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Objectives: This paper estimates the effect of changes in street lighting at night on levels of crime at street-level. Analyses investigate spatial and temporal displacement of crime into adjacent streets. Methods: Offense data (burglaries, robberies, theft of and theft from vehicles, and violent crime) were obtained from Thames Valley Police, UK. Street lighting data (switching lights off at midnight, dimming, and white light) were obtained from local authorities. Monthly counts of crime at street-level were analyzed using a conditional fixed-effects Poisson regression model, adjusting for seasonal and temporal variation. Two sets of models analyzed: (1) changes in night-time crimes adjusting for changes in day-time crimes and (2) changes in crimes at all times of the day. Results: Switching lights off at midnight was strongly associated with a reduction in night-time theft from vehicles relative to daytime (rate ratio RR 0.56; 0.41–0.78). Adjusted for changes in daytime, night-time theft from vehicles increased (RR 1.55; 1.14–2.11) in adjacent roads where street lighting remained unchanged. Conclusion: Theft from vehicle offenses reduced in streets where street lighting was switched off at midnight but may have been displaced to better-lit adjacent streets. Relative to daytime, night-time theft from vehicle offenses reduced in streets with dimming while theft from vehicles at all times of the day increased, thus suggesting temporal displacement. These findings suggest that the absence of street lighting may prevent theft from vehicles, but there is a danger of offenses being temporally or spatially displaced. [ABSTRACT FROM AUTHOR]

A street lighting system is a very essential part of the highways and streets of a smart city. Managing power consumption and maintenance of a street light system will be a challenging task in huge countries. The proposed work is mainly focused on the minimization of power consumption in the implementation of a smart street lighting system. Also, use a mobile application for setting up the brightness levels of the lamps in an encrypted form so that an unauthorized person will not be able to modify the settings. In the existing streetlight system, wireless sensors are installed to control and monitor the streetlamps. In the proposed system, using an nRF24L01 radio transceiver module, a secured communication link is established to operate the streetlights depending on the ambient weather conditions, movement of humans, vehicles and any other objects. A failsafe mechanism is implemented in the modules for conventional lamp operation in the case of module failures. Light-dependent resistor (LDR) is used to determine the ambient brightness levels to automatically turn on/off the streetlights based on weather conditions and lighting on roads. Using smartphones, we access and control the brightness information from the master node at which the nRF24L01 radio transceiver module is installed, and the same information is relayed to all the slave nodes. The results show that we could effectively monitor and control the brightness of streetlights in a secure way and there is a significant amount of power savings. The proposed system saves the average powers of 53.45%, 44.76%, 39.39%, and 32.25% respectively for 10%, 20%, 30%, and 50% idle mode brightness compared to the state-of-the-art techniques present in the literature. [ABSTRACT FROM AUTHOR]

To date, studies of the effects of artificial light on wildlife have mostly focused on street lighting. However, less attention has been paid to light from greenhouses, which can be sources of substantial light pollution from autumn to early spring. We investigated how daily 4–8 h of nocturnal illumination from a greenhouse influences the time of daily vocalization of forest birds in a non-urban area. Our study revealed that birds exposed to greenhouse lightning start dawn singing and calling earlier and stop dusk vocalization later, especially before the breeding season in the second half of February. The most affected species were the European Robin (entire study period) and Blackbird (early period only), but other species also revealed a significant impact on vocal activity (calls). Our study shows that even short-duration light pollution from greenhouses in a non-urban area in the breeding season can affect bird vocalization behavior. [ABSTRACT FROM AUTHOR]

Objectives: For more than one hundred years, street lighting has been one of the most ubiquitous capital investments in public safety. Prior research on street lighting is largely limited to ecological studies of very small geographic areas, creating substantial challenges with respect to both causal identification and statistical power. We address limitations of the prior literature by studying a natural experiment created by short-term disruptions to municipal street lighting. Methods: We leverage a natural experiment created by the differential timing of the repair of nearly 300,000 street light outages in Chicago. By conditioning on street segment fixed effects and focusing on a short window of time around the repair of a street light outage, we can credibly rule out confounding factors due to area-specific time trends as well as street segment-level correlates of crime. Results: We find that outdoor nighttime crimes change very little on street segments affected by street light outages, but that outages cause crime to spill over to nearby street segments. Effects are largest for robberies and motor vehicle theft. Conclusions: Despite strong environmental and social characteristics that tend to tie crime to place, we observe that street light outages are sufficiently salient to disrupt longstanding patterns. While the impact of localized street light outages can reverberate throughout a community, the findings imply that improvements in lighting can be defeated by the displacement of crime to adjacent spaces and therefore do not necessarily suggest that localized investments in municipal street lighting will yield a large public safety dividend. [ABSTRACT FROM AUTHOR]

