Demand-Responsive Transport: Models and Algorithms

Demand-responsive transport is a form of public transport between bus and taxi services, involving flexible routing of small or medium sized vehicles. This dissertation presents mathematical models for demand-responsive transport and methods that can be used to solve combinatorial problems related to vehicle routing and journey planning in a transport network. Public transport can be viewed as a market where demand affects supply and vice versa. In the first part of the dissertation related to vehicle routing, we show how a given demand for transportation can be satisfied by using a fleet of vehicles, assuming that the demand is known at the individual level. In the second part, by considering the journey planning problem faced by commuters, we study how the demand adapts to the supply of transport services, assuming that the supply remains unchanged for a short period of time. We also present a stochastic network model for determining the economic equilibrium, that is, the point at which the demand meets the supply, by assuming that commuters attempt to minimize travel time and transport operators aim to maximize profit. The mathematical models proposed in this work can be used to simulate the operations of public transport services in a wide range of scenarios, from paratransit services for the elderly and disabled to large-scale demand-responsive transport services designed to compete with private car traffic. Such calculations can provide valuable information to public authorities and planners of transportation services, regarding, for example, regulation and investments. In addition to public transport, potential applications of the proposed methods for solving vehicle routing and journey planning problems include freight transportation, courier and food delivery services, military logistics and air traffic.Kysyntäohjautuvalla joukkoliikenteellä tarkoitetaan bussi- ja taksipalvelujen välimuotoa, joka perustuu pienten tai keskisuurten ajoneuvojen joustavaan reititykseen. Tässä väitöskirjassa esitetään matemaattisia malleja kysyntäohjautuvalle joukkoliikenteelle, ja menetelmiä, joilla voidaan ratkaista ajoneuvojen reitinlaskentaan ja matkansuunnitteluun liittyviä kombinatorisia ongelmia liikenneverkossa. Joukkoliikennettä voidaan tarkastella markkinana, jossa kysyntä vaikuttaa tarjontaan ja päinvastoin. Väitoskirjan ensimmäisessa osassa, joka käsittelee ajoneuvojen reitinlaskentaa, näytetään miten tunnettuun kysyntään voidaan vastata käyttämällä tiettyä ajoneuvokantaa, kun oletetaan kysyntä tunnetuksi yhden matkustajan tarkkuudella. Toisessa osassa tarkastellaan matkustajien matkansuunnittelua joukkoliikenneverkossa, eli sitä miten kysyntä mukautuu liikennepalvelujen tarjonnan mukaan, kun oletetaan tarjonta muuttumattomaksi lyhyellä aikavälillä. Lopuksi esitetään menetelmä taloudellisen tasapainopisteen, eli kysynnän ja tarjonnan kohtaamispisteen, määrittämiseksi, kun oletetaan että matkustajat pyrkivät minimoimaan matka-aikaa ja liikennepalvelujen tarjoajat pyrkivät maksimoimaan taloudellista voittoa. Tässä työssä esiteltyjen mallien avulla voidaan simuloida useita erityyppisiä liikennepalvelujavanhuksille ja liikuntarajoitteisille suunnatuista kutsulinjoista henkilöautoliikenteen kanssa kilpaileviin laajamittaisiin kysyntäohjautuviin joukkoliikennejärjestelmiin. Mallien avulla tehdyt laskelmat voivat tuottaa arvokasta tietoa viranomaisille ja liikennepalvelujen suunnittelijoille liikenteen säännöstelyyn ja investointeihin liittyen. Joukkoliikenteen lisäksi esiteltyjä reitinlaskenta- ja matkansuunnittelumenetelmiä voidaan soveltaa muun muassa rahti- ja lentoliikenteessä, lähetti- ja ruoankuljetuspalveluissa sekä sotilaslogistiikassa

Adaptive Quantification and Subtyping of Pulmonary Emphysema on Computed Tomography

