coordinates using camera calibration data. During this process, a significant error between the center of a vehicle's bounding box and the real center of the vehicle in the world coordinates is generated due to the angled view of the vehicles by a camera installed on a traffic light pole. As a means of mitigating this vehicle localization error, two different types of regression models are trained and applied to the centers of the bounding boxes of the camera-detected vehicles. The accuracy of the proposed approach is validated using both static camera images and live-streamed traffic video. Based on the improved vehicle localization, it is expected that more accurate traffic signal control can be made to improve the overall network-wide energy efficiency and traffic flow at traffic intersections. [ABSTRACT FROM AUTHOR]

The object of research is the processes of automatic optimal passing of one’s own ship with many dangerous targets, including maneuvering ones, by the method of constructing the area of permissible passing parameters in the on-board computer. According to the European Maritime Safety Agency (EMSA), the largest number of ship accidents in 2014–2019 occurred due to collision (32 %). On modern ships, for observation and passing with targets, ARLM (automatic radar laying means) is used, which allows to automate manual operations, and the built-in function «Playing the maneuver» provides the navigator with a convenient graphic interface for solving passing problems. At the same time, ARLM is an automated system that assumes the presence of an operator in the control circuit. The presence of a person in the control circuit is related to the «human factor», which is a prerequisite for the occurrence of various types of accidents, including ship collisions. The most effective means of reducing the influence of the «human factor» on control processes is the introduction of automatic control modules in automated systems. The paper develops a method for the passing module, which allows automatic and optimal passing with many targets, including maneuvering ones. The number of targets for passing is not limited by the method, but is limited only by the capabilities of the ARLM to track the targets. The obtained results are explained by the fact that at each step of the on-board computer, a region of permissible passing parameters is constructed for all purposes, passing parameters that optimize a given optimality criterion are selected from the constructed region, the selected parameters are used as software in the control law. The developed method can be used on ships, subject to integration into the existing automated system of an on-board computer with an open architecture, to increase the capabilities of automatic traffic control, in this case, the possibility of automatic optimal passing with many objectives, including maneuvering. [ABSTRACT FROM AUTHOR]

As one key basis of traffic flow theory, the fundamental diagram (FD) has wide application in traffic management and control. However, the FD of mixed human-driven vehicles (HVs) and cooperative adaptive cruise control (CACC) vehicles remains unclear. The deterministic and stochastic FD of mixed traffic was obtained. First, the longitudinal control model (LCM) and connected LCM (CLCM) were selected as the car-following model of HVs and CACCs. Next, in deterministic simulation, the mathematical expression of mixed-traffic FD was conducted considering the CACC penetration rate and platooning intensity. Then, the stochastic distribution of perception and response time calibrated by the vehicle trajectory data set NGSIM was introduced to derive the stochastic FD. After that, in stochastic simulation, taking the variance and computation effort into account, the optimal number of simulated vehicles to obtain steady-state FD was obtained. The impact of CACC penetration rates and platooning intensity was also explored. It revealed that the optimal number of simulated vehicles is 400. CACC can improve the capacity and critical density of highways. In mixed traffic, due to the large variance of HV driver response time, scattering remains unchanged regardless of CACC penetration when there are enough vehicles. Pure CACC traffic has minimum scattering. More importantly, the proposed framework is applicable to other CACC, adaptive cruise control (ACC), and HV car-following models, including further experimental models. [ABSTRACT FROM AUTHOR]

String stability is important for understanding traffic flow dynamics, while its analytical study for mixed traffic is deficient. We focus on an analytical framework on string stability of mixed traffic consisting of cooperative adaptive cruise control (CACC), adaptive cruise control (ACC), and human vehicles. The analytical framework was conducted based on generalized car-following model formulations of the three vehicular types. Then string stability criterions of one-class traffic flow, mixed traffic flow, and local CACC/ACC platoons were derived. Taking into account the mixed flow with partial degenerations of CACC and three concrete car-following models, an example application was studied. The example application analyzed the string stability from the perspectives of the homogeneous flow, the mixed traffic flow, and the local CACC/ACC platoon, respectively. The example application also studied balance between stability and capacity for the mixed traffic with CACC vehicles. Results show the usefulness of the proposed analytical framework, in term of not only analyzing string stability but also providing suggestions for dynamic regulations of CACC/ACC management strategy to balance string stability and traffic capacity for the mixed CACC-human flow. [ABSTRACT FROM AUTHOR]

Cities need to develop, people's lives need to be improved, and the living environment needs to be enhanced. With the strengthening of the national policy of paying attention to people's livelihood, the speed of development, the increase of the number of cars, and the major demolition and construction of streets are in full swing everywhere. However, traffic congestion has become a serious social problem, which greatly affects economic development and people's daily life. Therefore, the study of signal coordination control of urban traffic arteries has become an urgent problem for all countries. In this paper, after a brief introduction of the basic concepts and related technologies of traffic control systems, we focus on the arterial coordinated control system. The adaptive control model studied in this paper reveals the vehicle arrival dynamics from a microscopic perspective, which helps to improve the adaptive control effect and alleviate urban intersection congestion. The use of traffic information acquisition and control systems can promote the application of vehicle networking technology, while moreover helping to provide important model simulations for future street simulation renovation. [ABSTRACT FROM AUTHOR]

