A great deal of research has been conducted on Central Florida toll roads to better understand the characteristics of the tolling operation. In this thesis, the development and calibration of a toll plaza simulation models will be analyzed using two simulation programs varying mostly in their modeling theory. The two models utilized are, SHAKER, a deterministic queuing model for vehicles utilizing toll collection facilities, and VISSIM, a globally popular stochastic simulation software. The benefits of simulation models leads to the purpose of this thesis, which is to examine the effectiveness of two toll modeling programs that are similar in purpose but vary in approach and methodology. Both SHAKER and VISSIM toll plaza models have the potential to work as a tool that can estimate the maximum throughput and capacity of toll plazas. Major operational benefits resulting from developing these models are to simulate and evaluate how traffic conditions will change when demand increases, when and if queues increase when a lane is closed due to maintenance or construction, the impact of constructing additional lanes, or determining whether or not the best lane type configuration is currently implemented. To effectively calibrate any model available site data must be used to compare simulation results to for model validity. In an effort to correctly calibrate the SHAKER toll plaza tool and VISSIM model, an extensive field collection procedure was conducted at four Florida Turnpike operated toll facilities located in Central Florida. Each site differed from the others in terms of number of lanes, lane configuration, toll base fee, highway location, traffic demand, and vehicle percentage. The sites chosen for data collection were: the Lake Jesup Mainline Plaza along the Seminole Expressway (SR-417), the Beachline West Expressway Toll Plaza along the SR-528, the Daniel Webster Western Beltway Plaza along SR-429, and the Leesburg Toll Plaza along the Florida Turnpike Mainline SR-91. Upon completion of calibration of the two simulation models it is determined that each of the two software are successful in modeling toll plaza capacity and queuing. As expected, each simulation model does possess benefits over the other in terms of set up time, analysis reporting time, and practicality of results. The SHAKER model setup takes mere seconds in order to create a network and input vehicle, another few seconds to calibrate driving parameters, and roughly 10 additional seconds to report analysis. Conversely, setting up the VISSIM model, even for the most experienced user, can take several hours and the report analysis time can take several more hours as it is dependant on the number of required simulation runs and complexity of the network. VISSIM is most beneficial by the fact that its modeling allows for driver variability while SHAKER assumes equilibrium amongst lane choice and queuing. This creates a more realistic condition to observed traffic patterns. Even though differences are prevalent, it is important that in each simulation model the capacity is accurately simulated and each can be used to benefit operational situations related to toll plaza traffic conditions.

Congestion on the freeways of the U.S. has increased multifold over the past few years. A significant portion of this congestion is caused by non-recurring events such as incidents. Diversion has been accepted as a method that can reduce delays during incidents. The process of diversion involves the selection of the alternate routes, which is currently done off-line and is not responsive to each incident case. The volumes on these preselected routes on that particular day are also ignored. The preselected routes, in most cases, serve only to bypass the link on which the incident occurs. Considering the volumes that flow on the freeways, this leads to considerable delays in terms of lost time and productivity. Another important issue that is currently neglected is user compliance. The network generator is used to reduce the delays in selection of these alternate routes. It uses characteristics such as the congestion levels and available capacities in selection of alternate routes in real-time. Also, used in selecting alternate routes are feasibility criteria, that significantly affect the available capacities on the links. These include presence of trip generators (schools, offices, etc.) or safety factors (icy bridges, height restrictions, etc.). The model thus generates a reduced network and a set of alternate routes to divert the traffic upstream of the incident. Disutilities that drivers associate with route-choice, such as the number of left-turns and signals, the relative time spent on the freeway and arterials are attached to each route. The routes with the minimum disutilities are displayed to the user. A user-equilibrium assignment module to predict traffic flows in the future is also incorporated into the framework. As a precursor to the network generator, there is a module which calculates the clearance time for an incident. It uses other characteristics of the incident such as the weather and time of occurrence in order to predict if the delays are significant to initiate diversion. Numerous tests were conducted in order to validate the rules and functions developed. The tests were based on varying incident and traffic conditions. The results showed that the model, was able to select better routes for off-peak conditions rather than peak conditions. There is a threshold value of the delay caused by the incident, beyond which the model is very effective. / Master of Science