Pulmonary emphysema contributes to the chronic airflow limitation characteristic of chronic obstructive pulmonary disease (COPD), which is a leading cause of morbidity and mortality worldwide. Computed tomography (CT) has enabled in vivo assessment of pulmonary emphysema at the macroscopic level, and is commonly used to identify and assess the extent of the disease. During the past decade, the availability of CT imaging data has increased rapidly, while the image quality has continued to improve. High-resolution CT is extremely valuable both for patient diagnosis and for studying diseases at the population level. However, visual assessment of these large data sets is subjective, inefficient, and expensive. This has increased the demand for objective, automatic, and reproducible image analysis methods. For the assessment of pulmonary emphysema on CT, computational models usually aim either to give a measure of the extent of the disease, or to categorize the emphysema subtypes apparent in a scan. The standard methods for quantitating emphysema extent are widely used, but they remain sensitive to changes in imaging protocols and patient inspiration level. For computational subtyping of emphysema, the methods remain at a developmental stage, and one of the main challenges is the lack of reliable label data. Furthermore, the classic emphysema subtypes were defined on autopsy before the availability of CT and could be considered outdated. There is also no consensus on how to match the subtypes on autopsy to the varying emphysema patterns present on CT. This work presents two methodological improvements for analyzing emphysema on CT. For the assessment of emphysema extent, a novel probabilistic approach is introduced and evaluated on a longitudinal data set with varying imaging protocols. The presented model is shown to improve significantly compared to standard methods, particularly at the presence of differing noise levels. The approach is also applied on quantifying emphysema on a large data set of cardiac CT scans, and is shown to improve the prediction of emphysema extent on subsequent full-lung CT scans. The second major contribution of this work applies unsupervised learning to recognizing patterns of emphysema on CT. Instead of trying to reproduce the classic subtypes, the novel approach aims to capture the most dominant variations of lung structure pertaining to emphysema. While removing the reliance on visually assigned labels, the learned patterns are shown to represent different manifestations of emphysema with distinct appearances and regular spatial distributions. The clinical significance of the patterns is also demonstrated, along with high-level performance in the application of content-based image retrieval. The contributions of this work advance the analysis of emphysema on CT by applying novel machine learning approaches to increase the value of the available imaging data. Probabilistic methods improve from the crude standard methods that are currently used to quantitate emphysema, and the value of learning disease patterns directly from image data is demonstrated. The common framework relying on replicating visually assigned labels of outdated subtypes has not achieved widespread acceptance. The methodology presented in this work may have a substantial impact on how emphysema subtypes on CT are recognized and defined in the future

Demonstration of large area forest volume and primary production estimation approach based on Sentinel-2 imagery and process based ecosystem modelling

Forest biomass and carbon monitoring play a key role in climate change mitigation. Operational large area monitoring approaches are needed to enable forestry stakeholders to meet the increasing monitoring and reporting requirements. Here, we demonstrate the functionality of a cloud-based approach utilizing Sentinel-2 composite imagery and process-based ecosystem model to produce large area forest volume and primary production estimates. We describe the main components of the approach and implementation of the processing pipeline into the Forestry TEP cloud processing platform and produce four large area output maps: (1) Growing stock volume (GSV), (2) Gross primary productivity (GPP), (3) Net primary productivity (NPP) and (4) Stem volume increment (SVI), covering Finland and the Russian boreal forests until the Ural Mountains in 10 m spatial resolution. The accuracy of the forest structural variables evaluated in Finland reach pixel level relative Root Mean Square Error (RMSE) values comparable to earlier studies (basal area 39.4%, growing stock volume 58.5%, diameter 35.5% and height 33.5%), although most of the earlier studies have concentrated on smaller study areas. This can be considered a positive sign for the feasibility of the approach for large area primary production modelling, since forest structural variables are the main input for the process-based ecosystem model used in the study. The full coverage output maps show consistent quality throughout the target area, with major regional variations clearly visible, and with noticeable fine details when zoomed into full resolution. The demonstration conducted in this study lays foundation for further development of an operational large area forest monitoring system that allows annual reporting of forest biomass and carbon balance from forest stand level to regional analyses. The system is seamlessly aligned with process based ecosystem modelling, enabling forecasting and future scenario simulation.Peer reviewe