In the environment of intelligent connected vehicles, the mixed traffic flow consisting of human-driven vehicles (HDVs), connected and automated vehicles (CAVs), and their platoons will coexist for a long time. Exploring and discovering the operational rules of mixed traffic flow is the foundation for its management and control. This study delves into the distinct characteristics of various vehicles within a mixed traffic environment, particularly focusing on HDVs, CAVs, and CAV platoons. Drawing on the psychological predisposition of HDVs to maintain a prudent following distance, we introduce a discrete motion safety distance model to establish the longitudinal movement rule for HDVs. Furthermore, a longitudinal movement rule for CAVs is established based on their ability to keep a constant headway using Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), alongside meeting specific speed and spacing criteria for platoon movement. On this basis, the paper proposes a cellular automaton model for single-lane mixed traffic flow that accounts for CAV platoon length and the decision-time disparity between HDVs and CAVs. The simulation results show that an increased proportion of CAVs can enhance the traffic capacity, elevate average speed, extend average platoon length, and reduce the congestion ratio and CACC degradation rate. Notably, in scenarios where the CAV proportion p > 0.7, the flow-density graph shows a trapezoidal shape, with the majority of CAVs in the traffic flow synchronizing their movements in CACC mode. Even when vehicular density surpasses the optimum threshold, the traffic flow maintains high-speed operation until reaching a critical congestion point, whereupon velocities precipitously decline. Through sensitivity analysis aimed at understanding the impact of maximum platoon length, it has been observed that, for CAV proportions below 0.7, variations in maximum platoon length negligibly impact traffic capacity, velocity, CACC degradation, and congestion levels. Only in scenarios dominated by CAVs does extending platoon length restrictions significantly enhance performance. However, as the maximum platoon length increases, the gains in traffic capacity, speed, CACC degradation rate, and congestion ratio diminish. When the platoon length of CACC exceeds a certain threshold, there will not be a significant improvement in traffic capacity or speed. [ABSTRACT FROM AUTHOR]

An adaptive traffic-control system (ATCS) is an advanced traffic-signal control system that updates traffic signal timing in an automated way to stabilise and smooth traffic. It is important to examine updated cost and benefits data for the entire life cycle of ATCSs to develop benefit/cost analysis frameworks. The application of a high-level benefit/cost analysis framework is presented in this paper. The framework was first tested on ramp metering systems in a previous study by the authors and its application in ATCSs produced promising results. The framework is composed of four main components: life-cycle cost analysis, benefits achieved through travel time savings, benefits achieved through reductions in energy consumption and benefits achieved through reductions in the number of accidents. A theoretical case study was used to demonstrate the application of the framework. A sensitivity analysis was conducted to determine the effects of higher traffic demand on the benefits achieved. The results showed that ATCSs can be feasible in relieving congestion at junctions. It is anticipated that the proposed framework can improve the higher-level decision-making procedures of transportation agencies concerning the deployment of ATCSs. [ABSTRACT FROM AUTHOR]

Intelligent traffic light control is one of the modern approaches to solve traffic congestion, where reinforcement learning is a widely used method. Conventionally, reinforcement learning is used to determine whether to change the current phase (or choose a traffic phase) after each small interval. One major drawback of these approaches is that it makes the current traffic light phase duration uncertain before the current phase terminates. Directly determining the duration of the traffic light phase can effectively avoid this shortcoming. An adaptive traffic light timing system is proposed in this paper which can directly control the phase duration. In the proposed system, the Q-learning algorithm is employed and the action space is defined as all possible phase durations. In addition, the reward function is redefined to guide the agent to balance more traffic metrics, and the state is redefined to reduce the state space. Finally, the proposed system is evaluated by equal, unequal, and complex traffic scenarios. Results show that the proposed system has a better performance compared with other methods in controlling traffic lights, even on complex traffic scenarios. [ABSTRACT FROM AUTHOR]

Adaptive control of traffic engineering (TE) based on 5G network function virtualization (NFV) authorizes the efficient and dynamic network resource allocation, whose utilization is increasingly wide and will become more widespread. In this paper, we first devise an adaptive control scheme for data-driven traffic migration engineering (TME) on the 5G virtual network. The proposed TME technology focuses on a 5G enhancing mobile broadband (eMBB) network application scenario and takes the network operating expenditure (OPEX) as the main research target. Firstly, we predict the network traffic of the virtual network through the constructed traffic predicted mathematical model. Then, based on the triangle inequality violation (TIV) theorem, some local network traffic is adaptively migrated when the predicted link traffic exceeds the peak rate. Consequently, the migrations of logical links in the virtual network layer are completed. Finally, our experiments show that the proposed protocol can effectively improve the key performance indicators (KPIs) of the reconfigured network, such as throughput, delay and energy consumption. Furthermore, the Fridman and Holm statistical hypothesis tests are also used to analyze the simulation data, which proves that the proposed approximate TME algorithm has statistical significance. [ABSTRACT FROM AUTHOR]

