Stoplight Research Articles

Unification of probabilistic graph model and deep reinforcement learning (UPGMDRL) for multi-intersection traffic signal control

Traffic signals play a pivotal role in modern life by preventing collisions, regulating traffic flow, and ensuring a predictable and efficient transportation system. Adaptive traffic light signal control (ATSC) is a promising paradigm for mitigating traffic congestion in Intelligent Transportation Systems (ITS). Among various AI-based approaches, Deep Reinforcement Learning (DRL) has gained widespread application, demonstrating superior performance. This paper aims to develop a latent space reinforcement learning method for intelligent traffic control, with a focus on making explainable decisions. According to the latent model and hidden Markov mixed model, this method integrated both to develop an ATSC framework for traffic networks with multiple intersections. Given the challenges posed by high-dimensional data and a limited understanding of the task, traditional decision-making methods often struggle with understanding the environment. This paper aims to provide semantic information and an enhanced understanding of the environment by offering interpretable states. The latent model is employed to extract task-relevant information from underlying representations within a framework that unifies representation learning and DRL. The experimental results demonstrate how our approach effectively and efficiently balances traffic flow, leading to improved traffic management.

Traffic signal phase control at urban isolated intersections: an adaptive strategy utilizing the improved D3QN algorithm

Abstract The intelligent control of traffic signals at urban single intersections has emerged as an effective approach to mitigating urban traffic congestion. However, the existing fixed phase control strategy of traffic signal lights lacks capability to dynamically adjust signal phase switching based on real-time traffic conditions leading to traffic congestion. In this paper, an adaptive real-time control method employed by the traffic signal phase at a single intersection is considered based on the improved Double Dueling Deep Q Network (D3QN) algorithm. Firstly, the traffic signal phase control problem is modeled as a Markov decision process, with its state, action, and reward defined. Subsequently, to enhance the convergence speed and learning performance of the D3QN algorithm, attenuation action selection strategy and priority experience playback technology based on tree summation structure are introduced. Then, traffic flow data from various traffic scenarios are utilized to train the traffic signal control model based on the improved D3QN to obtain the optimal signal phase switch strategy. Finally, the effectiveness and optimal performance of the improved D3QN-based traffic signal control strategy are validated across diverse traffic scenarios. The simulation results show that, compared with the control strategy based on AC, DQN, DDQN, D3QN, and C-D3QN algorithms, the cumulative reward of the proposed I-D3QN strategy is increased by at least 6.57%, and the average queue length and average waiting time are reduced by at least 9.64% and 7.61%, which can effectively reduce the congestion at isolated intersections and significantly improve traffic efficiency.

Plant Growth Optimization Using Amber Light Supplemented with Different Blue Light Spectra

Blue (400–500 nm) and red (600–700 nm) light regions have been investigated for their effects on photosynthesis and plant growth, yet evidence for specific blue light wavelengths in plant research is lacking. Investigations into amber (595 nm) light are similarly limited. To ‘shed light’ on these two important wavelengths, this study investigated the combined effects of blue and amber light on plant growth and development in two model plants: tomato (Solanum lycopersicum cv. Beefsteak) and lettuce (Lactuca sativa cv. Breen). Plant growth responses were determined with four light treatments: B+BA (blue + broad amber, 455–602 nm), RB-NA (royal blue + narrow amber, 430–602 nm), RB-BA (royal blue + broad amber, 423–595 nm), and high-pressure sodium at a PPFD of 250 µmol m−2 s−1. After 21 days, the highest fresh and dry mass for both plant species was obtained under the RB-BA light treatment. Shifting the blue wavelength from 430 nm to 455 nm with broad amber lighting led to 40% less fresh mass for tomatoes, whereas only an approximate 5% reduction in fresh mass was observed for lettuce plants. Our findings demonstrate that an alternate and combined blue + amber light spectrum is effective for optimizing plant productivity.

