Road lighting is installed mainly to increase traffic safety. Road lighting can reduce the number of collisions and fatalities significantly [1
]. For example, in a meta-analysis by Elvik [8
], road lighting was concluded to reduce fatal accidents by 65%, accidents with injuries by 30%, and collisions with only property damage by 15%. Moreover, correlations between speeding and lack of installed road lighting have been found [9
]. However, lighting installation, maintenance, and use are expenses for municipalities. Furthermore, excess lighting or light pollution is harmful for city dwellers and fauna [10
]. Hence, proper lighting design is crucial. Road lighting is installed following certain criteria or national regulations that often follow an international technical report such as ANSI/IES RP–8–14 (American National Standards Institute/Illuminating Engineering Society Recommended Practice) or CEN/TR (Comité Européen de Normalisation/Technical Report) 13201:2015 [12
]. Two important regulated measures are the overall uniformity and longitudinal uniformity of the road surface luminance distribution. Usually, the desired uniformity is present when the road lighting is installed. However, as the neighbouring vegetation grows, the lighting can become occluded and the safety critical uniformity in luminance distribution is compromised.
In urban landscaping, the effects of trees and green areas are considered almost solely positive [14
]. Green elements most certainly increase the attractiveness of city spaces, reduce stress levels of the city inhabitants, and provide cover from the weather or direct sunlight [16
]. Moreover, trees and vegetation increase the chance of introducing desired urban fauna to the built environment [19
]. However, as living things, trees are constantly under change. This can cause problems when the vegetation shares the location with safety-critical infrastructure such as road lighting.
Mobile mapping systems are widely used for the 3D measurement of roads and built environments [20
], forests and vegetation [25
], and especially for collecting data for urban tree inventories [26
]. Furthermore, luminance measurements have been integrated into point clouds scanned terrestrially and from mobile platforms [28
]. However, mobile luminance mapping systems have not yet been utilised to measure the effect that roadside vegetation pruning has on road surface luminance.
The objective of this study is to demonstrate a workflow in which a mobile mapping system is applied in order to assess the light-occluding effect caused by the roadside vegetation. Moreover, we measure how much the occlusion affects the luminance distribution on the road surface. The occlusion effect is presented by comparing road surface luminance uniformity before and after roadside tree pruning. The measurements were performed applying a mobile laser scanning system, and the road surface luminance uniformities were analysed and compared in three dimensions. Furthermore, the data were georeferenced. Finally, the benefits of measuring the light-occluding effect of roadside vegetation are discussed. This article contributes to the scientific discussion by presenting a novel and interdisciplinary approach to examine the connection between the road surface luminance metrics and light-occluding vegetation, which is an important aspect in traffic safety. This approach is presented with a usability study that was conducted on a part of a suburban street. This paper’s contribution is to ignite scientific discussion about two primary aspects. Firstly, we want to discuss the luminance measurement standard, and how it should be revisited, as novel measurement methods emerge. Secondly, we want to introduce the idea of nighttime measurement to the community of mobile mapping technology. The contribution of this paper is to present useful examples and evidence about these two aspects mentioned above.
In this study, we presented a measurement system and a workflow to assess the road lighting-occluding effect of roadside vegetation. In particular, we described a case in which we used a workflow involving a change analysis of the road surface luminance measures for the overall uniformity and longitudinal uniformity measured before and after roadside vegetation pruning. Applying the developed workflow, we verified the improvement in road surface overall and longitudinal luminance uniformities after vegetation pruning.