: In this paper, we presented a novel adaptive traffic control strategy for urban signalized interchanges based on traditional sensors and emerging connected and automated vehicle (CAV) technologies. The signalized interchanges in this paper refer to those controlled by one traffic signal controller while vehicles must cross two or more stop lines to cross. Examples included diamond interchanges (DIs), diverging diamond interchanges (DDIs), and single-point urban interchanges (SPUIs). With the expansion of urban areas, such interchanges are increasingly common and often become mobility bottlenecks. The traffic signal optimization in this paper was derived from the cumulative vehicle counting curves (A-D curves). An assumption of the A-D curves for control delay estimation is that vehicles are no longer restricted once they cross the stop line. However, at a signalized interchange, vehicles may stop multiple times before completely cross. This situation cannot be effectively reflected with the standard cumulative vehicle counting curves. The phasing sequence is also challenging due to the limited space within the interchange. To address these issues, we proposed a new adaptive traffic control framework based on a linear traffic control model, referred to as a phase-time network. The objective of this framework was to dynamically fine-tune control splits and optimize the phasing sequence according to the vehicle arrival counts (from infrastructure sensors) and turning movement ratios (from CAV technologies). The optimization problem was first formulated into a mixed-integer linear programming (MILP) formulation and validated through offline examples. Then, an online search algorithm was presented and evaluated within a microscopic traffic simulation environment. The proposed MILP formulation and algorithm were assessed in both offline and online experiments. The results of all numerical experiments validated the formulation and show promise for real-world implementations. [ABSTRACT FROM AUTHOR]

This paper evaluated the congestion reduction benefits over a 6-year period from the implementation of an adaptive traffic control system (ATCS) in a congested area of Manhattan, New York, bounded by 2nd and 6th Avenues and 42nd and 57th Streets—known as the Midtown-in-Motion (MIM) area. A methodology using taxi Global Positioning System (GPS) sensors was used to measure traffic speed. Traffic speeds were calculated for May weekdays before the ATCS was installed (May 2011) and for each subsequent year (May 2012 to May 2016). Within 1 to 2 years after the implementation of the ATCS, the area’s traffic speed increased on avenues and cross streets. This gain, however, could not be sustained in subsequent years because of intervening changes in key factors impacting traffic congestion. These factors included reduction in roadway space/capacity for motor vehicles, lack of effective traffic enforcement to maintain/protect available roadway capacity, increased vehicle miles traveled (VMT) from transportation network company vehicles, and increasing volume of bicycle trips sharing street space with vehicles/pedestrians. This paper’s two key findings are, first, traffic speed gains initially seen in the MIM area after 1 year of ATCS implementation could not be sustained because intervening external factors reduced capacity and increased VMT. However, during the same analysis period, the rest of the Midtown Core area (without ATCS deployment) experienced a greater speed loss than the MIM area, indicating the effectiveness of ATCS deployment in minimizing losses in traffic speed. Second, to protect street network capacity and to minimize VMT growth, midtown Manhattan requires adopting proactive, collaborative, and coordinated strategies by the three key agencies involved with traffic management in New York City (NYC): the NYC Department of Transportation (traffic control system technology), the NYC Police Department (traffic enforcement), and the Mayor’s office (traffic demand mitigation policies). [ABSTRACT FROM AUTHOR]

In this study, based on the comprehensive analysis of asymptotic stability and damping characteristics for the Intelligent Driver Model (IDM) car-following strategy, we propose a self-adaptive IDM (SA-IDM) car-following strategy, which is specifically designed for adaptive cruise control (ACC) vehicles. Using a coefficient of self-adaption, SA-IDM strategy can adaptively adjust the acceleration control function of following vehicle in real time given the velocity of preceding vehicle and time headway, in order to guarantee the asymptotically stable and overdamped condition for the vehicle platoon under any circumstances. The results of simulation experiment for a vehicle platoon with NGSIM dataset indicate that, vehicles can drive more stably and smoothly under traffic perturbation using the proposed SA-IDM strategy than the original IDM strategy, as well as the existing IDMM and E-IDM strategies. Meanwhile, SA-IDM strategy helps to improve the driving safety of vehicle platoon considerably. Overall, SA-IDM strategy provides a promising solution with higher stability, reliability, and safety for the longitudinal car-following control in the roadway traffic. • A self-adaptive IDM car-following strategy considering asymptotic stability and damping characteristics is proposed. • A coefficient of self-adaption in SA-IDM strategy guarantees vehicle's asymptotically stable and overdamped condition. • Vehicles drive more stably and smoothly using SA-IDM strategy under traffic perturbation. • SA-IDM strategy improves the driving safety of vehicle platoon considerably under traffic perturbation. [ABSTRACT FROM AUTHOR]

Data-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the traffic dynamics of the network regions are first transformed into MFAPC data models, and then, the derived MFAPC data models instead of mathematical traffic models serve as the prediction models in the distributed control design. The formulated control problem is finally solved with an alternating direction method of multipliers (ADMM)-based approach. The simulation results for the traffic network of Linfen, Shanxi, China, show the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Pragmatic design method of direct model reference adaptive control (MRAC) is developed for a class of uncertain systems, where various input channels might have different delays and taking into account the effect of control saturation. To overcome the difficulty of directly predict the plant state and cope with different kinds of input delays, the proposed control design relies on implicit time‐delay prediction by using auxiliary linear Smith‐like dynamic units with adjustable gains. The design is unified to different time delay categories, that is, constant, time‐varying, or dependent on the current plant state. Within the context of the proposed approach, various adaptive control feedback strategies are designed for problems of continuous and sample‐and‐hold control implementations. Simulation results of a perimeter control for two interacting aggregate urban traffic regions are presented. [ABSTRACT FROM AUTHOR]

