Context-Aware Intelligent Traffic Light Control through Secure Messaging

Introduction Drag on city traffic has escalated to be a pivotal problem in almost all cities in the world, and commuter travel time, fuel consumption and emission have also soared. Fixed-time signal control systems have been traditionally used in traffic signal control, but their optimality highly depends on the time of day they are applied and they are not capable of real-time traffic control. In order to overcome these drawbacks, intelligent and adaptive traffic control approaches are being explored by both scientists and traffic developers. Objective The purpose of this research is to create and assess a dynamic traffic light control system by utilizing Snake Optimization (SO), a novel meta-heuristic algorithm. In order to minimize vehicle queues, cut down on delays, and enhance overall traffic flow, the main goal is to examine how well SO optimizes traffic signal timings based on real-time traffic data. by contrasting conventional static control techniques and meta-heuristics with the SO-based dynamic approach. Proposed Method To find the best traffic light phase durations at intersections, the suggested approach uses the Snake Optimization (SO) algorithm, a bio-inspired meta-heuristic technique. Real traffic data from the Algerian city of Algiers, which depicts realistic traffic volumes and patterns, is used to test the method. The SUMO (Simulation of Urban Mobility) simulator, an open-source program popular for simulating microscopic traffic, is used to construct the experimental framework. Traffic scenario modeling, control algorithm implementation, and performance evaluation in a simulated urban setting are all made possible by SUMO. The following steps are part of the optimization process: gathering of data, using the SUMO simulator to model traffic flow and intersections, incorporating the SO algorithm to modify traffic light timings in real time in response to inputs, evaluation of performance using metrics like vehicle throughput, average waiting time, reduction of CO2 emission and total travel time, and evaluation of the benefits obtained by the SO-based approach through comparison with a baseline static control system and ACO-DSOS meta-heuristic. Conclusion According to the simulation results, the suggested approach, which is based on the Snake Optimization algorithm, performs better than other meta-heuristics and conventional static control systems. The algorithm's effectiveness in controlling traffic flows is validated by quantitative comparisons. According to the study's findings, SO is a practical and successful strategy for maximizing traffic light control in practical settings.

The congestion of the urban traffic is becoming one of critical issues with increasing population and automobiles in cities. Traffic jams not only cause extra delay and stress for the drivers, but also increase fuel consumption, add transportation cost, and increase carbon dioxide air pollution. The traffic controller is one of critical factors affecting the traffic flow. The conventional traffic patterns are nonlinear and complex. As a result, the fixed traffic light controller is not optimized to reduce the traffic jam. Moreover, it does not improve the response time for ambulances, fire trucks, police cars and other emergency vehicles. This paper is written with the endeavor to provide the readers an idea of the research that has been carried out in the intelligent traffic light field and the microcontroller based traffic light control system. The historical review of the technology and the recent development of the intelligent traffic light control system are stated in this paper. In addition, the future studies of the related work are also presented.

Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. In terms of how to dynamically adjust traffic signals' duration, existing works either split the traffic signal into equal duration or extract limited traffic information from the real data. In this paper, we study how to decide the traffic signals' duration based on the collected data from different sensors and vehicular networks. We propose a deep reinforcement learning model to control the traffic light. In the model, we quantify the complex traffic scenario as states by collecting data and dividing the whole intersection into small grids. The timing changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map the states to rewards. The proposed model is composed of several components to improve the performance, such as dueling network, target network, double Q-learning network, and prioritized experience replay. We evaluate our model via simulation in the Simulation of Urban MObility (SUMO) in a vehicular network, and the simulation results show the efficiency of our model in controlling traffic lights.

The conventional traffic light control frameworks are governed by fixed time interims of the traffic lights. These traditional fixed traffic light controllers have certain constraints and are less effective on the grounds that they utilize an equipment, which have capacities as indicated by the program that comes up with limitations of the inconsistent adaptability of adjustments. Along these lines, due to the fixed time interims of green and red signs, there is overabundance average waiting time and superfluous holding up time on lanes, due to which vehicles tend to devour more fuel. This in the long run indicates the ecological contamination and makes a few medical related problems among the individuals on street and living close by, as they tend to inhale the impure air. Likewise, these customary traffic light control frameworks do not have any advancements or arrangements to give any data on traffic densities on different streets, which prompt traffic clogs. Consequently, to make the traffic light scheme control and traffic guideline progressively productive, the proposed work makes use of the development of a novel strategy called as Adaptive Traffic Light Control System, modelled with highly proficient framework. The proposed framework utilizes a complete system of cluster of sensors for detecting the traffic with priority based traffic management. On positioning this effectively detected traffic, the planning interims of red and green light signals across the streets are cleverly chosen and changed in order to keep the holding up time least. In this way, enhancement of the traffic light with periodically exchanges, builds the street limit, spares time for voyaging and forestalls traffic blockages. The traffic related details are intermittently updated to the multi-cloud environment. The model is assessed by means of rebuilding in the Simulation of Urban Mobility framework (SUMO) in a vehicular system using Reinforced Decision Process (RDP), and the reenactment results show the significantly improved efficiency of the model in controlling and regulating the traffic with dynamic and self-adaptive management.

