Research on the Influence of Roadside Billboards on Cognitive Workload of Young Drivers and Traffic Safety

Based on a vast number of worldwide studies concerning driving behavior and traffic safety, lack of drivers\u27 attention and distraction is recognized as two of the most critical factors for road safety. While roadside advertising is often identified as a potential source of distraction, it has received less attention compared to other types of distractions such as texting or calling while driving. Young drivers, 18 - 25 years old, seem to invest more resources interacting with roadside advertising, suggesting a lower capacity to discriminate between relevant and irrelevant driving information. Combined with lesser driving experience, susceptibility to roadside distractions such as advertising signs, static or changeable, can increase traffic safety risks involving young drivers. Therefore, this study focused on the impact of roadside advertising signs on young drivers, specifically on their cognitive workload while driving in an urban environment cluttered with roadside billboards. The research has been conducted by simultaneously using driving simulator, wireless mobile EEG device for the measurement of the brain\u27s electrical activity, and mobile ETG (eye-tracking glasses) for the measurement of eye movement while driving. The research included 20 young drivers 18 - 25 years old. Research results from EEG device showed statistically significant difference in young drivers\u27 cognitive workload related to roadside advertising, with higher cognitive workload while driving in an environment including billboards. Research results from the simulator showed statistically significant driving speed change-drivers accelerating while driving through environment saturated with roadside billboards. Research results from ETG and questionnaire showed three common features of the static roadside billboards that draw more of driver\u27s attention: bigger billboards (mega-boards), well-known brands, and provocative design

Three significant builders of Lika historical roads

U vrijeme Vojne krajine na području Like izgrađene su mnoge važne ceste koje su i danas u uporabi, a plijene pozornost kvalitetom gradnje, vještim projektantskim rješenjima i interpolacijom u prostor. Izuzetno stručni cestarski graditelji toga vremena napravili su ceste koje su uz poneku rekonstrukciju, u uporabi i danas. U radu se donose kratki životopisi trojice istaknutih graditelja povijesnih ličkih cesta; Vinka Struppija, Josipa Filipa Vukasovića i Josipa Kajetana Knežića o kojima se do sada pisalo uglavnom rijetko, skromno i pojedinačno. Uz prikaz navedenih graditelja donosi se i opis novih tehnologija koje su uvodili u cestogradnju i njihova važnost za izgradnju suvremene cestovne infrastrukture u Lici.During the Military Frontier, many important roads were built in the Lika area which are still in use today, and they capture attention with the quality of construction, skilled design solutions and interpolation into space. Extremely professional roadbuilders of that time made roads that, with some reconstruction, are still in use today. The paper presents short biographies of three prominent builders of historic roads in Lika: Vinko Struppi, Josip Filip Vukasović and Josip Kajetan Knežić, about whom so far it has been written mostly rarely, modestly and individually. With representation of these builders, this paper also presents a description of new technologies that were introduced in road construction and their importance for the construction of modern road infrastructure in Lika

Temporal Patterns of Vehicle Collisions with Roe Deer and Wild Boar in the Dinaric Area

The purpose of this study was to determine the frequency of wildlife-vehicle collisions (WVC) based on the animal species, and to deepen the knowledge of temporal patterns of vehicle collisions with roe deer and wild boar. The study analyses the data from police reports on vehicle collisions with animals on state roads, by date and time, section of road, and animal species over a 5-year period (2012–2016). These data were analysed to determine the temporal dynamics of vehicle collisions with roe deer and wild boar by month, time of day, and moon phase. On the state roads in the Dinaric area, roe deer are most commonly involved in vehicle collisions (70.1% of all collisions), followed by wild boar (11.0%). Other large species involved in collisions were fallow deer (4.8%), brown bear (1.8%), red deer (0.9%), grey wolf (0.7%), and European mouflon (0.5%), respectively. Most collisions with roe deer occurred in the period April–August, with reduced frequency during autumn and winter. For wild boar, there was no association between month and frequency of collisions. At the annual level, collisions with roe deer were significantly higher during night (37%) and twilight (41%) than during the day (22%). For wild boar, most collisions occurred during twilight (26%) and night (72%), although the difference between these two periods was not statistically significant. For roe deer, collisions had no association with lunar phase, though wild boar collisions during twilight (dawn or dusk) were more common during twilight periods on days with less moonlight. Since vehicle collisions with wildlife showed certain temporal patterns, these should be taken into consideration in developing statistical models of spatial WVC patterns, and also in planning strategies and countermeasures to mitigate WVC issues

Expert approach to safety improvement in road work zones

Povećan obim prometa na hrvatskim cestama zahtijeva sve više radova na njihovom održavanju. Radovi na cestama smanjuju mobilnost prometa i sigurnost korisnika ceste, ali i samih izvođača radova. Povećanje mobilnosti i sigurnosti prometa u radnim zonama temeljni su problemi koje trebaju rješavati sve zainteresirane strane (oni koji planiraju i upravljaju radovima, ali i oni koji izvode radove na cesti). Ublažavanje navedenih negativnih posljedica moguće je uz pomoć mjera ekspertnih sustava. U radu su prikazane mogućnosti uvođenja ekspertnih sustava u zone radova na cestama kao element unapređenja održavanja cesta i povećanje sigurnosti protoka prometa u radnim zonama i sigurnosti cestarskih radnika koji rade u zonama radova na cesti. Popunjavanjem baze znanja ekspertnog sustava s mjerodavnim podacima dobiva se ekspertni sustav koji nudi vozaču koji dolazi do zone radova na cesti nekoliko alternativnih pravaca kretanja. U radu je prikazan dijagram upravljanja vozilom u zonama radova na cesti s posebnim osvrtom na smanjenje prilazne brzine vožnje pri dolasku vozila u zonu radova na cesti. Iz rezultata istraživanja vidljivo je da bi primjena ekspertnih sustava koji imaju popunjenu bazu znanja s mjerodavnim, vjerodostojnim podacima, značajno olakšala upravljanje prometom kroz radne zone i povećala sigurnost prometa i sigurnost radnika na održavanju cesta kao i samu učinkovitost radnika.An ever-increasing volume of traffic on Croatian roads increases the volume of maintenance work. Road works negatively impact traffic mobility and road user safety, and also safety of the maintenance workers. Improving traffic mobility and safety is the key issue that all interested parties (planning and managing road works and those executing them) should address. Mitigation of negative effects is possible via certain expert system measures. Presented in the paper are the options provided by expert systems implemented in the road work zones as factors for improving road maintenance and safe traffic flow, as well as road workers safety. Introducing relevant data into the data base, an expert system is created providing the driver approaching a road work zone with a number of alternative routes. Also shown is a driving diagram for road work zones with special focus on slowing down speed upon entering the road work zone. The results show that the implementation of expert systems based on relevant data would significantly facilitate traffic management in road work zones and improve the safety of traffic and road workers, as well as the workers\u27 efficacy

Site selection model for placing roadside billboards with regard to road traffic safety

