Street Light Poles Research Articles

Image Recognition and Early Warning System of Urban Waterlogging Based on Tensorflow

In recent years, heavy rainfall caused by low-lying roads, under-worn overpasses and tunnels to produce a large amount of water phenomenon occurs from time to time, to people’s travel brought great inconvenience, serious even will cause people’s lives, property and major losses. With the rapid development ofIo T technology and 5G, smart street light poles have become a hub for urban data transmission due to their wide range and integrated functions such as lighting control, environmental monitoring, 5G micro-base stations and meteorological monitoring. Therefore, with the powerful function of smart street light pole, the whole process of rain and sewage supervision can be realized in urban areas. Water conservancy departments can use the system as a whole to grasp the flood situation in the entire urban area, timely drainage scheduling, and can use TensorFlow’s original facilities to achieve early warning information LED screen display, video surveillance, sound column alarm and other functions to remind pedestrians to pay attention to safety.

Smart Street Lighting System using IoT and Cloud Computing

Street lights account for the maximum energy expense for any city. A smart street lighting system can cut out lighting costs by almost 70%. A smart street lighting system is a system that adjusts light output based on usage and movement, i.e., automatically detecting movements of either pedestrians or vehicles in the vicinity. For effective street light management, we propose installing a wireless-based system to remotely track and control the energy consumption of street lights of a particular city in real-time and take necessary energy conservation measures through power conditioning and adaptive control. The proposed system should be installed on the street light poles which consist of a wireless module that continuously transmits data to back-end IT systems where we can uninterruptedly monitor the condition of the lights. We can control the LED street lighting based on traffic flow in that particular area. The data from the system can be transferred to a central server using wireless technology and Cloud to have global access to the system. We can automate the system to switch on or off the lights at specific timings. We can also easily identify any glitches or failures in our system because everything is connected over the Internet.

Effects of land use and vegetation on nighttime air temperature in heat vulnerable neighborhoods in New York City

Background: Climate change is projected to increase the frequency and duration of extreme heat days in New York City (NYC), increasing heat risks among vulnerable populations. A Heat Vulnerability Index (HVI) previously developed in NYC based on epidemiological analysis, facilitated the City’s heat mitigation initiatives (i.e. planting street trees in high heat-vulnerable neighborhoods). The initiative also allowed an assessment of the role of land-use and vegetation on air temperature variations within these neighborhoods. Methods:Nearly 500 temperature sensors were installed on street trees and light poles in 16 medium to high HVI neighborhoods, with approximately 35 sensors within each neighborhood of size up to 1 km2, during the summer of 2018. The average nighttime air temperatures (3 to 5 am) at these locations were modeled as a function of buffer-based (along street blocks) tree canopy, grass/shrubs, impervious surfaces, and buildings, allowing for non-linear relationships in a land-use regression model, adjusting for inter-neighborhood variations as a smooth function of XY coordinatesResults:With all predictors, the model R-squared was 71%. Street block level grass/shrub density was negatively associated with nighttime air temperature, with a ~0.8 degrees °C reduction within its range (0-25% cover). Street block level tree-canopy was also negatively associated with nighttime temperature, though it was strongly modified by street orientation: a ~0.4 degrees °C reduction for east-west streets over its range 0-60% cover, but null for north-south streets, likely influenced by both sunlight duration and wind patterns. Street block level impervious surface was positively associated with nighttime temperature, with ~0.2 degrees °C increase over its range of 5 to 50% coverage. Conclusions: In this high density, hyper-local temperature data measured in heat vulnerable neighborhoods, vegetative covers were associated with reductions in nighttime air temperatures. Parameters obtained from this study will be incorporated into modeling and projections for future heat mitigation planning.

Effects of traffic-regulated street lighting on nocturnal insect abundance and bat activity