The authors deem the presented system to be an excellent utility for road lighting measurements. Compared to static measurements, mobile measuring enables fast coverage and data capture of large road or street entireties. With the presented system, the result is a three-dimensional luminance point cloud. The 3D luminance models are undeniably more versatile than the conventional 2D luminance images. Each measured luminance point is geo-referred and in scale, which is not possible with 2D imaging luminance photometry. For the specific case in this study—the verification of improvement in road surface luminance uniformities—the presented measurement system performed very well. Furthermore, the amount of vegetation removed between 2015 and 2019 was analysed applying a voxel grid. The amount of removed vegetation was compared to the improved overall luminance uniformity and longitudinal uniformity values. A correlation was found between these metrics. However, more research is needed to verify the correlation. Moreover, we applied a manual method for point cloud sectioning and occluding voxel detection. The manual method was manageable for our usability study in a limited area. For practical mobile mapping applications, these manual procedures are not feasible, and automation should be considered both for vegetation detection and for the division of measured area. For vegetation detection and feature extraction, machine learning tools such as support vector machines, associative Markov networks, or supervised classification could be applied [48
]. For road area sectioning, classification based on proximity of known road coordinates, and raster image processing techniques could be useful [51
However, the presented system is not without shortcomings. Firstly, the image capturing of the presented measurement system does not fully follow the guidelines of any road lighting measurement standard. It would naturally be possible to modify the MMS to follow the standards. In this study, this kind of modification would have drastically reduced the point density and accuracy of the point cloud. The authors considered this would have been counter-innovative and decided to use the mobile mapping system at its highest settings possible for the conditions. Likewise, the measurement standard could be updated to include the versatile possibilities of 3D mobile mapping. Secondly, the dynamics and the signal-to-noise ratio of MMS panoramic cameras are not yet optimal and cannot compete with stationary imaging luminance photometry in this respect. However, these technologies are continuously improving. The authors decided that the technology was developed enough to initiate a scientific conversation about mobile luminance measurement. Soon, the difference between mobile and static measurement quality will be negligible in terms of road surface luminance measurements.
In this study, the geometry of the road environment was measured solely with a laser scanner, and the captured digital images were projected onto the geometry. Urban vegetation has also been mapped using solely camera-based photogrammetry [53
]. However, the following aspects encourage the use of laser scanning. Firstly, a laser scanner measures absolute distances, whereas camera-based data are only relative in scale. Secondly, laser scanners are active sensors, which means they can measure the geometry in dark conditions, whereas camera-based photogrammetry always requires an external light source to emit light onto the measured surfaces. Especially when measuring under nighttime conditions, the geometry measurement quality would decrease if only camera-based photogrammetry was used. The third option would be a hybrid method in which the geometry is measured using both laser scanners and camera-based photogrammetry. This has been a favoured method for the daytime terrestrial measurement of built environments [54
]. The capabilities of this hybrid method should be assessed in future studies for the measurement of artificially lit road environments.
Urban green areas, trees, and vegetation will become even more emphasised and prominent in the future. The shading and temperature-reducing effect of urban vegetation helps to counter the effects of climate change [14
]. Furthermore, trees increase the property value [56
], potentially reduce crime [57
], and improve air quality [54
]. In terms of traffic infrastructure, street lighting obstruction is not the only negative effect of roadside vegetation. Growing trees can also cause damage to the pavement [59
]. Conveniently, the measurement system presented in this study could possibly also map pavement damage, and thus, measure these two negative effects caused by urban vegetation with one measurement.
The emergence of low-cost equipment further increases the feasibility of applying MLS in an increasing number of applications. Jaakkola et al [60
] have demonstrated the performance of affordable MLS systems. The reduction of the size and weight of MLS systems has also enabled their installation on UAVs, potentially offering a less occluded viewpoint of the urban environment. Highly portable MLS systems can also be utilised by pedestrians, further increasing their flexibility [61
]. Systems that rely on the SLAM principle are also able to operate in GNSS-occluded areas. These developments potentially expand the applicability of MLS-based luminance mapping to tunnels, the undersides of bridges, pedestrian areas etc., depending on the performance and suitability of the imaging sensors in these systems. As 3D mapping and imaging have also been demonstrated in near-consumer-level systems, it can also be argued that to some extent luminance mapping could also be carried out via a crowdsourcing-oriented approach in pedestrian areas, for example.
In addition to road lighting assessments, roadside vegetation mapping has various applications. Moose, deer, and elk collisions correlate with roadside vegetation, as they are browsing sites for Cervidae [62
]. On the other hand, roadside vegetation has been found to reduce frustration and aggression in drivers [63
]. Furthermore, roadside trees have the potential to reduce light pollution from street luminaires [64
]. Due to its positive outcome, roadside vegetation is preferred as long as it can be controlled. The measurement system presented in this study is an optimal utility for control.