Evaluating the influence of road lighting on traffic safety at accesses using an artificial neural network

Xu, Yueru; Ye, Zhirui; Wang, Yuan; Wang, Chao; Sun, Cuicui

doi:10.1080/15389588.2018.1471599

Cited by 16 publications

(14 citation statements)

References 19 publications

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“…23,24 The lighting effect is especially obvious on high-speed roads or roads with many lanes. 25 Wanvik 26 found that road lighting can reduce night-time injuries by 50% based on an interactive database of 763,000 injuries and 3.3 million property loss incidents from 1987 to 2006. The probability of night-time vehicle accidents also differs among different types of roads.…”

Section: Introductionmentioning

confidence: 99%

Exploring factors affecting the severity of night-time vehicle accidents under low illumination conditions

Liu

Wang

et al. 2019

Advances in Mechanical Engineering

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Night-time vehicle accidents under low illumination conditions are frequent and serious, and they have attracted widespread attention. The objective of this study was to explore how various factors affect night-time vehicle accidents using data collected from a city in China. Combined with logistic model theory, the occurrence or absence of a night-time fatal accident was set as the dependent variable. A total of 10 variables, including the accident site, road type and road surface conditions, were selected as independent factors. Based on 2106 valid night-time vehicle accidents, a binomial logistic model was established to evaluate the impact of contributing factors on the severity of these accidents. The results show that the accident site, accident type and presence of a median divider are important factors that affect the severity of night-time vehicle accidents under low illumination conditions. The probability of fatal night-time accidents on road segments is 2.387 times that at intersections. The probabilities of fatal single-vehicle and vehicle-pedestrian night-time accidents are also greater than that of fatal vehicle-vehicle night-time accidents, by factors of 7.591 and 1.749, respectively. The probability of fatal night-time accidents on roads with median dividers is 3.273 times greater than that on roads without median dividers.

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Section: Introductionmentioning

confidence: 99%

Exploring factors affecting the severity of night-time vehicle accidents under low illumination conditions

Liu

Wang

et al. 2019

Advances in Mechanical Engineering

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“…Many studies have explored the correlations between nighttime light and socioeconomical indices. However, previous studies have mainly employed nighttime light data to model socioeconomic phenomena, such as urban expansion [14], economic development [23], energy consumption [24], population density [25] and transportation mobility [26], and there has been a lack of studies that coupled nighttime light images and socioeconomic data to comprehensively explore the driving factors of artificial lighting changes. This study employed the driving factors according to different city groups distinguished by differences in urban economy, population density, geographical location, and industrial structure.…”

Section: Discussionmentioning

confidence: 99%

“…Most studies have shown that socioeconomic parameters, such as population [25,31,32], economy [33,34], transportation [26], and electricity consumption [23,24], are related to nighttime lighting. This study couples the socioeconomic data and nighttime light images to explore the driving factors of urban artificial lighting change.…”

Section: Study Area and Data Sourcesmentioning

confidence: 99%

“…In addition, research on the correlations between nighttime light and socioeconomical indices have extensive applications. However, most of them have been applied to analyse socioeconomic phenomena, such as urban expansion [14], economic development [23], energy consumption [24], population density [25] and transportation mobility [26], there currently remains a lack of documents to comprehensively reveal the driving factors of artificial lighting change.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Spatiotemporal Variability and Driving Factors of Artificial Lighting in the Beijing-Tianjin-Hebei Region Using Remote Sensing Imagery and Socioeconomic Data

Leng

Jian

2019

IJERPH

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With rapid urbanization and economic development, artificial lighting at night brings convenience to human life but also causes a considerable urban environmental pollution issue. This study employed the Mann-Kendall non-parametric test, nighttime light indices, and the standard deviation method to investigate the spatio-temporal characteristics of artificial lighting in the Beijing-Tianjin-Hebei region. Moreover, nighttime light imagery from the Defense Meteorological Satellite Program Operational Linescan System, socioeconomic data, and high-resolution satellite images were combined to comprehensively explore the driving factors of urban artificial lighting change. The results showed the following: (1) Overall, there was an increasing trend in artificial lighting in the Beijing-Tianjin-Hebei region, which accounted for approximately 56.87% of the total study area. (2) The change in artificial lighting in the entire area was relatively stable. The artificial lighting in the northwest area changed faster than that in the southeast area, and the areas where artificial lighting changed the most were Beijing, Tianjin and Tangshan. (3) The fastest growth of artificial lighting was in Chengde and Zhangjiakou, where the rates of increase were 334% and 251%, respectively. The spatial heterogeneity of artificial lighting in economically developed cities was higher than that in economically underdeveloped cities such as Chengde and Zhangjiakou. (4) Multi-source data were combined to analyse the driving factors of urban artificial lighting in the entire area. The Average Population of Districts under City (R2 = 0.77) had the strongest effect on artificial lighting. Total Passenger Traffic (R2 = 0.54) had the most non-obvious effect. At different city levels, driving factors varied with differences of economy, geographical location, and the industrial structures of cities. Urban expansion, transportation hubs, and industries were the major reasons for the significant change in nighttime light. Urban artificial lighting represents a trend of overuse closely related to nighttime light pollution. This study of artificial lighting contributes to the rational planning of urban lighting systems, the prevention and control of nighttime light pollution, and the creation of liveable and ecologically green cities.

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“…The integration of the IoT with intelligent traffic systems has recently been explored [1,2,3,4,5]. The IoT can provide both infrastructure and vehicles with sensors that can measure general traffic conditions, ambient variables, vehicle variables (such as position and speed), and driver conditions.…”

Section: Introductionmentioning

confidence: 99%

ITERL: A Wireless Adaptive System for Efficient Road Lighting

Gonzalez-Romo

Gómez-Galán

García-Martín

et al. 2019

Sensors

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This work presents the development and construction of an adaptive street lighting system that improves safety at intersections, which is the result of applying low-power Internet of Things (IoT) techniques to intelligent transportation systems. A set of wireless sensor nodes using the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standard with additional internet protocol (IP) connectivity measures both ambient conditions and vehicle transit. These measurements are sent to a coordinator node that collects and passes them to a local controller, which then makes decisions leading to the streetlight being turned on and its illumination level controlled. Streetlights are autonomous, powered by photovoltaic energy, and wirelessly connected, achieving a high degree of energy efficiency. Relevant data are also sent to the highway conservation center, allowing it to maintain up-to-date information for the system, enabling preventive maintenance.

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Evaluating the influence of road lighting on traffic safety at accesses using an artificial neural network

Cited by 16 publications

References 19 publications

Exploring factors affecting the severity of night-time vehicle accidents under low illumination conditions

Exploring factors affecting the severity of night-time vehicle accidents under low illumination conditions

Investigating the Spatiotemporal Variability and Driving Factors of Artificial Lighting in the Beijing-Tianjin-Hebei Region Using Remote Sensing Imagery and Socioeconomic Data

ITERL: A Wireless Adaptive System for Efficient Road Lighting

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