Complementary Nil Equitable Domination in Fuzzy Graphs

Objectives: To demonstrate an inventive showcase prominent as complementary nil equitable dominating set, complementary nil equitable domination value in fuzzy graphs (FGs), and pertinent innovations. Methods: Strong arc in FGs ideas are used to perceive complementary nil equitable domination value. Findings: Modern concepts of complementary nil equitable dominance in FGs will lead to a number of applications, including the creation of cell phone towers in remote places, traffic light signals, and Google Maps. Novelty: Employing the ideas of equitable and strong arc in FGs, the complementary nil equitable dominating set and complementary nil equitable domination value in FGs are obtained. Some points are observed in this concept and some theorem related to the complementary nil equitable dominant set, its numbers are proved. AMS Subject Classification: 05C72. Keywords: Fuzzy graph, Equitable dominating set, Equitable domination number, Complementary nil equitable dominating set, Complementary nil equitable domination number

Analysis of Sidewalk Traffic Lights Setting Modes Optimization

There are often some problems with the traditional setting mode of traffic lights, especially on some non-main roads. The unreasonable setting of pedestrian traffic lights can easily lead to empty spaces for people and vehicles, waste resources, and increase the risks of traffic congestion, Although the push-button traffic lights improves traffic efficiency, there is still a waste of waiting time for pedestrians and vehicles due to the fixed setting of the traffic time. This article mainly studies the use of machine learning to solve the recognition, motion direction, and time models of people and vehicles, and optimize pedestrian traffic light signals. By identifying pedestrians and vehicles and allocating release signal time reasonably, the waiting time of pedestrians and vehicles crossing the road can be reduced, and traffic accidents can be minimized. The optimized traffic system is applied to sidewalks on non-main roads, it can replace the push-button traffic lights, reduce manual intervention, save construction costs, and improve traffic efficiency.

Transient Photoactivation of Anionophores by Using Redshifted Fast-Relaxing Azobenzenes.

Photo-regulated transmembrane ionophores enable spatial and temporal control over activity, offering promise as targeted therapeutics. Key to such applications is control using bio-compatible visible light. Herein, we report red-shifted azobenzene-derived synthetic anionophores that use amber or red light to trigger (E)-(Z) photoisomerisation and activation of transmembrane chloride transport. We demonstrate that by tuning the thermal half-life of the more active, but thermodynamically unstable, Z isomer to relax on the timescale of minutes, transient activation of ion transport can be achieved by activating with solely with visible light and deactivating by thermal relaxation.

Feelings of safety for visitors recreating outdoors at night in different artificial lighting conditions

Artificial light can impact the actual and perceived safety of visitors recreating outdoors at night. Actual safety refers to how visitors identify aspects of their environment such as pathways and obstacles, while perceptions of safety refer to how safe they feel based on perceptions of the environment. Both influence the acceptability of different light conditions. The objective of this study is to better understand how light conditions influence visitors' perceptions of safety and their potential for pleasant experiences while recreating at night in a park-like setting. This paper explores study participants' self-reported feelings of safety across eight different light conditions using two different colors, 3000 K Correlated Color Temperature (warm white) and monochromatic amber, and 4 four different intensities (0.5, 1.0, 5.0, or 10.0 lux) during a nighttime recreation-focused experiment in a university's arboretum. Prior experience with nighttime outdoor recreation and community type (urban, suburban, rural) during adolescence were evaluated alongside light conditions as predictors of feelings of safety using ordered logistic regression. Participants of this study were 31.6% more likely to report higher feelings of safety in warm white light than amber light and 81.7% more likely to report higher feelings of safety at 5.0 lux than 0.5 lux. Further, individuals who felt safer in any lighting condition were 19.6 times more likely to feel greater potential for a pleasant experience while recreating than someone who did not report feeling safe or felt unsafe. Understanding the connection between light and perceived safety in park settings can help managers tailor lighting conditions to the needs of visitors while reducing ecological impacts - brighter and cooler temperature lighting have greater negative impacts on wildlife. Adjusting the distribution and intensity of light can increase the accessibility of nighttime recreation opportunities and the potential for comfortable and pleasant experiences for visitors.

Developing an eco-driving strategy in a hybrid traffic network using reinforcement learning.