Les recherches en sécurité routière suscitent largement l'intérêt des chercheurs. Indépendamment des techniques de modélisation, un facteur important d'imprécision -qui caractérise les études dans ce domaine- concerne le niveau d'agrégation des données. Aujourd'hui, la plupart des autoroutes sont équipées de systèmes permanents de surveillance qui fournissent des données désagrégées. Dans ce contexte, l'objectif de la thèse est d'exploiter les données trafic recueillies en temps réel au moment des accidents, afin d'élargir le champ des travaux précédents et de mettre en évidence un potentiel d'applications innovantes. À cette fin, nous examinons les effets du trafic sur le type d'accident ainsi que sur la gravité subie par les occupants des véhicules, tout en tenant compte des facteurs environnementaux et géométriques. Des modèles Probit sont appliqués aux données de trafic et d'accidents enregistrés pendant quatre années sur le tronc commun aux autoroutes A4 et A86 en Ile-de-France. Les résultats empiriques indiquent que le type d'accident peut être presque exclusivement défini par les conditions de trafic prévalant peu avant son occurrence. En outre, l'augmentation du débit s'avère exercer un effet constamment positif sur la gravité, alors que la vitesse exerce un effet différentiel sur la gravité en fonction des conditions d'écoulement. Nous établissons ensuite un cadre conceptuel pour des applications de gestion des incidents qui s'appuie sur les données trafic recueillies en temps réel. Nous utilisons les résultats de la thèse afin d'explorer des implications qui ont trait à la propension et à la détection des incidents, ainsi qu'à l'amélioration de leur gestion / Research on road safety has been of great interest to engineers and planners for decades. Regardless of modeling techniques, a serious factor of inaccuracy - in most past studies - has been data aggregation. Nowadays, most freeways are equipped with continuous surveillance systems making disaggregate traffic data readily available ; these have been used in few studies. In this context, the main objective of this dissertation is to capitalize highway traffic data collected on a real-time basis at the moment of accident occurrence in order to expand previous road safety work and to highlight potential further applications. To this end, we first examine the effects of various traffic parameters on type of road crash as well as on the injury level sustained by vehicle occupants involved in accidents, while controlling for environmental and geometric factors. Probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de -France region, France. Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Increased traffic volume is found to have a consistently positive effect on severity, while speed has a differential effect on severity depending on flow conditions. We then establish a conceptual framework for incident management applications using real-time traffic data on urban freeways. We use dissertation previous findings to explore potential implications towards incident propensity detection and enhanced management

