Abstract: Street lighting is an indispensable feature for the night landscape of cities. It is important for road safety, users visual comfort, crime prevention and to augment the perceived personal safety. Realize and maintain an adequate street lighting service is very expensive for municipalities with significant impact on their budgets. For this reason, special attention should be paid to the design of new street lighting systems and to the refurbishment of existing ones, since many of them are inadequate. In light of this it is very important to implement street lighting designs that fulfil lighting requirements avoiding energy waste and light pollution and, at the same time, result economically sustainable for municipalities. In this paper, an original step by step methodology for the lighting, energy and economic analysis of street lighting refurbishment designs has been introduced and explained in detail. The methodology is suitable for use in cities of different sizes. As an applicative example, the methodology has been tested in the town of Pontedera (Italy) and the results are discussed, also providing a sensitivity analysis of the economic feasibility with respect to the variations of electricity prices and investment costs.

Abstract: Big lighting data are required for evaluation of lighting performance and impacts on human beings, environment, and ecology for smart urban lighting. However, traditional approaches of measuring road lighting cannot achieve this aim. We propose a rule-of-thumb model approach based on some feature points to reconstruct road lighting in urban areas. We validated the reconstructed illuminance with both software simulated and real road lighting scenes, and the average error is between 6 and 19%. This precision is acceptable in practical applications. Using this approach, we reconstructed the illuminance of three real road lighting environments in a block and further estimated the mesopic luminance and melanopic illuminance performance. In the future, by virtue of Geographic Information System technology, the approach may provide big lighting data for evaluation and analysis, and help build smarter urban lighting.

Abstract: Most street lights currently deployed have constant illumination levels or vary
based on a predetermined schedule. However, with advances in lighting controls, intelligent transportation systems, and the efforts of transportation agencies at regional and national levels to better sustain and manage the transportation system by monitoring the roadway network, many different types of real-time traffic data are available; which enables the implementation of a traffic responsive outdoor light system. The International Commission on Illumination (CIE) has proposed a class-based lighting control model based on a number of roadway parameters, some of which are traffic related. However, the adaptation of the available traffic data to the existing model is not obvious. In addition, the CIE model can be improved to better reflect traffic characteristics to increase energy efficiency of the overall street lighting system. The intention of this research is to quantify the relationship between real-time traffic, and roadway lighting and to develop a control strategy based on real-time traffic data in order to reduce light energy consumption, enhance safety, and maximize throughput of the roadway. Significant energy savings were observed when the proposed control strategy was implemented in two case studies using available lighting and traffic data for Washington, DC, and Montgomery County, MD, representing urban and rural roadway networks, respectively.