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Urbanization has led to accelerated traffic congestion, posing a significant obstacle to urban development. Traditional traffic signal scheduling methods are often inefficient and cumbersome, resulting in unnecessary waiting times for vehicles and pedestrians, exacerbating the traffic situation. To address this issue, this article proposes a dynamic traffic signal scheduling system based on an improved greedy algorithm. Unlike conventional approaches, we introduce a reward function and a cost model to ensure fair scheduling plans. A constraint function is also established, and the traffic signal scheduling is iterated through the feasible matrix using the greedy algorithm to simplify the decision-making process and enhance solution efficiency. Moreover, an emergency module is integrated to prioritize special emergency vehicles, reducing their response time during emergencies. To validate the effectiveness of our dynamic traffic signal scheduling system, we conducted simulation experiments using the Simulation of Urban Mobility (SUMO) traffic simulation suite and the SUMO traffic control interface Traci. The results indicate that our system significantly improves intersection throughput and adapts well to various traffic conditions, effectively resolving urban traffic congestion while ensuring fair scheduling plans.
The paper is structured as follows: Section II describes the current state of research in signal scheduling. Section III describes the scheduling environment and the simulation model used for algorithm verification. Section IV presents the principles of the modified greedy algorithm for the dynamic signal light scheduling system with changing fixed order. Section V discusses the results and provides an analysis. Section VI outlines future work and potential developments. Finally, Section VII offers a summary.
To validate the effectiveness of the dynamic signal light scheduling system in simulating road traffic flow, the Simulation of Urban Mobility (SUMO) traffic simulation suite, along with the SUMO Traffic Control Interface (Traci), is utilized for simulation and evaluation.
This article proposes a dynamic signal light scheduling system that utilizes an improved greedy algorithm to change the fixed sequence. By employing image recognition and signal transmission [ 3 , 4 ], traffic conditions at intersections are collected, and the optimization of traffic conditions is formulated as a problem. The problem is then divided into several time intervals, and reward, cost, and constraint functions are defined [ 5 , 6 ]. Based on whether the routes conflict, the best scheduling plan is selected. Furthermore, this system fully considers the actions of special emergency vehicles, such as ambulances and fire trucks, to ensure their timely response.
In recent years, the rapid development of big data and artificial intelligence has significantly improved the efficiency and stability of road traffic signal scheduling systems. Currently, the primary traffic light scheduling system still relies on extending and shortening the signal timing based on fixed sequences. However, this approach often leads to the occurrence of &#;empty green lights&#; wasting valuable traffic resources. Literature review
Given the prevalence of congested roads, traffic police frequently resort to manually adjusting the duration of traffic signals. However, this approach is slow to respond and inefficient. Consequently, traditional traffic light scheduling is no longer suitable for today&#;s diverse traffic conditions.
Conventional traffic light scheduling, with a fixed duration and fixed sequence, often results in the frustrating occurrence of &#;green light for vehicles-free&#; reducing the road capacity and wasting valuable time for people. This phenomenon, commonly known as &#;empty green lights&#; exacerbates traffic congestion by allowing traffic signals to turn green even when no vehicles are waiting to proceed. Consequently, valuable green light time is squandered, exacerbating the overall traffic flow inefficiency. Additionally, &#;empty green lights&#; can lead to unnecessary casualties as emergency vehicles, such as ambulances and fire trucks, are unable to reach their destinations promptly.
The number of motor vehicles is steadily increasing, particularly during peak commuting periods, leading to a rise in urban congestion. This congestion not only causes inconvenience to travelers but also contributes to increased fuel consumption and higher emissions of car exhaust [ 1 ]. Such air pollution poses serious health risks to residents [ 2 ] and conflicts with the principles of ecological civilization construction.
By concentrating on adjusting the fixed sequence of signals, we aim to reduce technical support and maintenance costs while preserving the flexibility of the scheduling system as much as possible. This balance may be particularly suitable for urban traffic management, where sensitivity to real-time changes is crucial, but economic considerations must be carefully weighed. Through an in-depth exploration of altering fixed-sequence signal scheduling systems, we hope to identify a cost-effective and flexible solution to meet the demands of urban traffic efficiently.
Therefore, to strike a balance between cost considerations and scheduling efficiency, we are inclined to focus our research solely on altering fixed-sequence signal scheduling systems. This approach retains a degree of system simplicity and controllability while adapting to fluctuating traffic flow by flexibly adjusting the sequence of signal timings. Compared to fixed-duration systems, this method may offer enhanced performance in urban areas with variable and dynamic traffic volumes.
