The National Transport Data Framework

Report by Professor Peter Landshoff (Cambridge University) and Professor John Polak (Imperial College London) on a project for the Department for Transport. emails: [email protected] [email protected] NTDF is designed to be a resource for data owners to deposit descriptions into a central catalogue, so that people can search for data and find data and understand their characteristics. The value of this is to individuals, to commercial organizations, and to public bodies. For example, services that provide better information to travellers will help to make their journey less stressful and persuade them to make more use of public transport. Transport operators need very diverse information to help them plan developments to their services: demographic, geographical, economic etc. And policy makers need a similar range of information to help them decide how to divide their budget and afterwards to evaluate how valuable it has been.This work was supported by the Department for Transport (DfT)

Non-intrusive anomaly detection for encrypted networks

The use of encryption is steadily increasing. Packet payloads that are encrypted are becoming increasingly difficult to analyze using IDSs. This investigation uses a new non-intrusive IDS approach to detect network intrusions using a K-Means clustering methodology. It was found that this approach was able to detect many intrusions for these datasets while maintaining the encrypted confidentiality of packet information. This work utilized the KDD \u2799 and NSL-KDD evaluation datasets for testing

Fully automated urban traffic system

The replacement of the driver with an automatic system which could perform the functions of guiding and routing a vehicle with a human's capability of responding to changing traffic demands was discussed. The problem was divided into four technological areas; guidance, routing, computing, and communications. It was determined that the latter three areas being developed independent of any need for fully automated urban traffic. A guidance system that would meet system requirements was not being developed but was technically feasible