U ovom istraživanju prikazan je prijedlog ekspertnog sustava koji može biti koristan u detekciji područja koja su s aspekta sigurnosti povoljna za postavljanje oglasnih površina uz cestovne prometnice. Urbanizacija generira povećanu mobilnost u urbanim sredinama što čini upravljanje vozilom kompleksnijom i distraktibilnijom radnjom za vozače. Distrakcija postaje glavni izazov za sve koji se bave cestovnom prometnom sigurnošću (znanstvena zajednica, automobilska industrija, nadležna tijela), a definirana je kao odvraćanje pozornosti od prioritetno bitnih aktivnosti za sigurnu vožnju. Pozornost vozača može biti ugrožena nizom čimbenika koji se pojavljuju u raznim oblicima pa je time istraživanje distrakcija zahtjevno i treba biti pažljivo metodološki pripremljeno i isplanirano. Dosadašnja istraživanja dokazuju da je distrakcija vodeći uzročnik prometnih nesreća dok istraživanja provedena testnim vožnjama na autocestama gdje su kombinirana veća brzina i gusto postavljene oglasne površine dokazuju da distrakcije u takvim uvjetima ugrožavaju sigurno upravljanje vozilom. Istraživanjem tematike identificirani su glavni generatori distrakcija u cestovnom prometu koji imaju daljnju podjelu po utjecaju na sigurnu vožnju. U prvom dijelu istraživanja prikazani su osnovni pojmovi iz područja oglašavanja u cestovnom prometu. Autori su metodom modeliranja predložili model ekspertnog sustava koji može biti koristan u detekciji područja koja su s aspekta sigurnosti povoljna za postavljanje oglasnih površina uz cestovne prometnice. Metodom klasifikacije pomoću simulatora vožnje klasificirani su određeni elementi oglasnih površina koji utječu na koncentraciju vozača u vožnji te direktno na sigurnost cestovnog prometa. Deskriptivne i inferencijalne statističke analize, metoda indukcije, dedukcije i sinteze korištene su za prikaz rezultata istraživanja. Dobiveni rezultati istraživanja simulatorom i ETG naočalama daju jasan obrazac ponašanja ljudske percepcije u vožnji, što čini validnu podlogu za izradu ekspertnog sustava za poboljšanja sigurnosti u prometu s aspekta distrakcije od strane oglasnih površina.Presented in the study is the proposed system that may be useful in selecting the sites suitable for the placement of billboards along roads. Urbanization generates increased mobility in urban areas. Driving a vehicle in such areas is more demanding due to the visual distractions encountered by drivers. Distractions are a major issue for all those concerned with road traffic safety (professional community, automobile industry, authorities) and constitute anything and everything that may avert drivers’ attention from the key focus on safe driving. Drivers’ attention may be jeopardized by a number of factors, thus making the study of distractions a demanding task that needs to be carefully planned and prepared. Studies carried out to date prove that distractions are the chief cause of road accidents, and studies of test drives on motorways show that the combination of high speed and densely placed, visually distracting billboards pose a threat to safe driving. Additional studies have identified the main distractions in road traffic, providing also a classification with regard to their specific impacts on safe driving. In the first section, the study puts forth the basic terminology relating to billboards and road traffic. The model proposed by the authors may be useful in identifying the sites safe for putting up billboards alongside roads. Using a drive simulator for classification, certain billboard features are accented as potential distracters affecting drivers’ focus while driving, causing direct impact on road traffic safety. Descriptive and inferential statistic analyses, induction method, deduction, and synthesis were used to present the results. The results obtained using a simulator and ETG glasses help determine the behaviour pattern of human perception in driving, thus constituting a valid base for the development of traffic safety improvements with respect to the matter of roadside billboards

Model for identifying dangerous sections on public roads in areas frequent appearance of wild