The advantages of fixed-duration systems lie in their simplicity and controllability, making them suitable for relatively stable traffic patterns. However, in urban areas with fluctuating and dynamic traffic volumes, adaptive systems demonstrate clear advantages. These systems optimize traffic flow by adjusting signal timings based on real-time data, potentially reducing congestion and enhancing overall commuting experiences. Nevertheless, they may entail higher technical support and maintenance costs.
Adaptive traffic signal control systems employ two primary strategies to optimize traffic flow at intersections and road segments: adjusting fixed signal durations and altering the fixed sequence of signals. Fixed-duration systems, such as traditional traffic signal control, are suitable for situations where traffic flow remains relatively constant. These systems allocate green signal time based on preset intervals, providing stability but lacking adaptability to changing conditions. In contrast, adaptive systems like SCOOT and SCATS dynamically adjust signal durations and phase sequences in real-time, responding to the evolving traffic environment.
According to the operational situation, traffic signal controllers can be classified into two categories: timing signal controllers and adaptive signal controllers [ 7 ]. Timing signal controllers have fixed cycle lengths and green light splitting, while adaptive signal controllers can be adjusted dynamically, making the latter more effective in traffic control [ 33 ]. Despite the advances in various methods, designing effective and efficient urban traffic signal control systems still poses challenges.
As shown in , several traffic signal control systems, such as TRANSYT [ 24 , 25 ] and SCOOT [ 26 ] in the United Kingdom, SCATS in Australia [ 25 , 27 ], RHODES and OPAC in the United States [ 28 , 29 ], the CRONOS system in France [ 30 ], and the SPOT system in Italy [ 31 ], have been studied and developed worldwide.
Additionally, reinforcement learning has been applied to learn optimal signal control strategies [ 12 &#; 14 ]. The advanced Reinforced AIM (adv.RAIM) system, employing end-to-end Multi-Agent Deep Reinforcement Learning (MADRL), presents a novel paradigm for Autonomous Intersection Management (AIM) [ 15 ]. Its notable advantages over traditional traffic light control methods underscore its potential to revolutionize signal scheduling systems, offering improved adaptability and efficiency in traffic management. Van Der Pol investigated traffic signal coordination using a DQN, employing a testbed with a right-angle intersection and turn prohibitions. Simulation of Urban MObility (SUMO) was used for simulation, utilizing vehicle location from image data for the state, and selecting signal combinations as actions. The reward, a weighted average, considered vehicle delay, waiting time, stops, and signal changes [ 16 ]. Genders and Razavi optimized traffic signals using a DQN with a deep CNN. The testbed had a right-angle intersection with four lanes in each approach. SUMO simulated the scenario, extracting vehicle positions from images. The reward was based on the change in cumulative vehicle delay [ 17 ]. Evolutionary algorithms (EAs), including genetic algorithms and particle swarm optimization [ 18 &#; 23 ], have also been utilized for static timing optimization problems.
Road traffic lights have evolved significantly, moving away from the traditional fixed signal timing mode to embrace the more intelligent signal timing mode. In recent years, artificial intelligence (AI) has emerged as a promising solution for traffic signal control. Researchers have explored various AI-based approaches, such as fuzzy theory [ 7 , 8 ], fuzzy neural networks [ 9 ], and fuzzy control models [ 10 , 11 ], to design signal control schemes.
During each simulation, we gather vehicle and pedestrian information, which would typically be obtained from cameras and buttons in real-life scenarios, through Traci. Leveraging this data, we apply an improved greedy algorithm-based dynamic traffic signal control algorithm to generate the current optimal traffic signal control scheme. By using Traci and implementing the dynamic traffic signal control algorithm, we can effectively enhance the SUMO simulation to reflect real-world traffic conditions more accurately, enabling us to evaluate the performance of our proposed approach and optimize traffic signal timings based on the dynamic flow of vehicles and pedestrians.
In SUMO, the default traffic signal control relies on traditional static scheduling. However, we utilize Traci as a third-party library for Python, which serves as an interface connecting Python scripts to the traffic simulation software SUMO. This integration allows us to access real-time parameters from the simulation and control it accordingly.
