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In city population and workplace density distributions directly influences the number of trips, travel means and even leads to changes of energy consumption rate. This paper is aimed to perform Kaunas city workplace density and population density analysis and to evaluate the parameters impact on the mobility of the population. The main focus is on the Kaunas population and workplace distribution. For that population density and workplace density maps are made. Initial data is processed by geographic information system (GIS), allowing to represent them in a very clear way. Conclusions are given based on conducted analysis and other relevant sources of information. Gyventojų bei darbo vietų pasiskirstymas mieste turi tiesioginės įtakos gyventojų atliekamų kelionių skaičiui, kelionių būdui ir lemia transporto sistemos suvartojamą energijos kiekį. Šio darbo tikslas – atlikti Kauno miesto darbo vietų ir gyventojų tankio analizę bei įvertinti šių parametrų įtaką gyventojų mobilumui. Straipsnyje nagrinėjamas Kauno miesto gyventojų ir darbo vietų pasiskirstymas. Tam yra sudaromi gyventojų bei darbo vietų tankio žemėlapiai. Pradiniai duomenys apdorojami, analizuojami ir grafiškai pateikiami schemomis naudojant geografines informacines sistemas (toliau GIS). Remiantis analizės rezultatais ir atliktos apklausos rezultatais formuluojamos apibendrinančios išvados.

Advanced route planning algorithms are one of the key enabling technologies for emerging electric and autonomous mobility. Large realistic data sets are needed to test such algorithms under conditions that capture natural time-varying traffic patterns and corresponding travel-time and energy-use predictions. Further, the time-varying availability of charging infrastructure and vehicle-specific charging-power curves may be necessary to support advanced planning. While some data sets and synthetic data generators capture some of the aspects mentioned above, no integrated testbeds include all of them. We contribute with a modular testbed architecture. First, it includes a semi-synthetic data generator that uses a state-of-the-art traffic simulator, real traffic volume distribution patterns, EV-specific data, and elevation data. These elements support the generation of time-dependent travel-time and energy-use weights in a road-network graph. The generator ensures that the data satisfies the FIFO property, which is essential for time-dependent routing. Next, the testbed provides a thin layer of services that can serve as building blocks for future advanced routing algorithms. The experimental study demonstrates that the testbed can reproduce travel-time and energy-use patterns for long-distance trips similar to commercially available services.

The formulation of scenarios for developing the urban transport infrastructure requires decisions mainly based on the intuition of experts in transport and highly influenced by public interest groups, business entities and political opinions. However, the reached decisions sometimes fail to be the most efficient. Therefore, to avoid errors and ensure the development of a sustainable transport system, the economical appraisal of infrastructure development scenarios is necessary. The economic evaluation of the developed scenarios can be carried out through macro-simulation and cost-benefit analysis. This paper deals with the Kaunas City Master Plan providing solutions to transport infrastructure development. According to the Master Plan, solutions can be classified considering 3 cathegories (priorities), although the detailed sequence of implementation is not given. With the help of macro-simulation, this study arranged Master Plan solutions into scenarios, checked all 20 scenarious and established an implementation order based on the theory of cost benefit analysis. The identified order substantially differs from the priorities set in the Master Plan.

In city population and workplace density distributions directly influences the number of trips, travel means and even leads to changes of energy consumption rate. This paper is aimed to perform Kaunas city workplace density and population density analysis and to evaluate the parameters impact on the mobility of the population. The main focus is on the Kaunas population and workplace distribution. For that population density and workplace density maps are made. Initial data is processed by geographic information system (GIS), allowing to represent them in a very clear way. Conclusions are given based on conducted analysis and other relevant sources of information. Article in Lithuanian. Kauno miesto gyventojų ir darbo vietų tankio analizė transportiniu požiūriu Santrauka  Gyventojų bei darbo vietų pasiskirstymas mieste turi tiesioginės įtakos gyventojų atliekamų kelionių skaičiui, kelionių būdui ir lemia transporto sistemos suvartojamą energijos kiekį. Šio darbo tikslas – atlikti Kauno miesto darbo vietų ir gyventojų tankio analizę bei įvertinti šių parametrų įtaką gyventojų mobilumui. Straipsnyje nagrinėjamas Kauno miesto gyventojų ir darbo vietų pasiskirstymas. Tam yra sudaromi gyventojų bei darbo vietų tankio žemėlapiai. Pradiniai duomenys apdorojami, analizuojami ir grafiškai pateikiami schemomis naudojant geografines informacines sistemas (toliau GIS). Remiantis analizės rezultatais ir atliktos apklausos rezultatais formuluojamos apibendrinančios išvados. Reikšminiai žodžiai: Kauno miestas, darbo vietų tankis, gyventojų tankis, regresinė analizė.