Nanoparticle volatility and growth : Implications for interactions between biogenic and anthropogenic aerosol components

Aerosol particles are important atmospheric constituents. They exist in both polluted and remote areas but the sizes and concentrations of these particles vary greatly depending on location. Aerosol particles damage human health via inhalation, reduce visibility with high mass loadings, and among all, contribute to climate change. Particles directly scatter and absorb solar radiation. In addition, particles that are large enough can participate in cloud formation and affect cloud properties by acting as cloud condensation nuclei (CCN). A notable fraction of submicron atmospheric aerosol mass consists of organic compounds, and a large fraction of this material has been formed through condensation of organic vapors onto aerosol particles (secondary organic aerosol, SOA). Most of the global SOA mass is deemed to be biogenic in origin, but recent studies suggest that a significant fraction of it may be controlled by anthropogenic pollution. However, due to poor understanding of this anthropogenic enhancement in biogenic SOA formation, it is not systematically accounted for in current atmospheric models. Due to these kind of uncertainties in global SOA mass burden and lack of detailed knowledge of chemical, physical and optical properties of SOA, estimates of organic aerosol effect on the climate are highly uncertain. To decrease the uncertainty in the climate effects of the organic aerosol, an improved understanding of the formation mechanisms and properties of SOA is needed. In addition, nanoparticle growth to CCN-sizes by condensation of secondary organic matter needs to be accurately described in atmospheric models. In this thesis the formation of SOA is investigated in the presence of both biogenic and anthropogenic compounds. The chemical and physical properties volatility and hygroscopicity of SOA are examined via field and laboratory experiments combined with process modeling. The thesis introduces improvements for the treatment of SOA related to nanoparticle growth in atmospheric models and evaluates their performance. The thesis shows that interactions between atmospheric biogenic and anthropogenic aerosol components can form aerosol material of low-volatility. For instance organic salt formation via chemical reactions between organic acids and inorganic salts can lower aerosol volatility. Particulate-phase processing may also alter aerosol hygroscopic properties. Description of nanoparticle growth by low-volatility secondary organics is important in improving the estimates of particle and CCN numbers. The thesis highlights the significance of biogenic organic matter formed under anthropogenic influence in the nanoparticle growth. This warrants future studies focusing on the formation mechanisms and properties of anthropogenically driven biogenic organic aerosol.Aerosolihiukkasia on ilmakehässä kaikkialla, niin saastuneilla kuin syrjäisemmilläkin alueilla. Ilmakehän aerosolihiukkaset aiheuttavat ihmisille vakavia terveyshaittoja ja huonontavat näkyvyyttä erityisesti saastuneissa suurkaupungeissa. Tämän lisäksi aerosolihiukkaset vaikuttavat ilmastoon ja sen muutoksiin. Hiukkaset sirottavat ja absorboivat auringon lähettämää säteilyä sekä toimivat pilvien tiivistymisytiminä vaikuttaen pilvien muodostukseen ja ominaisuuksiin. Nykytiedon mukaan aerosolihiukkasten nettoilmastovaikutukset ovat ilmastoa viilentäviä, joskin epävarmuustekijät ovat suuria. Jotta aerosolihiukkasten ilmastovaikutus voitaisiin arvioida luotettavammin, tulee pienhiukkasten lähteet, määrä ja ominaisuudet tuntea paremmin. Alle mikrometrin halkaisijaltaan olevat aerosolihiukkaset selittävät suuren osan ilmakehän hiukkasten lukumääräpitoisuuksista. Merkittävä osa näiden pienhiukkasten kemiallisesta koostumuksesta selittyy luonnollisista biogeenisistä lähteistä peräisin olevilla orgaanisilla yhdisteillä. Nämä yhdisteet ovat päätyneet hiukkasiin pääosin orgaanisten höyryjen tiivistyessä hiukkasten pinnalle (sekundäärinen orgaaninen aerosoli). Tämän hetkisen tiedon mukaan merkittävä osuus luonnollisista orgaanisista yhdisteistä ei kuitenkaan olisi hiukkasfaasissa ilman ihmisen toimintaa. Toisin sanoen ihmisen toiminnalla on vaikutusta biogeenisen orgaanisen aerosolin muodostumiseen. Syntymekanismit ovat kuitenkin vielä epäselviä eikä niitä siksi ole järjestelmällisesti huomioitu ilmakehää ja sen aerosolihiukkasia kuvaavissa malleissa. Tämä johtaa merkittäviin epävarmuuksiin arvioitaessa ihmisperäisten orgaanisten aerosolihiukkasten ilmastovaikutuksia. Tässä väitöskirjatyössä tutkitaan sekundäärisen orgaanisen aerosolin muodostumista ja ominaisuuksia biogeenisten ja ihmisperäisten yhdisteiden läsnä ollessa. Lisäksi pienimpien aerosolihiukkasten (nanohiukkanen) kasvun kuvausta suuren skaalan ilmakehämalleissa parannetaan mittaustuloksiin pohjautuvalla parametrisaatiolla. Nanohiukkasten kasvu sekundäärisen orgaanisen aerosoliaineksen muodostumisen avulla on merkittävä pilvien tiivistymisytimien lähde ilmakehässä. Monet tämän hetkiset ilmakehämallit kuitenkin aliarvioivat nanohiukkasten kasvun merkitystä ilmastoon. Väitöskirjan tutkimusaineisto koostuu kenttä- ja laboratoriomittauksista, joita analysoidaan mallinnuksen keinoin. Tämä väitöstutkimus osoittaa että biogeenisen ja ihmisperäisen aerosoliaineksen keskinäiset vuorovaikutukset muuntavat orgaanisen aerosolin ominaisuuksia, esimerkiksi alentavat hiukkasten haihtuvuutta ja vaikuttavat niiden vedenottokykyyn. Alhainen haihtuvuus kertoo siitä, että yhdiste pysyy hiukkasessa, mukaan lukien nanohiukkasissa, eikä helposti siirry kaasufaasiin. Hiukkasessa olevien yhdisteiden haihtuvuuden aleneminen siirtää kaasu-hiukkastasapainoa hiukkasfaasiin päin. Tämä edesauttaa sekundäärisen orgaanisen aerosolin muodostumista. Väitöstutkimus tukee omalta osaltaan viimeaikaisia tutkimustuloksia siitä, että ihmisen toiminta edesauttaa biogeenisen orgaanisen aerosolin muodostumista. Väitöskirja korostaa, että tämä biogeeninen orgaaninen aines on tärkeää huomioida mallinnettaessa nanohiukkasten kasvua ilmastollisesti merkittäviin kokoihin