As for special emergency vehicles, such as ambulances, they are designated with a cross displayed on their body above . These vehicles have the characteristic of ignoring red lights, enabling them to pass through the intersection without stopping when necessary, ensuring a swift response during emergencies. Additionally, the green elliptical object in symbolizes a pedestrian, indicating their presence within the monitored area.
In , the blue area represents the region where real intersection cameras detect vehicles. These cameras monitor this area to gather information about the presence and movement of vehicles, allowing for efficient traffic signal control. For pedestrians, the intersection itself provides pedestrian information, and their movements are monitored within the intersection area to ensure their safety.
Based on these advantages, we selected the open-source traffic simulation software SUMO as the simulation model for our dynamic traffic signal scheduling system. We created a map featuring a four-way intersection with three lanes (left turn, right turn, and straight) and one pedestrian lane in each direction, resulting in a total of sixteen routes in the road network requiring traffic control.
SUMO is an excellent traffic simulation software widely used in traffic planning, management, and intelligent transportation systems [ 35 , 36 ]. It offers several advantages: 1) it is open source, allowing users to utilize, modify, and distribute it freely; 2) it is flexible and capable of simulating various road traffic scenarios, automatically adhering to the regulations of the &#;Traffic Safety Law of the People&#;s Republic of China&#; for vehicle acceleration and deceleration; 3) It provides a user-friendly interface, detailed documentation, Python API, and command-line tools, as well as various formats of simulation data for easy analysis and system validation; 4) it is extendable, allowing users to customize algorithms, models, and write plugins to enhance its functionality.
Currently, we employ a pedestrian crosswalk button on the traffic light pole, which is a widely used approach in the United States [ 34 ]. This button serves multiple purposes: it facilitates traffic signal scheduling at intersections and ensures pedestrian safety while crossing the road. However, to accommodate special emergency vehicles, we propose equipping them with remote control devices that can communicate with emergency signal receivers installed in traffic signal lights, allowing for priority emergency scheduling. This measure aims to expedite the passage of emergency vehicles during critical situations.
To control all signal lights at an intersection and achieve global traffic signal scheduling, it is crucial to detect the traffic flow in each lane accurately. For vehicle detection, we propose the installation of a camera in each direction on the traffic light pole to monitor and integrate real-time traffic flow at the intersection. However, for pedestrians, relying solely on cameras may not yield optimal results due to challenges such as overlapping pedestrians, greenery, and building obstructions. Therefore, we need to explore more effective detection methods for pedestrian flow.
Dynamic time-sequencing optimization problems necessitate real-time adaptation to changing conditions, and one effective approach is through the application of greedy algorithms. Rooted in local optimization, these algorithms make sequential, myopic choices at each step, aiming to approximate the global optimum. This optimization challenge involves determining the most efficient arrangement of elements over time, considering the evolving nature of the system.
Building upon the broader application of greedy algorithms in optimization problems [37,38], our emphasis on dynamic time-sequencing optimization highlights the versatility of this approach. Recognized for their locally optimal decision-making, greedy algorithms play a pivotal role in resource-constrained scenarios, spanning time, space, and cost considerations. This adaptability proves especially valuable in domains like sensor scheduling, where these algorithms excel in minimizing estimation errors and enhancing overall system performance [39]. In the context of production scheduling, the Iterated Greedy Algorithm has proven efficient in resolving intricate problems [40]. Moreover, our focus on dynamic time-sequencing optimization resonates in domains such as wind turbine positioning, where the integration of the greedy algorithm with incremental calculation and iterative adjustments surpasses alternative methods, establishing itself as a highly effective strategy for optimal solutions [41]. This underscores the relevance and efficacy of the greedy algorithm in addressing a spectrum of optimization challenges.
To address the dynamic traffic light scheduling optimization problem, characterized by the imperative to minimize traffic congestion and waiting times while enhancing overall traffic efficiency, the application of the greedy algorithm proves instrumental [42]. Greedy algorithms, renowned for their ability to make immediate, locally optimal decisions, exhibit a fast execution that aligns seamlessly with the real-time demands of traffic management. Through the utilization of greedy algorithms in this context, our approach significantly improves traffic efficiency and reduces congestion, accomplishing the objectives of dynamic traffic light scheduling.