Uočen je trend povećanja prometnih nesreća naleta vozila na divljač koji zahtijeva prijedloge mjera kako bi se prevenirala pojava divljači na rizičnim dionicama cesta. Da bi se došlo do određenih mjera spriječavanja naleta vozila na divljač, potrebno je prepoznati rizične dionice cesta s obzirom na mogućnost pojave divljači. U tom smislu razvijen je model prepoznavanja opasnih dionica na javnim cestama i istražene su mjere učinkovitog gospodarenja cestovnom mrežom koje će doprinijeti smanjenju pojave divljači na cestama. Zbog velikog raspona nadmorskih visina (0 – 1 011 m. n. v.) cestovne mreže, klimatskih razlika, različitosti konfiguracije terena te obitavanja svih vrsta divljači Republike Hrvatske, istraživanje doktorskog rada provedeno je na cestama Ličko-senjske županije. U razdoblju istraživanja najveći broj nesreća naleta vozila na divljač dogodio se na državnim cestama i uzrok nesreća uglavnom je bila krupna divljač. Stoga su modeli napravljeni na državnim cestama za krupnu divljač, srnu običnu i divlju svinju te za krupnu divljač ukupno. Posebna pažnja posvećena je vremenskim i prostornim obrascima naleta vozila na divljač koji su poslužili kao podloga razvoja modela prepoznavanja opasnih dionica od pojave divljači. Analiziranje prostornih obrazaca daleko je složenije od vremenskih obrazaca i zahtijevalo je upotrebu velikog broja nazavisnih varijabli, odnosno pretkazivača (pretkazivači ceste, krajobraza, reljefa, brojnosti divljači i broj naleta u ćeliji). Kako bi se dobio što precizniji i pouzdaniji model prepoznavanja opasnih dionica od pojave divljači ceste su podijeljene na dionice te su oko središta dionica ucrtani krugovi/ćelije određenih polumjera. Za svaku ćeliju određene su nezavisne varijable odnosno (pretkazivači). Prostorni podaci su pripremljeni i obrađeni u programskom paketu ArcGIS 9.2. U doktorskom radu predložene su dvije vrste modela prepoznavanja opasnih dionica od pojave divljači, i to model procjene vjerojatnosti naleta i model procjene broja naleta vozila na divljač. Za izbor najpouzdanijeg modela prepoznavanja opasnih dionica od pojave divljači korišten je Akaike Information Criterion (AIC). Izbor pouzdanog modela uslijedio je ukoliko je ΔAIC < 2 jedinice. Isto tako je određena i Akaike-ova težina (wi), koja predstavlja vjerojatnost da je model najbolji odnosno najpouzdaniji u usporedbi s ostalim modelima. Rizične dionice cesta od pojave divljači rangirane su višekriterijskom analizom primjenom analitičkog hijerarhijskog procesa (AHP metodom). Na osnovi provedenog istraživanja definirani su modeli procjene vjerojatnosti naleta i modeli procjene broja naleta vozila na divljač koji se temelje na pouzdanim podacima naleta vozila na divljač u istraživanom razdoblju. Svaki model za sebe relativno je manjkav. Prvi tip modela (model procjene vjerojatnosti naleta) dosta precizno procjenjuje vjerojatnost nastanka naleta. Ako se za procjenu vjerojatnosti naleta koristi i drugi model (model procjene broja naleta), tada kombinacija ova dva tipa modela daje dosta visoku točnost u procjeni opasnih dionica od naleta vozila na divljač. Modeli će omogućiti pravnim osobama koje gospodare javnim cestama da dobiju prikaz opasnih dionica i djeluju preko mjera održavanja i opremanja cesta na sigurnost prometa. Terenskim istraživanjem na kritičnim dionicama cesta ispitan je utjecaj pojedinih mjera održavanja na pojavu divljači na cestama. Predložen je novi postupak održavanja javnih cesta s kojim bi se smanjili prelasci divljači preko javnih cesta. Predloženi model prepoznavanja opasnih dionica na javnim cestama i postupak održavanja funkcionalno su provjereni na mreži javnih cesta Ličko-senjske županije.Along with the increase in the number of road motor vehicles there is also an increase in the number of collisions of vehicles and wildlife (WVC) that for some reason come out onto the road. The consequences of such collisions are sometimes very severe. Apart from material damage on the vehicle, wild animals also get injured, and there is great danger for human lives as well. The observed trend of the increasing number of traffic accidents involving collisions between vehicles and wildlife (WVC) requires proposals of measures in order to prevent the appearance of game at risky road sections. In order to take certain measures of preventing vehicle collision with the game (WVC), it is necessary to identify the high-risk road sections regarding the possible appearance of game. Therefore, a model of identifying dangerous sections on public roads, and measures of efficient management of the road network have been studied, and this will contribute to reducing the occurrence of wildlife on the roads. In practice, the question of liability for the damage caused by wildlife in collision with the vehicles or the vehicles colliding with wildlife (WVC) is often raised. The law stipulates that the game are the assets of interest for the Republic of Croatia and has its protection. The game lives in the hunting grounds with roads passing through. The hunting grounds are managed by the hunting licensees or hunting right owners, who are either legal or natural persons (craftsmen). The legislation in the Republic of Croatia, related to this issue is based on four laws of the Republic of Croatia. The area of compensation for the damage and liability caused by the vehicle colliding with the game is regulated by the Hunting Act, the Roads Act, the Road Traffic Safety Act, and the Obligations Act. The judicial practice of passing judgements lacks uniformity and there are different court judgements both in the first and the second court instances. According to the verdicts, sometimes the driver is to blame and sometimes the hunting licensee, and sometimes the legal entity that manages the road on which the vehicle collided with the game. The purpose of this doctoral thesis is to produce a maximally reliable model of forecasting the collision of vehicle with the wildlife (game) based on relatively easily accessible data. The objective of research is to propose measures that would significantly increase the traffic safety and reduce the number of vehicle-game collisions (WVC). Research hypotheses have been set, i.e. by in-depth analysis of data on traffic accidents caused by vehicle colliding with the game, it is possible to determine the elements of road and environment that highlight the high-risk sections of public roads and it is possible to develop a model of identifying the dangerous sections of public roads regarding the occurrence of wild animals on them. Lika-Senj County, selected as the research area, has central geographical position, and therefore an important connecting significance within the Republic of Croatia. Lika-Senj County occupies 9,46 % of the Croatian territory. Most of the County belongs to the mountainous area and includes the mountains of Velebit, Plješivica and Velika and Mala Kapela. The area of the County includes also the karst fields separated by the mountain ridges: fields of Lika, Gacka, Krbava, Drežnica, Korenica, Lapacand Gračac. The County also includes the Adriatic coast as well as a part of the island of Pag, i.e. a part of the territorial sea (596,63 km2 or 1.9 % of the Croatian sea area) and 2,29 km2 of the island area or 0,07 % of the area of all the islands of the Republic of Croatia. The mainland area of the Lika-Senja County covers an area of 535 113 ha, and stretches from 0 to 1 738 metres above sea level. Considering the division of climate according to Köppen, several different climate types change in the entire Lika-Senj County (climate type Cfb – 85,6 % of the area; climate type Cfa – 6,7 % of the area and climate type Df – 7,7 % of the Lika-Senj County area). The dominant type of the game habitat in Lika-Senj County are forests, which make up 65 % i.e. together with shrubs, brake-grown areas, and heaths the closed habitats make up almost 70 % of the observed area. Carbonate rocks make up 74 % of the researched area, which makes this part of the research area porous in terms of precipitation retention. Consequently, and due to a large range of the altitudes of the road network (0 – 1 011 metres a.s.l.), climatic differences, differences in terrain configuration and habitat of almost all species of game in the Republic of Croatia, the research of the doctoral dissertation was conducted on the roads of the Lika- Senj County. A prerequisite for determining the dangerous road sections regarding the occurrence of wildlife are the collected relevant data about the vehicle collisions with wildlife. The data on traffic accidents of vehicle collisions with wildlife have been collected by the employees of the Ministry of the Interior of the Republic of Croatia through going to the scene of accident, and for the purposes of this thesis the data have been collected by the Lika-Senj Police Department, i.e. police stations Gospić, Otočac, Senj, Donji Lapac, Korenica, Karlobag and Novalja. The data about vehicle collisions with wildlife have been taken from the police records on vehicle collisions with wildlife in the time period from 2012. to 2016.. There are 63 established hunting grounds in this area, which are managed by slightly fewer hunting licensees (some hunting licensees lease two or more hunting grounds). In order to verify the accuracy of the obtained data the hunting licensees of the hunting grounds in the Lika-Senj County have been surveyed, and because of the sensitivity of the data that impact the value of the hunting grounds, no newer relevant data could be obtained. In the research period there were 548 accidents involving vehicles colliding with wildlife, and the largest number of accidents of vehicle and wildlife collisions occurred on state roads, as many as 441, mostly collisions with roe deer (Capreolus capreolus) and wild boar (Sus scrofa). Having in mind the frequency of traffic accidents and the possibility of fatal outcomes of the traffic accidents, the work started on developing a model for recognizing dangerous sections on state roads, for the cases of collisions with roe deer, wild boar and large game in total. Special attention was paid to the temporal and spatial patterns of vehicle collisions with wildlife that served as the basis for developing the model of recognizing the dangerous sections regarding the appearance of wildlife. In order to determine the relevant minimum section length for the calculation of the collision probability estimate, state roads were divided into sections of 200, 500, 1 000, 2 000 and 12 000 m. Circles (cells) of radii of 100, 250, 500, 1 000 and 6 000 m were drawn around the centres of the sections and for each circle (cell) independent variables, that is, predictors were determined. In developing temporal patterns regarding the time of the occurrence of vehicle – wildlife collision, the 24-hour day was divided regarding the time of dawn and dusk into day, night and dusk. The time of sunrise and sunset was calculated by means of the algorithm provided by the Zagreb Observatory website for every day of the research period. When calculating the lunar phases, the international standard of the US Maritime Oceanographic Portal (lunar cycle of eight lunar months) was used. Analysing spatial patterns is far more complex than the temporal patterns and required the use of a large number of independent variables, i.e. predictors (predictors of the road, landscape, relief, number of wildlife, and number of collisions in the cell). The observed characteristics of the road included: average annual daily traffic (AADT), average summer daily traffic (ASDT) and curves parameter. The habitat data that were used included: share of water, shores, bare grounds, heaths and brake-grown areas, thickets, forests, grasslands, builtup land, neglected agricultural land, arable land. The index of topographic position (TPI or TOPEX) was used for the relief as a predictor of topographic characteristics of road. Regarding the index of topographic position and slope, the terrain has been classified into six categories: valleys, less steep terrains, medium steep terrains, extremely steep terrains, upper parts of the slopes and ridges. The data on the number of wildlife are relatively unreliable as predictor of population density, and therefore the data on game shooting in individual hunting grounds were used; however, they have been reduced to the unit of the hunting area. In order to obtain a maximally precise model of identifying dangerous sections on state roads, spatial patterns of vehicle collisions with large game were used. The spatial data were prepared in the software package Arc GIS 9.2., and processed in the software package Statistica 13.4.014 TIBCO Software Inc., 2018. The doctoral thesis proposes two types of patterns of recognizing dangerous sections regarding appearance of wildlife. These are: collision probability estimate model and vehiclegame collision number estimate. For the selection of the most reliable model of recognizing dangerous road sections regarding appearance of wildlife the software tools Akaike Information Criterion (AIC) was used. The selection of a reliable model followed if ΔAIC < 2 units. Akaike weight (wi) was also determined, and it represents the probability that the model is the best, that is, the most reliable compared to other models. Logistic regression was used to calculate the prediction of the probability of a vehicle colliding with wildlife. AIC analysis provided collision estimate models for every cell radius separately, and the logistic regression provided reliability of the results in estimation percentages. For the estimate of the collision probability with roe deer the smallest road section would be 2 000 m (cell radius 1 000 m), and the prediction accuracy is 68,20 %. The used independent variables (predictors) are the number of roe deer, share of the neglected agricultural land and the shares of bare land and the sea. The number of collisions with roe deer increases with the population density of roe deerfor radiiof 500 and 1 000 m, roe deer and wild boarfor radius of 250 m and wild boarfor radii of 6 000 m; the share of neglected agricultural land for sections of 500, 1 000 and 2 000 m; share of heaths and brake-grown areas on road sections of 500 and 1 000 m; distances from possible watering places (fresh water) on road sections of 500 and 1 000 m; AADT on sections of 500 and 12 000 m and with lower TPI (TOPEX) value, i.e. in valleys and less steep terrain, but this applies only to road sections of 12 000 m. The number of collisions with roe deer gets reduced with the increase of curves on a section (sections of 1 000 m) or road share (sections of 2 000 m); share of bare land (on all sections, except sections of 12 000 m); share of the sea (on all sections, except sections of 12 000 m) and built-up land on sections of 500 m. In the wild boar collision estimation model, AIC analysis provided quite a lot of collision estimation models for each cell radius separately, and the logistic regression gave reliable results only for road sections of 12 000 m. The collision probability on the section can be estimated in 73,33 % of cases. In the wild boar collision estimation model the game population density proved to be the key independent variable (predictor). The number of wild boar collisions will be larger if the population density of roe deer, wild boar or large game in total is higher; higher share of neglected agricultural land and higher ASDT. The number of collisions will be smaller if the share of built-up land is higher; higher share of forests; higher share of roads; higher share of the sea (exceptions are cells of radius of 6 000 m) and higher AADT. For the models estimating the probability of collision with large game in total, all the large game killed during the research period were included as well as grey wolf (legally grey wolf is not game, but is included in the model development due to similar consequences of the collision). For models of estimating the collision probability with large game in total the wildlife population density as independent variable comes in all cell radii. Respecting the results of the logistic regression, it may be said that the shortest section on which the probability of collision with large game in total may be reliably determined is a section of 2 000 m. On this section it is possible to predict the collision of vehicle on wildlife with a certainty of 70,11 %, based on the relative density of game, share of bare land, share of the sea, and the proximity to the nearest watering place. The number of collisions with large game in total will be greater if the population density of roe deer or large game is higher; higher share of neglected agricultural land; higher share of heaths and brake-grown areas; greater distance to the watering place; higher AADT. The number of collisions on large game in total will be smaller if the road features more curves or more intersections; higher share of bare land; higher share of built-up land; higher share of the sea and more indented relief (higher TPI value). Although the collision or non-collision location model can be estimated with an accuracy of 70,11 %, there are also certain model errors. Three outcomes of the model operation can be expected, and these are: the model has estimated correctly that on a certain section there will not occur or there will come to a collision; there were no collisions on the section during the research period, but the model predicted that a collision might occur there (collision overestimation error) and on the section collisions were recorded during the research period, but the model predicts that no collision can occur there (collision underestimation error) Each model for itself is relatively deficient. The first type of model (collision probability estimation) estimates quite accurately the collision occurrence probability. If another model were used for the collision probability estimation (estimation of the number of collisions) then the combination gives quite high accuracy in estimating the danger of vehicle collision with wildlife. The proposed models will enable the legal entities that manage public roads to get a view of dangerous sections and to act through measures of maintaining and equipping the roads for traffic safety. The road sections at risk of the appearance of wildlife have been ranked by multicriteria analysis by applying the Analytic Hierarchy Process (AHP method), using the software tools Expert Choice. Before using the software tools the objective was set, and it is to rank the dangerous road sections on state roads of the Lika-Senj County regarding the appearance of wildlife. The criteria that have been used are the number of collisions, section length, number of collisions per 100 km and number of collisions per 100 km annually. Appropriate matrices have been developed comparing the criteria with each other in relation to the set objective and based on these the values from the Saaty evaluation scale were added. The program requires also setting of variants, and they are several dangerous sections. Field research on critical road sections examined the impact of certain maintenance measures on the appearance of wildlife on the roads. The tested maintenance measures include installation of protective wire fences along the road; thorough cleaning of the protective road belt; mounting of optical and sound sensors on signposts; installation of adequate traffic signs – game on the road; planting of plant species that repel game, and also the impact of the location of game feeding grounds on the wildlife population density along the road was investigated. A new public road maintenance procedure has been proposed that would reduce wildlife crossing the public roads by increasing the patrol hours on high-risk sections; installation of protective fences to prevent the appearance of wild animals on the roads; cleaning the full profile of road protection zone in order to reduce the occurrence of wildlife on the roads; planting of game-repellent plant species along the road; installation of optical and sound sensors on signposts that drive off the game, and amendments to the Ordinance on traffic signs, signalization and road equipment. By proving the set objective and hypotheses, this doctoral dissertation opens up the possibilities of further research in preventing the wildlife to appear on the roads by developing a model with and without large predators; classification of killed game by gender and estimated age of the individual; unification of temporal and spatial patterns; separation of spatial patterns of collision according to the season, time of day and phases of the moon. Furthermore, in further research it is of extreme importance to separately study the killing on the roads of fallow deer, as a separate species. According to the conducted research in this doctoral thesis, this is the game which suffers major casualties in vehicle collisions, although they are small in number and living in a confined area

