Traffic Control Model Research Articles

Full Vehicle Trajectory Planning Model for Urban Traffic Control Based on Imitation Learning

Connected and automated vehicles (CAVs) extend urban traffic control from temporal to spatiotemporal by enabling the control of CAV trajectories. Most of the existing studies on CAV trajectory planning only consider longitudinal behaviors (i.e., in-lane driving), or assume that the lane changing can be done instantaneously. The resultant CAV trajectories are not realistic and cannot be executed at the vehicle level. The aim of this paper is to propose a full trajectory planning model that considers both in-lane driving and lane changing maneuvers. The trajectory generation problem is modeled as an optimization problem and the cost function considers multiple driving features including safety, efficiency, and comfort. Ten features are selected in the cost function to capture both in-lane driving and lane changing behaviors. One major challenge in generating a trajectory that reflects certain driving policies is to balance the weights of different features in the cost function. To address this challenge, it is proposed to optimize the weights of the cost function by imitation learning. Maximum entropy inverse reinforcement learning is applied to obtain the optimal weight for each feature and then CAV trajectories are generated with the learned weights. Experiments using the Next Generation Simulation (NGSIM) dataset show that the generated trajectory is very close to the original trajectory with regard to the Euclidean distance displacement, with a mean average error of less than 1 m. Meanwhile, the generated trajectories can maintain safety gaps with surrounding vehicles and have comparable fuel consumption.

Robustness Analysis of Discrete State-Based Reinforcement Learning Models in Traffic Signal Control

With the growing traffic congestion problem, more and more deep reinforcement learning (DRL) methods have been applied in traffic signals control(TSC). But researches show that DRL is very fragile with abnormal data. In this paper, special traffic state abnormal data (TSAD) are simulated, based on which the robustness of DRL is analyzed and improved for traffic signals control. Firstly, the perturbation noise is generated based on the Discrete Carlin&Wagner attack, which is then added to the normal data to simulate the TSAD. Secondly, under different type of TSAD, the robustness of DRL models for traffic signals control is explored, which are demonstrated to have certain vulnerability, especially with high traffic flows. Finally, induction model based on reward detection(IMR) and mask the activation values of decision neurons (MVN) are proposed to effectively improve the robustness of DRL models for traffic signals control.

Lyapunov exponent using Euler’s algorithm with applications in optimization problems

The difference and differential equations have played an eminent part in nonlinear dynamics systems, but in the last two decades one-dimensional difference maps are considered in the forefront of nonlinear systems and the optimization of transportation problems. In the nineteenth century, the nonlinear systems have paved a significant role in analyzing nonlinear phenomena using discrete and continuous time interval. Therefore, it is used in every branch of science such as physics, chemistry, biology, computer science, mathematics, neural networks, traffic control models, etc. This paper deals with the maximum Lyapunov exponent property of the nonlinear dynamical systems using Euler?s numerical algorithm. The presents experimental as well as numerical analysis using time-series diagrams and Lyapunov functional plots. Moreover, due to the strongest property of Lyapunov exponent in nonlinear system it may have some application in the optimization of transportation models.

Shared-phase-dedicated-lane based intersection control with mixed traffic of human-driven vehicles and connected and automated vehicles

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

A deterministic predictive traffic signal control model in intelligent transportation and geoinformation systems

In this paper, we propose a traffic signal control method in intelligent transportation and geoinformation systems, based on a deterministic predictive model. The method provides adaptive control based on traffic data, including data from connected and autonomous vehicles. The proposed method is compared with the state-of-the-art traffic signal control solutions: empirical control algorithms and reinforcement learning-based control methods. An advantage of the proposed method is shown and directions of further research are outlined.

Traffic Efficiency Models for Urban Traffic Management Using Mobile Crowd Sensing: A Survey