New technological developments modulate the light levels of LED street luminaires according to traffic volumes: light levels are increased given traffic and reduced in its absence. Such dimming of street lights reduces the level of artificial light at night (ALAN) and may thus contribute to mitigate light pollution. To quantify the impact of traffic-driven dimming of street lights on nocturnal insect abundance and bat activity in comparison to full light (i.e., dimming functions of luminaires switched off), we mounted 20 insect flight-interception traps and ten batloggers on street light poles along two dimmable street light sections. Insect abundance and bat activity were measured alternately with one week of full street lighting followed by a week with light levels modulated by traffic volumes. In total, 16 dimmed and 16 full-light days were investigated. Overall, traffic-driven dimming reduced light levels by 35%. Weather conditions (warm, dry nights) were the main drivers of insect abundance and bat activity, but traffic-driven dimming resulted in lower numbers of insects caught and reduced bat activity. Among insect groups, Heteroptera benefited most from dimming. For bats, urban exploiters (Pipistrellus spp.) benefited from increased availability of prey at brightly lit street lights, while less frequent species (Myotis spp.) did not benefit from street lighting. We conclude that street light dimming technology may contribute to mitigate negative effects of ALAN on nocturnal organisms, although the measure may not be efficient enough to support light-sensitive and threatened species.

Development trends and construction strategies of smart city and the ubiquitous power Internet of Things with smart streetlight pole as carrier

Development trends and construction strategies of smart city and the ubiquitous power Internet of Things with smart streetlight pole as carrier

Strretlight pole metal as the medium of Tetragonula sp nest in Indralaya, South Sumatra

An investigation had been done to Tetragonula (Tetragona) sp nest at Indralaya, South Sumatra to describe the Tetragonula sp nest that use streetlight pole as nest medium during April - May 2019. Purpossive sampling is used to select the target nest. Two streetlight pole found be used by Tetragonula sp as their home. The coordinate of location, heght from ground surface, diameter of streetlight pole, air temperature and humidity, and floral species around nest, were noted. Spot coordinate are S 30 14’ 19.2498’’ and E 1040 39’ 15,3288’’ ; 1,5 m above the ground surface, 12 cm diameter pole, highest air temperature was 35 o C at daylight (April and May 2019), 80 – 90 % humidity at April-May 2019; which Switenia macrophyla, Hevea brasiliensis, Zea mays, and Citrullus lanatus floral species are planted around. Air temperature in the pole is very high, around 40 0 C during daylight.

Circularly Polarized RFID Tag Antenna Design for Metallic Poles Using Characteristic Mode Analysis

This letter presents a novel circularly polarized ultra-high frequency radio frequency identification (902–928 MHz) tag antenna design for metallic poles using characteristic mode analysis (CMA). The traditional tags are usually mounted on the surface of the pole, which is a vulnerable position for tag mounting since it can be easily broken or anyone can easily remove the tag from the pole surface, whereas the proposed tag is placed on the chassis of the metallic pole with the metallic pole cut through the center of the tag. According to the CMA, the conical radiating mode will not be so effective in our application because it will provide the main beam in an upward direction, which made this tag difficult to read from the side direction. Therefore, the proposed tag exploits the broadside radiating mode as the optimal radiation mode to meet the application requirements such as main beam direction at 45° to provide long read range. Moreover, a Wilkinson power divider is used to provide equal power with a 90° phase difference to form the circularly polarized radiation. Furthermore, the proposed tag provides a read range of more than 7 m with and without the metallic pole in the center of the tag. The simulated and measured results show that the proposed tag design is a suitable candidate for tagging metallic poles such as traffic signal, guide boards, and street light poles, which is vital for the Internet-of-Things applications.

Semantic segmentation of road furniture in mobile laser scanning data

Road furniture recognition has become a prevalent issue in the past few years because of its great importance in smart cities and autonomous driving. Previous research has especially focussed on pole-like road furniture, such as traffic signs and lamp posts. Published methods have mainly classified road furniture as individual objects. However, most road furniture consists of a combination of classes, such as a traffic sign mounted on a street light pole. To tackle this problem, we propose a framework to interpret road furniture at a more detailed level. Instead of being interpreted as single objects, mobile laser scanning data of road furniture is decomposed in elements individually labelled as poles, and objects attached to them, such as, street lights, traffic signs and traffic lights.In our framework, we first detect road furniture from unorganised mobile laser scanning point clouds. Then detected road furniture is decomposed into poles and attachments (e.g. traffic signs). In the interpretation stage, we extract a set of features to classify the attachments by utilising a knowledge-driven method and four representative types of machine learning classifiers, which are random forest, support vector machine, Gaussian mixture model and naïve Bayes, to explore the optimal method. The designed features are the unary features of attachments and the spatial relations between poles and their attachments. Two experimental test sites in Enschede dataset and Saunalahti dataset were applied, and Saunalahti dataset was collected in two different epochs. In the experimental results, the random forest classifier outperforms the other methods, and the overall accuracy acquired is higher than 80% in Enschede test site and higher than 90% in both Saunalahti epochs. The designed features play an important role in the interpretation of road furniture. The results of two epochs in the same area prove the high reliability of our framework and demonstrate that our method achieves good transferability with an accuracy over 90% through employing the training data of one epoch to test the data in another epoch.