Model for identifying dangerous sections on public roads in areas frequent appearance of wild

Uočen je trend povećanja prometnih nesreća naleta vozila na divljač koji zahtijeva prijedloge mjera kako bi se prevenirala pojava divljači na rizičnim dionicama cesta. Da bi se došlo do određenih mjera spriječavanja naleta vozila na divljač, potrebno je prepoznati rizične dionice cesta s obzirom na mogućnost pojave divljači. U tom smislu razvijen je model prepoznavanja opasnih dionica na javnim cestama i istražene su mjere učinkovitog gospodarenja cestovnom mrežom koje će doprinijeti smanjenju pojave divljači na cestama. Zbog velikog raspona nadmorskih visina (0 – 1 011 m. n. v.) cestovne mreže, klimatskih razlika, različitosti konfiguracije terena te obitavanja svih vrsta divljači Republike Hrvatske, istraživanje doktorskog rada provedeno je na cestama Ličko-senjske županije. U razdoblju istraživanja najveći broj nesreća naleta vozila na divljač dogodio se na državnim cestama i uzrok nesreća uglavnom je bila krupna divljač. Stoga su modeli napravljeni na državnim cestama za krupnu divljač, srnu običnu i divlju svinju te za krupnu divljač ukupno. Posebna pažnja posvećena je vremenskim i prostornim obrascima naleta vozila na divljač koji su poslužili kao podloga razvoja modela prepoznavanja opasnih dionica od pojave divljači. Analiziranje prostornih obrazaca daleko je složenije od vremenskih obrazaca i zahtijevalo je upotrebu velikog broja nazavisnih varijabli, odnosno pretkazivača (pretkazivači ceste, krajobraza, reljefa, brojnosti divljači i broj naleta u ćeliji). Kako bi se dobio što precizniji i pouzdaniji model prepoznavanja opasnih dionica od pojave divljači ceste su podijeljene na dionice te su oko središta dionica ucrtani krugovi/ćelije određenih polumjera. Za svaku ćeliju određene su nezavisne varijable odnosno (pretkazivači). Prostorni podaci su pripremljeni i obrađeni u programskom paketu ArcGIS 9.2. U doktorskom radu predložene su dvije vrste modela prepoznavanja opasnih dionica od pojave divljači, i to model procjene vjerojatnosti naleta i model procjene broja naleta vozila na divljač. Za izbor najpouzdanijeg modela prepoznavanja opasnih dionica od pojave divljači korišten je Akaike Information Criterion (AIC). Izbor pouzdanog modela uslijedio je ukoliko je ΔAIC < 2 jedinice. Isto tako je određena i Akaike-ova težina (wi), koja predstavlja vjerojatnost da je model najbolji odnosno najpouzdaniji u usporedbi s ostalim modelima. Rizične dionice cesta od pojave divljači rangirane su višekriterijskom analizom primjenom analitičkog hijerarhijskog procesa (AHP metodom). Na osnovi provedenog istraživanja definirani su modeli procjene vjerojatnosti naleta i modeli procjene broja naleta vozila na divljač koji se temelje na pouzdanim podacima naleta vozila na divljač u istraživanom razdoblju. Svaki model za sebe relativno je manjkav. Prvi tip modela (model procjene vjerojatnosti naleta) dosta precizno procjenjuje vjerojatnost nastanka naleta. Ako se za procjenu vjerojatnosti naleta koristi i drugi model (model procjene broja naleta), tada kombinacija ova dva tipa modela daje dosta visoku točnost u procjeni opasnih dionica od naleta vozila na divljač. Modeli će omogućiti pravnim osobama koje gospodare javnim cestama da dobiju prikaz opasnih dionica i djeluju preko mjera održavanja i opremanja cesta na sigurnost prometa. Terenskim istraživanjem na kritičnim dionicama cesta ispitan je utjecaj pojedinih mjera održavanja na pojavu divljači na cestama. Predložen je novi postupak održavanja javnih cesta s kojim bi se smanjili prelasci divljači preko javnih cesta. Predloženi model prepoznavanja opasnih dionica na javnim cestama i postupak održavanja funkcionalno su provjereni na mreži javnih cesta Ličko-senjske županije.Along with the increase in the number of road motor vehicles there is also an increase in the number of collisions of vehicles and wildlife (WVC) that for some reason come out onto the road. The consequences of such collisions are sometimes very severe. Apart from material damage on the vehicle, wild animals also get injured, and there is great danger for human lives as well. The observed trend of the increasing number of traffic accidents involving collisions between vehicles and wildlife (WVC) requires proposals of measures in order to prevent the appearance of game at risky road sections. In order to take certain measures of preventing vehicle collision with the game (WVC), it is necessary to identify the high-risk road sections regarding the possible appearance of game. Therefore, a model of identifying dangerous sections on public roads, and measures of efficient management of the road network have been studied, and this will contribute to reducing the occurrence of wildlife on the roads. In practice, the question of liability for the damage caused by wildlife in collision with the vehicles or the vehicles colliding with wildlife (WVC) is often raised. The law stipulates that the game are the assets of interest for the Republic of Croatia and has its protection. The game lives in the hunting grounds with roads passing through. The hunting grounds are managed by the hunting licensees or hunting right owners, who are either legal or natural persons (craftsmen). The legislation in the Republic of Croatia, related to this issue is based on four laws of the Republic of Croatia. The area of compensation for the damage and liability caused by the vehicle colliding with the game is regulated by the Hunting Act, the Roads Act, the Road Traffic Safety Act, and the Obligations Act. The judicial practice of passing judgements lacks uniformity and there are different court judgements both in the first and the second court instances. According to the verdicts, sometimes the driver is to blame and sometimes the hunting licensee, and sometimes the legal entity that manages the road on which the vehicle collided with the game. The purpose of this doctoral thesis is to produce a maximally reliable model of forecasting the collision of vehicle with the wildlife (game) based on relatively easily accessible data. The objective of research is to propose measures that would significantly increase the traffic safety and reduce the number of vehicle-game collisions (WVC). Research hypotheses have been set, i.e. by in-depth analysis of data on traffic accidents caused by vehicle colliding with the game, it is possible to determine the elements of road and environment that highlight the high-risk sections of public roads and it is possible to develop a model of identifying the dangerous sections of public roads regarding the occurrence of wild animals on them. Lika-Senj County, selected as the research area, has central geographical position, and therefore an important connecting significance within the Republic of Croatia. Lika-Senj County occupies 9,46 % of the Croatian territory. Most of the County belongs to the mountainous area and includes the mountains of Velebit, Plješivica and Velika and Mala Kapela. The area of the County includes also the karst fields separated by the mountain ridges: fields of Lika, Gacka, Krbava, Drežnica, Korenica, Lapacand Gračac. The County also includes the Adriatic coast as well as a part of the island of Pag, i.e. a part of the territorial sea (596,63 km2 or 1.9 % of the Croatian sea area) and 2,29 km2 of the island area or 0,07 % of the area of all the islands of the Republic of Croatia. The mainland area of the Lika-Senja County covers an area of 535 113 ha, and stretches from 0 to 1 738 metres above sea level. Considering the division of climate according to Köppen, several different climate types change in the entire Lika-Senj County (climate type Cfb – 85,6 % of the area; climate type Cfa – 6,7 % of the area and climate type Df – 7,7 % of the Lika-Senj County area). The dominant type of the game habitat in Lika-Senj County are forests, which make up 65 % i.e. together with shrubs, brake-grown areas, and heaths the closed habitats make up almost 70 % of the observed area. Carbonate rocks make up 74 % of the researched area, which makes this part of the research area porous in terms of precipitation retention. Consequently, and due to a large range of the altitudes of the road network (0 – 1 011 metres a.s.l.), climatic differences, differences in terrain configuration and habitat of almost all species of game in the Republic of Croatia, the research of the doctoral dissertation was conducted on the roads of the Lika- Senj County. A prerequisite for determining the dangerous road sections regarding the occurrence of wildlife are the collected relevant data about the vehicle collisions with wildlife. The data on traffic accidents of vehicle collisions with wildlife have been collected by the employees of the Ministry of the Interior of the Republic of Croatia through going to the scene of accident, and for the purposes of this thesis the data have been collected by the Lika-Senj Police Department, i.e. police stations Gospić, Otočac, Senj, Donji Lapac, Korenica, Karlobag and Novalja. The data about vehicle collisions with wildlife have been taken from the police records on vehicle collisions with wildlife in the time period from 2012. to 2016.. There are 63 established hunting grounds in this area, which are managed by slightly fewer hunting licensees (some hunting licensees lease two or more hunting grounds). In order to verify the accuracy of the obtained data the hunting licensees of the hunting grounds in the Lika-Senj County have been surveyed, and because of the sensitivity of the data that impact the value of the hunting grounds, no newer relevant data could be obtained. In the research period there were 548 accidents involving vehicles colliding with wildlife, and the largest number of accidents of vehicle and wildlife collisions occurred on state roads, as many as 441, mostly collisions with roe deer (Capreolus capreolus) and wild boar (Sus scrofa). Having in mind the frequency of traffic accidents and the possibility of fatal outcomes of the traffic accidents, the work started on developing a model for recognizing dangerous sections on state roads, for the cases of collisions with roe deer, wild boar and large game in total. Special attention was paid to the temporal and spatial patterns of vehicle collisions with wildlife that served as the basis for developing the model of recognizing the dangerous sections regarding the appearance of wildlife. In order to determine the relevant minimum section length for the calculation of the collision probability estimate, state roads were divided into