This research: (1) examines the impact of missing data from permanent counters on the accuracy of the AADT; and (2) analyses the effect of varying short term count durations on the accuracy of AADT estimates. Data gaps can occur at permanent counters due to equipment malfunction and lane closures and can result in no available useable data. For short term counts a balance between accuracy and cost efficiency drives a need for an ideal count duration. Using data from Manitoba’s permanent counters, controlled data gaps and simulated short term counts were created to estimate AADTs. 150,000 AADTs were obtained from the analysis and were then compared to the true AADT to determine the overall error. The findings of this research showed that larger data gaps and shorter duration counts carry more error. Additionally, factors including month of the year and traffic pattern group impact AADT estimates illustrating the need for context sensitivity when rejecting data from a permanent counter and selecting an appropriate count duration. / October 2015

No description available.

Traffic is one of the key elements nowadays that affect our lives more or less in a every day basis. Traffic is present when we go to work, is on week ends, on holidays, even if we go shopping in our neighborhood, traffic is present. Every year, we can see on TV how after a bank holiday (United Kingdom public holiday), the traffic incidents are a figure that all TV news are obliged to report. If we see the accident causes, the ones caused by mechanical failures are always a minimal part, being human causes the majority of times. In our society, the tasks where technology help us to complete those with 100% success are very frequent. We tune our TVs for digital broadcasting, robots complete thousands of mechanical tasks, we work with computers that do not crash for months or years, even weather forecasting is more accurate than ever. All those aspects in our lives are successfully carried out on a daily basis. Nowadays, in traffic and road transport, we are starting a new era where driving a vehicle can be assisted partially or totally, parking our car can be done automatically, or even detecting a child in the middle of the road can be automatically done instead of leaving those tasks to the prone-to-fail human. The same features that today amaze us (as in the past did the TV broadcast in colour), in the future, those safety features will be a common thing in our cars. With more and more vehicles in the roads, cars, motorbikes, bicycles, more people in our cities and the necessity to be in a constant move, our society needs a zero-car-accidents conception, as we have now the technology to achieve it. Computer Vision is the computer science field that since the 80s has been obsessed with emulating the way human see and perceive their environment and react to it in an intelligent way. One decade ago, detecting complex objects in a scene as a human was impossible. All we could do was to detect the edges of an object, to threshold pixel values, detect motion, but nothing as the human capability to detect objects and identify their location. The advance in GPUs technology and the development of neural networks in the computer vision community has made those impossible tasks possible. GPUs now being a commodity item in our lives, the increase of amount and speed of RAM and the new and open models developed by experts in neural networks, make the task of detecting a child in the middle of a road a reality. In particular, detections with 99.79% probability are now possible, and the 100% probability goal is becoming a closer reality. In this thesis we have approached one of the key safety features in systems for traffic analysis, that is monitoring pedestrian crossing. After researching the state-of-the-art in pedestrian movement detection, we have presented a novel strategy for such detection. By locating a fixed camera in a place where pedestrians move, we are able to detect the movement of those and their direction. We have achieved that task by using a mix of old and new methodologies. Having a fixed camera, allow us to remove the background of the scene, only leaving the moving pedestrians. Once we have this information, we have created a dataset of moving people and trained a CNN able to detect in which direction the pedestrian is moving. Another work that we present in this thesis is a traffic dataset and the research with state-of.the-art CNN models to detect objects in traffic environments. Crucial objects like cars, people, bikes, motorbikes, traffic signals, etc. have been grouped in a novel dataset to feed state-of-the-art CNNs and we carried out an analysis about their ability to detect and to locate those objects from the car point of view. Moreover, with the help of tracking techniques, we improved efficiency and robustness of the proposed method, creating a system capable of performing real-time object detection (urban objects). In this thesis, we also present a traffic sign dataset, which comprises 45 different traffic signs. This dataset has been used for training a traffic sign classifier that is used a second step of our urban object detector. Moreover, a very basic but important aspect in safety driving is to keep the vehicle within the allowed space in the road (within the limits of the road). SLAM techniques have been used in the past for such tasks, but we present an end-to-end approach, where a CNN model learns to keep the vehicle within the limits of the road, correcting the angle of the vehicle steering wheel. Finally, we show an implementation of the presented systems running on a custom-built electric car. A series of experiments were carried out on a real-life traffic environment for evaluating the steering angle prediction system and the urban object detector. A mechanical system was implemented on the car to enable automatic steering wheel control.

No description available.