To further optimize traffic flow control, we propose introducing reward functions, cost functions, and constraint functions to design more efficient greedy algorithms. Specifically, we obtain information on the number of vehicles and pedestrians on the current road, establish reward and cost functions to calculate the total loss value of each route, and define constraint functions. The algorithm then selects the first route and iteratively schedules the signal lights through the feasible matrix, dynamically adjusting the signal light status on each route to optimize traffic flow. The utilization of greedy algorithms enables the division of the optimized traffic flow into several periods, simplifying the decision-making process and significantly improving solution efficiency. This approach facilitates the efficient and rapid transmission of dynamic signal light scheduling decisions to signal light devices, as depicted in .
To find out the best signal scheduling scheme in the current time interval, we need the current traffic situation at the intersection, and we take the average waiting time of vehicles and pedestrians in the period as the current traffic situation.
We divide the optimization traffic flow problem into several time intervals and calculate the optimal scheduling scheme for each interval. Decomposing the problem into smaller intervals enhances the efficiency and accuracy of the solution, thereby maximizing traffic flow.
The greedy strategy is designed to assign green lights to the direction with the highest vehicle traffic passing through the intersection at each time interval. The traffic light status of the intersection is determined based on the detected traffic flow status at each intersection ( ).
Open in a separate windowTaking into full consideration the disadvantages of the local optimal solution caused by the greedy algorithm, the cost function can be employed to prevent a constant state of red lights in specific lanes, reduce the standard deviation of the average vehicle waiting time, and ensure fairness for both vehicles and pedestrians. The rules for setting the cost function are as follows:
Pi={0Notrafficontheroad1Thefirsttrafficontheroad(Pi&#;1+1)Pi&#;1otherwise
(1)
Thereinto, i is the route i at an intersection, Pi is the generation value assigned to each route.
To determine the optimal state of the current traffic lights, we consider the reward value associated with allowing traffic on specific routes, which represents the benefit of permitting traffic flow on those roads. Set the reward function to be equal to the cost function, namely Ri = Pi.
As shown in , the intersection consists of four directions: east, west, south, and north, which are controlled by corresponding traffic lights, resulting in a total of sixteen routes. These routes encompass various movements, including West - Left, West - Direct, West - Right, West - Pedestrian, South - Left, South - Direct, South - Right, South - Pedestrian, East - Left, East - Direct, East - Right, East - Pedestrian, North - Left, North - Direct, North - Right, and North - Pedestrian. Among these sixteen routes, some are independent of each other, while others conflict with each other. To represent the relationship between routes, we define a 16*16 two-dimensional matrix A. If matrix[i][j] is 1, then route i is independent of route j. If matrix[i][j] is 0, then route i conflicts with route j. If matrix[i][j] is -1, then route i and route j are the same. Thus, we can initialize the independent matrix A according to the traffic rules.
Open in a separate windowFor the independent matrix A, we need to transform it as follows:
E[i][j]=A[i][j](1,&#;1&#;0,0&#;1)&#;E=[]
(2)
Based on the reward function and the cost function, the cost value of the traffic on that road can be calculated as Ci(i = 1,2,&#;,16). The cost value of each road Ci is calculated as follows:
Ci=&#;i=116(E[i][j])*Pi
(3)
With reward value and cost value, the loss value for each road over the current period can be calculated.
M=Ci&#;Ri
(4)
At the same time, to solve the situation that different routes have the same loss value, we introduce a constraint function &#;n, Set the value of the initial constraint function for each road to 0. The value of the route constraint function that is not enabled in the nth dispatch is increased by 1, and the value of the enabled road constraint function is reset to 0. The rules for setting constraint functions are as follows.
{εn=0,irouteisenabledεn=εn&#;1+1,irouteisnotenabled
(5)
Selecting the route with the minimum loss value M as the first path in the scheduling scheme.
Upon selecting the first route, continuous iterations are performed based on the current traffic conditions. Traffic signal states for all directions are updated within fixed time intervals to achieve the optimization of traffic flow.
As shown in the traffic section of above, after solving the first route using the greedy strategy, we proceed to solve the remaining routes. To facilitate this process, we transform the independent matrix A as follows to obtain the one-dimensional feasible array F:
{S[i][j]=A[i][j](&#;1&#;0,1&#;1,0&#;0)&#;S=[]Fi=S[i]
(6)
Open in a separate windowAccording to F the calculated cost value:
{Cj=&#;i=116(E[i][j])*Pi*FiMj=Cj&#;Rj
(7)
After finding the second route with the least loss value, it is necessary to iteratively update the variable F according to the following strategy for the nth iteration:
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F=S[i]*S[j],εn&#;1&#;εn,Mn&#;1&#;Mn
After iterating until the feasible array F is all 0, the scheduling is completed, and the optimal traffic flow is found. The traffic conditions after the dispatch are shown in below, demonstrating that the scheduling algorithm can effectively alleviate the traffic congestion problem.