sections of 200, 500, 1 000, 2 000 and 12 000 m. Circles (cells) of radii of 100, 250, 500, 1 000 and 6 000 m were drawn around the centres of the sections and for each circle (cell) independent variables, that is, predictors were determined. In developing temporal patterns regarding the time of the occurrence of vehicle – wildlife collision, the 24-hour day was divided regarding the time of dawn and dusk into day, night and dusk. The time of sunrise and sunset was calculated by means of the algorithm provided by the Zagreb Observatory website for every day of the research period. When calculating the lunar phases, the international standard of the US Maritime Oceanographic Portal (lunar cycle of eight lunar months) was used. Analysing spatial patterns is far more complex than the temporal patterns and required the use of a large number of independent variables, i.e. predictors (predictors of the road, landscape, relief, number of wildlife, and number of collisions in the cell). The observed characteristics of the road included: average annual daily traffic (AADT), average summer daily traffic (ASDT) and curves parameter. The habitat data that were used included: share of water, shores, bare grounds, heaths and brake-grown areas, thickets, forests, grasslands, builtup land, neglected agricultural land, arable land. The index of topographic position (TPI or TOPEX) was used for the relief as a predictor of topographic characteristics of road. Regarding the index of topographic position and slope, the terrain has been classified into six categories: valleys, less steep terrains, medium steep terrains, extremely steep terrains, upper parts of the slopes and ridges. The data on the number of wildlife are relatively unreliable as predictor of population density, and therefore the data on game shooting in individual hunting grounds were used; however, they have been reduced to the unit of the hunting area. In order to obtain a maximally precise model of identifying dangerous sections on state roads, spatial patterns of vehicle collisions with large game were used. The spatial data were prepared in the software package Arc GIS 9.2., and processed in the software package Statistica 13.4.014 TIBCO Software Inc., 2018. The doctoral thesis proposes two types of patterns of recognizing dangerous sections regarding appearance of wildlife. These are: collision probability estimate model and vehiclegame collision number estimate. For the selection of the most reliable model of recognizing dangerous road sections regarding appearance of wildlife the software tools Akaike Information Criterion (AIC) was used. The selection of a reliable model followed if ΔAIC < 2 units. Akaike weight (wi) was also determined, and it represents the probability that the model is the best, that is, the most reliable compared to other models. Logistic regression was used to calculate the prediction of the probability of a vehicle colliding with wildlife. AIC analysis provided collision estimate models for every cell radius separately, and the logistic regression provided reliability of the results in estimation percentages. For the estimate of the collision probability with roe deer the smallest road section would be 2 000 m (cell radius 1 000 m), and the prediction accuracy is 68,20 %. The used independent variables (predictors) are the number of roe deer, share of the neglected agricultural land and the shares of bare land and the sea. The number of collisions with roe deer increases with the population density of roe deerfor radiiof 500 and 1 000 m, roe deer and wild boarfor radius of 250 m and wild boarfor radii of 6 000 m; the share of neglected agricultural land for sections of 500, 1 000 and 2 000 m; share of heaths and brake-grown areas on road sections of 500 and 1 000 m; distances from possible watering places (fresh water) on road sections of 500 and 1 000 m; AADT on sections of 500 and 12 000 m and with lower TPI (TOPEX) value, i.e. in valleys and less steep terrain, but this applies only to road sections of 12 000 m. The number of collisions with roe deer gets reduced with the increase of curves on a section (sections of 1 000 m) or road share (sections of 2 000 m); share of bare land (on all sections, except sections of 12 000 m); share of the sea (on all sections, except sections of 12 000 m) and built-up land on sections of 500 m. In the wild boar collision estimation model, AIC analysis provided quite a lot of collision estimation models for each cell radius separately, and the logistic regression gave reliable results only for road sections of 12 000 m. The collision probability on the section can be estimated in 73,33 % of cases. In the wild boar collision estimation model the game population density proved to be the key independent variable (predictor). The number of wild boar collisions will be larger if the population density of roe deer, wild boar or large game in total is higher; higher share of neglected agricultural land and higher ASDT. The number of collisions will be smaller if the share of built-up land is higher; higher share of forests; higher share of roads; higher share of the sea (exceptions are cells of radius of 6 000 m) and higher AADT. For the models estimating the probability of collision with large game in total, all the large game killed during the research period were included as well as grey wolf (legally grey wolf is not game, but is included in the model development due to similar consequences of the collision). For models of estimating the collision probability with large game in total the wildlife population density as independent variable comes in all cell radii. Respecting the results of the logistic regression, it may be said that the shortest section on which the probability of collision with large game in total may be reliably determined is a section of 2 000 m. On this section it is possible to predict the collision of vehicle on wildlife with a certainty of 70,11 %, based on the relative density of game, share of bare land, share of the sea, and the proximity to the nearest watering place. The number of collisions with large game in total will be greater if the population density of roe deer or large game is higher; higher share of neglected agricultural land; higher share of heaths and brake-grown areas; greater distance to the watering place; higher AADT. The number of collisions on large game in total will be smaller if the road features more curves or more intersections; higher share of bare land; higher share of built-up land; higher share of the sea and more indented relief (higher TPI value). Although the collision or non-collision location model can be estimated with an accuracy of 70,11 %, there are also certain model errors. Three outcomes of the model operation can be expected, and these are: the model has estimated correctly that on a certain section there will not occur or there will come to a collision; there were no collisions on the section during the research period, but the model predicted that a collision might occur there (collision overestimation error) and on the section collisions were recorded during the research period, but the model predicts that no collision can occur there (collision underestimation error) Each model for itself is relatively deficient. The first type of model (collision probability estimation) estimates quite accurately the collision occurrence probability. If another model were used for the collision probability estimation (estimation of the number of collisions) then the combination gives quite high accuracy in estimating the danger of vehicle collision with wildlife. The proposed models will enable the legal entities that manage public roads to get a view of dangerous sections and to act through measures of maintaining and equipping the roads for traffic safety. The road sections at risk of the appearance of wildlife have been ranked by multicriteria analysis by applying the Analytic Hierarchy Process (AHP method), using the software tools Expert Choice. Before using the software tools the objective was set, and it is to rank the dangerous road sections on state roads of the Lika-Senj County regarding the appearance of wildlife. The criteria that have been used are the number of collisions, section length, number of collisions per 100 km and number of collisions per 100 km annually. Appropriate matrices have been developed comparing the criteria with each other in relation to the set objective and based on these the values from the Saaty evaluation scale were added. The program requires also setting of variants, and they are several dangerous sections. Field research on critical road sections examined the impact of certain maintenance measures on the appearance of wildlife on the roads. The tested maintenance measures include installation of protective wire fences along the road; thorough cleaning of the protective road belt; mounting of optical and sound sensors on signposts; installation of adequate traffic signs – game on the road; planting of plant species that repel game, and also the impact of the location of game feeding grounds on the wildlife population density along the road was investigated. A new public road maintenance procedure has been proposed that would reduce wildlife crossing the public roads by increasing the patrol hours on high-risk sections; installation of protective fences to prevent the appearance of wild animals on the roads; cleaning the full profile of road protection zone in order to reduce the occurrence of wildlife on the roads; planting of game-repellent plant species along the road; installation of optical and sound sensors on signposts that drive off the game, and amendments to the Ordinance on traffic signs, signalization and road equipment. By proving the set objective and hypotheses, this doctoral dissertation opens up the possibilities of further research in preventing the wildlife to appear on the roads by developing a model with and without large predators; classification of killed game by gender and estimated age of the individual; unification of temporal and spatial patterns; separation of spatial patterns of collision according to the season, time of day and phases of the moon. Furthermore, in further research it is of extreme importance to separately study the killing on the roads of fallow deer, as a separate species. According to the conducted research in this doctoral thesis, this is the game which suffers major casualties in vehicle collisions, although they are small in number and living in a confined area