Efficient traffic control and management is a fundamental and urgent challenge in our ever-growing cities. Maximizing roadway capacity usage, balancing traffic flows, decreasing emission, improving traffic safety and providing motorists with the best end-to-end transportation experience are the most important goals of modern intelligent traffic control infrastructures. Evolving traffic light or traffic signal controller systems are playing an important role in these architectures by adaptively and dynamically coordinating individual traffic lights to achieve city-wide objectives of traffic operations. The integration of such systems with vehicle-to-vehicle (V2V) and vehicle-to- infrastructure (V2I) communication techniques (together referred as V2X) could easily be the next step of this evolution allowing for dynamic real-time information exchange between all the players of the traffic controlling ecosystem and fostering cooperative urban mobility. In this paper we propose a possible architecture for a V2X-integrated traffic light controller systems and present the testbed with our proof-of-concept implementation of the proposed scheme.

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

Traffic congestion is a major concern for many cities throughout the world. Developing a sophisticated traffic monitoring and control system would result in an effective solution to this problem. In a conventional traffic light controller, the traffic lights change at constant cycle time. Hence it does not provide an optimal solution. Many traffic light controllers implemented in current practice, are based on the 'time-of-the-day' scheme, which use a limited number of predetermined traffic light patterns and implement these patterns depending upon the time of the day. These automated systems do not provide an optimal control for fluctuating traffic volumes. A traffic light controller based on fuzzy logic can be used for optimum control of fluctuating traffic volumes such as over saturated or unusual load conditions. The objective is to improve the vehicular throughput and minimize delays. The rules of fuzzy logic controller are formulated by following the same protocols that a human operator would use to control the time intervals of the traffic light. The length of the current green phase is extended or terminated depending upon the 'arrival' i.e. the number of vehicles approaching the green phase and the 'queue' that corresponds to the number of queuing vehicles in red phases. A prototype system for controlling traffic at an intersection is designed using VB6 and Matlab tool. The traffic intersection is simulated in VB6 and the data regarding the traffic parameters is collected in VB6 environment. The decision on the duration of the extension is taken using the Matlab tool. This decision is based on the Arrival and Queue of vehicles, which is imported in Matlab from VB6 environment. The time delay experienced by the vehicles using the fixed as well as fuzzy traffic controller is then compared to observe the effectiveness of the fuzzy traffic controller.

Traffic crisis often happen because of traffic burden by the large number automobiles on the path. Increasing transportation move and decreasing the average waiting time of each vehicle are the aims of cooperative intelligent traffic control system. Each signal wishes to catch better travel move. During the course, signals form a strategy of cooperation in addition to restriction for neighboring signals to exploit their individual benefit. A superior traffic signal scheduling strategy is used to resolve the difficulty. The several parameters may influence the traffic control model. So it is hard to learn the best possible result. Traffic light controllers are not expert to study from previous results. Due to this, they are unable to include the uncertain transformation of traffic flow. Reinforcement learning algorithm based traffic control model used to get fine timing rules by properly defining real-time parameters of the real traffic scenario. The projected real-time traffic control optimization prototype is able to continue with the traffic signal scheduling rules successfully. The model expands traffic value of the vehicle, which consists of delay time, the number of vehicles stopped at a signal, and the newly arriving vehicles to learn and set up the optimal actions. The experimentation outcome illustrates a major enhancement in traffic control, demonstrating the projected model is competent of making possible real-time dynamic traffic control.

Traffic crisis frequently happen because of traffic burden by the large number automobiles are on the path. Increasing transportation move and decreasing the average waiting time of each vehicle are the objectives of cooperative intelligent traffic control system. Each signal wishes to catch better travel move. During the course, signals form a strategy of cooperation in addition to restriction for neighboring signals to exploit their individual benefit. A superior traffic signal scheduling strategy is useful to resolve the difficulty. The several parameters may influence the traffic control model. So it is hard to learn the best possible result. Traffic light controllers are not expert to study from previous results. Due to this they are unable to include uncertain transformation of traffic flow. Reinforcement learning algorithm based traffic control model can be used to obtain fine timing rules by properly defining real time parameters of the real traffic scenario. The projected real-time traffic control optimization prototype is able to continue with the traffic signal scheduling rules successfully. The model expands traffic value of the vehicle, which consists of delay time, the number of vehicles stopped at signal, and the newly arriving vehicles to learn and establish the optimal actions. The experimentation outcome illustrates a major enhancement in traffic control, demonstrating the projected model is competent of making possible real-time dynamic traffic control.

