<p>In today’s fast-paced data-driven world, accumulating and organizing streams of high-resolution information plays a vital role in numerous decision and design tasks. The transportation sector is a prime example of this. Fine-scale information on traffic exposure at specific observation periods is critical to the successful analysis of road safety. Annual Average Daily Traffic (AADT) and hourly traffic volumes represent essential statistics to predict crash risk under time-dependent conditions, such as, weather and seasonal traffic variations. State highway agencies including the Indiana Department of Transportation (INDOT) collect traffic count data using multiple permanent and coverage count stations. However, approximately ten percent of the local-administered road segments in Indiana are included in their database. To impute the missing data, predictive models that can accurately forecast AADT and consequently, hourly traffic volumes, will be of great value.</p> <p><br></p> <p>To address this problem, this thesis proposes a methodology to predict traffic volumes in different classes of urban road segments in Indiana. Two sets of regression models have been developed: (1) AADT Estimation Model, and (2) Hourly Traffic Volume Model. These models include effects of spatial and temporal variations, land use, roadway characteristics and, previously-overlooked in such models, road network connectivity and route selection. These, in turn, address two important research questions: (1) how trips are generated and (2) how people choose routes. The spatial and temporal effects that were considered in the analysis are travel propensity, travel time excess index, road class, hour of day, day of week and seasonal variations. While travel propensity captures particulars of network connectivity and land-use characteristics in traffic analysis zones (TAZ), the travel time excess index accounts for commuters’ route-choice. The estimation results indicate that all these variables are strongly correlated with traffic volumes on considered roadways. Reasonable estimations of hourly traffic volumes on a network scale can be achieved using the proposed model. In addition to aiding safety management at disaggregate level, hourly traffic predictions can help highway agencies in other system-wide analysis where such traffic information is needed.</p>

Syftet med denna studie var att undersöka vilka effekter luftföroreringar har på lavar och grönalger som växer på lindar, genom att jämföra lavfloran i urban miljö och i landsbygdsmiljö. Mer specifikt var syftet att utreda hur avstånd till närmaste väg, trafikintensitet och trädets exponerade respektive icke-exponerade sida gentemot en väg påverkar lavfloran i urban miljö. 18 utvalda lavarter samt grönalger eftersöktes på 86 lindar i urban miljö och 37 i landsbygdsmiljö inom Norrköpings kommun. Resultaten visade att artantalet, samt förekomsten och täckningsgraden för flertalet lavar var större i landsbygdsmiljö jämfört med urban miljö. Kortare avstånd till närmaste väg och hög trafikintensitet hade negativ effekt på flertalet lavar. I båda fallen visades det motsatta för grönalger. Det fanns ingen skillnad i förekomster av lavar om de exponerades utåt mot vägen eller bort från vägen. / The aim of this study was to investigate which effects air pollution has on epiphytic lichens and green algae in urban and rural environment, respectively.  More specifically, the aim was to investigate the effects on the lichen flora of distance to the nearest road, traffic volume and to compare the effect of tree sides exposed and not exposed to roads in the urban environment. 18 selected lichen selected species and green algae as a taxonomic group where searched on 86 lime trees in urban and 37 in rural environments in the Norrköping municipality. The species number was higher and the majority of the lichens occurred more often and showed a higher cover on trees in rural areas compared to trees in the urban environment. Shorter distance to the nearest road and high traffic volume had a negative impact on the majority of lichens. In both cases the opposite was shown for the green algal group. The lichens occurred equally often on the sides of the trees that were exposed to the road as on the sides that were not exposed to the road.

This paper reviews the effectiveness of vehicle activated signs. Vehicle activated signs are being reportedly used in recent years to display dynamic information to road users on an individual basis in order to give a warning or inform about a specific event. Vehicle activated signs are triggered individually by vehicles when a certain criteria is met. An example of such criteria is to trigger a speed limit sign when the driver exceeds a pre-set threshold speed. The preset threshold is usually set to a constant value which is often equal, or relative, to the speed limit on a particular road segment. This review examines in detail the basis for the configuration of the existing sign types in previous studies and explores the relation between the configuration of the sign and their impact on driver behavior and sign efficiency. Most of previous studies showed that these signs have significant impact on driver behavior, traffic safety and traffic efficiency. In most cases the signs deployed have yielded reductions in mean speeds, in speed variation and in longer headways. However most experiments reported within the area were performed with the signs set to a certain static configuration within applicable conditions. Since some of the aforementioned factors are dynamic in nature, it is felt that the configurations of these signs were thus not carefully considered by previous researchers and there is no clear statement in the previous studies describing the relationship between the trigger value and its consequences under different conditions. Bearing in mind that different designs of vehicle activated signs can give a different impact under certain conditions of road, traffic and weather conditions the current work suggests that variable speed thresholds should be considered instead.