Open in a separate windowWe propose the introduction of an emergency module in the traffic light dispatch system to prioritize the passage of special emergency vehicles, such as ambulances and police cars, thereby enhancing the efficiency and safety of the city&#;s transportation system.
In the dynamic signal dispatch system&#;s emergency module, our primary objective is to ensure traffic safety and prioritize public interest during emergencies. Different priorities can be assigned to various vehicles and pedestrians in the traffic scheduling algorithm. During emergencies, emergency service vehicles like police cars, ambulances, and fire engines are granted the highest priority to ensure the uninterrupted execution of their critical tasks. Consequently, we optimize the traffic scheduling mechanism based on the priority algorithm.
The vehicle type-based priority system is a traffic management approach that classifies vehicles according to type and assigns traffic priority accordingly. This method categorizes vehicles into two groups: emergency service vehicles and the rest of the vehicles. Emergency service vehicles, such as ambulances, police cars, and fire engines, are granted top priority due to their crucial role in responding to emergencies and ensuring rapid arrival at their destinations. The rest of the vehicles, excluding emergency service vehicles, are assigned the second priority to discourage private vehicle usage during peak periods.
Pi represents the priority of route i; if an emergency vehicle is present on route i, Pi = 1, otherwise, Pi = 0. Mi represents the loss value on route i under normal scheduling. An emergency scheduling Ei can be determined using a function:
Ei={0,Pi=01,Pi=1andMiistheminimum
(8)
In this function, the values of Pi and Mi determine the final emergency scheduling scheme Ei = 1 indicates that route i is designated as an emergency passageway, while Ei = 0 means route i is not designated as an emergency passageway. When an emergency vehicle is detected on the road, the emergency module is immediately activated, indicating Pi = 1, prioritizing the passage of route i. If multiple routes have Pi values equal to 1, the loss values Mi are compared, and the route i with the smallest Mi value is selected, setting Ei = 1 as the first emergency route. The process iterates, considering other routes with Mi = 1 until the feasible array F for routes with loss value Mi = 1 is all 0. Then, routes without emergency vehicles are iterated. When all initial routes have Ei = 0 indicating no emergency situation, the normal traffic light scheduling module is executed.
By considering both the priority (Pi) and traffic route loss values (Mi), the model effectively determines the emergency scheduling (Ei) for the roads, enabling rapid adjustments in traffic flow during emergencies. Additionally, the model intelligently selects routes with the minimum loss values when multiple roads face emergency situations, progressively managing other routes to minimize traffic congestion and enhance efficiency. Its flexibility and adaptability are commendable, allowing adjustments according to changing traffic conditions. Furthermore, even in the absence of specific emergencies, the model seamlessly executes normal traffic light scheduling, ensuring the smooth operation of the traffic system.
The improved greedy algorithm is implemented using the Python programming language and PyCharm software. The code is shown in Algorithm 1.