Advanced techniques for traffic monitoring using inductive sensors

Programa Oficial de Doutoramento en Tecnoloxías da Información e Comunicación en Redes Móbiles. 553V01[Resumen] El objetivo principal de este proyecto es el desarrollo de técnicas avanzadas para gestión del tráfico de vehículos usando un Detector de Bucles Inductivos (ILDs). Para ello, en primer lugar se desarrolla e implementa un ILD que va a proporcionar huellas inductivas de los vehículos que transitan por una vía. Además de las funciones tradicionales de medida de aforamientos de tráfico, tales como densidad, ocupación y clasificación de vehículos, se pretende conseguir el reconocimiento de los mismos mediante el análisis de la señal de su huella. Basándose en la infraestructura existente en las carreteras para realizar los aforamientos de tráfico que usa fundamentalmente bucles inductivos, modificaciones de los equipos detectores van a permitir incluir además la función de re-identificación, para su uso en aplicaciones de control y supervisión de tráfico de vehículos. Por lo tanto, y aunque la tecnología de los detectores de bucles inductivos está totalmente extendida y en uso en este momento, se le añade una función de captura de las huellas inductivas del vehículo que permite aplicaciones adicionales de reconocimiento de los mismos para mejorar la clasificación, detección de velocidad con una sola espira, y re-identificación para aplicaciones de control y supervisión del tráfico rodado. Este trabajo presenta un sistema completo para clasificación de vehículos compuesto de un detector de bucles inductivos y los correspondientes algoritmos o.ff-line. El sistema detecta la presencia de vehículos mediante un desplazamiento en el periodo de oscilación del bucle de forma que las huellas de los vehículos detectados se registran mediante la duración de un número prefijado de pulsos de oscilación. En este trabajo nos centraremos en la cuestión, todavía no resuelta a día de hoy, de contar el número de vehículos (clasificándolos en tres tipos, coches, furgonetas y camiones) que circulan por una carretera. El método clásico para tal propósito consiste en la estimación de la longitud del vehículo usando las huellas inductivas obtenidas en dos bucles y, a continuación, las clasifica de acuerdo con un umbral preestablecido. Para la clasificación de los vehículos que circulan por una vía, presentamos un sistema bastante simple que usa esas huellas inductivas y la transformada discreta de Fourier (OFf, del inglés Discrete Fourier Transfonn). Para abordar el problema de clasificación en tres tipos de vehículos (como mencionábamos antes, coches, furgonetas y camiones) se propone un algoritmo heurístico basado en decisión por umbrales y en la magnitud del primer máximo espectral obtenido aplicando el análisis DFf a la huella inductiva del vehículo obtenida a partir de un único blucle. Además, el método aquí desarrollado puede aplicarse a huellas de vehículos capturadas con otros tipos de sensores. En este trabajo compararemos nuestro sistema con métodos de clasificación clásicos basados en la estimación de la longitud del vehículo obtenida a partir de dos bucles. Los resultados experimentales muestran que el criterio basado en la magnitud de la DFT exhibe un error de clasificación más bajo que el alcanzado con dichos métodos, con la enorme ventaja de la utilización de un único bucle. Por último, dado el elevado coste de estas pruebas en escenarios reales cada vez que una nueva técnica está siendo estudiada, hemos desarrollado un modelo avanzado del detector de bucles inductivos que claramente supera los modelos que se han usado tradicionalmente con unos resultados muy similares a los obtenidos directamente usando el prototipo de ILD que hemos desarrollado.[Resumo]O obxetivo principal deste proxecto é o desenvolvemento de técnicas avanzadas para a xestión do tráfico de vehículos usando un Detector de Bucles Inductivos (ILD). Así, desenvólvese e impleméntase un ILD que vai a proporcionar pegadas inductivas dos vehículos que transitan por unha vía. Ademáis das funcións tradicionais de medidas de aforamentos de tráfico, tales como densidade, ocupación e clasificación de vehículos, preténdese conseguir o recoñecemento dos mesmos mediante a análise do sinal da pegada. Baseándose na infraestrutura existente nas estradas para realizar os aforamentos de tráfico que usa fundamentalmente bucles inductivos, modifi.cacións dos equipos detectores permiten incluir ademáis a función de re-identificación, para o seu uso en aplicacións de control e supervisión de tráfico de vehículos. Polo tanto, e aímla que a tecnoloxfa dos detectores de bucle inductivos está totalmente extendida e en uso neste momento, engádese unha función de captura das pegadas inductivas do vehículo que pennite aplicacións adicionais de recoñecemento dos mesmos para mellorar a clasificación, detección de velocidade cunha soa espira, e re-identificación para aplicaci6ns de control e supervisión do tráfico rodado. Este traballo presenta un sistema completo para clasificación de vehículos composto dun detector de bucles inductivos e dos correspondentes algoritmos off-line. O sistema detecta a presenza de vehículos mediante un desprazamento no periodo de oscilación do bucle de xeito que as pegadas dos vehículos detectados se rexistran mediante a duración dun número prefixado de pulsos de oscilación. Neste traballos imos focalizarnos na cuestión, aínda non resalta a día de hoxe, de contar o número de vehículos (clasificándoos en coches, furgonetas e camións) que circulan por unha estrada. O método clásico para este propósito consiste na estimación da lonxitude do vehículo usando as pegadas inductivas obtidas en dous bucles e, a continuación, clasificalas dacordo a un umbral preestablecido. Para a clasificación dos vehículos que circulan por unha vía, presentamos un sistema bastante sinxelo que usas esas pegadas inductivas e a transformada discreta de Fourier (OFf, do inglés Discrete Fourier Transform). Para abordar o problema de clasificación en tres tipos de vehículos (como comentabamos antes, coches, furgonetas e camións) proponse un algoritmo heurístico baseado en decisión por umbrais e na magnitude do primeiro máximo espectral obtido aplicando a análise DFf á pegada inductiva do vehículo obtida a partir dun único bucle. Ademáis, o método proposto pode aplicarse a pegadas de vehículos capturadas con outros tipos de sensores. Neste traballo compararemos o noso sistema a métodos de clasificación clásicos baseados na estimación da Jonxitude do vehfculo obtida a partir de dous bucles. Os resultados experimentais amasan que o criterio baseado na magnitude da OFf presenta un erro de clasificación máis baixo que o que acadan estos métodos, coa enorma avantaxe da súa utilización dun único bucle. Por último, dado o elevado custo das probas realizadas en escearios reais cada vez que unha nova técnica está baixo estudo, desenvolvemos tamén un modelo avanzado de detector de bucles inductivos que claramente supera os modelos que se están a usar tradicionalmente con esta finalidade cuns resultados moi similares aos obtidos directamente usando o prototipo de ILD proposto neste traballo.[Abstract] The main goal of this work is the development of advanced techniques for vehicle traffic monitoring using Jnductive Loop Detectors (ILD). Thus, we develop an implementation of an ILD that will provide vehicle inductive signatures passing on a road. Severa! traditional functions of traffic monitoring are intensity, density or vehicle classification, but mon:over we want to identify those vehicles using their inductive signatures. Based on the infrastructure already available under the road pavements for traffic applications using inductive sensors, sorne modifications on the detector equipments allow us to include re-identification functions to be used for vehicle traffic control and management. Therefon:, although the technology of inductive loop detectors is widely used in many countries, we will add a module for capturing the inductive signatun:s leading to additional applications of vehicle recognising to improve the classification, the vehicle detection, and their re-identification useful for vehicular traffic control and surveillance tasks. This work presents a complete system for vehicle classification composed by an inductive-loop detector and the corresponding off-line algorithms. The system detects the presence of vehicles by means of a shift in the loop oscillation period so that the signature of the detected vehicles is registered by measuring the duration of a fixed number of oscillator pulses. We focus on the open issue of counting the number of vehicles (classified into cars, vans and trucks) on a roadway. The classical method for such purpose consists of estimating the vehicle length using the inductive signatures obtained from two loops and, subsequently, it classifies them taking into account a prefixed threshold. We presenta simple system to classify vehicles travelling along a road using inductive signatures and the Discrete Fourier Transform (DFf). We focus on the problem of classifying those vehicles into three types (cars, vans, and trucks) using a heuristic algorithm based on threshold decision and on the magnitude of the first spectral maximum obtained applying the DFT analysis to the vehicle inductive signature from only one loop. Moreover, the method here developed can be applied to vehicle signatures captured with other types of sensors. In this dissertation we will compare our system to classical methods based on estimating the vehicle length obtained from two loops. Experimental results show that the magnitude of the DFT exhibits a lower classifying error rate than that achieved using the lenglh-based rnethod, with the enormous advantage of requiring only one loop. Finally, due to the high cost of testing in real scenarios each new technique under study, we also develop an advanced model of an ILD that clearly outperforms the traditional ones with similar results to those directly obtained from the hardware prototype of ILD proposed in this work