Hierarchical extraction of pole-like objects from scene point clouds

We present a hierarchical extraction algorithm to extract pole-like objects (PLOs) from scene point clouds. First, the point clouds are divided into a set of data blocks along the x- and y-axes after computing the dimensionality structure of each point. An effective height constrained voxel-based segmentation algorithm is proposed to segment the scene point clouds. The adjacent voxels with similar heights are grouped into an individual object based on the spatial proximity and height information. The objects consisting of a smaller number of voxels and most of the linear points are extracted and regarded as the pole-like candidates (PLCs). Then a Euclidean distance clustering algorithm is adopted to segment the PLCs and remove the floating and short segments. Next, each PLC is divided along the z-axis to extract the vertical structure. The straightness of the vertical structure is computed to remove the false PLOs. Finally, a collection of characteristics, such as point distribution and size, are applied to classify the PLOs into a street light pole, high-mast light, beacon light, and single pole. The experimental results demonstrate that our method can extract PLOs quickly and effectively.

Using tweets to understand changes in the spatial crime distribution for hockey events in Vancouver.

The use of social media data for the spatial analysis of crime patterns during social events has proven to be instructive. This study analyzes the geography of crime considering hockey game days, criminal behaviour, and Twitter activity. Specifically, we consider the relationship between geolocated crime‐related Twitter activity and crime. We analyze six property crime types that are aggregated to the dissemination area base unit in Vancouver, for two hockey seasons through a game and non‐game temporal resolution. Using the same method, geolocated Twitter messages and environmental variables are aggregated to dissemination areas. We employ spatial clustering, dictionary‐based mining for tweets, spatial autocorrelation, and global and local regression models (spatial lag and geographically weighted regression). Findings show an important influence of Twitter data for theft‐from‐vehicle and mischief, mostly on hockey game days. Relationships from the geographically weighted regression models indicate that tweets are a valuable independent variable that can be used in explaining and understanding crime patterns.

Construction of remote control for web based street light pole

Background/Objectives: It is not easy to ascertain a maximum electricity production in case of solar energy generation and there is problem not to recognize failures for a long time even iffailures occurred in facility. Measures need to be taken not only to check irregularities and replacement time but to maximize overall efficiency of solar energy generation system.Methods/Statistical analysis: It is necessary to build a control system based on web to analysis of various statistics information and real time index visibility. It is also necessary to verify SMS notification in the number of lightning in case of the falling of a thunderbolt, date and time of the occurrence, and correct operation of circuit breaker in the connection board.Findings: It is needed to be introduced the system which not only considers excessive communication cost as well as failure treatment of communication device and replacement cost but also makes full use of a dispersed generating system. Control system which is able to do scientific action of making up for fault and proper monitoring as well as maintenance in a solar power plant is constructed in this study.Improvements/Applications: Smart control system that electricity generation is stopped or reactivated in case of emergency through making an autonomous judgment of difficulties or faulted conditions will be extended. Technology of new renewable energy such as wind power and geothermal power will be applied through enhanced design technology of communication interface.

DETECTION OF STREET LIGHT POLES IN ROAD SCENES FROM MOBILE LIDAR MAPPING DATA FOR ITS APPLICATIONS

Abstract. Identification of street light poles is very significant and crucial for intelligent transportation systems. Automatic detection and extraction of street light poles are a challenging task in road scenes. This is mainly because of complex road scenes. Nowadays mobile laser scanners have been used to acquire three-dimensional geospatial data of roadways over a large area at a normal driving speed. With respect to the high density of such data, new and beneficial algorithms are needed to extract objects from these data. In this article, our proposed algorithm for extraction of street light poles consists of five main steps: 1. Preprocessing, 2. Ground removal, 3. 3D connected components analysis, 4. Local geometric feature generation, 5. Extraction of street light poles using Bhattacharya distance metric. The proposed algorithm is tested on two rural roadways, called Area1 and Area2. Evaluation results for Area1 report 0.80, 0.72 and 0.62 for completeness, correctness and quality, respectively.