Eco-driving has garnered considerable research attention owing to its potential socio-economic impact, including enhanced public health and mitigated climate change effects through the reduction of greenhouse gas emissions. With an expectation of more autonomous vehicles (AVs) on the road, an eco-driving strategy in hybrid traffic networks encompassing AV and human-driven vehicles (HDVs) with the coordination of traffic lights is a challenging task. The challenge is partially due to the insufficient infrastructure for collecting, transmitting, and sharing real-time traffic data among vehicles, facilities, and traffic control centers, and the following decision-making of agents involved in traffic control. Additionally, the intricate nature of the existing traffic network, with its diverse array of vehicles and facilities, contributes to the challenge by hindering the development of a mathematical model for accurately characterizing the traffic network. In this study, we utilized the Simulation of Urban Mobility (SUMO) simulator to tackle the first challenge through computational analysis. To address the second challenge, we employed a model-free reinforcement learning (RL) algorithm, proximal policy optimization, to decide the actions of AV and traffic light signals in a traffic network. A novel eco-driving strategy was proposed by introducing different percentages of AV into the traffic flow and collaborating with traffic light signals using RL to control the overall speed of the vehicles, resulting in improved fuel consumption efficiency. Average rewards with different penetration rates of AV (5%, 10%, and 20% of total vehicles) were compared to the situation without any AV in the traffic flow (0% penetration rate). The 10% penetration rate of AV showed a minimum time of convergence to achieve average reward, leading to a significant reduction in fuel consumption and total delay of all vehicles.

Distributed MPC-Based Hierarchical Cooperative Control for Mixed Vehicle Groups With T-CPS in the Vicinity of Traffic Signal Light

Distributed MPC-Based Hierarchical Cooperative Control for Mixed Vehicle Groups With T-CPS in the Vicinity of Traffic Signal Light

The use of LEDs for the stomatal response, light compensation points, and storage of spinach and kale

The spectral composition of some light-emitting diodes (LEDs) reportedly results in higher crop yield, prevents wilting, and reduces thermal damage to plants. The use of LEDs for postharvest storage and shelf-life extension has been limited, but the potential of this technology will allow for greater applications in horticulture and the food industry. In this experiment, ‘Winterbor’ kale (Brassica oleracea) and ‘Melody’ spinach (Spinacia oleracea) plants were measured for the light compensation point and stomatal response under 14 different wavelengths of light ranging from 405 to 661 nm. Data collected from these measurements were used to select two different wavelengths of LEDs and determine the proper irradiance levels for an LED irradiance storage test on spinach and kale. Treatments comprising blue, red, and amber lights were effective at increasing the stomatal opening, while the green light resulted in reduced stomatal opening. For spinach, the light response curve showed that light compensation points at 500 nm and 560 nm were 65.3 and 64.7 μmol m−2 s−1, respectively. For kale, the light compensation points at 500 nm and 560 nm were 50.8 and 44.1 μmol m−2 s−1, respectively. For the storage test experiment at room temperature, kale and spinach were stored under four different treatments: dark treatment (control), standard white fluorescent light, 500 nm, and 560 nm LED wavelengths. For spinach, the moisture content was 70.1% at 560 nm and 53.7% for dark, moisture losses of 41.5% under the 560-nm treatment and 52.0% for the dark treatment. The fresh basis moisture content was 74.6% at 560 nm and 59.3% in the dark. Moisture loss under the 560 nm treatment was 39.6% while the dark treatment had a 54.0% moisture loss. A visual assessment scale was monitored, 560 nm resulted in the top visual quality for kale compared to the other treatments with the lowest visual quality under the dark treatment at day 4. For spinach, the visual quality for 560 nm treatment was statistically the standard white fluorescent light and 500 nm, with poor-quality product occurring by day 4 and the lowest-quality product occurring at day 5. The LED treatments improved the shelf life of spinach and kale, likely as a result of stomatal aperture closure, photosynthetic rate near the light compensation point and stability of the atmospheric moisture content. This study provides valuable information on the extension of the shelf life of leafy greens during storage. Reducing fresh produce waste in grocery stores will increase revenue, thereby benefiting the Canadian economy while providing social and environmental benefits that entail increased food security and reduced food waste.