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Advantages offered by the double magnetic loops versus the conventional single ones

[EN] Due to their simplicity and operating mode, magnetic loops are one of the most used traffic sensors in Intelligent Transportation Systems (ITS). However, at this moment, their potential is not being fully exploited, as neither the speed nor the length of the vehicles can be surely ascertained with the use of a single magnetic loop. In this way, nowadays the vast majority of them are only being used to measure traffic flow and count vehicles on urban and interurban roads. This is the reason why we presented in a previous paper the double magnetic loop, capable of improving the features and functionalities of the conventional single loop without increasing the cost or introducing additional complexity. In that paper, it was introduced their design and peculiarities, how to calculate their magnetic field and three different methods to calculate their inductance. Therefore, with the purpose of improving the existing infrastructure and providing it with greater potential and reliability, this paper will focus on justifying and demonstrating the advantages offered by these double loops versus the conventional ones. This will involve analyzing the magnetic profiles generated by the passage of vehicles over double loops and comparing them with those already known. Moreover, it will be shown how the vehicle speed, the traffic direction and many other data can be obtained more easily and with less margin of error by using these new inductance signatures.This research has been funded by the Universitat Politecnica de Valencia through its internal project 'Equipos de deteccion, regulacion e informacion en el sector de los sistemas inteligentes de transporte (ITS). Nuevos modelos y ensayos de compatibilidad y verificacion de funcionamiento', which has been carried out at the ITACA InstituteMocholí-Belenguer, F.; Mocholí Salcedo, A.; Guill Ibáñez, A.; Milian Sanchez, V. (2019). Advantages offered by the double magnetic loops versus the conventional single ones. PLoS ONE. 14(2):1-24. https://doi.org/10.1371/journal.pone.0211626S12414

Accurate vehicle classification including motorcycles using piezoelectric sensors