This paper is intended to improve the performance of signalized intersections, one of the most important systems of traffic control explained within the scope of smart transportation systems. These structures, which have the main role in ensuring the order and flow of traffic, are alternative systems depending on the different methods and techniques used. Determining the operation principles of these systems requires an extremely careful and planned study, considering their important role. Performance outputs obtained from the queue analyses made in previous studies formed the input of this study. The most important techniques are used in the effective control of intersections, such as signal timing: in particular, the use of effective green time and order of the transitions between phases. In this research, a traffic-sensitive signalized intersection control system was designed with the suggested methods against these two problems. The sample intersections were selected from three cities with the highest population density as the case study area. In the analysis of the performance of the connection arms of the selected intersections, flow intensity data were taken into consideration, as well as the arrival and service rates. Based on this, the outputs of the two proposed models regarding the use of phase transitions and effective green durations were compared with two other adaptive control systems and their positive results were shared. The results showed that signalized intersections, operating with a well-planned and correctly chosen technique, better regulate density and queuing. [ABSTRACT FROM AUTHOR]

In this paper, to fully utilize the urban traffic flow characteristics of similarity and repeatability without using a mathematical traffic model, a data‐driven urban traffic control strategy based on model‐free adaptive iterative learning control (MFAILC) scheme is put forward. Firstly, by dynamically linearizing the urban traffic dynamics along the iteration axis, the traffic network system is transformed into a MFAILC data model with the help of repetitive pattern of urban traffic flow. Then, the traffic controller is designed based on the derived MFAILC data model only using the I/O data of the traffic network. Finally, a traffic data compensation method is proposed to deal with data dropout problem. Simulation study verifies the feasibility and effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

With the emerging application of low-level driving automation technology, heterogeneous traffic flow mixed with human-driven vehicles and low-level autonomous vehicles is dawning. In this context, it is imperative to investigate its effect on mixed traffic flow. As a key component for adaptive cruise control (ACC) which is a practical low-level application of driving automation, the time gap policy determines the dynamic of ACC-equipped vehicles and plays a crucial role in traffic flow stability and efficiency. There are two main time gap policies used for ACC at present, namely, constant time gap (CTG) policy and variable time gap (VTG) policy. In this study, we carried out a detailed comparison between these time gap policies to investigate their potential effect on mixed traffic flow, where the analytical- and simulation-based approaches are both considered. Analytical results show that VTG policy is superior to CTG policy in stabilizing the mixed traffic flow. In addition, numerical simulations are also conducted and simulation results further support the analytical results. As for throughput, there is no difference between CTG policy and VTG policy in analytical progress when the same time gap is set at the equilibrium. However, simulation results based on an on-ramp scenario show that the throughput of mixed traffic flow with VTG policy is slightly higher than that of CTG policy. Meanwhile, the scatter of mixed traffic flow with VTG policy in the flow-density diagram gradually clusters in the middle range of density (i.e., 20–40 veh/km) with the increase of the penetration rates of ACC vehicles, where the traffic flow operates more efficiently. These results indicate that VTG policy is better than CTG policy when designing controllers for ACC in the context of traffic flow operation and control. [ABSTRACT FROM AUTHOR]

Fluctuations in traffic flows at isolated intersections throughout the day have a negative impact on the control effects of certain traffic signal control methods. To solve this problem, adaptive signal control of isolated intersections based on sequential signal-stage optimisation is proposed under a multi-stage optimal decision framework to reduce vehicle delays and increase traffic efficiency. In this paper, to maximise the traffic capacity of an intersection during a signal stage, a primary and secondary control mechanism is designed in which the signal-stage phase (SSP) is composed of primary and secondary compatible phases. The SSP can be dynamically obtained and its maximum duration can be determined in a predictable manner. The terminal conditions are also designed to end the current SSP in a timely manner to increase the traffic throughput of an isolated intersection during a signal cycle. For easier understanding, the adaptive signal control algorithm is elaborated in detail. Numerous simulation experiments were performed in the Vissim 5.4 software program and the simulation results demonstrated that, compared with fixed timing control, segmented timing control and fully actuated control, the proposed control method can clearly reduce the average vehicle delay and the impact on average vehicle stops is acceptable. [ABSTRACT FROM AUTHOR]