The population is increasing rapidly, due to which the number of vehicles has increased, but the transportation system has not yet developed as development occurred in technologies. Currently, the lowest capacity and old infrastructure of roads do not support the amount of vehicles flow which cause traffic congestion. The purpose of this survey is to present the literature and propose such a realistic traffic efficiency model to collect vehicular traffic data without roadside sensor deployment and manage traffic dynamically. Today’s urban traffic congestion is one of the core problems to be solved by such a traffic management scheme. Due to traffic congestion, static control systems may stop emergency vehicles during congestion. In daily routine, there are two-time slots in which the traffic is at peak level, which causes traffic congestion to occur in an urban transportation environment. Traffic congestion mostly occurs in peak hours from 8 a.m. to 10 a.m. when people go to offices and students go to educational institutes and when they come back home from 4 p.m. to 8 p.m. The main purpose of this survey is to provide a taxonomy of different traffic management schemes for avoiding traffic congestion. The available literature categorized and classified traffic congestion in urban areas by devising a taxonomy based on the model type, sensor technology, data gathering techniques, selected road infrastructure, traffic flow model, and result verification approaches. Consider the existing urban traffic management schemes to avoid congestion and to provide an alternate path, and lay the foundation for further research based on the IoT using a Mobile crowd sensing-based traffic congestion control model. Mobile crowdsensing has attracted increasing attention in traffic prediction. In mobile crowdsensing, the vehicular traffic data are collected at a very low cost without any special sensor network infrastructure deployment. Mobile crowdsensing is very popular because it can transmit information faster, collect vehicle traffic data at a very low cost by using motorists’ smartphone or GPS vehicular embedded sensor, and it is easy to install, requires no special network deployment, has less maintenance, is compact, and is cheaper compared to other network options.

Traffic Signal Control Model on Isolated Intersection Using Reinforcement Learning: A Case Study on Algiers City, Algeria

Traffic jams and congestion in our cities are a major problem because of the huge increase in the number of cars on the road. To remedy this problem, several control methods are proposed to prevent or reduce traffic congestion based on traffic lights. There are few works using reinforcement learning technique for traffic light control and recent studies have shown promising results. However, existing works have not yet tested the methods on the real-world traffic data and they only focus on studying the rewards without interpreting the policies. In this paper, we proposed a reinforcement learning algorithm to address the traffic signal control problem in real multi-phases isolated intersection. A case study based on Algiers city is conducted the simulation results from the different scenarios show that our proposed scheme reduces the total travel time of the vehicles compared to those obtained with traffic-adaptive control.

Deep Reinforcement Learning Model to Mitigate Congestion in Real-Time Traffic Light Networks

Urban traffic congestion has a significant detrimental impact on the environment, public health and the economy, with at a high cost to society worldwide. Moreover, it is not possible to continually modify urban road infrastructure in order to mitigate increasing traffic demand. Therefore, it is important to develop traffic control models that can handle high-volume traffic data and synchronize traffic lights in an urban network in real time, without interfering with other initiatives. Within this context, this study proposes a model, based on deep reinforcement learning, for synchronizing the traffic signals of an urban traffic network composed of two intersections. The calibration of this model, including training of its neural network, was performed using real traffic data collected at the approach to each intersection. The results achieved through simulations were very promising, yielding significant improvements in indicators measured in relation to the pre-existing conditions in the network. The model was able to deal with a broad spectrum of traffic flows and, in peak demand periods, reduced delays and queue lengths by more than 28% and 42%, respectively.

Deep Q-network-based traffic signal control models.

Traffic congestion has become common in urban areas worldwide. To solve this problem, the method of searching a solution using artificial intelligence has recently attracted widespread attention because it can solve complex problems such as traffic signal control. This study developed two traffic signal control models using reinforcement learning and a microscopic simulation-based evaluation for an isolated intersection and two coordinated intersections. To develop these models, a deep Q-network (DQN) was used, which is a promising reinforcement learning algorithm. The performance was evaluated by comparing the developed traffic signal control models in this research with the fixed-time signal optimized by Synchro model, which is a traffic signal optimization model. The evaluation showed that the developed traffic signal control model of the isolated intersection was validated, and the coordination of intersections was superior to that of the fixed-time signal control method.

A genetic timing scheduling model for urban traffic signal control

As urban traffic condition is diverse and complicated, maximizing the use of urban traffic signal control system to solve traffic issues becomes one of the hot and promising topics. Especially, how to control traffic signals at multiple intersections is a key challenge. To speed up urban traffic flow, this research presents a genetic timing scheduling model (GTSM) for urban traffic signal control. GTSM constructs cellular automata to update the timing cycles of traffic signals at multiple intersections, where state update functions are formulated to coordinate traffic signals. In addition, a proposed genetic optimization algorithm (GOA) in GTSM optimizes the timing cycles of traffic signals at multiple intersections in the dynamic timing optimization. The experimental results on urban road networks show that the performance of our model is excellent in various scenarios to control traffic signals to speed up urban traffic flow.