Merge junctions are the key elements in the freeway system, as they are likely to function as bottlenecks. Investigations into breakdown occurrence at ramp junctions have demonstrated that when the groups of several vehicles following each other enter the freeway from the ramp, they are expected to create 'turbulence' resulting from lane changes, decelerations of vehicles on the mainline and inevitably by the cars merging from the on-ramp. This turbulence can lead to breakdown when the level of mainline demand is adequately high. In other words, the impact of a ramp vehicle on capacity is higher than that of a mainline vehicle, which indicates that a part of vehicles will simultaneously occupy two lanes during the process of changing them thus momentarily decreasing the capacity of the link. This feature becomes particularly important near bottlenecks where it might reduce the already limited throughput. The article introduces the main approaches, methodology, principles and stages of transport demand management on freeways that are aimed at improving the operation quality of transport facilities, including road safety. The technique allows evaluating and optimizing a Ramp-Metering (RM) concept from the viewpoint of minimizing the length of queues on ramps and a possibility of transport congestion. The proposed algorithm estimates the probability of starting congestion formation on the ramp using objective information on traffic conditions in each segment of the highway, which is based on the criterion for vehicle density on the lane. The last chapter shows the examples of traffic flow optimization on Western bypass ramps in Vilnius comparing two strategies for access control management using one or several vehicles per lane. Conclusions, trends and work on future investigations are presented at the end of the article.

The growing rate of motorization and the use of passenger cars have a worsening effect on traffic conditions in the streets of Vilnius City. Moreover, adverse urbanisation processes (i.e. migration to suburban areas) make a huge effect on the behaviour of travelling. A deeper analysis of these processes requires large data amounts and techniques for analysing transportation. The study is aimed at preparing and assessing the scenarios of developing passenger car transport through the prism of sustainability indicators. A plan for case study-based hypothetical mobility management explores a series of future scenarios improving transportation diversity and changes in modes for travellers. These scenarios are developed with respect to developments anticipated in the Master Plan of the City of Vilnius and aims at identifying the effect of a new public transport network on the motorized transport system during the morning peak in the hypothetical year 2025. Mobility management through transportation diversity increases travelling options, encourages travellers to choose the most efficient mode and tends to eliminate car dependency that otherwise occurs in urban areas. The development of a public transport route network in Vilnius City creates real preconditions for implementing a sustainable transport system thus giving a priority to the development of a new and fast transport mode. The planned routes of the new transport mode allows significantly reducing the necessity for the use of private motorized transport and influencing the total structure of travels thus making it possible for a large number of people to reach destination by public transport.

Researchers and practitioners often discuss which types of junctions are more efficient in urban territories. Consequently, a proper decision is important in order to minimize the costs of infrastructure for users and government. The aim of the study is to assess and compare operation of roundabouts and signal-controlled intersections under various traffic conditions. The study is based on traffic measurements and data about road accidents. Efficiency of operation is calculated and compared, proposals are given.