Aufgrund der steigenden Verkehrsnachfrage und der begrenzten Resourcen zum Ausbau der Straßenverkehrsnetze werden zukünftig größere Anforderungen an die Effektivität von Telematikanwendungen gestellt. Die Erhebung und Bereitstellung aktueller Verkehrsdaten durch geeignete Sensoren ist dazu eine entscheidende Voraussetzung. Gegenstand dieser Arbeit ist die großflächige Analyse des Straßenverkehrs auf der Basis bodengebundener und verteilter opto-elektronischer Sensoren. Es wird ein Konzept vorgestellt, dass eine von der Bilddatenerhebung bis zur Bereitstellung der Daten für Verkehrsanwendungen durchgehende Verarbeitungskette enthält. Der interdisziplinäre Ansatz bildet die Basis zur Verknüpfung eines solchen Sensorsystems mit Verkehrstelematik. Die Abbildung des Verkehrsgeschehens erfolgt im Gegensatz zu herkömmlichen bodengebundenen Messsystemen innerhalb größerer zusammenhängender Ausschnitte des Verkehrsraums. Dadurch können streckenbezogene Verkehrskenngrößen direkt bestimmt werden. Die Georeferenzierung der Verkehrsobjekte ist die Grundlage für eine optimale Verkehrsanalyse und Verkehrssteuerung. Die generierten Daten sind Basis zur Findung und Verifizierung von Theorien und Modellen sowie zur Entwicklung verkehrsadaptiver Steuerungsverfahren auf mikroskopischer Ebene. Es wird gezeigt, wie aus der Fusion gleichzeitig erhaltener Daten mehrerer Sensoren, die im Bereich des Sichtbaren und im thermalen Infrarot sensitiv sind, ein zusammengesetztes Abbildungsmosaik eines vergrößerten Verkehrsraums erzeugt werden kann. In diesem Abbildungsmosaik werden Verkehrsdatenmodelle unterschiedlicher räumlicher Kategorien abgeleitet. Die Darstellung des Abbildungsmosaiks mit seinen Daten erfolgt auf unterschiedlichen Informationsebenen in geokodierten Karten. Die Bewertung mikroskopischer Verkehrsprozesse wird durch die besondere Berücksichtigung der Zeitkomponente bei der Visualisierung möglich. Die vorgestellte Verarbeitungskette beinhaltet neue Anwendungsbereiche für geografische Informationssysteme (GIS). Der beschriebene Ansatz wurde konzeptionell bearbeitet, in der Programmiersprache IDL realisiert und erfolgreich getestet. / The growing demand of urban and interregional road traffic requires an improvement regarding the effectiveness of telematics systems. The use of appropriate sensor systems for traffic data acquisition is a decisive prerequisite for the efficiency of traffic control. This thesis focuses on analyzing road traffic based on stationary and distributed ground opto-electronic matrix sensors. A concept which covers all parts from image data acquisition up to traffic data provision is presented. This interdisciplinary approach establishes a basis for the integration of such a sensor system into telematics systems. Unlike conventional ground stationary sensors, the acquisition of traffic data is spread over larger areas in this case. As a result, road specific traffic data can be measured directly. Georeferencing of traffic objects is the basis for optimal road traffic analysis and road traffic control. This approach will demonstrate how to generate a spatial mosaic consisting of traffic data generated by several sensors with different spectral resolution. For traffic flow analysis the realisation of special 4D data visualisation methods on different information levels was an essential need. The data processing chain introduces new areas of application for geographical information systems (GIS). The approach utilised in this study has been worked out conceptually and also successfully tested and applied in the programming language IDL.

La circulation routière est une source majeure de pollution atmosphérique dans les zones urbaines. Les décideurs insistent pour qu’on leur propose de nouvelles solutions, y compris de nouvelles stratégies de management qui pourraient directement faire baisser les émissions de polluants. Pour évaluer les performances de ces stratégies, le calcul des émissions de pollution devrait tenir compte de la dynamique spatiale et temporelle du trafic. L’utilisation de capteurs traditionnels sur route (par exemple, capteurs inductifs ou boucles de comptage) pour collecter des données en temps réel est nécessaire mais pas suffisante en raison de leur coût de mise en oeuvre très élevé. Le fait que de telles technologies, pour des raisons pratiques, ne fournissent que des informations locales est un inconvénient. Certaines méthodes devraient ensuite être appliquées pour étendre cette information locale à une grande échelle. Ces méthodes souffrent actuellement des limites suivantes : (i) la relation entre les données manquantes et la précision de l’estimation ne peut être facilement déterminée et (ii) les calculs à grande échelle sont énormément coûteux, principalement lorsque les phénomènes de congestion sont considérés. Compte tenu d’une simulation microscopique du trafic couplée à un modèle d’émission, une approche innovante de ce problème est mise en oeuvre. Elle consiste à appliquer des techniques de sélection statistique qui permettent d’identifier les emplacements les plus pertinents pour estimer les émissions des véhicules du réseau à différentes échelles spatiales et temporelles. Ce travail explore l’utilisation de méthodes statistiques intelligentes et naïves, comme outil pour sélectionner l’information la plus pertinente sur le trafic et les émissions sur un réseau afin de déterminer les valeurs totales à plusieurs échelles. Ce travail met également en évidence quelques précautions à prendre en compte quand on calcul les émissions à large échelle à partir des données trafic et d’un modèle d’émission. L’utilisation des facteurs d’émission COPERT IV à différentes échelles spatio-temporelles induit un biais en fonction des conditions de circulation par rapport à l’échelle d’origine (cycles de conduite). Ce biais observé sur nos simulations a été quantifié en fonction des indicateurs de trafic (vitesse moyenne). Il a également été démontré qu’il avait une double origine : la convexité des fonctions d’émission et la covariance des variables de trafic. / Road traffic is a major source of air pollution in urban areas. Policy makers are pushing for different solutions including new traffic management strategies that can directly lower pollutants emissions. To assess the performances of such strategies, the calculation of pollution emission should consider spatial and temporal dynamic of the traffic. The use of traditional on-road sensors (e.g. inductive sensors) for collecting real-time data is necessary but not sufficient because of their expensive cost of implementation. It is also a disadvantage that such technologies, for practical reasons, only provide local information. Some methods should then be applied to expand this local information to large spatial extent. These methods currently suffer from the following limitations: (i) the relationship between missing data and the estimation accuracy, both cannot be easily determined and (ii) the calculations on large area is computationally expensive in particular when time evolution is considered. Given a dynamic traffic simulation coupled with an emission model, a novel approach to this problem is taken by applying selection techniques that can identify the most relevant locations to estimate the network vehicle emissions in various spatial and temporal scales. This work explores the use of different statistical methods both naïve and smart, as tools for selecting the most relevant traffic and emission information on a network to determine the total values at any scale. This work also highlights some cautions when such traffic-emission coupled method is used to quantify emissions due the traffic. Using the COPERT IV emission functions at various spatial-temporal scales induces a bias depending on traffic conditions, in comparison to the original scale (driving cycles). This bias observed in our simulations, has been quantified in function of traffic indicators (mean speed). It also has been demonstrated to have a double origin: the emission functions’ convexity and the traffic variables covariance.