With recent development of mobile Internet technology and connected vehicle technology, vehicle trajectory data are readily available and exhibit great potential to be used as an alternative data source for urban traffic signal control. In this study, a Queue Intensity Adaptive (QIA) algorithm is proposed, using vehicle trajectory data as the only input to perform adaptive signal control. First, a Kalman filter-based method is employed to estimate real-time queue state with vehicle trajectories. Then, based on queue intensity that quantifies queuing pressure, five control situations are defined, and different min-max optimization models are designed correspondingly. Last, a situation-aware signal control optimization procedure is developed to adapt intersection's queue intensity. QIA algorithm optimizes phase sequence and green time simultaneously. One case study was conducted at a field intersection in Shenzhen, China. The results show that provided with 7.4% penetrated vehicle trajectories, QIA algorithm effectively prevented queue spillback by constraining temporal percentage of queue spillback under 2.4%. The performance of QIA was also compared with the algorithm in Synchro and Max Pressure (MP) method. It was found that compared with Synchro, the extreme queue intensity, temporal percentage of queue spillback, delay, and stops were decreased by 54.7%, 97%, 22.3%, and 45.1%, respectively, and compared with MP the above four indices were decreased by 16%, 61.5%, −1.8%, and 49.4%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, two new adaptive traffic signal control algorithms are proposed based on data from probe vehicles to realize the coordinated signal control of arterial roads. One is an iterative signal control algorithm, and the other is an optimized signal control algorithm. The proportion of vehicles in the nonstop group is selected as the indicator of the traffic state. The value for this indicator can be accurately estimated by data from probe vehicles. Our goal is to ease traffic congestion and enhance the capacities of traffic networks. Compared with the Webster fixed-time signal control algorithm, these two new adaptive signal control algorithms are evaluated on a microscopic simulation platform. Simulation results show that the average travel time is reduced by 32% under the iterative signal control algorithm and by 23% under the optimized signal control algorithm, and the average delay times are reduced by 36% and 35%, respectively. In the meantime, the average number of stops under the iterative signal control algorithm is reduced by 43%, and under the optimized signal control algorithm, by 67%. They indicate that the two new adaptive signal control algorithms are effective for easing traffic congestion and achieve the adaptive signal control objectives using real-time traffic information. [ABSTRACT FROM AUTHOR]

Improving the resilience of urban road networks suffering from various disruptions has been a central focus for urban emergence management. However, to date the effective methods which may mitigate the negative impacts caused by the disruptions, such as road accidents and natural disasters, on urban road networks is highly insufficient. This study proposes a novel adaptive signal control strategy based on a doubly dynamic learning framework, which consists of deep reinforcement learning and day-to-day traffic dynamic learning, to improve the network performance by adjusting red/green time split. In this study, red time split is regarded as extra traffic flow to discourage drivers to use affected roads, so as to reduce congestion and improve the resilience when urban road networks are subject to different levels of disruptions. In addition, we utilize the convolution neural network as Q-network to approximate Q values, link flow distribution and link capacity are regarded as the state space, and actions are denoted as red/green time split. A small network is utilized as a numerical example, and a fixed time signal control and other two adaptive signal controls are employed for the comparisons with the proposed one. The results show that the proposed adaptive signal control based on deep reinforcement learning can achieve better resilience in most of the cases, particularly in the scenarios of moderate and severe disruptions. This study may shed light on the advantages of the proposed adaptive signal control dealing with major emergencies compared to others. [ABSTRACT FROM AUTHOR]

Cooperative adaptive cruise control (CACC) vehicles need vehicle-to-vehicle (V2 V) communication to achieve CACC function. When a CACC vehicle follows a manual-driven vehicle (MDV) without V2 V communication, it needs degenerate to adaptive cruise control (ACC). By using real experiments, California PATH program indicated that ACC vehicles are apt to be unstable, which may have negative influence on fuel consumption and traffic emissions. Hence, this paper studies the impacts of the mixed CACC-MDV traffic on fuel consumption and emissions, by taking into consideration partial degenerations from stable CACC vehicles to unstable ACC vehicles. To deal with this, microscopic simulations were adopted by using car-following models. Then, an appropriate emission model was used for evaluating the emission impacts under different CACC market penetration rates (MPRs). In order to obtain reliable evaluation results, the models validated by PATH program using real experimental data were employed as the CACC and ACC car-following models. In addition, we also analytically investigated stability of the mixed traffic flow under different CACC MPRs, in order to explore its relationship with the emission impacts. The results show that the fuel consumption and emissions firstly increase and then decrease with the increase of the CACC MPR. This means the mixed traffic under some ranges of CACC MPRs will produce more fuel consumption and emissions, compared with the full MDVs traffic. It indicates that stability situations of the mixed traffic qualitatively influence the impact trend of CACC MPRs on fuel consumption and emissions. Then, V2 V communication equipments on MDVs are not only encouraging but also essential to avoid the deterioration of fuel consumption and emissions of the mixed traffic flow. From a novel perspective, emission impacts of mixed CACC-HDVs flow are studied by considering partial degeneration from stable CACC to unstable ACC. It showed that stability of the mixed traffic flow qualitatively influences the impact trend of emissions with respect to CACC market penetration rates (MPR). Full V2 V communication environment is essential to reduce fuel consumption and emissions in the mixed traffic flow. [ABSTRACT FROM AUTHOR]

Cooperative adaptive cruise control (CACC) system possesses more remarkable ability to suppress disturbance and enhance the traffic capacity than adaptive cruise control (ACC). However, CACC asks for strict requirement on wireless communication and precise equipment, which remains a big difficulty to implement. This paper extends a new ACC model by introducing the self-stabilizing control with historical data, aimed at achieving the close performance of CACC and make it practicable. Substituting real-time information with pre-stored data substantially reduces the technical demand and offers high reliability to withstand the network delay. Linear stability analysis for this model points out enhancing the value of the gain or time delay of self-stabilizing control benefits to stabilize the traffic. The theories are corroborated via the simulation and further numerical simulations explicate the impact on fuel consumption and emissions and traffic capacity. [ABSTRACT FROM AUTHOR]