Algorithm 1 The code of the improved greedy Algorithm
Input: emergency_vehicles_list, normal_vehicles_list, pedestrian_list, dispatch_interval
Output: Traffic Light Scheduling Scheme
1: Function TrafficLightScheduler(emergency_vehicles_list, normal_vehicles_list, pedestrian_list, dispatch_interval):
2:&#;traffic_light_matrix = InitializeTrafficLightMatrix()
3:&#;Loop Forever:
4:&#;&#;selected_lights = []
5:&#;&#;total_loss = CalculateTotalLoss(traffic_light_matrix)
6:&#;&#;feasible_array = GenerateFeasibleArray(traffic_light_matrix)
7:&#;&#;constraint_value = CalculateConstraintValue(traffic_light_matrix)
8:&#;&#;&#;has_emergency_vehicle = CheckForEmergencyVehicles(emergency_vehicles_list)
9:&#;&#;If has_emergency_vehicle:
10:&#;&#;&#;&#;HandleEmergencyVehiclePriority(traffic_light_matrix)
11:&#;&#;&#;Else:
12:&#;&#;&#;&#;SelectNormalTrafficLights(traffic_light_matrix)
13:&#;&#;feasible_array = UpdateFeasibleArray(feasible_array)
14:&#;&#;If feasible_array is not empty:
15:&#;&#;&#;new_selected_lights = OptimizeLightSelection(feasible_array)
16:&#;&#;&#;selected_lights.extend(new_selected_lights)
17:&#;&#;WaitFor(dispatch_interval)
18:&#;End Loop
19:&#;Return selected_lights
20: End Function
Street lighting plays an important role in cities. When night falls, they are activated, however, sometimes due to malfunctions, the lights do not function properly and even remain on during the day. In some cases, the brightness of the lamps is still too high late at night when there are few people and cars. These all reflect the failure of effective management of road lighting. Street lighting control is a technology used to manage and optimize the operation of street lights. It offers a variety of options, including traditional switches, photo sensors, dimming (intelligent control), time control, motion sensors, wireless communications, and more. Selecting the appropriate control method depends on several factors, including project needs, budget, energy efficiency goals, and the capabilities of the street manager. This article will focus on these different street light control methods, and their respective advantages and disadvantages, hoping to help street light providers, project managers and street system operators more fully understand these control methods, so as to optimize the use of road lighting and improve the performance of street lights.
Street lighting control systems such as traditional switches, photocell, dimming (intelligent control), time control, motion sensors, and wireless communications have their necessity and benefits. Different types of street lighting controls provide a variety of benefits that not only help improve the efficiency, safety and sustainability of urban lighting systems, but also promote the development of smart lighting. Here are some common benefits of different types of street lighting controls:
Traditional switches are a common way to control lighting, whether they are found in homes, offices, or in industrial and outdoor settings. These simple and affordable devices control lights on and off by connecting or breaking an electrical circuit. One of the main advantages of traditional switches is their simplicity. They are easy to use and require no additional equipment or complicated installation processes. Just press or turn the switch to control the status of the lamp. Another obvious advantage is the low cost. Traditional switches are more affordable than other more complex lighting control systems, making them ideal for projects on a budget. In the street lighting, traditional street lighting systems usually use traditional power switches to turn on and off power. The power supply is transmitted via underground cables to street light poles or facilities, and power switches are installed near a group of street light poles. After these street light systems are connected to the power supply network, street light managers can manually control the on and off circuits through switches to control the turning on and off of street lights.
However, traditional switches also have some limitations. Their functions are limited and can only achieve basic switching operations and cannot meet other lighting needs, such as dimming. Additionally, they require manual operation, which may be inconvenient in some situations. For large facilities or outdoor areas, planning requirements are higher and not easy to achieve. Therefore, some traditional street lighting systems are usually equipped with timers and photocell controllers in the distribution cabinets, through which the street lights can be automatically turned on and off based on the timer (preset schedule) and photocell controller (according to the ambient light level). This not only saves manpower but also ensures adequate lighting for the road when needed.
Although traditional power switching systems are still widely used in many places, dimming control has also become increasingly popular in street lighting in recent years. This dimming method is generally equipped with a dimmer and a dimmable LED driver. The former can emit or indirectly emit dimming signals such as PWM, 0-10V and DALI, while the latter receives these signals and adjusts the output of the LED driver as needed to achieve street light brightness adjustment. This method of dimming has many benefits. First, it helps save energy and money by reducing electricity consumption and extending the life of street lights. For example, by dimming street lights by 50% in the middle of the night, we can save about 40-50% of energy. Another benefit is that users can customize the brightness of street lights based on project needs, real-time road conditions and other special situations. Among them, wireless intelligent control systems are the most widely used in street lighting, and their rise has changed the way street lights are controlled. Smart street light systems use wireless communication technology and Internet connections to remotely monitor and control street lights, improve the efficiency of energy management, and provide more flexible control options to adapt to different lighting needs. In the field of street lighting, it usually needs to be used with NEMA and Zhaga socket instead of traditional wired wiring. If the project requires wireless intelligent control at the beginning, we can equip the customer with the corresponding wireless control module, and the customer needs to conduct corresponding learning to achieve real-time street light control. If there is no need for this at the beginning, the NEMA and Zhaga sockets of street lights can also facilitate future project upgrades. Of course, wireless control also has some disadvantages, such as higher initial investment, longer learning period, and the installation of centralized controllers. These shortcomings limit its wide application to a certain extent, but with the development of society, wireless intelligent control will gradually become popular, leading cities to a digital and intelligent future.