No description available.

Intelligenta Transportsystem (ITS) utgör idag en central del i arbetet att försöka höja kvaliteten i transportnätverken, genom att exempelvis ge stöd i arbetet att leda trafik i realtid och att ge trafikanter större möjlighet att ta informerade beslut gällandes sin körning. Kortsiktig prediktion av trafikdata, däribland trafikvolym, spelar en central roll för de tjänster ITS-systemen levererar. Den starka teknologiska utvecklingen de senaste decennierna har bidragit till en ökad möjlighet till att använda datadriven modellering för att utföra kortsiktiga prediktioner av trafikdata. Säsongsbaserad ARIMA (SARIMA) är en av de vanligaste datadrivna modellerna för modellering och predicering av trafikdata, vilken använder mönster i historisk data för att predicera framtida värden. Vid modellering med SARIMA behöver en mängd beslut tas gällandes de data som används till modelleringen. Exempel på sådana beslut är hur stor mängd träningsdata som ska användas, vilka dagar som ska ingå i träningsmängden och vilket aggregationsintervall som ska användas. Därtill utförs nästintill enbart enstegsprediktioner i tidigare studier av SARIMA-modellering av trafikdata, trots att modellen stödjer predicering av flera steg in i framtiden. Besluten gällandes de parametrar som nämnts saknar ofta teoretisk motivering i tidigare studier, samtidigt som det är högst troligt att dessa beslut påverkar träffsäkerheten i prediktionerna. Därför syftar den här studien till att utföra en känslighetsanalys av dessa parametrar, för att undersöka hur olika värden påverkar precisionen vid prediktion av trafikvolym. I studien utvecklades en modell, med vilken data kunde importeras, preprocesseras och sedan modelleras med hjälp av SARIMA. Studien använde trafikvolymdata som insamlats under januari och februari 2014, med hjälp av kameror placerade på riksväg 40 i utkanten av Göteborg. Efter differentiering av data används såväl autokorrelations- och partiell autokorrelationsgrafer som informationskriterier för att definiera lämpliga SARIMA-modeller, med vilka prediktioner kunde göras. Med definierade modeller genomfördes ett experiment, där åtta unika scenarion testades för att undersöka hur prediktionsprecisionen av trafikvolym påverkades av olika mängder träningsdata, vilka dagar som ingick i träningsdata, längden på aggregationsintervallen och hur många tidssteg in i framtiden som predicerades. För utvärdering av träffsäkerheten i prediktionerna användes MAPE, RMSE och MAE. Resultaten som experimentet visar är att definierade SARIMA-modeller klarar att predicera aktuell data med god precision oavsett vilka värden som sattes för de variabler som studerades. Resultaten visade dock indikationer på att en träningsvolym omfattande fem dagar kan generera en modell som ger mer träffsäkra prediktioner än när volymer om 15 eller 30 dagar används, något som kan ha stor praktisk betydelse vid realtidsanalys. Därtill indikerar resultaten att samtliga veckodagar bör ingå i träningsdatasetet när dygnsvis säsongslängd används, att SARIMA-modelleringen hanterar aggregationsintervall om 60 minuter bättre än 30 eller 15 minuter samt att enstegsprediktioner är mer träffsäkra än när horisonter om en eller två dagar används. Studien har enbart fokuserat på inverkan av de fyra parametrarna var för sig och inte om en kombinerad effekt finns att hitta. Det är något som föreslås för framtida studier, liksom att vidare utreda huruvida en mindre träningsvolym kan fortsätta att generera mer träffsäkra prediktioner även för andra perioder under året. / Intelligent Transport Systems (ITS) today are a key part of the effort to try to improve the quality of transport networks, for example by supporting the real-time traffic management and giving road users greater opportunity to take informed decisions regarding their driving. Short-term prediction of traffic data, including traffic volume, plays a central role in the services delivered by ITS systems. The strong technological development has contributed to an increased opportunity to use data-driven modeling to perform short-term predictions of traffic data. Seasonal ARIMA (SARIMA) is one of the most common models for modeling and predicting traffic data, which uses patterns in historical data to predict future values. When modeling with SARIMA, a variety of decisions are required regarding he data used. Examples of such decisions are the amount of training data to be used, the days to be included in training data and the aggregation interval to be used. In addition, one-step predictions are performed most often in previous studies of SARIMA modeling of traffic data, although the model supports multi-step prediction into the future. Often, in previous studies, decisions are made concerning mentioned variables without theoretical motivation, while it is highly probable that these decisions affect the accuracy of the predictions. Therefore, this study aims at performing a sensitivity analysis of these parameters to investigate how different values affect the accuracy of traffic volume prediction. The study developed a model with which data could be imported, preprocessed and then modeled using a SARIMA model. Traffic volume data was used, which was collected during January and February 2014, using cameras located on highway 40 on the outskirts of Gothenburg. After differentiation of data, autocorrelation and partial autocorrelation graphs as well as information criteria are used to define appropriate SARIMA models, with which predictions could be made. With defined models, an experiment was conducted in which eight unique scenarios were tested to investigate how the prediction accuracy of traffic volume was influenced by different amount of exercise data, what days was included in training data, length of aggregation intervals, and how many steps into the future were predicted. To evaluate the accuracy of the predictions, MAPE, RMSE and MAE were used. The results of the experiment show that developed SARIMA models are able to predict current data with good precision no matter what values were set for the variables studied. However, the results showed indications that a training volume of five days can generate a model that provides more accurate predictions than when using 15 or 30-day volumes, which can be of great practical importance in real-time analysis. In addition, the results indicate that all weekdays should be included in the training data set when daily seasonality is used, SARIMA modeling handles aggregation intervals of 60 minutes better than 30 or 15 minutes, and that one-step predictions are more accurate than when one or two days horizons are used. The study has focused only on the impact of the four parameters separately and not if a combined effect could be found. Further research is proposed for investigating if combined effects could be found, as well as further investigating whether a lesser training volume can continue to generate more accurate predictions even for other periods of the year.