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

In a connected vehicle environment, vehicle location, speed, and other traffic information are readily available; hence, such environments provide new data sources for traffic signal control optimization. Existing adaptive signal control systems based on fixed detectors cannot directly obtain vehicle location and speed information, and thus, cannot provide accurate information about real-time traffic flow changes. This study presents a dynamic optimization method for adaptive signal control in a connected vehicle environment. First, the proposed method developed a dynamic platoon dispersion model to predict vehicle arrivals by using connected vehicle data. Then, a signal timing optimization model is constructed by regarding the minimization of average vehicle delay as the optimization objective, and setting the green time duration of each phase as a constraint. To achieve real-time adaptive signal control, a genetic algorithm is adopted to solve the optimization model through rolling optimization. Finally, a real-world road network was modeled in Vissim to validate the proposed method. Simulation results show that compared with the classical adaptive signal control algorithm, the proposed method is able to reduce vehicle delays and queue lengths at least 50% penetration rates. At 100% penetration rate, the proposed method improved the average vehicle delay and the average queue length by 22.7% and 24.8%, respectively. Moreover, it catered to all directions in a balanced manner. [ABSTRACT FROM AUTHOR]

This evaluation ascertained the operational impacts of the SUTRAC (Scalable Urban Traffic Control) Adaptive Signal Control Technology (ASCT) in an urban corridor consisting of 23 intersections in Pittsburgh, Pennsylvania. A combination of real-world GPS floating car runs and private sector probe data from INRIX was used to assess the impact of the ASCT. Data were collected with the ASCT active and inactive to determine the operational impacts on the mainline and cross streets. The ASCT was found to produce significant improvements in the number of stops made along the corridor. On Baum and Center, travel times improved during the AM and PM peak in the WB direction. Speeds were also observed to improve significantly during the Midday period on Baum EB and during the AM and PM peak periods on Baum WB. Similarly, statistically significant improvements in speed were observed on Center WB during the AM and PM periods, while a statistically significant decrease in speed was observed during the Midday period. Six months of private sector probe data was used to examine travel time reliability along the corridor, and reliability was also found to have improved. Further, Bayesian models were calibrated to account for variations in speeds and acceleration/deceleration. The Bayesian models revealed that driving was less volatile with the ASCT system in operation over instantaneous periods, which also points towards improved operations. The findings of this study are generally consistent with past evaluations of other ASCTs, indicating that the SURTRAC system is another potential tool for managing congestion on signalized urban arterial networks. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) approaches have recently been spotlighted for use in adaptive traffic-signal control on an area-wide level. Most researchers have employed multi-agent reinforcement learning (MARL) algorithms wherein each agent shares a holistic traffic state and cooperates with other agents to reach a common goal. However, MARL algorithms cannot guarantee a global optimal solution unless the actions of all agents are fully coordinated. The present study employs a RL algorithm that recognizes an entire traffic state and jointly controls all the traffic signals of multiple intersections. With this approach, a deep Q-network (DQN) that depends solely on traffic images is extended to overcome the curse of dimensionality that is associated with a large state and action space. Several front layers in a deep convolutional neural network (CNN) to approximate the true Q-function are shared by each intersection approach. Weight parameters connecting the last hidden layer to the output layer are fixed. The proposed methodology outperforms a fixed-signal operation, a fully actuated signal operation, a multi-agent RL control without coordination, and a multi-agent RL control with partial coordination. [ABSTRACT FROM AUTHOR]

This study develops and proposes a unifying control scheme to address the ramp and mainline metering problems arising in freeways. A distinguishing novelty of the proposed approach is that it can inherently be applied both at local and coordinated levels. The proposed strategy is based on a recently developed nonlinear adaptive control scheme, consisting of a nominal feedback law in conjunction with a nonlinear observer, which aims to estimate some unknown system variables. The present study provides insights into and demonstrates the properties and the performance of the proposed methodology, which is tested for realistic traffic scenarios using a macroscopic traffic flow simulator as a surrogate for potential field application. Comparison tests have been performed with other control strategies proposed in the literature and employed already in the field. [ABSTRACT FROM AUTHOR]

This paper analyses the management process of the vessel traffic control on one-way section on navigable canal with the adaptive time-sequential filter (traffic lights). One-way section on canal significantly decreases waterway capacity and requests special attention in control and regulation of the vessel traffic. The vessel traffic is a stochastic variable, and the vessel traffic control needs to be flexible and adaptive in order to achieve the required traffic flow with minimal delays. On the one-way section, two independent variable vessel flows from opposite directions are encountered, and fixed (predefined) signal plans lead to an increase in vessel delays. An appropriate solution is development of a Fuzzy Control System (FCS) for the vessel traffic control. A control algorithm is designed according to a set of linguistic rules that describes input parameters for the control strategy. The estimated and approximate input parameters are implemented in the algorithm as fuzzy sets. The final result of the developed algorithm is the traffic light scheme (duration of green light for certain direction). The presented control system can be used as an adaptive automatic control system for the vessel traffic control processes on navigable canals or on critical sections of other waterways. [ABSTRACT FROM AUTHOR]