A data analysis approach for designing an energy efficient street lighting framework is proposed to maximize both energy efficiency and uniformity of the system. A multiobjective optimization problem on obtaining energy efficiency is formulated in a comprehensive manner. Three multiobjective evolutionary optimization algorithms such as nondominated sorting genetic algorithm II, strength Pareto evolutionary algorithm 2 and multiobjective differential evolutionary algorithm are used to analyse the approximated Pareto solutions of our proposed model. The performance of considered algorithms are presented and compared with regard to different metrics. The results from the best algorithm, in terms of convergence and diversity, among the algorithms are then validated using DIALux to ensure the recommendation for the standardization in different aspects. The proposed work contributes a comprehensive data analysis on genetic algorithm solutions towards obtaining a multiobjective energy efficient street lighting which is beyond the scope of the existing works. The results obtained by the proposed method are also compared with existing DIALux results. The improvement of energy efficiency obtained by the proposed methodology over existing works is shown in terms of various aspects. [ABSTRACT FROM AUTHOR]

Large-scale installation of broad-spectrum (white) road lighting has been claimed to bring about a substantial reduction in road traffic collisions (RTCs). This confirmatory study estimates the effect on personal injury RTCs of a relighting programme that installed nearly 80,000 new white lamps, between the years 2005 and 2013, throughout the large UK city of Leeds. Time series of weekly RTC personal injury counts in 107 areas, within the city over nearly 9 years when its road lighting was almost completely relit, were analysed, using multilevel modelling. The background change in each area when and where no lighting was being changed was separated from that associated with when and where new replacement white lamps were installed by including a polynomial for the underlying time-trend. The key interest is how the installation of the new lamps affects the ratio of the rate of collisions occurring in darkness to those occurring in daylight. The measure sought is given by the daylight adjusted darkness collision rate ratio (CRR) for the specified amount of relighting, that is the factor by which the daylight adjusted collision rate changes by the specified change in lighting. The daylight adjusted darkness collision rate ratio (CRR) has a point estimate of 0.990 and a 95% confidence interval, CI (0.971, 1.010) for 100 replaced lamps; representing a range between a 3% reduction in the collision rate to a 1% increase. Using the series truncated at the end of 2011 that seems more trustworthy, gives a revised point estimate of 0.993 and a CI (0.971, 1.015). Both CIs include one, therefore no effect on road safety was detected. The CIs for any other number of lamps installed will also include one. Typically, the plausible range of road safety impact brought by the new white lamps for a typical area with 700 lamps, roughly spans 20% reduction to a 10% increase for the daylight adjusted darkness collision rate. No convincing evidence was found for an improvement (or detriment) in road safety by relighting with white lamps, despite the extensive, city-wide installation efforts and associated costs. [ABSTRACT FROM AUTHOR]

Street lamp is a great asset for human society with a narrow beam spread light. The extensive proliferation of solar power in street lamps causes power outages due to their variable power-generated profiles. Thus Smart Street Lamp Fog Intelligence (SSLFI) framework based on hierarchical learning was proposed for efficient energy management in solar street lamps. Smart Street Lamp (SSL) shifts its brightness at higher and lower light levels with a comforting, energy-efficient gleam of light. The fog intelligence framework forecasts the SSL output power through short-term probabilistic energy consumption forecasts using Q-NARX-BiLSTM (Quantile Regression-Nonlinear Auto-Regressive Neural Networks with exogenous input-Bidirectional Long short-term memory) model. NARX-BiLSTM of two module types: (1) NARXNN (Nonlinear Auto-Regressive Neural Networks with exogenous input) model generates SSL power consumption and (2) BiLSTM (Bidirectional Long short-term memory) model generates SSL power forecasts. The quantile regression with the NARX-BiLSTM (Nonlinear Auto-Regressive Neural Networks with exogenous input-Bidirectional Long short-term memory) model forecasts the seasonal patterns achieving non-parametric interval predictions. The probabilistic predictions of power consumption are determined based on the conditional quantile using an improved kernel density estimation approach. The fuzzy inference system adopts forecasting results to diagnose fault conditions in street lamps. The experiment results show that the proposed framework SSLFI outperformed the state-of-the-art models forecasting under different weather conditions. [ABSTRACT FROM AUTHOR]