Errors related to the automatized satellite-based change detection of boreal forests in Finland

Highlights • Forest changes were automatically modelled from multitemporal Sentinel-2 images. • Errors were evaluated based on visually interpreted VHR images. • Extraction of clear-cuts was accurate whereas thinnings had more variation. • Image quality and translucent clouds had most significant effect on errors. • Results were regarded applicable for operational change monitoring.The majority of the boreal forests in Finland are regularly thinned or clear-cut, and these actions are regulated by the Forest Act. To generate a near-real time tool for monitoring management actions, an automatic change detection modelling chain was developed using Sentinel-2 satellite images. In this paper, we focus mainly on the error evaluation of this automatized workflow to understand and mitigate incorrect change detections. Validation material related to clear-cut, thinned and unchanged areas was collected by visual evaluation of VHR images, which provided a feasible and relatively accurate way of evaluating forest characteristics without a need for prohibitively expensive fieldwork. This validation data was then compared to model predictions classified in similar change categories. The results indicate that clear-cuts can be distinguished very reliably, but thinned stands exhibit more variation. For thinned stands, coverage of broadleaved trees and detections from certain single dates were found to correlate with the success of the modelling results. In our understanding, this relates mainly to image quality regarding haziness and translucent clouds. However, if the growing season is short and cloudiness frequent, there is a clear trade-off between the availability of good-quality images and their preferred annual span. Gaining optimal results therefore depends both on the targeted change types, and the requirements of the mapping frequency