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

• A backpressure approach built on macroscopic traffic theory. • A decentralized approach suitable for implementation in large urban networks. • Theoretical proof of network-wide traffic stability properties. • Very low computational costs. • Extensive experiments and comparisons that highlight the superiority of the proposed approach. Decentralized intersection control techniques have received recent attention in the literature as means to overcome scalability issues associated with network-wide intersection control. Chief among these techniques are backpressure (BP) control algorithms, which were originally developed of for large wireless networks. In addition to being light-weight computationally, they come with guarantees of performance at the network level, specifically in terms of network-wide stability. The dynamics in backpressure control are represented using networks of point queues and this also applies to all of the applications to traffic control. As such, BP in traffic fail to capture the spatial distribution of vehicles along the intersection links and, consequently, spill-back dynamics. This paper derives a position weighted backpressure (PWBP) control policy for network traffic applying continuum modeling principles of traffic dynamics and thus capture the spatial distribution of vehicles along network roads and spill-back dynamics. PWBP inherits the computational advantages of traditional BP. To prove stability of PWBP, (i) a Lyapunov functional that captures the spatial distribution of vehicles is developed; (ii) the capacity region of the network is formally defined in the context of macroscopic network traffic; and (iii) it is proved, when exogenous arrival rates are within the capacity region, that PWBP control is network stabilizing. We conduct comparisons against a real-world adaptive control implementation for an isolated intersection. Comparisons are also performed against other BP approaches in addition to optimized fixed timing control at the network level. These experiments demonstrate the superiority of PWBP over the other control policies in terms of capacity region, network-wide delay, congestion propagation speed, recoverability from heavy congestion (outside of the capacity region), and response to incidents. [ABSTRACT FROM AUTHOR]

Considering the influence of slope sections and random degradation of the communication function of cooperative adaptive cruise control vehicles and the effect of the cooperative adaptive cruise control vehicle group driving, a linear stability criterion for heterogeneous traffic flow composed of cooperative adaptive cruise control vehicles and humanly-driven vehicles on slopes is derived, and a parameter sensitivity analysis is carried out. On this foundation, the safety of traffic flow on two-lane roads up and down hills is simulated while considering the lane-changing behavior of automobiles. The stability study shows increased cooperative adaptive cruise control vehicle penetration and uphill gradient will help improve traffic flow stability. In contrast, the increase in the downhill slope will intensify the transmission amplitude of the disturbance. When vehicle starting sensitivity is 2.5, and the gradient is 60, the minimum cooperative adaptive cruise control vehicle penetration required for mixed traffic flow to maintain stability on a downhill at any initial equilibrium speed is 85%, and the minimum penetration required on an uphill is 73%, and this proportion decreases with the increase in the uphill gradient and increases with the rise in the downhill slope. According to the safety study, increasing cooperative adaptive cruise control vehicle penetration and implementing lane-changing rules will improve traffic flow safety. Compared with the straight section, under the same conditions, the traffic flow safety of the uphill section is the best, and the safety of the downhill section is poor, which is consistent with the stability analysis results. This study is of great significance for future traffic control strategies for mountain roads. [ABSTRACT FROM AUTHOR]

• CAV data enabled adaptive signal control strategies to enhance safety environment. • Innovated mechanism and data models to evaluate CAV data potentials for safety. • SSAM integrated into the developed data fusion models to support safety evaluation. • The proof-of-concept study suggests positive benefits from operation and safety. Given the connected and autonomous vehicle (CAV) generated trajectories as a "floating sensor" data source to obtain high resolution CAV-generated mobility data at intersections, to ensure maximum safety effect while maintaining efficient operations at the same time is actually a complex task in traffic management. Literature indicates that methods for evaluating the CAV-generated data potentials focusing on safety benefits are still immature. The primary reason lies in lack of underlying mechanism and data models to make the data intelligent to enhance safety environment through adaptive traffic signal control. On top of the developed intelligent CAV-generated mobility data fusion model framework in support of adaptive traffic signal control, parameters and models included in Surrogate Safety Assessment Model (SSAM) are integrated to indicate the risk of near crashes and then evaluate the safety environment. A proof-of-concept study is conducted in Uptown Cincinnati, Ohio to test the developed data fusion models in terms of safety enhancement, along with operational benefits. In the tests, the CAV-generated data supported developed adaptive signal plan is compared with the basic signal plans (i.e., pretimed signal plan, actuated signal plan) that supported by traditional detection systems. The results indicate that the adaptive signal plan has a great potential to decrease at most 91% of total collision risk (measured in probability), 71% of crossing collision risk, 90% of rear end collisions risk and 100% of lane-changing collisions risk, compared with basic signal plans. Meanwhile, it increases up to 6.8% of throughput, and decreases up to 91.49% of average delay, 96.23% of queue length and 75.00% of number of stops. The benefits of operation efficiency include reduced average delay and reduced number of stops; but no improvement in reducing collisions severity that is reflected by high maximum speed and relative speed of two vehicles involved in a potential collision. [ABSTRACT FROM AUTHOR]