This study assesses the suitability of UAV aerial imagery-based photogrammetry for reconstructing underwater riverbed topography and its application in two-dimensional (2D) hydrodynamic modelling, with a particular focus on comparing RGB, multispectral, and fused RGB–multispectral imagery. Four Lithuanian rivers—Verknė, Šušvė, Jūra, and Mūša—were selected to represent a wide range of hydromorphological and hydraulic conditions, including variations in bed texture, vegetation cover, and channel complexity. High-resolution digital elevation models (DEMs) were generated from field-based surveys and UAV imagery processed using Structure-from-Motion photogrammetry. Two-dimensional hydrodynamic models were created and calibrated in HEC-RAS 6.5 using measurement-based DEMs and subsequently applied using photogrammetry-derived DEMs to isolate the influence of terrain input on model performance. The results showed that UAV-derived DEMs systematically overestimate riverbed elevation, particularly in deeper or vegetated sections, resulting in underestimated water depths. RGB imagery provided greater spatial detail but was more susceptible to local anomalies, whereas multispectral imagery produced smoother surfaces with a stronger positive elevation bias. The fusion of RGB and multispectral imagery consistently reduced spatial noise and improved hydrodynamic simulation performance across all river types. Despite moderate vertical deviations of 0.10–0.25 m, relative flow patterns and velocity distributions were reproduced with acceptable accuracy. The findings demonstrate that combined spectral UAV aerial imagery in photogrammetry is a robust and cost-effective alternative for hydrodynamic modelling in shallow lowland rivers, particularly where relative hydraulic characteristics are of primary interest.

Urban change detection is an important part of sustainable urban planning, regional development, and socio-economic analysis, especially in regions with limited access to economic and demographic statistical data. The goal of this research is to create a strategy that enables the extraction of indicators from large-scale orthoimages of different resolution with practically acceptable accuracy after a short training process. Remote sensing data can be used to detect changes in number of buildings, forest areas, and other landscape objects. In this paper, aerial images of a digital raster orthophoto map at scale 1:10,000 of the Republic of Lithuania (ORT10LT) of three periods (2009–2010, 2012–2013, 2015–2017) were analyzed. Because of the developing technologies, the quality of the images differs significantly and should be taken into account while preparing the dataset for training the semantic segmentation model DeepLabv3 with a ResNet50 backbone. In the data preparation step, normalization techniques were used to ensure stability of image quality and contrast. Focal loss for the training metric was selected to deal with the misbalanced dataset. The suggested model training process is based on the transfer learning technique and combines using a model with weights pretrained in ImageNet with learning on coarse and fine-tuning datasets. The coarse dataset consists of images with classes generated automatically from Open Street Map (OSM) data and the fine-tuning dataset was created by manually reviewing the images to ensure that the objects in images match the labels. To highlight the benefits of transfer learning, six different models were trained by combining different steps of the suggested model training process. It is demonstrated that using pretrained weights results in improved performance of the model and the best performance was demonstrated by the model which includes all three steps of the training process (pretrained weights, training on coarse and fine-tuning datasets). Finally, the results obtained with the created machine learning model enable the implementation of different approaches to detect, analyze, and interpret urban changes for policymakers and investors on different levels on a local map, grid, or municipality level.

Parking lots have wide variety of shapes because of surrounding environment and the objects inside the parking lot, such as trees, manholes, etc. In the case of paving the parking lot, as much area as possible should be covered by the construction vehicle to reduce the need for manual workforce. Thus, the coverage path planning (CPP) problem is formulated. The CPP of the parking lots is a complex problem with constraints regarding various issues, such as dimensions of the construction vehicle and data processing time and resources. A strategy based on convolutional neural networks (CNNs) for the fast estimation of the CPP’s average track length, standard deviation of track lengths, and number of tracks was suggested in this article. Two datasets of different complexity were generated to analyze the suggested approach. The first case represented a simple case with a working polygon constructed out of several rectangles with applied shear and rotation transformations. The second case represented a complex geometry generated out of rectangles and ellipses, narrow construction area, and obstacles. The results were compared with the linear regression models, with the area of the working polygon as an input. For both generated datasets, the strategy to use an approximator to estimate outcomes led to more accurate results compared to the respective linear regression models. The suggested approach enables us to have rough estimates of a large number of geometries in a short period of time and organize the working process, for example, planning construction time and price, choosing the best decomposition of the working polygon, etc.