Splitting light pollution: Wavelength effects on the activity of two sandy beach species

Artificial Light at Night (ALAN) threatens to disrupt most natural habitats and species, including those in coastal settings, where a growing number of studies have identified ALAN impacts. A careful examination of the light properties behind those impacts is important to better understand and manage the effects of this stressor. This study focused on ALAN monochromatic wavelengths and examined which types of light spectra altered the natural activity of two prominent coastal species from the Pacific southeast: the talitroid amphipod Orchestoidea tuberculata and the oniscoid isopod Tylos spinulosus. We compared the natural daylight/night activity of these organisms with the one they exhibit when exposed to five different ALAN wavelengths: lights in the violet, blue, green, amber, and red spectra. Our working hypothesis was that ALAN alters these species’ activity at night, but the magnitude of such impact differs depending on light wavelengths. Measurements of activity over 24 h cycles for five consecutive days and in three separate experiments confirmed a natural circadian activity pattern in both species, with strong activity at night (∼90% of probability) and barely any activity during daylight. However, when exposed to ALAN, activity declined significantly in both species under all light wavelengths. Interestingly, amphipods exhibited moderate activity (∼40% of probability) when exposed to red lights at night, whereas isopods shifted some of their activity to daylight hours in two of the experiments when exposed to blue or amber lights, suggesting a possible alteration in this species circadian rhythm. Altogether, our results were consistent with our working hypothesis, and suggest that ALAN reduces night activity, and some wavelengths have differential effects on each species. Differences between amphipods and isopods are likely related to their distinct adaptations to natural low-light habitat conditions, and therefore distinct sensitivity to ALAN.

Light emitting diode effect of red, blue, and amber light on photosynthesis and plant growth parameters

The visible light spectrum (400–700 nm) powers plant photosynthesis and innumerable other biological processes. Photosynthesis curves plotted by pioneering photobiologists show that amber light (590–620 nm) induces the highest photosynthetic rates in this spectrum. Yet, both red and blue light are viewed superior in their influence over plant growth. Here we report two approaches for quantifying how light wavelength photosynthesis and plant growth using light emitting diodes (LEDs). Resolved quantum yield spectra of tomato and lettuce plants resemble those acquired earlier, showing high quantum utilization efficiencies in the 420–430 nm and 590–620 nm regions. Tomato plants grown under blue (445 nm), amber (595 nm), red (635 nm), and combined red-blue-amber light for 14 days show that amber light yields higher fresh and dry mass, by at least 20%. Principle component analysis shows that amber light has a more pronounced and direct effect on fresh mass, whereas red light has a major effect on dry mass. These data clarify amber light's primary role in photosynthesis and suggest that bandwidth determines plant growth and productivity under sole amber lighting. Findings set precedence for future work aimed at maximizing plant productivity, with widespread implications for controlled environment agriculture.

Amber LEDs outperform red, blue, and red-blue-amber LEDs for lettuce

ABSTRACT Debate persists on light in controlled environment agriculture. To dissect the effects of conventionally used wavelengths on a crop and provide new information to this expanding food production sector, this study investigated the effects of monochromatic and combined red, blue, and amber light on lettuce growth with increasing light intensities (up to 1300 µmol·m−2·sec−1) for 18 days with a 16 h·d−1. Under amber light at PPFDs ranging from 500 to 700 µmol·m−2·sec−1, fresh mass displayed a 33.3% greater yield when compared to red light at the same PPFD. Suppressed growth was observed with either red or amber alone at high PPFD (>800 µmol·m−2·sec−1). Blue light was the least productive of the four treatments, yet lettuce plant growth was not suppressed at high intensities. No growth suppression was observed for lettuce plants grown under combined red-blue-amber light at high intensities, and these plants exhibited greater biomass yield than blue light alone. Varied degrees of pigmentation occurred under each light treatment, yet bleaching was only observed in plants grown under amber light alone at PPFD above 1000 µmol·m−2·sec−1. Findings present novel plant responses to high intensity light, setting precedence for future experiments aimed at expanding the use of LEDs in horticulture.