Thesis subject is a project documentation as a proposal of spiral roundabout replacing current luminous controlled transverse crossing. In the proposal is designed foot - path, cycling transport and stops for public transport. Vehicles will be rectified by the help of protective, divisive islets. We also measured actual traffic flow intensity. Current and newly designed state is modelled with using of programme Aimsun, where we validate proposed solution.

Thesis subject is a project documentation as a proposal of reconstruction of level crossing in Hradecká street to elevated crossing. Proposal is based on research of traffic intensity and consequential assessment of traffic capacity. Another thesis subject is a verification of potencial proposal of elevated crossing in Sportovní street and Hradecká street, which exists only in one way in present. This proposal is only indicative and can be used for next stage of project documentation. Proposal of crossing in Hradecká and Palackého třída includes design of beam of elevated crossing and design of cover crossing. Elevated crossing of Sportovní and Hradecká is designed as a beam of crossing, which runs on bridge construction of concrete.

Abstract: Road traffic accidents claim more than 1,500 lives each year in Canada and affect society adversely, so transport authorities must reduce their impact. This is a major concern in Quebec, where the traffic-accident risks increase year by year proportionally to provincial population growth. In reality, the occurrence of traffic crashes is rarely random in space-time; they tend to cluster in specific areas such as intersections, ramps, and work zones. Moreover, weather stands out as an environmental risk factor that affects the crash rate. Therefore, traffic-safety engineers need to accurately identify the location and time of traffic accidents. The occurrence of such accidents actually is determined by some important factors, including traffic volume, weather conditions, and geometric design. This study aimed at identifying hotspot locations based on a historical crash data set and spatiotemporal patterns of traffic accidents with a view to improving road safety. This thesis proposes two new methods for identifying hotspot locations on a road network. The first method could be used to identify and rank hotspot locations in cases in which the value of traffic volume is available, while the second method is useful in cases in which the value of traffic volume is not. These methods were examined with three years of traffic-accident data (2011–2013) in Sherbrooke. The first method proposes a two-step integrated approach for identifying traffic-accident hotspots on a road network. The first step included a spatial-analysis method called network kernel-density estimation. The second step involved a network-screening method using the critical crash rate, which is described in the Highway Safety Manual. Once the traffic-accident density had been estimated using the network kernel-density estimation method, the selected potential hotspot locations were then tested with the critical-crash-rate method. The second method offers an integrated approach to analyzing spatial and temporal (spatiotemporal) patterns of traffic accidents and organizes them according to their level of significance. The spatiotemporal seasonal patterns of traffic accidents were analyzed using the kernel-density estimation; it was then applied as the attribute for a significance test using the local Moran’s I index value. The results of the first method demonstrated that over 90% of hotspot locations in Sherbrooke were located at intersections and in a downtown area with significant conflicts between road users. It also showed that signalized intersections were more dangerous than unsignalized ones; over half (58%) of the hotspot locations were located at four-leg signalized intersections. The results of the second method show that crash patterns varied according to season and during certain time periods. Total seasonal patterns revealed denser trends and patterns during the summer, fall, and winter, then a steady trend and pattern during the spring. Our findings also illustrated that crash patterns that applied accident severity were denser than the results that only involved the observed crash counts. The results