Intelligent control of complex traffic conditions based on machine learning

In order to improve the simulation effect of complex traffic conditions, based on machine learning algorithms, this paper builds a simulation model. Starting from the macroscopic traffic flow LWR theory, this paper introduces the process of establishing the original CTM mathematical model, and combines it with machine learning algorithms to improve it, and establishes the variable cell transmission model VCTM ordinary transmission, split transmission, and combined transmission mathematical expressions. Moreover, this paper establishes a road network simulation model to calibrate related simulation parameters. In addition, this paper combines the actual needs of complex traffic conditions analysis to construct a complex traffic simulation control model based on machine learning, and designs a hybrid microscopic traffic simulation system architecture to simulate all relevant factors of complex road conditions. Finally, this paper designs experiments to verify the performance of the simulation model. The research results show that the simulation control model of complex traffic conditions constructed in this paper has certain practical effects.

Controlling the security of the airport airspace using the digital twin

The article aims to develop a neural model for digital air traffic control. The proposed approach uses the concept of socio-cyber-physical self-organization of distributed organizational and technical system, whose components are connected to 4G and 5G generation wireless networks. The advantage of the approach is complex integration of promising analytical management principles and their representation in hybrid intellect.

Smart traffic control: Identifying driving-violations using fog devices with vehicular cameras in smart cities

Growing vehicular traffic in urban areas creates a mess for authorities to handle city traffic. With the lack of human resources, authorities are moving towards the use of smart and auto-traffic control systems to manage an increasing volume of traffic. Mostly, these systems monitor traffic using street cameras and identify illegal traffic behaviors, such as signal violations. However, it is not feasible to employ humans or static cameras everywhere in the city in order to cover all the urban roads. These days, modern automobiles come with cameras to store videos as a black-box in case of an accident. In this paper, we exploited the use of vehicular cameras and proposed a smart traffic control model to report any traffic violation on the road. To this end, the vehicle's camera monitors all front cars on the road and transmits videos to the car's attached fog device. The fog device analyses the captured video for unlawful behavior and reports to traffic authorities in case of any violation. Initially, front vehicles are recognized using Single Shot MultiBox Detector (SSD), whereas road lanes are marked using Hough transform. Later, the violations are identified using the violation-detection algorithm. As a use case, the algorithm is designed for the fog device to identify driving violations, including wrong U-turn and driving on a central divider line or a yellow line. The role of the fog device is implemented on a GTX750-Ti GPU-based machine. Finally, the system's performance is evaluated in terms of accuracy and efficiency.

Research on Urban Traffic Active Control in Cooperative Vehicle Infrastructure

Urban intersection control mainly undertakes two tasks: traffic safety and traffic efficiency. Traditional intersection control models and methods have been insufficient in improving traffic efficiency, which is composed of the increase in traffic demand and the complexity of demand at present. In this paper, we propose a novel model and method called ATCM, which is based on the advanced technology of cooperative vehicle infrastructure. In this paper, a novel active traffic control model (ATCM) is proposed, which is based on the advanced technology of cooperative vehicle infrastructure. ATCM increases the intersection control model variables from the traditional two dimensions to five dimensions. It reshapes intersection control from the perspective of road designers and managers, so it can achieve more flexible and efficient traffic control. To this end, a multivariable active traffic control model is constructed, which includes road speed, lane control, sequence, phase, and green light time; a D-double layer optimization method is designed for this model. The first part of this optimization method combines speed control and dynamic phase sequence control. The second part is realized by the combination of lane control and dynamic phase sequence control. By conducting comprehensive experiments, the results demonstrate that the proposed approach is more flexible and efficient than traditional methods.

Discrete Chaotification of a Modulated Logistic System

In the last few decades, the discrete chaotification of difference equations has gained the considerable attention of academicians and scholars due to its tremendous applications in many branches of science, such as cryptography, traffic control models, secure communications, weather forecasting and engineering. In this article, a modulated logistic system is studied and superior chaos is reported through period-doubling bifurcation, period-three orbit and Lyapunov exponent properties. It is interesting to see that the modulated system admits the superiority in chaos due to the presence of an extra degree of freedom of an ordered parameter [Formula: see text]. Analytical as well as numerical simulations are demonstrated to examine the period-doubling and Lyapunov exponent properties for some particular values of parameter [Formula: see text]. Moreover, it is speculated that the superiority in chaos may be used in chaos-based cryptography, in the future.