Research on road safety has been of great interest to engineers and planners for decades. Regardless of modeling techniques, a serious factor of inaccuracy - in most past studies - has been data aggregation. Nowadays, most freeways are equipped with continuous surveillance systems making disaggregate traffic data readily available ; these have been used in few studies. In this context, the main objective of this dissertation is to capitalize highway traffic data collected on a real-time basis at the moment of accident occurrence in order to expand previous road safety work and to highlight potential further applications. To this end, we first examine the effects of various traffic parameters on type of road crash as well as on the injury level sustained by vehicle occupants involved in accidents, while controlling for environmental and geometric factors. Probit models are specified on 4-years of data from the A4-A86 highway section in the Ile-de -France region, France. Empirical findings indicate that crash type can almost exclusively be defined by the prevailing traffic conditions shortly before its occurrence. Increased traffic volume is found to have a consistently positive effect on severity, while speed has a differential effect on severity depending on flow conditions. We then establish a conceptual framework for incident management applications using real-time traffic data on urban freeways. We use dissertation previous findings to explore potential implications towards incident propensity detection and enhanced management

Rising from the “excess demand” modern societies and economies place on limited road resources, congestion causes increased vehicle emissions, decreases national efficiency, and wastes time (Downs, 2004). In order to minimise congestion’s impacts, traffic management systems gather traffic data and use it to implement efficient management algorithms (Downs, 2004). This dissertation’s purpose has been the development of a distributable vehicle presence detection sensor, which will wirelessly provide vehicle presence information in real time. To address the sensor’s wireless power requirements, the feasibility of self-powering the device via harvested energy has been investigated. Piezoelectric, electrostatic, and electromagnetic energy harvesting devices’ principles of operation and underlying theory has been investigated in detail and an overview presented alongside a literature review of previous vibration energy harvesting research. An electromagnetic energy harvesting device was designed, which consists of: a nylon reinforced rubber bladder, hydraulic piston, neodymium magnets, and wire-wound coil housing. Preliminary testing demonstrated a harvested energy between 100mJ and 205mJ per axle. This amount is able to be transferred to a 100O load when driven over at speeds between 10km/h and 50km/h. Combined with an embedded circuit, the energy harvester facilitated the development of a passive sensor, which is able to wirelessly transmit a vehicle’s presence signal to a host computer. The vehicle detected event is displayed via a graphical user interface. Energy harvesting’s ability to power the embedded circuit’s wireless transmission, demonstrated the feasibility of developing systems capable of harvesting energy from their environment and using it to power discrete electronic components. The ability to wirelessly transmit a vehicle’s presence facilitates the development of distributable traffic monitoring systems, allowing for remote traffic monitoring and management.