The optimization of intersection signal control can improve traffic efficiency, reduce congestion degree, and improve traffic safety. Aiming at implementing the coordinated adaptive traffic signal control (ATSC) across large-scale arterial network, multi-agent reinforcement learning (MARL) has been widely concerned and lucubrated. Nevertheless, the existing MARL-based ATSC studies suffers from several limitations: (1) While most existing researches focused on the mobility performance of controlled corridor, there calls for a methodology that aims at combine multi-objective performance on traffic safety, efficiency, and network coordination simultaneously; (2) Most methods ignore the correlations between multiple agents, nor considers the spatial–temporal dependencies among the corelated neighboring intersections due to high communications requirements, which can hardly be achieved in real adaptive coordination control. To overcome the aforementioned difficulties, a multi-objective reinforcement learning model (NACRL) for network-wide coordinated signal control is proposed. Firstly, to enforce a coordinated network control with safety and efficiency considerations, a reward mechanism inspecting both traffic safety and traffic efficiency indicators was designed to achieve ideal performance in terms of mobility, safety and smooth. Secondly, the proposed NACRL conducted a centralized training-decentralized execution framework, this overcomes the critical limitation of data transmission in the field implementation while explicitly analyzing the traffic state over the entire network instead of examining each isolated intersection. Last but not least, the proposed algorithm utilized the attention mechanism to dynamically capture the sophisticated spatial–temporal dependencies over the complex arterial network, which aids the better coordinated control over multi-agents deployed at the intersections across the corridor. To testify the effeteness of the proposed algorithm, extensive experiments were implemented in both large-scale synthetic traffic grid and real-world arterial network. The experiment demonstrated that the proposed NACRL algorithm outperforms other state-of-the-art baselines with simultaneously improved performance in terms of traffic safety, traffic efficiency and network coordination, as well as improved algorithm convergence and interpretability. [ABSTRACT FROM AUTHOR]

A formation tracking control problem for uncertainty traffic cone robots (TCRs) is considered. In this paper, the TCRs can not only perform formation behaviors but also track placement targets under environmental constraints (i.e., the position constraints in the y -direction.). To deal with the environmental constraints, a differential homeomorphism transformation is selected to eliminate the constraints. Moreover, the uncertainty of the TCRs is bounded, including matching portions and mismatching portions. For the matching uncertainty, a series of adaptive parameters are introduced to estimate the real-time information of the uncertainty. The mismatching uncertainty is tackled by a geometric decomposition, which is orthogonal to the range space of the formation. At the same time, a fuzzy uncertainty optimization design problem is presented. The global solution to the resulting optimization problem always exists and is unique. Using adaptive robust control, TCRs' performance is deterministically and fuzzy-optimized. The system performance is demonstrated by a range of numerical simulations and real experiments with three TCRs. • An adaptive robust formation tracking control design method is proposed. • The artificial potential field function has the property of "repulsion at near and attraction at far". • The uncertainty of the TCR system is divided into a matching part and a mismatching part. • A constrained optimization problem based on uncertain fuzzy information is presented. [ABSTRACT FROM AUTHOR]

Cooperative adaptive cruise control (CACC) has been regarded as a promising approach to improving traffic stability and safety with vehicle-to-vehicle communication. However, current communication system is vulnerable to all kinds of cyberattacks, bringing potential security risks to the transportation system. Although cyberattacks have been well documented recently, realistic impact of cyberattacks is not replicated by reason of: First, real-world cyberattacks can take various attack forms and cause diverse damages, but there has been limited research in generalizing their effects on inter-vehicle communication or providing a comprehensive investigation into the resulting response in vehicle dynamics. Second, some adaptive traffic operations, e.g., platooning under a connected environment and dynamic controls under disturbance, were usually neglected. Third, cyberattacks were thought to have constant properties in terms of attack targets and attack severity, which goes against the stochastic nature of cyberattacks. To this end, the objective of this study is to address these existing gaps by generalizing multi-class stochastic cyberattacks and examining their impact in a more realistic traffic stream. Specifically, we categorized different cyberattacks as bogus messages, delay/replay, and malicious messages, and proposed their general forms with stochastic noise or failures on control variables, i.e., location, velocity, and acceleration. Moreover, the research scenario was expanded to a heterogeneous traffic comprising of CACC vehicles, ACC vehicles, and manually driven vehicles, incorporating adaptive traffic operations such as dynamic controls, vehicle degradation, and platooning. Finally, we analysed​ the rear-end collision risks in the presence of multi-class cyberattacks with surrogated safety measures and identified the most critical circumstances under the cyberattack combinations. The findings provide evidences for the adverse effects of cyberattacks on velocity oscillation and collision risks, based on our general modelling of cyberattacks in a more practical scenario. • Propose a framework for the general modelling of multi-class stochastic cyberattacks. • Extend the stochasticity of cyberattacks to stochastic targets and stochastic severity. • Expand the cyberattack scenarios to heterogeneous traffic with dynamical controls. • Assess the impact of cyberattacks on both velocity oscillation and collision risks. [ABSTRACT FROM AUTHOR]