Dimming street light with smart controlSensors work by detecting changes in the environment and then sending signals to the lighting system, which adjusts the light output accordingly. There are two main types of sensors used for lighting control: occupancy sensors (motion sensors) and phtotocell sensors.
Microwave sensors are another common control technology in street lights, which use microwave radiation to detect motion and presence in the surrounding environment and automatically adjust the brightness of the street lights based on the detection results. Similar to infrared (PIR) sensors, it is also a type of occupancy sensor because it is used to detect the presence of people in a space. In road lighting applications, when there is no one under the streetlight lighting area, the sensor will not be triggered and the streetlight will operate at low power. When the sensor detects a person or vehicle in a specific area under the streetlight, it triggers corresponding controls, causing the streetlight to operate at higher power to meet the visual needs of pedestrians and drivers.
As mentioned earlier, one of the main advantages of microwave sensors is to reduce unnecessary lighting while ensuring that sufficient lighting is provided when necessary, helping to reduce energy consumption and electricity costs. It also provides convenience and enhanced safety since users do not need to manually adjust or turn these lights on and off. This makes microwave sensors widely used in areas with low traffic volume in the middle of the night, such as parking lots, roads and parks, as well as indoor lighting, such as corridors, warehouses and promenades.
However, there are some drawbacks to microwave sensors, such as the fact that they can be triggered by any moving object, including small animals, insects, and trees, resulting in unnecessary switches that can be uncomfortable or distracting. At the same time, microwave sensors have limited detection capabilities for fast-moving objects. In addition, frequent switching and adjustment will bring certain tests to the service life of microwave sensors and lamps.
Street light with motion sensorPhotocell (Photo sensor) is a common control technology in street lamps. It uses light-sensitive components to sense the surrounding ambient light level and control the switching status of the lamp according to the light intensity. When the ambient light is strong, the lamp will automatically turn off and vice versa. In addition, some light controls are also equipped with dimming functions, which will gradually increase the brightness of the street lights as the environment darkens.
One of the main advantages of light sensors is their plug-and-play nature. For example, once a streetlight is equipped with a light control, users no longer need to worry about turning the light on and off manually because the photocell will automatically complete this task. In addition, the application of photocell helps reduce energy consumption, thereby extending the service life of lamps. In general, photocell is a simple, low-cost and relatively energy-saving control method. However, photo sensors also have some potential drawbacks. One issue is their sensitivity to changes in natural light levels, which can cause the light sensor to incorrectly perceive true ambient light levels, as buildings, trees and dirt on the light sensor lens can all affect its judgment. Additionally, it&#;s not always very smart, as it may not proactively reduce lighting brightness to save energy, even late at night when there&#;s less traffic.
Street light with photocellIn street lighting systems, this type of timer is often used to control the operation of street lights according to a predetermined schedule to ensure that sufficient light is provided when needed during the night and turned off during the daytime to save energy.
In contrast, &#;timer dimming&#; divides the night into different stages based on the changing characteristics of pedestrian and vehicle flow at various times of the night. For instance, during the first stage when the flow of vehicles and pedestrians is high, street lamps operate at full power. In the second stage, as the number of vehicles and pedestrians gradually decreases, the lamps start operating at reduced power. In the third stage (usually from midnight to 4:00 AM), the lamps run at a lower power level, such as 20%, as there are minimal pedestrians and vehicles at that time. In the fourth stage, the lamps return to full power operation as early morning city activities resume, and adequate lighting is needed to ensure pedestrian and driver safety. It&#;s evident that &#;timer dimming&#; is an enhanced version of a standard &#;timer,&#; better suited to the characteristics of street lighting. When combined with a photocell, it is widely used in road lighting. For more details, please refer to our other blog timer dimming.
It&#;s worth noting that both &#;timer&#; and &#;timer dimming&#; are highly beneficial for automating lighting control, improving energy efficiency, reducing operating costs, and ensuring the right level of street lighting at the appropriate times. However, timers can make setting up and adjusting schedules complicated or challenging. For example, seasonal changes necessitate frequent adjustments to accommodate shifts in sunrise and sunset times. Furthermore, once &#;timer dimming&#; settings are configured, making real-time and flexible changes to dimming settings can be difficult.