This thesis deals with the data mining in data stream which represents fast developing area of information technology. The text describes common principles of data mining, explains what data stream is and shows methods for its preprocessing and algorithms for following data mining. The special attention is given to the VFDT and the CVDT algorithm. The next mentioned are the spatiotemporal data and related data mining. The second part describes the design and implementation of the application for classification over spatiotemporal data stream represented by road traffic data and following prediction of spatiotemporal events (traffic-jams). The classification is performed by the VFDT and CVFDT algorithm. The application has been tested on the data set obtained by the simulation tool SUMO.

Accurate and reliable traffic state estimation is essential for the identification of congested areas and bottleneck locations. It enables the quantification of congestion characteristics, such as intensity, duration, reliability, and spreading which are indispensable for the deployment of appropriate traffic management plans that can efficiently ameliorate congestion problems. Similarly, it is important to categorize known congestion patterns throughout a long period of time, so that corresponding traffic simulation models can be built for the investigation of the performance of different traffic management plans. This study conducts cluster analysis to identify days with similar travel conditions and congestion patterns. To this end, travel, traffic and weather data from the Smart Mobility Living Lab of Thessaloniki, Greece is used. Representative days per cluster are determined to facilitate the development of traffic simulation models that typify average traffic conditions within each cluster. Moreover, spatio-temporal matrices are developed to illustrate time-varying traffic conditions along different routes for the representative days. Results indicate that the proposed clustering technique can produce valid classification of days in groups with common characteristics, and that spatio-temporal matrices enable the development of traffic management plans which encompass routing information for competing routes in the city of Thessaloniki.

<p>EU direktiv 2005/0182 röstades igenom i början av år 2006. All trafik-data kring Internet- och telekommunikation ska lagras mellan sex må-nader och ett år. Direktivet kommer innebära gemensamma regler för EU:s medlemsstater. Syftet med direktivet är att data ska lagras för till-gänglig vid utredning, avslöjande och åtal av grov organiserad brottslig-het och terrorism.</p><p>Studien innehåller tre problemområden som behandlar andra gällande direktiv och konventioner, personlig integritet och etik. Först undersöks det nya direktivet i relation till European Convention on human rights (ECHR) Vidare förs diskussioner kring lagring av trafikdata och lokali-seringsdata i förhållande till personlig integritet. Även etikens syn på hur lagring av personuppgifter ska hanteras och rättfärdigas tas upp.</p><p>Studien har kommit fram till ett resultat kring varje problemområde. I relation till redan gällande direktiv och konventioner visar studien att det är väldigt öppet för tolkningar. Dock är det framförallt mot artikel 8 i ECHR som det nya direktivet strider mot.</p><p>Peter Seipel har definierat sex olika teorier kring synen på personlig in-tegritet. Direktivets påverkan på den personliga integriteten har analy-serats med hjälp av dessa teorier. Sammanfattningsvis kan det konstate-ras att lagring av trafikdata och lokaliseringsdata kan ses som ett in-trång i den personliga integriteten, frågan är om detta intrång är berät-tigat?</p><p>Inom etiken är synen på direktivet koncentrerat till vems nytta direkti-vet är och vilket mål direktivet ska uppnå. Ofta hamnar olika regler och ställningstagande i konflikt med varandra där två saker kan ses som rätt, men de båda inte kan samexistera.</p> / <p>At the beginning of year 2006 EU directive 2005/0182 was approved by the European parliament. The directive contains rules concerning the retention of traffic and localization data created with electronically communication. This data will be stored between six months and 1 year depending on the data type. The intension is to use the data to detect, investigate and prosecute heavy criminals and terrorists.</p><p>This study contains three different problem areas, if the directive op-poses to existing directives and conventions. The relationship between personal integrity and storage of personal information is also investi-gated. The last area is ethical issues with the storing of personal data.</p><p>The result is divided into three parts, one for each problem area. It shows that the new directive don’t comply with article 8 in the European convention on human rights. Although all the articles presented are open for wide interpretation.</p><p>Peter Seipel has made a categorization of six different views of the per-sonal integrity. It is clear that the new directive will affect the personal integrity.</p><p>The ethical view on the directive is concentrated to who will benefit from the changes and witch goals are the directive set to meet. Often the rules of ethics will conflict with each other because of two sets of rules can’t exist together.</p>