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.

This study explores the potential of vehicle trajectory data obtained from Wide Area Motion Imagery for modeling and analyzing traffic characteristics. The data in question is collected by PV Labs and also known as persistent wide-area video. This video, in combination with PVLab's integrated Tactical Content Management System's spatiotemporal capability, automatically identifies and captures every vehicle in the video view frame, storing each vehicle with a discrete ID, track ID, and time-stamped location. This unique data capture provides comprehensive vehicle trajectory information. This thesis explores the use of data collected by the PVLab's system for an approximate area of 4 square kilometers area in the CBD area of Hamilton, Canada for use in understanding traffic characteristics. The data was collected for two three-hour continuous periods, one in the morning and one in the evening of the same day. Like any other computer vision algorithm, this data suffers from false detection, no detection, and other inaccuracies caused by faulty image registration. Data filtering requirements to remove noisy trajectories and reduce error is developed and presented. A methodology for extracting microscopic traffic data (gap, relative velocity, acceleration, speed) from the vehicle trajectories is presented in details. This study includes the development of a data model for storing this type of large-scale spatiotemporal data. The proposed data model is a combination of two efficient trajectory data storing techniques, the 3-D schema and the network schema and was developed to store trajectory information along with associated microscopic traffic information. The data model is designed to run fast queries on trajectory information. A 15-minute sample of tracks was validated using manual extraction from imagery frames from the video. Microscopic traffic data is extracted from this trajectory data using customized GIS analysis. Resulting tracks were map-matched to roads and individual lanes to support macro and microscopic traffic characteristic extraction. The final processed dataset includes vehicles and their trajectories for an area of approximately 4-square miles that includes a dense and complex urban network of roads over two continuous three-hour periods. Two subsets of the data were extracted, cleaned, and processed for use in calibrating car-following sub-models used in microscopic simulations. The car-following model is one of the cornerstones of any simulation based traffic analysis. Calibrating and validating these models is essential for enhancing the ability of the model's capability of representing local traffic. Calibration efforts have previously been limited by the availability and accuracy of microscopic traffic data. Even datasets like the NGSIM data are restricted in either time or space. Trajectory data of all vehicles over a wide area during an extended period of time can provide new insight into microscopic models. Persistent wide-area imagery provides a source for this data. This study explores data smoothing required to handle measurement error and to prepare model input for calibration. Three car-following models : the GHR model, the linear Helly model, and the Intelligent Driver model are calibrated using this new data source. Two approaches were taken for calibrating model parameters. First, a least square method is used to estimate the best fit value for the model parameter that minimizes the global error between the observed and predicted values. The calibration results outline the limitation of both the WAMI data source and the models themselves. Existing model structures impose limitations on the parameter values. Models become unstable beyond these parameter values and these values may not be near global optima. Most of the car-following models were developed based upon some kinematic relation between driver reaction and expected stimuli of that response. For this reason, models in their current form are ill-suited for calibration with noisy microscopic data. On the other hand, the limitation of the WAMI data is the inability of obtaining an estimate of the measurement errors. With unknown measurement errors, any model development or calibration becomes questionable irrespective of the data smoothing or filtering technique undertaken. These findings indicate requirements for development of a new generation of car-following model that can accommodate noisy trajectory data for calibration of its parameters. / MS / The decision making process undertaken by transportation agencies for planning, evaluating, and operating transportation facilities relies on analyzing traffic and driver behavior in both aggregated and disaggregated manner. Different computational tools relying on representative models of aggregate traffic flow measures and/or driver behavior are used in the decision support system tools. Field data is used not only as an input for the computational tools but also to develop, calibrate, and validate the models representing a particular aspect of traffic and driver behavior. Different approaches have been undertaken to collect the data required for analyzing traffic and driver behavior. One of the applied approach is to collect trajectory (i.e. position, speed, acceleration) information of vehicles in the analysis zone. However, this data collection approach is often limited to relatively small stretch of a roadway for short duration due to high cost of collection and limitation of technology. As a result, the models developed and calibrated using these data often lack generalization power for different situation. This study explores the potential of a new data source to address the aforementioned limitations. The data used in this study collects the trajectory information for the whole population of vehicles in the study area by collecting wide-area (WAMI) video data. The data is collected by Canada based imaging solution company PV Labs. The collection area is relatively large to cover wide range of roadway types and traffic operation system. A framework has been developed to extract traffic flow measures from the trajectory data. The extracted traffic flow measures are then applied to calibrate the car-following model. The car-following model attempts to mimic the longitudinal movement of real-world drivers following another vehicle in front of them. The calibration results outline the limitations of the WAMI data. Although, this dataset is capable of capturing traffic measures for different driving condition, the lack of information about measurement error imposes limits on the direct application of the data for model calibration. Findings of this study can be applied for refinement of the video data capture technology and subsequent application in modelling traffic characteristics as well as development of new and calibration of existing driver behavior model.