Objectives: This paper offers novel experimental evidence that violent crimes can be successfully reduced by changing the situational environment that potential victims and offenders face. We focus on a ubiquitous but understudied feature of the urban landscape—street lighting—and report the first experimental evidence on the effect of street lighting on crime. Methods: Through a unique public partnership in New York City, temporary street lights were randomly allocated to 40 of the city's public housing developments. Results: We find evidence that communities that were assigned more lighting experienced sizable reductions in nighttime outdoor index crimes. We also observe a large decline in arrests indicating that deterrence is the most likely mechanism through which the intervention reduced crime. Conclusion: Results suggests that street lighting, when deployed tactically, may be a means through which policymakers can control crime without widening the net of the criminal justice system. [ABSTRACT FROM AUTHOR]

Bioluminescence is a chemical phenomenon common in fireflies and aquatic life. A team of MIT engineers has taken the first step towards leveraging this phenomenon using plant nanobionics to create light-emitting trees. These light-emitting trees can serve as our desk lamps or maybe even street lamps in the future. The team has packaged the light-emitting molecules, enzymes, and coenzymes responsible for bioluminescence in living organisms, into nanoparticles and inserted them into plants to perform the chemical reaction, which emits light in the visible region. [ABSTRACT FROM AUTHOR]

Street lighting, as the most essential and universal component of the urban lighting system, accounts for a large portion of public electricity usage. Therefore, improving street lamps working efficiency is vital for energy savings. This paper demonstrates the design of a smart street lighting system supported by the combination of NB-IoT and LoRa communication technology. By adopting an optimized street lamp control algorithm, the system can realize the automatic control of street lights according to the real-time traffic flow information. This system has been installed on Luyang Avenue, Lucheng City, Shanxi Province, China in May 2019. It managed to reduce the electricity consumption in this region significantly in June 2019, which was 18% lower than that in April 2019 and 19.7% lower than that in June 2018. By illustrating the unique advantages of this system in energy savings and cost reduction, this paper displays its potential for further application in the construction of smart cities on a large scale. [ABSTRACT FROM AUTHOR]

This paper introduces a study on using solar energy instead of fossil fuel energy to light the dark and gloomy streets. An intelligent smart street light system is implemented and the feasibility of SSL is evaluated using a case study of a remotely street located Real Estate Developer of Cairo University in Bolak Al Dakrour district in the state of Giza, Egypt. The main objective is to investigate the technical design feasibility of standalone solar systems, to evaluate cost–benefit analysis of solar LED luminaries compared to convention electrical luminaries for the outdoor street lighting system and to determine the sizing of system components as well as simulation of lighting arrangements is performed using DIALUX 4.12 software package. Results show that the integrated solar street light including motion sensor is used here in this study including LAMP of 15 W LED PHILIPS, 45 W Monocrystalline panel, 12 V 37.5 AH Lithium-ion battery and 10 A 12 V charge controller. This proposed system is designed according to 12 operation hours: 4 h with 100% efficiency, 4 h 75% with efficiency and 4 h with 50% efficiency. Luminaries' arrangements are two sides offset, pole height is 7 m and pole distance 32 m. It is found the cost benefit of using solar LED luminaries against normal electric luminaries reaches about 62% and payback period is 2 years. So, the proposed solar-powered LED street lighting system is technically feasible in Egyptian streets; LED lamps can save more than half of the total needed energy, allowing for the use of a small PV system as well financially viable and environmental impact of CO2 emissions. Furthermore, if the decreasing trend in PV system costs persists and electricity prices rise, solar lighting systems could be feasible in the future. [ABSTRACT FROM AUTHOR]

In the severe weather such as heavy fog, there are car serial rear-end collision accidents in the expressway sometimes. In order to avoid such accidents, the expressway is often closed. The reason for the rear-end collision is that the drivers' vision is limited in heavy fog and the vehicle ahead is not clear. There are many problems in the traditional street lighting system, such as the inability to intelligently control street lights according to the weather conditions, so it has been unable to meet the lighting needs of the highway. For this reason, this paper proposes a design scheme of intelligent street lamp control system for preventing heavy fog of Expressway Based on ZigBee technology. The system mainly uses CC2530 as the core device to collect the temperature and humidity on site in real time and automatically control the LED street lamp switch for lighting according to the collected data. The experimental results show that the system is easy to operate, responsive, energy-saving and emissions reducing, and can meet the needs of intelligent lighting in the expressway. [ABSTRACT FROM AUTHOR]