Street light with timer and timer dimmingIn the previous chapter, we introduced in detail the various control methods of street lights, their principles, and their advantages and disadvantages. Now we make the following summary:
Lighting ControlsFeatureProsConsTraditional switchTurn lights on or off by Switch on or off the electrical circuitWidely used; Low costNo dimming function; Depend on manual operationDimming + smart controlAdjust the brightness of light in real timeSave energy and money; Customize the light level according to requirementHigh cost; Training is required before operationMotion sensor(Occupancy sensor)Detect the present or motion of pedestrians/vehicles under the lamp and adjust the lighting level accordinglyMinimize energy usage and reduce electricity cost; Enhance convenience and safetySometimes insensitivity; False triggers by animals/leavesPhotocell(Daylight sensor)Detect the ambient light level and turn on/off lighting accordinglyEasy for application(plug and play);Reduce labor costsNo dimming; False triggers when the window is blocked by trees, construction and othersTimer or timer dimmingSet timelines for lights to turn on/off and dimmingSave energy and reduce costs; Meet the lighting standards and dark sky guidelinesThe light level cannot be adjusted in real time; Timelines can not easily be re-programmed afterwardsZGSM is a professional manufacturer of LED lights. ZGSM has a wealth of knowledge and project experience in street lighting control. At the same time, our streetlights can be equipped with various features, including photocell control, microwave sensing, dimming power supply with NEMA/Zhaga interfaces, time-based dimming power supply, and intelligent control, among others. We are well-versed in configuring these features, and if you have such requirements, please feel free to contact us. Below are various series of streetlights offered by ZGSM for your reference.
This article introduces in detail the various controllers of street lighting, including traditional switches, photocells, timer dimming, intelligent dimming, microwave sensors and wireless communications. Each of these different control methods has a set of advantages and disadvantages, and understanding these advantages and disadvantages can help us better select the control method suitable for a specific project. Intelligent control technology is currently at the most advanced stage. Although it has excellent performance, it also requires road operators to have corresponding professional knowledge and operating skills, and its cost is also relatively high. In contrast, technologies such as light-controlled switches, time-division dimming and microwave sensors are relatively mature and low-cost, but they have some shortcomings, such as false responses, inability to adjust light in real time, and inability to quickly modify settings. Wireless communication technology is an emerging technology, but it is more suitable for the civilian field. ZGSM provides a variety of street lighting solutions, but we encourage readers to learn about various street lighting control methods through this article and choose the appropriate method based on the needs of their own projects. If you need more information, feel free to contact us.
Dusk-to-dawn is also called light control. It and motion sensors are two common lighting control technologies in street lighting. Dusk to dawn light control turns lighting equipment on and off based on changes in natural light levels, it turns the fixture on after the sun has set (dusk) and turns it off when the sun rises in the morning (dawn), hence the name dusk to dawn sensor. Motion sensors use infrared or microwave technology to detect objects or object movement. There are certain differences between the two. Taking the microwave sensor as an example, it will start lighting when there are moving objects under the lamp. It is suitable for determining the switch and brightness of the lamp based on the occupancy (activity) in the area, such as warehouses and parking lots.
In this case, it&#;s usually because the light fixture is malfunctioning. Dusk to dawn sensors (Photocell or photosensor) usually operate in &#;fail on&#; mode, which means that if the sensor itself fails, the light fixture will remain on during the day to alert the user that the fault is with the dusk to dawn sensor, while the problem isn&#;t from LED driver or LED module. In addition, it is also possible that some external factors cause the light control sensor to incorrectly detect that the ambient light is too dark and trigger the lamp to light up, such as the threshold setting is too high or the photosensitive window is blocked (for example, trees, buildings, etc.). These factors may cause false triggering of the light control sensor. Therefore, when encountering this situation, you need to first check whether the light control is accidentally triggered. If it is not a false trigger, please replace the old light control with a new one and try again. By this way you will find the cause of the problem.
Photocell sensors are different from microwave/PIR sensing. Rather than detecting motion or heat signatures, it measures ambient lighting levels, turning lights on at dusk and off at dawn. However, with the advancement of technology, some manufacturers are now integrating microwave sensors into light controls, so that lamps can achieve the following functions:
My name is Taylor Gong, I&#;m the product manager of ZGSM Tech. I have been in the LED lights industry for more than 13 years. Good at lighting design, street light system configuration, and bidding technology support. Feel free to contact us. I&#;m happy to provide you with the best service and products.
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