clearly show that the proposed methods could assist transport authorities in quickly identifying the most hazardous sites in a road network, prioritizing hotspot locations in a decreasing order more efficiently, and assessing the relationship between traffic accidents and seasons. / Les accidents de la route sont responsables de plus de 1500 décès par année au Canada et ont des effets néfastes sur la société. Aux yeux des autorités en transport, il devient impératif d’en réduire les impacts. Il s’agit d’une préoccupation majeure au Québec depuis que les risques d’accidents augmentent chaque année au rythme de la population. En réalité, les accidents routiers se produisent rarement de façon aléatoire dans l’espace-temps. Ils surviennent généralement à des endroits spécifiques notamment aux intersections, dans les bretelles d’accès, sur les chantiers routiers, etc. De plus, les conditions climatiques associées aux saisons constituent l’un des facteurs environnementaux à risque affectant les taux d’accidents. Par conséquent, il devient impératif pour les ingénieurs en sécurité routière de localiser ces accidents de façon plus précise dans le temps (moment) et dans l’espace (endroit). Cependant, les accidents routiers sont influencés par d’importants facteurs comme le volume de circulation, les conditions climatiques, la géométrie de la route, etc. Le but de cette étude consiste donc à identifier les points chauds au moyen d’un historique des données d’accidents et de leurs répartitions spatiotemporelles en vue d’améliorer la sécurité routière. Cette thèse propose deux nouvelles méthodes permettant d’identifier les points chauds à l’intérieur d’un réseau routier. La première méthode peut être utilisée afin d’identifier et de prioriser les points chauds dans les cas où les données sur le volume de circulation sont disponibles alors que la deuxième méthode est utile dans les cas où ces informations sont absentes. Ces méthodes ont été conçues en utilisant des données d’accidents sur trois ans (2011-2013) survenus à Sherbrooke. La première méthode propose une approche intégrée en deux étapes afin d’identifier les points chauds au sein du réseau routier. La première étape s’appuie sur une méthode d’analyse spatiale connue sous le nom d’estimation par noyau. La deuxième étape repose sur une méthode de balayage du réseau routier en utilisant les taux critiques d’accidents, une démarche éprouvée et décrite dans le manuel de sécurité routière. Lorsque la densité des accidents routiers a été calculée au moyen de l’estimation par noyau, les points chauds potentiels sont ensuite testés à l’aide des taux critiques. La seconde méthode propose une approche intégrée destinée à analyser les distributions spatiales et temporelles des accidents et à les classer selon leur niveau de signification. La répartition des accidents selon les saisons a été analysée à l’aide de l’estimation par noyau, puis ces valeurs ont été assignées comme attributs dans le test de signification de Moran. Les résultats de la première méthode démontrent que plus de 90 % des points chauds à Sherbrooke sont concentrés aux intersections et au centre-ville où les conflits entre les usagers de la route sont élevés. Ils révèlent aussi que les intersections contrôlées sont plus à risque par comparaison aux intersections non contrôlées et que plus de la moitié des points chauds (58 %) sont situés aux intersections à quatre branches (en croix). Les résultats de la deuxième méthode montrent que les distributions d’accidents varient selon les saisons et à certains moments de l’année. Les répartitions saisonnières montrent des tendances à la densification durant l’été, l’automne et l’hiver alors que les distributions sont plus dispersées au cours du printemps. Nos observations indiquent aussi que les répartitions ayant considéré la sévérité des accidents sont plus denses que les résultats ayant recours au simple cumul des accidents. Les résultats démontrent clairement que les méthodes proposées peuvent: premièrement, aider les autorités en transport en identifiant rapidement les sites les plus à risque à l’intérieur du réseau routier; deuxièmement, prioriser les points chauds en ordre décroissant plus efficacement et de manière significative; troisièmement, estimer l’interrelation entre les accidents routiers et les saisons.