Signal Master using YOLO

As urban populations and automobile numbers continue to swell, traffic congestion emerges as a pressing concern, inflicting delays, stress, heightened fuel consumption, and increased air pollution. Megacities, in particular, bear the brunt of this escalating issue. Real-time assessment of road traffic density becomes imperative for efficient signal control and traffic management. Among the pivotal factors influencing traffic flow, the traffic controller stands paramount, necessitating optimization to meet the surging demand. Our proposed system capitalizes on live camera feeds from traffic junctions to conduct traffic density calculations through image processing techniques and artificial intelligence (AI). By harnessing these technologies, the system aims to offer a dynamic solution to the persistent challenge of congestion. The core focus lies in devising an algorithm that dynamically adjusts traffic light signals based on the detected vehicle density, thereby mitigating congestion and fostering smoother transit experiences for commuters. This innovative approach not only promises expedited travel times but also holds the potential to curtail pollution levels, contributing to a more sustainable urban environment. By seamlessly integrating image processing and AI-driven traffic control mechanisms, our system endeavors to pave the way towards a more efficient and eco-friendly transportation infrastructure

Sistema de gestión y operación de la red semafórica de la ciudad de Lima

The research problem arose from the need to implement a system to remotely manage the traffic operations of the network of traffic lights to reduce vehicular congestion. The procedure involved designing the placement of electronic sensors to track the number of vehicles in the area. This allowed for the testing of different operational strategies for modifying the timing of the traffic light signals. A simulation model was used to test these strategies in seven intersections on Av. Petit Thouars in Lima, between Av. Alejandro Tirado and Av. 28 de Julio. Detectors were installed at the access points of secondary access streets to the main road (Av. Petit Thouars) which yielded cumulative positive results. Alternatives 1 and 2 produced a 59% reduction in queues, thus demonstrating the validity of the general hypothesis.

RTAIAED: A Real-Time Ambulance in an Emergency Detector with a Pyramidal Part-Based Model Composed of MFCCs and YOLOv8.

In emergency situations, every second counts for an ambulance navigating through traffic. Efficient use of traffic light systems can play a crucial role in minimizing response time. This paper introduces a novel automated Real-Time Ambulance in an Emergency Detector (RTAIAED). The proposed system uses special Lookout Stations (LSs) suitably positioned at a certain distance from each involved traffic light (TL), to obtain timely and safe transitions to green lights as the Ambulance in an Emergency (AIAE) approaches. The foundation of the proposed system is built on the simultaneous processing of video and audio data. The video analysis is inspired by the Part-Based Model theory integrating tailored video detectors that leverage a custom YOLOv8 model for enhanced precision. Concurrently the audio analysis component employs a neural network designed to analyze Mel Frequency Cepstral Coefficients (MFCCs) providing an accurate classification of auditory information. This dual-faceted approach facilitates a cohesive and synergistic analysis of sensory inputs. It incorporates a logic-based component to integrate and interpret the detections from each sensory channel, thereby ensuring the precise identification of an AIAE as it approaches a traffic light. Extensive experiments confirm the robustness of the approach and its reliable application in real-world scenarios thanks to its predictions in real time (reaching an fps of 11.8 on a Jetson Nano and a response time up to 0.25 s), showcasing the ability to detect AIAEs even in challenging conditions, such as noisy environments, nighttime, or adverse weather conditions, provided a suitable-quality camera is appropriately positioned. The RTAIAED is particularly effective on one-way roads, addressing the challenge of regulating the sequence of traffic light signals so as to ensure a green signal to the AIAE when arriving in front of the TL, despite the presence of the "double red" periods in which the one-way traffic is cleared of vehicles coming from one direction before allowing those coming from the other side. Also, it is suitable for managing temporary situations, like in the case of roadworks.