Neue Methoden zur Verkehrsdatenerfassung wie die Fahrzeug-Infrastruktur-Kommunikation, der Floating Car-Ansatz und die Videodetektion eröffnen die Möglichkeit, neue Verfahren zur verkehrsabhängigen Lichtsignalanlagensteuerung zu realisieren. In dem Beitrag wird ein Verfahren beschrieben, das aus diesen Quellen Daten in Form von Fahrzeugverlustzeiten direkt zur Steuerung eines Einzelknotens verwendet. Die robuste Ausgestaltung des Verfahrens sorgt dabei dafür, dass auch mit einer lückenhaften Datenlage, wie z. B. aufgrund geringer Ausstattungsraten kommunikationsfähiger Fahrzeuge, angemessen umgegangen werden kann. Mit Hilfe einer mikroskopischen Simulationsstudie wird nachgewiesen, dass das neue Verfahren bei der Qualität des Verkehrsablaufs das gleiche Niveau wie eine traditionelle Zeitlückensteuerung erreicht oder dieses unter bestimmten Bedingungen sogar übersteigt. Mit abnehmender Ausstattungsrate ergibt sich dabei allerdings ein Qualitätsverlust, der ebenfalls mit Hilfe der mikroskopischen Simulation quantifiziert wird und wichtige Erkenntnisse für einen möglichen Praxistest liefert. / State-of-the-art traffic data sources like Car-to-Infrastructure communication, Floating Car Data and video detection offer great new prospects for vehicle-actuated traffic signal control. Due to this, the article deals with a recent approach which uses vehicles’ delay times for real-time control of traffic signals at an isolated intersection. One of the strengths of the new approach is that it can handle also incomplete data sets, e.g. caused by low penetration rates of vehicles equipped with Car-to-Infrastructure communication technology, in an appropriate manner. Based on a microscopic simulation study the high quality of this innovative approach is demonstrated, which is equal or even outperforms the well-known headway-based control. However, a decreasing penetration rate of equipped vehicles means a reduced quality of signals’ control, which is quantified in the microscopic simulation study, too, and provides useful information for tests in the field.

Denial of Service (DoS) attacks aim at rendering a computer or network incapable of providing normal services by exploiting bugs or holes of system programs or network communication protocols. Existing DoS attack defense mechanisms (e.g., firewalls, intrusion detection systems, intrusion prevention systems) typically rely on data gathered from gateways of network systems. Because these data are IP-layer or above packet information, existing defense mechanisms are incapable of detecting internal attacks or attackers who disguise themselves by spoofing the source IP addresses of their packets. To address the aforementioned limitations of existing DoS attack defense mechanisms, we propose a classification-based DoS attack detection technique on the basis of the SNMP MIB II data from the network interface to induce a DoS detection model from a set of training examples that consist of both normal and attack traffic data). The constructed DoS detection model is then used for predicting whether a network traffic from the network interface is a DoS attack. To empirically evaluate our proposed classification-based DoS attack detection technique, we collect, with various traffic aggregation intervals (including 1, 3, and 5 minutes), normal network traffic data from two different environments (including an enterprise network, and a university campus network) and attack network traffics (including TCP SYN Flood, Land, Fake Ping, and Angry Ping) from an independent experimental network. Our empirical evaluation results show that the detection accuracy of the proposed technique reaches 98.59% or above in the two network environments. The evaluation results also suggest that the proposed technique is insensitive to the traffic aggregation intervals examined and has a high distinguishing power for the four types of DoS attacks under investigation.

abstract: Concern regarding the quality of traffic data exists among engineers and planners tasked with obtaining and using the data for various transportation applications. While data quality issues are often understood by analysts doing the hands on work, rarely are the quality characteristics of the data effectively communicated beyond the analyst. This research is an exercise in measuring and reporting data quality. The assessment was conducted to support the performance measurement program at the Maricopa Association of Governments in Phoenix, Arizona, and investigates the traffic data from 228 continuous monitoring freeway sensors in the metropolitan region. Results of the assessment provide an example of describing the quality of the traffic data with each of six data quality measures suggested in the literature, which are accuracy, completeness, validity, timeliness, coverage and accessibility. An important contribution is made in the use of data quality visualization tools. These visualization tools are used in evaluating the validity of the traffic data beyond pass/fail criteria commonly used. More significantly, they serve to educate an intuitive sense or understanding of the underlying characteristics of the data considered valid. Recommendations from the experience gained in this assessment include that data quality visualization tools be developed and used in the processing and quality control of traffic data, and that these visualization tools, along with other information on the quality control effort, be stored as metadata with the processed data. / Dissertation/Thesis / M.S. Civil and Environmental Engineering 2011