This thesis builds on previous work on an Institute of Roads FAST VUT dealing with modifications of road I/43 Brno – Svitavy. A conversion of the existing temporary three leg single lane roundabout to egg turbo roundabout with four legs, is proposed there, in accordance with local plan of Lipůvka. The intersection of road I/43 and II/379 can be characterized by large congestions in the morning peak hours during weekdays and afternoon peak hours on Saturday and Sunday. That is the reason for implementation of capacity assessment of designed turbo roundabout and comparison with current situation. Construction of fourth leg is motivated by the planned expansion of residential area and the development of industrial zone on the south-western outskirts of the village and rerouting of the county road II/379 out of built-up area as well. Road I/43 is an important thoroughfare north of Brno, so it is obvious effort to eliminate the bottlenecks on the route. The thesis also includes a theoretical introduction dealing with the design of turbo roundabouts abroad.

Technology innovations and capacity upgrades in optical networks have influenced the optical transmission. As a result, network operators are considering separating hardware and software components in order to achieve efficiency and promising savings during network operation and network lifecycle. This leads to resolving the vendor lock-in and opening up the optical networks architectures such that different kinds of disaggregation models can be supported in the DWDM transport layer.In this master thesis, a techno-economic analysis which compares the economical differences between an integrated and disaggregated optical network has been conducted. For the analysis, an actual test case scenario of a European interregional network has been utilized. The line system and transponders components, price lists and features are based on actual vendor components in today’s optical transport networks.According to the results, it has been concluded that all total cost investments required for an integrated or disaggregated approach of DWDM system are traffic dependent. Moreover, capital expenses of open architecture are influenced by business model policy in price reduction due to multiple vendors’ competition on transponders. Calculations demonstrated that a disaggregated approach can introduce considerable total cost savings about 21% less total investments that include 25% less costs for capital expenses and 3% higher cost for operational expenses on an average traffic volume of European network. Finally, by increasing 5-10 times the traffic volume of European network it has been noticed that disaggregated system reduced total cost by 39% and 43% respectively. / Tekniska innovationer och trafiktillväxt påverkar utvecklingen av optisk transmission. En konsekvens av detta är att nätoperatörer undersöker möjligheten att separera hårdvara och mjukvarukomponenter för öka effektiviteten och sänka kostnader för drift. Detta minskar inlåsning till enskilda leverantörer och öppnar upp dom optiska näten för att byggas med olika typer av disaggregering i DWDM transporten.I den här masteruppsatsen har en tekno-ekonomisk analys utförts avseende den ekonomiska skillnaden mellan integrerade och disaggregerade optiska nät. Ett verkligt nät scenario användes för analysen. Komponenter och kostnader för linjesystem och transpondrar är baserade på existerande system och prislistor i dagens nät. Enligt det erhållna resultatet har slutsatsen dragits att totala kostnaden för integrerade och öppna system är trafikberoende. Vidare så är investeringen för öppna system påverkad av pris reduktionen pga. konkurrens på transpondrar.Beräkningarna visar att disaggregerade system kan sänka kostnaderna med 21%, i dessa siffror ingår 25% lägre kostnad för investeringar och 3% högre driftkostnader.Slutligen, genom att öka trafikvolymen med 5 respektive 10 ggr från den ursprungliga trafik matrisen så kan de totala kostnaderna sänkas med 39% och 43%.