A Robust Traffic Control Model Considering Uncertainties in Turning Ratios

The effects of model parameter uncertainty on traffic flow control problems have recently drawn research attention. While the uncertainty in fundamental diagram related parameters has been investigated in the past, few articles have focused on network parameters uncertainty, including turning ratio uncertainty. To fill this gap, this article proposes a robust control model to deal with the uncertainties in the turning ratio by using distributionally robust chance constraints. The model allows one to compute the optimal control action that maximizes some objective, under all possible distributions of network parameters. We then apply this robust control framework to both a freeway network and an urban network, and evaluate the impact of uncertainty on optimal control inputs, over the test networks. The case studies show that compared to non-robust control, the proposed robust model can reduce congestion brought by the uncertainties and improve the overall throughput.

Self-Adaptive Traffic Control Model With Behavior Trees and Reinforcement Learning for AGV in Industry 4.0

Automated guided vehicles (AGVs) are considered as an enabling technology to realize smart manufacturing in the upcoming Industrial 4.0 era. However, several challenges including efficiency, timeliness, and safety still exist in AGVs system in discrete manufacturing shopfloor. To address these challenges, a self-adaptive traffic control model combining behavior trees (BTs) and reinforcement learning (RL) is proposed to implement optimal decisions according to diverse, dynamic and complex situations in Industry 4.0 environments. A cyber-physical systems using multiagent system technology is designed in which components such as AGVs and traffic commander are defined as specific agent that cooperates autonomously with each other. Then, the behavior construction model is constructed by BTs to enumerate all the possible states in AGVs traffic control. An RL model is further developed based on the BTs. By using this approach, in this article, AGVs have the ability to adaptively choose the optimal rule-based strategy from existing optional strategies. The case study of the scenario avoiding collisions at intersections illustrates that the proposed model can enhance self-adaptive capability of AGVs traffic control and simultaneously guarantees efficiency, timeliness, and safety.

Adaptive collaborative optimization of traffic network signal timing based on immune-fireworks algorithm and hierarchical strategy

In contemporary urban areas, the construction speed of urban roads lags far behind the growth rate of the number of vehicles. The traffic delay caused by excessive vehicles is a major challenge for modern transportation systems. In this paper, we design an adaptive coordination traffic signal control system based on a three-level framework. In the framework, we propose a hierarchical strategy, which is helpful in avoiding possible offset conflicts and configuring the offsets reasonably. In addition, we establish a multi-intersection traffic signal control model with the goal of minimizing traffic delays. To solve the proposed model, we propose a new algorithm, called the Immune-Fireworks algorithm (IM-FWA), on the basis of the artificial immune and fireworks algorithms. Inspired by the antibody maintenance mechanism, diversity mechanism and communication mechanism of the artificial immune algorithm, IM-FWA can effectively overcome the shortcomings of the fireworks algorithm, such as its limited search range and lack of interaction among fireworks. The experiments show that the proposed model and algorithm have good practicability and that our control system can obtain a better signal timing schedule to effectively reduce traffic delays.

CHAOTIC EVOLUTION OF DIFFERENCE EQUATIONS IN MANN ORBIT

Because of the tremendous characteristics of discrete chaos and sensitivity to their initial parameters, discrete one-dimensional maps are extensively used in every branch of science and engineering such as a security system, cryptography and traffic control models. In this article, it is proposed to examine the chaotic characteristics of a logistic-type difference equation using Mann orbit. Due to the presence of an added parameter <i>α</i> the resulting orbit provides superior chaotic characteristics from those of the existing characteristics in chaotic maps. As compared to existing chaotic maps it provides more efficient and effective chaotic characteristics like better sensitivity, suitable maximum Lyapunov exponent value and superior stability behavior. The results are carried out mathematically as well as experimentally followed by theorems, a few counterexamples and some concluding remarks. Moreover, a superior discrete traffic flow model using macroscopic approach and Greenshield's model is also described.

Hybrid Recurrent Traffic Flow Model with Artificial Neural Networks For Approximation of Unknown Urban Road Subnetworks

The article considers a hybrid recurrent mathematical model of traffic flow control, that consists of two types of models: a universal recurrent finite-difference model for describing subnetworks with known parameters, and a model based on the artificial neural networks to describe subnetworks with partially unknown parameters. The choice of Elman recurrent neural network is substantiated and a method for its training based on a variational genetic algorithm is described.