Traffic control at road intersections is based on traffic lights (TLs). The control mechanism typically used for traffic lights operates based on a periodic schedule to change the light (red/yellow/green). In many cases, a different schedule is used in late night/early morning hours. This fixed control mechanism does not adequately account for changing traffic conditions, and is unaware of/unresponsive to congestion. In this work, we propose a framework for dynamic traffic light control at intersections. The framework relies on a simple sensor network to collect traffic data and includes novel protocols for traffic flow control to handle congestion and facilitate flow. We show that our proposed algorithms have low overhead and are practical to employ in live traffic flow scenarios. Through extensive simulations, we demonstrate the benefits of our framework in optimizing traffic flow metrics, such as traffic throughput, average vehicle waiting time, and vehicle waiting line length.

This chapter provides an in-depth overview of the state of the art on traffic light control and optimization. Several classical control systems, methods, concepts, tools, and strategies are described, and their related pros and cons are discussed. Four different types of control strategies are considered, pretimed, actuated, adaptive, and self-organized, and it is demonstrated that some strategies can be appropriate for local control, area control, or both (local and area). Further, the chapter develops a system of coupled nonlinear oscillators, that is used for traffic light control and optimization both at isolated junctions (i.e., local control) and in a network of coupled traffic junctions (i.e., area control). The case of an isolated traffic junction is modeled by coupled oscillators, each of which represents a specific phase group of the traffic light at the junction. The case of a network of coupled traffic junctions is also considered, and we show the possibility of modeling the traffic light at each junction by a single oscillator. The system developed is viewed as a modified version of the self-organized Kuramoto model for traffic light control due to some important features that are common to both systems (i.e., Kuramoto model and the system developed). The main advantage of the system developed is the possibility of dynamically monitoring the signals’ phases (delays), signal-splits, or signal timings according to the dynamic variation of the traffic demand in all conflicting approaches of the traffic junctions under investigation. The system of coupled nonlinear oscillators is considered a flexible platform, which is appropriate for modeling the four types of traffic light control strategies. Another advantage of the system developed is the possibility of an easy hardware implementation using electronic devices. This allows a straightforward possibility for designing appropriate electronic prototypes of traffic light controllers (appropriate for both local and area controls).

Traffic congestion especially at road intersections is becoming an issue for which road traffic users contend with daily. The conventional traffic light applies a fixed logic of allocating equal “GO” time to lanes of traffic at road intersections irrespective of the density of traffic on each lane. Using the PIC18F4550 microcontroller interfaced with infrared sensors, a new traffic light control system was developed to ease the flow of traffic at a particular troubled spot in Uyo Metropolis (Abak Road, Udo Eduok, Udo Obio streets intersection) in Akwa Ibom State, Nigeria. Simulation of the proposed design was performed using the Proteus Software while implementation was carried out on a prototype. Performance evaluation of the prototype implemented showed that the 4 arrays (IR Transmitter and Receiver) of infrared sensors interfaced with the microcontroller using AND gates and strategically positioned to read the density of traffic on each lane at the intersection triggered when a vehicle comes between the transmitter and the receiver. Obtaining inputs from these sensors together with the logic from the microcontroller, a new traffic light control system was developed capable of controlling traffic based on the density of each lane of traffic. Results obtained from simulation and implementation of the design indicates that the traffic control system with the PIC18F4550 microcontroller and the infrared sensors gives a better performance compared to the conventional traffic light control system.Keywords: Road traffic density, Traffic light, Uyo metropolis, ‘+’ road intersection, Proteus

An optimization model and traffic light control scheme for heterogeneous traffic systems

Contemporary traffic light control systems rely on sensors for detection of traffic which are costly in purchase, installation and maintenance. Emerging cooperative technology offers an attractive alternative where only one road side unit per intersection is required, instead of several infrastructure sensors per lane. However, studies showed that traffic control with cooperative detection requires a penetration rate of at least 20% to function effectively. To show the potential of cooperative traffic control, this study presents three algorithms: (i) the SWARM control algorithm, which is designed to work with very low penetration rates; (ii) an extension to the adaptive control algorithm, ImFlow, which uses cooperative data for enhanced queue modelling; and (iii) an ImFlow extension to stabilise green planning to enable green light optimal speed advice. The results from micro‐simulation show a 7.8% improvement for stops and delay time over traditional adaptive control for Swarm, and 14.9% for Cooperative ImFlow. Adding planning stabilisation reduced the average perceived change for end users from 9.0–2.3%, without performance loss for the overall traffic flow. This shows the large potential of cooperative traffic control.

Urban traffic situation information is the basis of effective traffic guidance and traffic control. In order to promote communication and practical applications of traffic information, then can provide full, accurate, real time and sufficient traffic information service to public, government and correlative enterprises, in this paper, the authors proposed the dynamic traffic information collection and processing methods of intelligent transportation systems (ITS) common information platform (CIP) based on floating car technology. According to the characteristics of the ITS CIP data collection, they were required large scale, accurate and dynamic data collecting. A complete set of dynamic traffic information processing methods based on floating car technology were studied, including road data storage by two queues, error data elimination by taximeter, map matching by region dividing method and boundary rectangle, and traffic condition calculation by circle queues and A* algorithm. The application of the methods in the ITS CIP of Guangzhou City proved that the methods were quite effective and valuable to be popularized.