Le trafic routier évolue dans un contexte qui a connu trois changements majeurs ces deux dernières décennies : changement politique tout d'abord, avec la remise en cause de la place jusque-là occupée par la voiture en ville ; changement technologique ensuite, par lequel tant le véhicule que son conducteur produisent et reçoivent des données indépendamment des infrastructures de gestion du gestionnaire ; changement financier enfin, alors que les systèmes de gestion du trafic sont très dépendants de finances publiques de plus en plus contraintes.Dans ce contexte, l'auteur, du fait de ses fonctions, adopte le point de vue d'un gestionnaire de voirie clé, la Ville de Paris. En charge de l'évaluation des conséquences techniques des politiques de circulation sur l'écoulement du trafic motorisé, il s'intéresse ici à la manière dont les nouvelles données de trafic renouvellent la connaissance technique du gestionnaire sur la demande.Pour ce faire, l'auteur montre d'abord que les données de trafic et l'information trafic ont toujours été au cœur des préoccupations du gestionnaire. Données et information sont profondément liées à la technologie disponible et aux missions mêmes du gestionnaire. Les développements théoriques, alimentés par les données, tentent ainsi de lier les technologies avec les missions du gestionnaire.Ensuite, à travers l'évaluation technique de politiques de circulation (fermetures de voie, réduction de la vitesse limite) sur la base de deux types de nouvelles données (vitesses GPS et temps de parcours Bluetooth), l'auteur analyse les caractéristiques de ces jeux de données, les résultats auxquels ils permettent de parvenir, et la manière dont ils complètent la connaissance tirée des capteurs fixes historiques. Ces nouveaux jeux de données permettent au gestionnaire d'obtenir une connaissance de la demande du point de vue des usagers, alors que les capteurs fixes fournissent principalement un point de vue collectif de flux. Cette richesse nouvelle d'information redéfinit les schémas de décision du gestionnaire de voirie / Road traffic evolves in a context which has undergone three major changes in the past two decades: first, a political change, reshaping the car's role in cities; second, a technical change, through which both vehicles and drivers emit and receive information independently of road authorities' roadside infrastructure; and finally, a financial change, as traffic management infrastructure has heavily relied on public funding which now becomes scarcer.From the perspective of a key road authority, the City of Paris, the Author, in charge of assessing the impact on traffic flow of major disruptive policies, addresses how new traffic data renews the road authority's knowledge of the traffic, on technical grounds.The Author has worked on Bluetooth travel-time and GPS based Floating Car Data datasets. He believes he makes two major contributions in the field.He first shows that traffic data and traffic information have always been at the core of the road authority's concerns, deeply related to the available technology, the missions of the road authority, and the theory attempting to bridge the gap between the two.Through the technical assessment of traffic-related policies (road closures, speed-limit reduction), based on two types of new traffic data (GPS speeds and Bluetooth travel-times), the Author analyzes the characteristics of the two datasets, the results they yield and how they complement legacy fixed-sensor based data. They allow the road authority to grasp user-perspective information whereas legacy data mostly offered a collective flow perspective. This, in turn, reshapes the decision-making process of road authorities

Cílem práce je vytvoření přehledu o existujících přístupech pro předzpracování dopravních dat se zaměřením na objevování sémantických trajektorií a návrh a vývoj rámce, který integruje dopravní data z GPS senzorů se sémantikou. Problém analýzy nezpracovaných trajektorií spočíva v tom, že není natolik vyčerpávající, jako analýza trajektorií, které obsahují smysluplný kontext. Po nastudování různých přístupů a algoritmů sleduje návrh a vývoj rámce, který objevuje semantická místa pomocí schlukovací metody záložené na hustotě, aplikované na body zastavení v trajektoriích. Návrh a implementace rámce byl zhodnotěn na veřejně přístupných datových souborech obsahujících nezpracované GPS záznamy.

Vehicle velocity distributions are of utmost relevance for the efficiency, safety, and sustainability of road traffic. Yet, due to technical limitations, they are often empirically analyzed using spatiotemporal averages. Here, we instead study a novel set of microscopic traffic data from Dresden comprising 346 million data points with a resolution of one vehicle from 145 detector sites with a particular focus on extreme events and distribution tails. By fitting q-exponential and Generalized Extreme Value distributions to the right flank of the empirical velocity distributions, we establish that their tails universally exhibit a power-law behavior with similar decay exponents. We also find that q-exponentials are best suitable to model the vast extent to which speed limit violations in the data occur. Furthermore, combining velocity and time headway distributions, we obtain estimates for free flow velocities that always exceed average velocities and sometimes even significantly exceed speed limits. Likewise, congestion effects are found to play a very minor, almost negligible role in traffic flow at the detector sites. These results provide insights into the current state of traffic in Dresden, hinting toward potentially necessary policy amendments regarding road design, speed limits, and speeding prosecution. They also reveal the potentials and limitations of the data set at hand and thereby lay the groundwork for further, more detailed traffic analyses.