With the introduction of mechanization in agriculture, the area of terraced slopes has increased. However, in most cases, the planning of terracing in practice remains experience-based, which is no longer effective from an agricultural, geological, and hydrological point of view. The usual method of building terraces, especially terraces with earth risers, is therefore outdated, and a new method must be found for planning and building terraced areas. In addition to geographical information system (GIS) tools, parametric design tools for planning terraced landscapes are now available. Based on the design approaches for a selected plot in the Gorizia Hills in Slovenia, where we used a trial-and-error method, we improved previous results by defining a model using a computer algorithm that generates a terraced landscape on a selected slope depending on various input parameters such as the height of the terrace slope, the inclination of the terrace slope, the width of the terrace platform, and the number of terraces. For the definition of the algorithm we used the visual program tool Grasshopper. By changing the values of the input data parameters, the algorithm was able to present combinatorial simulations through a variety of different solutions with all the corresponding statistics. With such results it is much easier to make a conscious decision on which combination of parameters is optimal to prevent landslides, plan adequate drainage, and control soil movements when building terraces. The controlled slope intervention is further optimized by the introduction of a usage index (Tx), defined as the quotient of the sum of all flat areas (terrace platforms) and the total area of the plot.

The southern inner ring road in Ljubljana, Slovenia was equipped with low-cost sensors supported by the Telraam integrated platform. The sensors were built with open-source components (Raspberry PI). The software is running, and the counting data is collected and analysed via an internet portal ( www.telraam.com ). The Telraam sensor counts pedestrians, cyclists, cars and freight/heavy vehicles using the images provided by the device sensor and the analysis performed by the “Raspberry Pi” (a small computer on which the device is based). The sensor software uses the size and speed of the passing object to determine and classify the different vehicles. The classification is based on the average observed full value and the axis ratio of each observed object (which meets a set of criteria that helps filter out any movement in the field of view that should be associated with road users). The five traffic sensors camera is mounted directly on the inside of the window glass facing the street at varying distances from the road (from 3 to 15 meters), where they count traffic only during daylight hours, update their count every hour and separate car traffic by direction.

No abstract

No abstract