USEFUL CONTRAST INDEX FOR ROADWAY LIGHTING STANDARDS

This article proposes a new metric for roadway lighting design that has the potential to improve visibility for drivers during nighttime. The new metric is named as Useful Contrast Index (UCI) and it relates the contrast of obstacles (targets) to the contrast threshold. We define the useful contrast index as the percentage of targets that have a contrast value greater than the contrast threshold along the length of the road. We conclude that the Useful Contrast Index has merit and could be used to provide a better visual environment for drivers.

HCL Control Strategy for an Adaptive Roadway Lighting Distribution

This study aims to develop a human-centric, intelligent lighting control system using adaptive LED lights in roadway lighting, integrated with an imaging luminance meter that uses an IoT sensor driver to detect the brightness of road surfaces. AI image data are collected for luminance and vehicle conditions analyses to adjust the output of the photometric curve. Type-A lenses are designed for R3 dry roads, while Type-B lenses are designed for W1 wet roads, to solve hazards caused by slippery roads, for optimizing safety and for visual clarity for road users. Data are collected for establishing formulae to optimize road lighting. First, the research uses zonal flux analysis to design secondary optical components of LED roadway lighting. Based on the distribution of LED lights and the target photometric curve, the freeform surface calculation model and formula are established, and control points of each curved surface are calculated using an iterative method. The reflection coefficient of a roadway is used to design optical lenses that take into account the illuminance and luminance uniformity to produce photometric curves accordingly. This system monitors roadway luminance in real time, which simulates drivers’ visual experiences and uses the ZigBee protocol to transmit control commands. This optimizes the output of light according to weather and produces quality roadway lighting, providing a safer driving environment.

Development of models to study traffic accidents on the final sections of access roads to the cities: a case study of three major Iranian cities

The final sections of main access roads to the cities require especial attention as the frequency of accidents in these road sections are considerably higher than other parts of interurban roads. These road sections operate as an interface between the rural roads and urban streets. The previous researches available on this subject are limited and they have also mainly focused on a narrow range of factors contributing to the accidents in these areas. The main contribution of this research is to consider a relatively comprehensive range of potential factors , and to examine their impacts through the development and comparison of both conventional probabilistic models and Artificial Neural Network (ANN) models. For this purpose, information related to the main access roads of three major Iranian cities were collected. This information consisted of accident frequency data together with the field observations of traffic characteristics, road-way conditions and roadside features of these roads. Various ANN and probabilistic models were developed. The frequency of accidents, i.e. fatal, injured, or damaged accidents, was considered as the output of the developed models. The results indicated that a hybrid of ANN models, each comprised of 10 input variables representing traffic, roadway and roadside conditions, outperformed several probabilistic models, i.e. Poisson, Negative binomial, Zero-truncated Poisson, and Zero-truncated Negative Binomial models, also developed under similar conditions in this study. Moreo-ver, effective roadway width, roadway lighting condition, the standard deviation of vehicles speed, percentage of drivers violating the speed limit, average annual daily traffic, percentage of heavy goods vehicles, the density of road-side commercial and industrial landuses, the density of median U-turns, the density of local access roads, and the effective width of the left-side shoulder were identified as the most effective factors contributing to the accidents in these areas. The developed ANN model can be used as a tool to predict accident rates in these road sections, and to estimate a potential reduction in the accident rates, following any improvements in the major factors contributing to the traffic accidents in these areas.

A Spatial Comparison of Roadway Lighting and Nonmotorist Crashes in Cambridge, MA

Dark lighting conditions, including those occurring at dawn and dusk, are correlated with increased nonmotorist crash frequency owing to reduced visibility, but little research has been done that investigates the spatial relationship between roadway lights and nonmotorist crashes on a community scale. This research used kernel density estimation methods to calculate the commonalities between geolocated streetlight data and non-motorist-vehicle crashes from 2010 to 2018 in Cambridge, Massachusetts. It was observed that dawn, dusk, and darkness showed a significant correlation between nonmotorist crashes and the absence of roadway lighting, all exceeding the control analysis undertaken with crashes occurring in daylight. The Getis-Ord [Formula: see text] hot spot cluster analysis indicated that areas with the greatest density of streetlights were associated with fewer nonmotorist crash hot spots. Future research seeks to corroborate these findings with data from other cities and to assess roadway lighting as a facet of pedestrian network connectivity.