Accessibility evaluations for nighttime walking and bicycling for low-income shift workers

This paper addresses an important issue related to nighttime commuting of low-income shift workers who walk and/or bike to their workplace using public transit. A shift worker is anyone who follows a work schedule that is outside of the typical daytime working hours of a business day and commute after dark - by walking or bicycling to a transit stop. However, poor visibility conditions on sidewalks and bicycle lanes often thwart safety of their walking and bicycling activities. Therefore, this paper develops two simple scores - nighttime accessibility score for walking (NASW) and nighttime accessibility for bicycling (NASB) - for evaluating nighttime infrastructure for pedestrians and bicyclist. The scores consider the employment data, travel time and the physical distribution of streetlight poles along the sidewalks and bicycle lanes. Data from the city of El Paso in Texas is used to demonstrate the applicability of the two scores. Employment data from three prominent service industry sectors known to employ low-income shift workers – i) Retail Trade, ii) Accommodation and Food Services, and iii) Health Care and Social Assistance – are used for demonstration purposes. It is observed that amongst the three sectors analyzed, both NASW and NASB values are higher for regions in El Paso with low-income employment concentrations from the Health Care and Social Assistance sector. It is also observed that some prominent regions in north-east, south-east and west of El Paso need improvements in streetlight systems, sidewalks and bicycle network to facilitate walking and bicycling amongst low-income shift workers employed in Retail Trade, and Accommodation and Food Services industry sectors to access transit stops at night.

A Hierarchical Smart Street Lighting System With Brute-Force Energy Optimization

This paper presents a novel smart street lighting (SmSL) system in which energy consumption by a group of street lighting poles is minimized based on Brute-Force search algorithm. While outdoor lighting imposes considerable cost, maintenance, safety, and environmental issues; utilization of advanced street lighting system with energy saving, autonomous fault detection, and monitoring capabilities benefits all the players involved including municipalities and distribution companies. The proposed SmSL has a hierarchical platform. The segment or intermediate controller determines the scheduling, switching, and dimming level of each pole based on the proposed optimization subroutine and transmits the controller set points to the local pole controller through power line communications. Optimization of street lighting electrical energy is achieved by minimizing a cost function, considering operational constraints, ambient luminance, and local traffic flow. The local controller acts as an actuator and applies the received commands. The controller inherently responses to lamp fault. Moreover, pole electrical parameters and status of the lamp and its capacitor is transmitted to the intermediate controller. The supervisory controller, which is installed on a server in distribution center monitors the whole system and sends appropriate commands such as minimum required luminance in the area to the segment controller based on WiMAX wireless communications. The whole system is developed and implemented in a pilot street. The experimental results show considerable energy saving with the proposed SmSL and reduced maintenance costs.

Rapid Localization and Extraction of Street Light Poles in Mobile LiDAR Point Clouds: A Supervoxel-Based Approach

This paper presents a supervoxel-based approach for automated localization and extraction of street light poles in point clouds acquired by a mobile LiDAR system. The method consists of five steps: preprocessing, localization, segmentation, feature extraction, and classification. First, the raw point clouds are divided into segments along the trajectory, the ground points are removed, and the remaining points are segmented into supervoxels. Then, a robust localization method is proposed to accurately identify the pole-like objects. Next, a localization-guided segmentation method is proposed to obtain pole-like objects. Subsequently, the pole features are classified using the support vector machine and random forests. The proposed approach was evaluated on three datasets with 1,055 street light poles and 701 million points. Experimental results show that our localization method achieved an average recall value of 98.8%. A comparative study proved that our method is more robust and efficient than other existing methods for localization and extraction of street light poles.

Recognizing Street Lighting Poles From Mobile LiDAR Data

In this paper, a novel segmentation and recognition approach to automatically extract street lighting poles from mobile LiDAR data is proposed. First, points on or around the ground are extracted and removed through a piecewise elevation histogram segmentation method. Then, a new graph-cut-based segmentation method is introduced to extract the street lighting poles from each cluster obtained through a Euclidean distance clustering algorithm. In addition to the spatial information, the street lighting pole's shape and the point's intensity information are also considered to formulate the energy function. Finally, a Gaussian-mixture-model-based method is introduced to recognize the street lighting poles from the candidate clusters. The proposed approach is tested on several point clouds collected by different mobile LiDAR systems. Experimental results show that the proposed method is robust to noises and achieves an overall performance of 90% in terms of true positive rate.