Increasing Energy Efficiency of Autonomous Indication and Lighting Systems for Unregulated Ground Pedestrian Crossings

The work deals with the problem of energy efficiency of autonomous indication and lighting systems for unregulated ground pedestrian crossings equipped with a solar panel being significantly important in conditions of insufficient insolation and low temperatures. The scientific objectives of the work are considered to be as follows: analysis of the conditions affecting the uninterrupted functioning of autonomous indication and lighting systems of unregulated ground pedestrian crossings equipped with a solar panel; selection and justification of objective factors, considering which allow the implementation of battery charge saving modes by optimizing the functioning algorithm of autonomous indication and lighting systems for unregulated ground pedestrian crossings equipped with a solar panel. The work presents the directions for possible technical implementation of increasing the energy efficiency of autonomous indication and lighting systems for ground pedestrian crossings equipped with a solar panel, based on obtaining optical information about vehicles moving in the direction of the pedestrian crossing and stabilizing the brightness contrast of the traffic light signal and the background where it is observed by vehicle drivers.

IMPROVING THE EFFICIENCY OF TRAFFIC LIGHT CONTROL AT ROAD INTERSECTIONS

From the functional point of view, the intersection is the most complex element of the road network. It is here that the traffic flows in different directions cross, and various maneuvers take place. This indicates that the intersection is a place with an increased concentration of conflict situations and an increased risk of traffic accidents. At most high-flow intersections, traffic is controlled by traffic lights, and their inefficient operation can lead to unnecessarily long wait times and overall increase in traffic delays. The research analyzed a regulated intersection at the crossing of Stepana Bandery and Viacheslava Chornovola main streets in the city of Rivne. This intersection is characterized by constant significant traffic jams. To simulate traffic conditions at this intersection, the research used PTV Vissim software. Observations of traffic flows, their distribution by directions, and traffic light regulation parameters were used as initial data for modeling the intersection traffic scheme. The main problem that must be solved when considering isolated traffic light intersections is the calculation and optimization of the signal plan, which involves: determining the number of phases; determining cycle duration; distribution, i.e. determining parts of available green time for each phase; modeling of traffic situations that may arise due to the passage of priority vehicles, congestion of vehicles in intersection areas during peak periods or other situations. At the same time, it is necessary to achieve the best possible characteristics of the intersection functioning. According to the research results, the road conditions were modeled and the signal plan was optimized with the use of PTV Vissim software. Simulation modeling of the intersection included drawing a road network, installing traffic lights (signal controllers) with a description of their work (choosing the type of light signaling devices, creating signal groups and traffic light signals, parameters for coordinating signals), forming pedestrian zones and a node, performing calculations with subsequent analysis of the received data. In order to improve the efficiency of the intersection, two options for the operation of the traffic light controllers are offered: 1. Lengthening of the “green” phase, for the convenience of turning to the left (by reducing the phase of oncoming traffic in one direction by 5 seconds). The total duration of the cycle of 70 seconds will not change; 2. Implementation of the third phase – fully pedestrian in all directions, lasting 20 seconds. The total duration of the cycle will increase to 90 seconds. A more progressive measure can be the introduction of adaptive systems, which are based on new traffic monitoring technologies and allow obtaining accurate data on traffic flows in real time and performing adaptive control of traffic lights, that is, adapting the signal plan in real time to changes in traffic flows.

Amber rainbow ribbon effect in broadband optical metamaterials

Using the trapped rainbow effect to slow down or even stop light has been widely studied. However, high loss and energy leakage severely limited the development of rainbow devices. Here, we observed the negative Goos-Hänchen effect in film samples across the entire visible spectrum. We also discovered an amber rainbow ribbon and an optical black hole due to perfect back reflection in optical waveguides, where little light leaks out. Not only does the amber rainbow ribbon effect show an automatic frequency selection response, as predicted by single frequency theoretical models and confirmed by experiments, it also shows spatial periodic regulation, resulting from broadband omnidirectional visible metamaterials prepared by disordered assembly systems. This broadband light trapping system could play a crucial role in the fields of optical storage and information processing when being used to construct ultra-compact modulators and other tunable devices.

Optimal control of traffic light signals using stochastic simulation

Optimal control of traffic light signals using stochastic simulation