Model for identifying dangerous sections on public roads in areas frequent appearance of wild

Uočen je trend povećanja prometnih nesreća naleta vozila na divljač koji zahtijeva prijedloge mjera kako bi se prevenirala pojava divljači na rizičnim dionicama cesta. Da bi se došlo do određenih mjera spriječavanja naleta vozila na divljač, potrebno je prepoznati rizične dionice cesta s obzirom na mogućnost pojave divljači. U tom smislu razvijen je model prepoznavanja opasnih dionica na javnim cestama i istražene su mjere učinkovitog gospodarenja cestovnom mrežom koje će doprinijeti smanjenju pojave divljači na cestama. Zbog velikog raspona nadmorskih visina (0 – 1 011 m. n. v.) cestovne mreže, klimatskih razlika, različitosti konfiguracije terena te obitavanja svih vrsta divljači Republike Hrvatske, istraživanje doktorskog rada provedeno je na cestama Ličko-senjske županije. U razdoblju istraživanja najveći broj nesreća naleta vozila na divljač dogodio se na državnim cestama i uzrok nesreća uglavnom je bila krupna divljač. Stoga su modeli napravljeni na državnim cestama za krupnu divljač, srnu običnu i divlju svinju te za krupnu divljač ukupno. Posebna pažnja posvećena je vremenskim i prostornim obrascima naleta vozila na divljač koji su poslužili kao podloga razvoja modela prepoznavanja opasnih dionica od pojave divljači. Analiziranje prostornih obrazaca daleko je složenije od vremenskih obrazaca i zahtijevalo je upotrebu velikog broja nazavisnih varijabli, odnosno pretkazivača (pretkazivači ceste, krajobraza, reljefa, brojnosti divljači i broj naleta u ćeliji). Kako bi se dobio što precizniji i pouzdaniji model prepoznavanja opasnih dionica od pojave divljači ceste su podijeljene na dionice te su oko središta dionica ucrtani krugovi/ćelije određenih polumjera. Za svaku ćeliju određene su nezavisne varijable odnosno (pretkazivači). Prostorni podaci su pripremljeni i obrađeni u programskom paketu ArcGIS 9.2. U doktorskom radu predložene su dvije vrste modela prepoznavanja opasnih dionica od pojave divljači, i to model procjene vjerojatnosti naleta i model procjene broja naleta vozila na divljač. Za izbor najpouzdanijeg modela prepoznavanja opasnih dionica od pojave divljači korišten je Akaike Information Criterion (AIC). Izbor pouzdanog modela uslijedio je ukoliko je ΔAIC < 2 jedinice. Isto tako je određena i Akaike-ova težina (wi), koja predstavlja vjerojatnost da je model najbolji odnosno najpouzdaniji u usporedbi s ostalim modelima. Rizične dionice cesta od pojave divljači rangirane su višekriterijskom analizom primjenom analitičkog hijerarhijskog procesa (AHP metodom). Na osnovi provedenog istraživanja definirani su modeli procjene vjerojatnosti naleta i modeli procjene broja naleta vozila na divljač koji se temelje na pouzdanim podacima naleta vozila na divljač u istraživanom razdoblju. Svaki model za sebe relativno je manjkav. Prvi tip modela (model procjene vjerojatnosti naleta) dosta precizno procjenjuje vjerojatnost nastanka naleta. Ako se za procjenu vjerojatnosti naleta koristi i drugi model (model procjene broja naleta), tada kombinacija ova dva tipa modela daje dosta visoku točnost u procjeni opasnih dionica od naleta vozila na divljač. Modeli će omogućiti pravnim osobama koje gospodare javnim cestama da dobiju prikaz opasnih dionica i djeluju preko mjera održavanja i opremanja cesta na sigurnost prometa. Terenskim istraživanjem na kritičnim dionicama cesta ispitan je utjecaj pojedinih mjera održavanja na pojavu divljači na cestama. Predložen je novi postupak održavanja javnih cesta s kojim bi se smanjili prelasci divljači preko javnih cesta. Predloženi model prepoznavanja opasnih dionica na javnim cestama i postupak održavanja funkcionalno su provjereni na mreži javnih cesta Ličko-senjske županije.Along with the increase in the number of road motor vehicles there is also an increase in the number of collisions of vehicles and wildlife (WVC) that for some reason come out onto the road. The consequences of such collisions are sometimes very severe. Apart from material damage on the vehicle, wild animals also get injured, and there is great danger for human lives as well. The observed trend of the increasing number of traffic accidents involving collisions between vehicles and wildlife (WVC) requires proposals of measures in order to prevent the appearance of game at risky road sections. In order to take certain measures of preventing vehicle collision with the game (WVC), it is necessary to identify the high-risk road sections regarding the possible appearance of game. Therefore, a model of identifying dangerous sections on public roads, and measures of efficient management of the road network have been studied, and this will contribute to reducing the occurrence of wildlife on the roads. In practice, the question of liability for the damage caused by wildlife in collision with the vehicles or the vehicles colliding with wildlife (WVC) is often raised. The law stipulates that the game are the assets of interest for the Republic of Croatia and has its protection. The game lives in the hunting grounds with roads passing through. The hunting grounds are managed by the hunting licensees or hunting right owners, who are either legal or natural persons (craftsmen). The legislation in the Republic of Croatia, related to this issue is based on four laws of the Republic of Croatia. The area of compensation for the damage and liability caused by the vehicle colliding with the game is regulated by the Hunting Act, the Roads Act, the Road Traffic Safety Act, and the Obligations Act. The judicial practice of passing judgements lacks uniformity and there are different court judgements both in the first and the second court instances. According to the verdicts, sometimes the driver is to blame and sometimes the hunting licensee, and sometimes the legal entity that manages the road on which the vehicle collided with the game. The purpose of this doctoral thesis is to produce a maximally reliable model of forecasting the collision of vehicle with the wildlife (game) based on relatively easily accessible data. The objective of research is to propose measures that would significantly increase the traffic safety and reduce the number of vehicle-game collisions (WVC). Research hypotheses have been set, i.e. by in-depth analysis of data on traffic accidents caused by vehicle colliding with the game, it is possible to determine the elements of road and environment that highlight the high-risk sections of public roads and it is possible to develop a model of identifying the dangerous sections of public roads regarding the occurrence of wild animals on them. Lika-Senj County, selected as the research area, has central geographical position, and therefore an important connecting significance within the Republic of Croatia. Lika-Senj County occupies 9,46 % of the Croatian territory. Most of the County belongs to the mountainous area and includes the mountains of Velebit, Plješivica and Velika and Mala Kapela. The area of the County includes also the karst fields separated by the mountain ridges: fields of Lika, Gacka, Krbava, Drežnica, Korenica, Lapacand Gračac. The County also includes the Adriatic coast as well as a part of the island of Pag, i.e. a part of the territorial sea (596,63 km2 or 1.9 % of the Croatian sea area) and 2,29 km2 of the island area or 0,07 % of the area of all the islands of the Republic of Croatia. The mainland area of the Lika-Senja County covers an area of 535 113 ha, and stretches from 0 to 1 738 metres above sea level. Considering the division of climate according to Köppen, several different climate types change in the entire Lika-Senj County (climate type Cfb – 85,6 % of the area; climate type Cfa – 6,7 % of the area and climate type Df – 7,7 % of the Lika-Senj County area). The dominant type of the game habitat in Lika-Senj County are forests, which make up 65 % i.e. together with shrubs, brake-grown areas, and heaths the closed habitats make up almost 70 % of the observed area. Carbonate rocks make up 74 % of the researched area, which makes this part of the research area porous in terms of precipitation retention. Consequently, and due to a large range of the altitudes of the road network (0 – 1 011 metres a.s.l.), climatic differences, differences in terrain configuration and habitat of almost all species of game in the Republic of Croatia, the research of the doctoral dissertation was conducted on the roads of the Lika- Senj County. A prerequisite for determining the dangerous road sections regarding the occurrence of wildlife are the collected relevant data about the vehicle collisions with wildlife. The data on traffic accidents of vehicle collisions with wildlife have been collected by the employees of the Ministry of the Interior of the Republic of Croatia through going to the scene of accident, and for the purposes of this thesis the data have been collected by the Lika-Senj Police Department, i.e. police stations Gospić, Otočac, Senj, Donji Lapac, Korenica, Karlobag and Novalja. The data about vehicle collisions with wildlife have been taken from the police records on vehicle collisions with wildlife in the time period from 2012. to 2016.. There are 63 established hunting grounds in this area, which are managed by slightly fewer hunting licensees (some hunting licensees lease two or more hunting grounds). In order to verify the accuracy of the obtained data the hunting licensees of the hunting grounds in the Lika-Senj County have been surveyed, and because of the sensitivity of the data that impact the value of the hunting grounds, no newer relevant data could be obtained. In the research period there were 548 accidents involving vehicles colliding with wildlife, and the largest number of accidents of vehicle and wildlife collisions occurred on state roads, as many as 441, mostly collisions with roe deer (Capreolus capreolus) and wild boar (Sus scrofa). Having in mind the frequency of traffic accidents and the possibility of fatal outcomes of the traffic accidents, the work started on developing a model for recognizing dangerous sections on state roads, for the cases of collisions with roe deer, wild boar and large game in total. Special attention was paid to the temporal and spatial patterns of vehicle collisions with wildlife that served as the basis for developing the model of recognizing the dangerous sections regarding the appearance of wildlife. In order to determine the relevant minimum section length for the calculation of the collision probability estimate, state roads were divided into