Deep Learning Model Transfer in Forest Mapping Using Multi-Source Satellite SAR and Optical Images

Deep learning (DL) models are gaining popularity in forest variable prediction using Earth observation (EO) images. However, in practical forest inventories, reference datasets are often represented by plot- or stand-level measurements, while high-quality representative wall-to-wall reference data for end-to-end training of DL models are rarely available. Transfer learning facilitates expansion of the use of deep learning models into areas with sub-optimal training data by allowing pretraining of the model in areas where high-quality teaching data are available. In this study, we perform a “model transfer” (or domain adaptation) of a pretrained DL model into a target area using plot-level measurements and compare performance versus other machine learning models. We use an earlier developed UNet based model (SeUNet) to demonstrate the approach on two distinct taiga sites with varying forest structure and composition. The examined SeUNet model uses multi-source EO data to predict forest height. Here, EO data are represented by a combination of Copernicus Sentinel-1 C-band SAR and Sentinel-2 multispectral images, ALOS-2 PALSAR-2 SAR mosaics and TanDEM-X bistatic interferometric radar data. The training study site is located in Finnish Lapland, while the target site is located in Southern Finland. By leveraging transfer learning, the SeUNet prediction achieved root mean squared error (RMSE) of (Formula presented.) m and R2 of 0.882, considerably more accurate than traditional benchmark methods. We expect such forest-specific DL model transfer can be suitable also for other forest variables and other EO data sources that are sensitive to forest structure.</p

A hierarchical clustering method for land cover change detection and identification

A method to detect abrupt land cover changes using hierarchical clustering of multi-temporal satellite imagery was developed. The Autochange method outputs the pre-change land cover class, the change magnitude, and the change type. Pre-change land cover information is transferred to post-change imagery based on classes derived by unsupervised clustering, enabling using data from different instruments for pre- and post-change. The change magnitude and change types are computed by unsupervised clustering of the post-change image within each cluster, and by comparing the mean intensity values of the lower level clusters with their parent cluster means. A computational approach to determine the change magnitude threshold for the abrupt change was developed. The method was demonstrated with three summer image pairs Sentinel-2/Sentinel-2, Landsat 8/Sentinel-2, and Sentinel-2/ALOS 2 PALSAR in a study area of 12,372 km2 in southern Finland for the detection of forest clear cuts and tested with independent data. The Sentinel-2 classification produced an omission error of 5.6% for the cut class and 0.4% for the uncut class. Commission errors were 4.9% for the cut class and 0.4% for the uncut class. For the Landsat 8/Sentinel-2 classifications the equivalent figures were 20.8%, 0.2%, 3.4%, and 1.6% and for the Sentinel-2/ALOS PALSAR classification 16.7%, 1.4%, 17.8%, and 1.3%, respectively. The Autochange algorithm and its software implementation was considered applicable for the mapping of abrupt land cover changes using multi-temporal satellite data. It allowed mixing of images even from the optical and synthetic aperture radar (SAR) sensors in the same change analysis

Energy from biomass : Assessing sustainability by geoinformation technology

Publisher Copyright: © 2021 Austrian Acedemy of Sciences Press. All rights reserved.Forest Flux https://www.forestflux.eu/ will renew forestry value-added services in Earth Observation (EO) by creating and piloting cloud-based services for committed users on forest carbon assimilation and structural variable prediction. Forest Flux exploits the explosive increase of high-resolution EO data from the Copernicus program and developments of cloud computing technology. It implements a world-first service platform for high-resolution maps of traditional forestry variables together with forest carbon fluxes. Forest Flux will allow the users to improve the profitability of forest management while taking care of ecological sustainability. The Forest Flux services are implemented on the Forestry Thematic Exploitation cloud platform https://f-tep.com/. In 2020, nearly 700 thematic maps on forest stand and carbon flux variables were delivered to nine specific users in a form that was applicable to their operational forest management systems. The last project year 2021 focuses on map product refinement and improving user services, which eventually lead to operational service concepts.Peer reviewe