Complex three- dimensional-shaped knitting preforms for composite application

For decades, street lighting and electric poles are made of metal and it is vulnerable to corrosion due to the harsh weather and chemicals. To overcome such essential problems, galvanized iron is used although it adds more hard work to increase the manufacturing cost. Therefore, fiber reinforced polymer lighting pole is proposed. Fiber reinforced polymer materials possess many advantages such as corrosion resistance, high specific strength and stiffness, etc. Two-dimensional woven fabrics and three-dimensional woven fabrics preforms are used to produce composite structures. However, complex shapes cannot be manufactured as a one piece preform. Woven fabrics, whether two-dimensional or three-dimensional need to be cut into patterns to finally produce the complex shapes. These processes add more cost and time to the final composite products. In this research, innovative technique to produce a three-dimensional complex shape knitted preform using regular flat-knitting machine will be presented. Production of such shaped three-dimensional preform permits the production of one piece-shaped preform without any connection or further sewing processes. Produced knitted preform can be used for various reinforcement applications such as light and communication poles, scaffold façades, traffic sign, oars, and wind mill blades.

Automated Extraction of Urban Road Facilities Using Mobile Laser Scanning Data

This paper proposes a novel, automated algorithm for rapidly extracting urban road facilities, including street light poles, traffic signposts, and bus stations, for transportation-related applications. A detailed description and implementation of the proposed algorithm is provided using mobile laser scanning data collected by a state-of-the-art RIEGL VMX-450 system. First, to reduce the quantity of data to be handled, a fast voxel-based upward growing method is developed to remove ground points. Then, off-ground points are clustered and segmented into individual objects via Euclidean distance clustering and voxel-based normalized cut segmentation, respectively. Finally, a 3-D object matching framework, benefiting from a locally affine-invariant geometric constraint, is developed to achieve the extraction of 3-D objects. Quantitative evaluations show that the proposed algorithm attains an average completeness, correctness, quality, and F1-measure of 0.949, 0.971, 0.922, and 0.960, respectively, in extracting 3-D light poles, traffic signposts, and bus stations. Comparative studies demonstrate the efficiency and feasibility of the proposed algorithm for automated and rapid extraction of urban road facilities.

A Low-Cost and Noninvasive System for the Measurement and Detection of Faulty Streetlights

Badly lit roads lead to vehicle accidents and encourage crime. Therefore, it is important to rapidly detect and report faulty streetlights (FSLs) to the relevant authorities to keep roads safe. Currently, communities primarily depend on electrical inspectors to check streetlights regularly, which may result in long and unnecessary delays prior to repair. Recent studies have focused on adding a networking capability (i.e., a wireless sensor network) into street light poles to enable real-time status reports. However, a smart system that would incorporate sensors and network modules into every streetlight would be expensive; therefore, it would be nearly impossible to realize this system quickly. In this paper, we propose a noninvasive method for detecting faulty lights that involves designing special equipment, called the Hitchhiker, which could be installed on vehicles and would collect information about streetlights' intensity. This system would not require the modification of conventional streetlights. The collected data would be used to create illumination maps (IMaps), the analysis of which could help identify changes in lighting intensity in specific regions. As far as we know, this is an unprecedented approach; no other approaches use IMaps to find FSLs and consider cost and invasiveness. The proposed system could be extended to a citywide scale with minimal cost, and could be used as a complementary system for electrical inspectors possibly identifying FSLs sooner and shortening the duration of poor lighting on streets.

Semiautomated Extraction of Street Light Poles From Mobile LiDAR Point-Clouds

This paper proposes a novel algorithm for extracting street light poles from vehicleborne mobile light detection and ranging (LiDAR) point-clouds. First, the algorithm rapidly detects curb-lines and segments a point-cloud into road and nonroad surface points based on trajectory data recorded by the integrated position and orientation system onboard the vehicle. Second, the algorithm accurately extracts street light poles from the segmented nonroad surface points using a novel pairwise 3-D shape context. The proposed algorithm is tested on a set of point-clouds acquired by a RIEGL VMX-450 mobile LiDAR system. The results show that road surfaces are correctly segmented, and street light poles are robustly extracted with a completeness exceeding 99%, a correctness exceeding 97%, and a quality exceeding 96%, thereby demonstrating the efficiency and feasibility of the proposed algorithm to segment road surfaces and extract street light poles from huge volumes of mobile LiDAR point-clouds.