sections of 200, 500, 1 000, 2 000 and 12 000 m. Circles (cells) of radii of 100, 250, 500, 1 000 and 6 000 m were drawn around the centres of the sections and for each circle (cell) independent variables, that is, predictors were determined. In developing temporal patterns regarding the time of the occurrence of vehicle – wildlife collision, the 24-hour day was divided regarding the time of dawn and dusk into day, night and dusk. The time of sunrise and sunset was calculated by means of the algorithm provided by the Zagreb Observatory website for every day of the research period. When calculating the lunar phases, the international standard of the US Maritime Oceanographic Portal (lunar cycle of eight lunar months) was used. Analysing spatial patterns is far more complex than the temporal patterns and required the use of a large number of independent variables, i.e. predictors (predictors of the road, landscape, relief, number of wildlife, and number of collisions in the cell). The observed characteristics of the road included: average annual daily traffic (AADT), average summer daily traffic (ASDT) and curves parameter. The habitat data that were used included: share of water, shores, bare grounds, heaths and brake-grown areas, thickets, forests, grasslands, builtup land, neglected agricultural land, arable land. The index of topographic position (TPI or TOPEX) was used for the relief as a predictor of topographic characteristics of road. Regarding the index of topographic position and slope, the terrain has been classified into six categories: valleys, less steep terrains, medium steep terrains, extremely steep terrains, upper parts of the slopes and ridges. The data on the number of wildlife are relatively unreliable as predictor of population density, and therefore the data on game shooting in individual hunting grounds were used; however, they have been reduced to the unit of the hunting area. In order to obtain a maximally precise model of identifying dangerous sections on state roads, spatial patterns of vehicle collisions with large game were used. The spatial data were prepared in the software package Arc GIS 9.2., and processed in the software package Statistica 13.4.014 TIBCO Software Inc., 2018. The doctoral thesis proposes two types of patterns of recognizing dangerous sections regarding appearance of wildlife. These are: collision probability estimate model and vehiclegame collision number estimate. For the selection of the most reliable model of recognizing dangerous road sections regarding appearance of wildlife the software tools Akaike Information Criterion (AIC) was used. The selection of a reliable model followed if ΔAIC < 2 units. Akaike weight (wi) was also determined, and it represents the probability that the model is the best, that is, the most reliable compared to other models. Logistic regression was used to calculate the prediction of the probability of a vehicle colliding with wildlife. AIC analysis provided collision estimate models for every cell radius separately, and the logistic regression provided reliability of the results in estimation percentages. For the estimate of the collision probability with roe deer the smallest road section would be 2 000 m (cell radius 1 000 m), and the prediction accuracy is 68,20 %. The used independent variables (predictors) are the number of roe deer, share of the neglected agricultural land and the shares of bare land and the sea. The number of collisions with roe deer increases with the population density of roe deerfor radiiof 500 and 1 000 m, roe deer and wild boarfor radius of 250 m and wild boarfor radii of 6 000 m; the share of neglected agricultural land for sections of 500, 1 000 and 2 000 m; share of heaths and brake-grown areas on road sections of 500 and 1 000 m; distances from possible watering places (fresh water) on road sections of 500 and 1 000 m; AADT on sections of 500 and 12 000 m and with lower TPI (TOPEX) value, i.e. in valleys and less steep terrain, but this applies only to road sections of 12 000 m. The number of collisions with roe deer gets reduced with the increase of curves on a section (sections of 1 000 m) or road share (sections of 2 000 m); share of bare land (on all sections, except sections of 12 000 m); share of the sea (on all sections, except sections of 12 000 m) and built-up land on sections of 500 m. In the wild boar collision estimation model, AIC analysis provided quite a lot of collision estimation models for each cell radius separately, and the logistic regression gave reliable results only for road sections of 12 000 m. The collision probability on the section can be estimated in 73,33 % of cases. In the wild boar collision estimation model the game population density proved to be the key independent variable (predictor). The number of wild boar collisions will be larger if the population density of roe deer, wild boar or large game in total is higher; higher share of neglected agricultural land and higher ASDT. The number of collisions will be smaller if the share of built-up land is higher; higher share of forests; higher share of roads; higher share of the sea (exceptions are cells of radius of 6 000 m) and higher AADT. For the models estimating the probability of collision with large game in total, all the large game killed during the research period were included as well as grey wolf (legally grey wolf is not game, but is included in the model development due to similar consequences of the collision). For models of estimating the collision probability with large game in total the wildlife population density as independent variable comes in all cell radii. Respecting the results of the logistic regression, it may be said that the shortest section on which the probability of collision with large game in total may be reliably determined is a section of 2 000 m. On this section it is possible to predict the collision of vehicle on wildlife with a certainty of 70,11 %, based on the relative density of game, share of bare land, share of the sea, and the proximity to the nearest watering place. The number of collisions with large game in total will be greater if the population density of roe deer or large game is higher; higher share of neglected agricultural land; higher share of heaths and brake-grown areas; greater distance to the watering place; higher AADT. The number of collisions on large game in total will be smaller if the road features more curves or more intersections; higher share of bare land; higher share of built-up land; higher share of the sea and more indented relief (higher TPI value). Although the collision or non-collision location model can be estimated with an accuracy of 70,11 %, there are also certain model errors. Three outcomes of the model operation can be expected, and these are: the model has estimated correctly that on a certain section there will not occur or there will come to a collision; there were no collisions on the section during the research period, but the model predicted that a collision might occur there (collision overestimation error) and on the section collisions were recorded during the research period, but the model predicts that no collision can occur there (collision underestimation error) Each model for itself is relatively deficient. The first type of model (collision probability estimation) estimates quite accurately the collision occurrence probability. If another model were used for the collision probability estimation (estimation of the number of collisions) then the combination gives quite high accuracy in estimating the danger of vehicle collision with wildlife. The proposed models will enable the legal entities that manage public roads to get a view of dangerous sections and to act through measures of maintaining and equipping the roads for traffic safety. The road sections at risk of the appearance of wildlife have been ranked by multicriteria analysis by applying the Analytic Hierarchy Process (AHP method), using the software tools Expert Choice. Before using the software tools the objective was set, and it is to rank the dangerous road sections on state roads of the Lika-Senj County regarding the appearance of wildlife. The criteria that have been used are the number of collisions, section length, number of collisions per 100 km and number of collisions per 100 km annually. Appropriate matrices have been developed comparing the criteria with each other in relation to the set objective and based on these the values from the Saaty evaluation scale were added. The program requires also setting of variants, and they are several dangerous sections. Field research on critical road sections examined the impact of certain maintenance measures on the appearance of wildlife on the roads. The tested maintenance measures include installation of protective wire fences along the road; thorough cleaning of the protective road belt; mounting of optical and sound sensors on signposts; installation of adequate traffic signs – game on the road; planting of plant species that repel game, and also the impact of the location of game feeding grounds on the wildlife population density along the road was investigated. A new public road maintenance procedure has been proposed that would reduce wildlife crossing the public roads by increasing the patrol hours on high-risk sections; installation of protective fences to prevent the appearance of wild animals on the roads; cleaning the full profile of road protection zone in order to reduce the occurrence of wildlife on the roads; planting of game-repellent plant species along the road; installation of optical and sound sensors on signposts that drive off the game, and amendments to the Ordinance on traffic signs, signalization and road equipment. By proving the set objective and hypotheses, this doctoral dissertation opens up the possibilities of further research in preventing the wildlife to appear on the roads by developing a model with and without large predators; classification of killed game by gender and estimated age of the individual; unification of temporal and spatial patterns; separation of spatial patterns of collision according to the season, time of day and phases of the moon. Furthermore, in further research it is of extreme importance to separately study the killing on the roads of fallow deer, as a separate species. According to the conducted research in this doctoral thesis, this is the game which suffers major casualties in vehicle collisions, although they are small in number and living in a confined area

