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

Effective parameter estimation and low computational complexity are the two major challenges involved in traffic light control. Most traffic light scheduling strategies focus on developing well-tuned off-line solutions. This paper focuses on the design of a hybrid traffic light control strategy. A macroscopic traffic network model is proposed to depict the traffic dynamics and a closed-loop traffic control strategy is designed based on the estimation of branching ratios at intersections. To reduce the computational complexity, a distributed algorithm is proposed based on the congestion level identification and system partitioning method, which is based on machine learning algorithms. Simulation results show the effectiveness of the proposed methodologies.

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 lights in cities play a crucial role in regulating the complex dynamics of diverse vehicles, giving priority to certain users or vehicles on the road. However, the alternating stop-and-go patterns induced by these lights have an impact on desired speed of vehicles and energy dissipation. In this paper, we examine the impacts of traffic signals, focusing on a two-lane cellular automaton model. Our model accounts for traffic heterogeneity by differentiating vehicles based on speed (slow and fast) and size (large, slow vehicles and small, fast vehicles). In our study, we concentrated on a case in which the lane-changing rules were asymmetric., this regulation stipulates that slower-moving traffic should keep to the righthand lane (or the left-hand lane in countries where driving is on the left) to allow faster vehicles to pass. Although these vehicles occasionally change lanes, they revert to their preferred lane at the earliest opportunity. Hence, we examined two traffic light control strategies: the green wave and synchronized methods. Our findings indicate that both strategies exhibit comparable performance for vehicles in terms of traffic flow parameters. However, differences emerge when comparing the two lanes or vehicle types. Specifically, the slow lane demonstrates a higher traffic flow for slower vehicles and a reduced flow for faster vehicles. When examining satisfaction rates and energy dissipation, notable variations emerge, especially in extremely low densities. The green wave demonstrates marginally superior performance compared to the synchronized traffic light control. This investigation deepens our understanding of the impact of various control strategies on performance. The findings can serve as a foundation for exploring more intricate aspects of traffic management, enhancing the potential for creating efficient and adaptive urban transportation systems.

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.

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.

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.

Environmentally friendly traffic control strategy - A case study in Xi’an city

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.

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.

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.

Intelligent Traffic Lights: Green Time Period Negotiaton

The traffic congestion problem in urban areas is worsening since traditional traffic signal control systems cannot provide efficient traffic control. Therefore, dynamic traffic signal control in intelligent transportation system (ITS) recently has received increasing attention. This study devised an adaptive and cooperative multi-agent fuzzy system for a decentralized traffic signal control. To achieve this goal we have worked on a model, which has three levels of control. Every intersection is controlled by its own traffic situation, its neighboring intersections recommendations and a knowledge base, which provides the traffic pattern of each intersection in any particular day of the week and hour of the day. The proposed architecture comprises a knowledge base, prediction module and a traffic observer that provide data to real traffic data preparation module, then a decision-making layer takes decision to how long should the intersection green light be extended. The proposed architecture can achieve dynamic traffic signal control. We have also developed a NetLogo-based traffic simulator to serve as the agents' world. Our approach is tested with traffic control of a large connected junction and the result obtained is promising; The average delay time can be reduced by 21.76% compared to the conventional fixed sequence traffic signal and 14.77% compared to the vehicle actuated traffic signal control strategy.

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.

A Report on Urban Traffic Control Research Conducted by the Federal Highway Administration

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.