Unmanned Aerial Vehicles (UAVs) represent easy, affordable, and simple solutions for many tasks, including the collection of traffic data. The main aim of this study is to propose a new, low-cost framework for the determination of highly accurate traffic flow parameters. The proposed framework consists of four segments: terrain survey, image processing, vehicle detection, and collection of traffic flow parameters. The testing phase of the framework was done on the Zagreb bypass motorway. A significant part of this study is the integration of the state-of-the-art pre-trained Faster Region-based Convolutional Neural Network (Faster R-CNN) for vehicle detection. Moreover, the study includes detailed explanations about vehicle speed estimation based on the calculation of the Mean Absolute Percentage Error (MAPE). Faster R-CNN was pre-trained on Common Objects in COntext (COCO) images dataset, fine-tuned on 160 images, and tested on 40 images. A dual-frequency Global Navigation Satellite System (GNSS) receiver was used for the determination of spatial resolution. This approach to data collection enables extraction of trajectories for an individual vehicle, which consequently provides a method for microscopic traffic flow parameters in detail analysis. As an example, the trajectories of two vehicles were extracted and the comparison of the driver’s behavior was given by speed—time, speed—space, and space—time diagrams.
Public transport (PT) subsidy provides the means to impose the optimal combination of fare and Level of Service (LoS)
Geoinformation inventories are often employed as a tool for providing a comprehensive view onto the required state of traffic control infrastructure. They are especially important in road safety inspection where, in combination with georeferenced video, they enable repeatable off-line and off-site assessments as an attractive aternative to classic onsite inspection. Nevertheless, manual assessments are tedious and time-consuming even when performed off-line, and this seriously impairs the potential of the geoinformation inventory concept. This paper therefore researches a hypothesis that suitable georeferenced video processing techniques would allow reliable automation of the following operations: i) creation of the traffic inventory from the given video, and ii) assessing the video against the state in the inventory. Prominent computer vision approaches have been rigorously and systematically evaluated and the obtained results are presented. The results seem to support the hypothesis, although further work is required for a more definite answer.
Original scientific paper Planning of road transport infrastructure is a complex process. One of the major issues is determining the design elements of road infrastructure (road type and number of traffic lanes, cross-sectional profile, etc.). When designing roads, parameters from the international literature are used, containing values derived empirically from local data on traffic flow (mostly from northern American cities). The goal of this paper was to explore traffic flows for the purpose of developing a model that will enable scientifically exact description of traffic flows in urban areas of Central Europe. Study of basic parameters of traffic flow included the selection of road location, survey time, traffic survey, analysis of video recordings, as well as statistical analysis and calculation of basic parameters of traffic flow. Added value of this research is demonstrated through the method of collecting and analysing the data for each lane (or roadway) separately in order to detect the difference in the values of the basic parameters of traffic flows. The research was conducted on various urban roads and in various traffic conditions and in this way the basic parameters of traffic flow were obtained. These parameters were used to develop diagrams of relations between speed, traffic density and volume, resulting in cumulative functions of traffic flow parameters for the entire urban traffic network. This made it possible to develop new equations enabling theoretical determination of flow volume, speed and density for a given road. Methods established in this work and the results of the research present a useful and applicable tool for benchmarking road capacity and finding relevant coefficients significant for dimensioning the road cross-sections in urban areas, but also on all other categories of roads. Keywords: density and volume; road infrastructure design; road network; traffic flow; traffic modelling; traffic speed Modeliranje prometnog toka na prometnoj mreži u gradovimaIzvorni znanstveni članak Planiranje cestovne prometne infrastrukture je složen proces. Jedan od najvećih problema je određivanje projektnih elemenata cestovne infrastrukture (vrsta ceste i broj prometnih traka, poprečni profil i sl). Za potrebe dimenzioniranja prometnica koriste se parametri iz strane literature s vrijednostima koje su empirijskim metodama derivirane iz lokalnih podataka o prometnom toku (najčešće sjevernoamerički gradovi). Cilj ovog rada bio je istraživanje prometnih tokova radi izrade modela koji će pružiti mogućnost egzaktnijeg opisa lokalnih karakteristika prometnih tokova na području srednjoeuropskih gradova. Istraživanje osnovnih parametara prometnog toka sastojalo se od izbora lokacije cesta, vremena snimanja, snimanja prometa, analize video zapisa istatističke analize i proračuna osnovnih parametara prometnog toka. Posebnost provedenog istraživanja sadržana je u načinu prikupljanja i analizi podataka za svaki prometni trak (ili kolnik) radi uočavanja razlike u vrijednostima osnovnih parametara prometnih t...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.