Three significant builders of Lika historical roads

U vrijeme Vojne krajine na području Like izgrađene su mnoge važne ceste koje su i danas u uporabi, a plijene pozornost kvalitetom gradnje, vještim projektantskim rješenjima i interpolacijom u prostor. Izuzetno stručni cestarski graditelji toga vremena napravili su ceste koje su uz poneku rekonstrukciju, u uporabi i danas. U radu se donose kratki životopisi trojice istaknutih graditelja povijesnih ličkih cesta; Vinka Struppija, Josipa Filipa Vukasovića i Josipa Kajetana Knežića o kojima se do sada pisalo uglavnom rijetko, skromno i pojedinačno. Uz prikaz navedenih graditelja donosi se i opis novih tehnologija koje su uvodili u cestogradnju i njihova važnost za izgradnju suvremene cestovne infrastrukture u Lici.During the Military Frontier, many important roads were built in the Lika area which are still in use today, and they capture attention with the quality of construction, skilled design solutions and interpolation into space. Extremely professional roadbuilders of that time made roads that, with some reconstruction, are still in use today. The paper presents short biographies of three prominent builders of historic roads in Lika: Vinko Struppi, Josip Filip Vukasović and Josip Kajetan Knežić, about whom so far it has been written mostly rarely, modestly and individually. With representation of these builders, this paper also presents a description of new technologies that were introduced in road construction and their importance for the construction of modern road infrastructure in Lika

Organization of Passenger Transport in Town of Zapresic by Harmonizing Operator Services

The paper presents the current situation in public passengertransport in the town of Zapresic. The ana lysis of the passengertransport in urban and suburban traffic has shown that the carriersproviding the service of passenger transport in urban, suburbanand interurban traffic act individually, that there is nocoordination among them the aim of which would be cheaper,more economical and higher quality public transport of passengers.The activities have been proposed to introduce the traffictechnologies in urban, suburban and interurban transport ofpassengers with the proposal of a unique tariff system