Thesis (M.S. ECE)--University of Oklahoma, 2012.Includes bibliographical references (leaves 88-90).State and federal departments of transportation are charged with classifying vehicles and monitoring mileage traveled. Accurate data reporting enables suitable roadway design for safety and capacity. Vehicle classifier devices currently employ inductive loops, piezoelectric sensors, or some combination of both, to aid in the identification of 13 Federal Highway Administration (FHWA) classifications. However, systems using inductive loops have proven unable to accurately classify motorcycles and record pertinent data. Previous investigations undertaken to overcome this problem have focused on classification techniques utilizing inductive loops signal output, magnetic sensor output with neural networks, or the fusion of several sensor outputs. Most were off-line classification studies with results not directly intended for product development. Vision, infrared, and acoustic classification systems among others have also been explored as possible solutions. This thesis presents a novel vehicle classification setup that uses a single piezoelectric sensor placed diagonally on the roadway to accurately identify motorcycles from among other vehicles, as well as identify vehicles in the remaining 12 FHWA classifications. An algorithm was formulated and deployed in an embedded system for field testing. Both single element and multi-element piezoelectric sensors were investigated for use as part of the vehicle classification system. The piezoelectric sensors and vehicle classification system reported in this thesis were subsequently tested at the University of Oklahoma-Tulsa campus. Various vehicle types traveling at limited vehicle speeds were investigated. The newly developed vehicle classification system demonstrated results that met expectation for accurately identifying motorcycles

Weigh-in-Motion Auto-Calibration Using Automatic Vehicle Identification

Weigh-in-Motion (WIM) sensors are installed on mainline lanes at highway locations to record vehicle weights, axle spacing, vehicle class, travel speed, vehicle length, and traffic volume. These data elements support effective transportation planning, infrastructure design, and policy development. Therefore, it is important that WIM sensors supply accurate data. After initial installation and calibration, WIM systems may experience measurement drifts in weight and axle detection. Recalibration takes two general forms: (a) On-site calibration involving running trucks of known weight over WIM scales and (b) Auto-calibration methods involving comparisons to assumed reference weights. Auto-calibration can be more cost and time effective than on-site calibration. This paper leverages the increasing prevalence of truck tracking technologies like Global Positioning Systems (GPS) to improve auto-calibration methods and was divided into three aims: (i) data collection, (ii) data processing and (iii) model development. Truck GPS data from a national provider, WIM recorded truck weights, and static weights collected at weight enforcement station were gathered at several highway locations in Arkansas. A “matching” algorithm was developed to automatically match each GPS record to a WIM record based on timestamp and vehicle configuration. Algorithm performance was assessed via manual video verification of matches. Approximately, 75% of WIM and truck GPS records were correctly paired. Lastly, an auto-calibration model was developed to estimate lane and site specific calibration factors. The algorithm estimates hourly calibration factors by comparing the front axle weight of the same truck as it passes multiple WIM sites. Algorithm performance was measured by comparing estimated front axle and gross vehicle weights to known weights of the same truck measured at a static enforcement scale. The algorithm achieved Median Absolute Percent Error (MdAPE) of 11-23% for front axle weight and 15-45% for gross vehicle weight. These results can be improved by increasing the number of trucks that are able to be tracked across WIM sites with Automatic Vehicle Identification

Wide area detection system: Conceptual design study

An integrated sensor for traffic surveillance on mainline sections of urban freeways is described. Applicable imaging and processor technology is surveyed and the functional requirements for the sensors and the conceptual design of the breadboard sensors are given. Parameters measured by the sensors include lane density, speed, and volume. The freeway image is also used for incident diagnosis

A Kalman filter approach for exploiting bluetooth traffic data when estimating time-dependent OD matrices

Time-dependent origin–destination (OD) matrices are essential input for dynamic traffic models such as microscopic and mesoscopic traffic simulators. Dynamic traffic models also support real-time traffic management decisions, and they are traditionally used in the design and evaluation of advanced traffic traffic management and information systems (ATMS/ATIS). Time-dependent OD estimations are typically based either on Kalman filtering or on bilevel mathematical programming, which can be considered in most cases as ad hoc heuristics. The advent of the new information and communication technologies (ICT) provides new types of traffic data with higher quality and accuracy, which in turn allows new modeling hypotheses that lead to more computationally efficient algorithms. This article presents ad hoc, Kalman filtering procedures that explicitly exploit Bluetooth sensor traffic data, and it reports the numerical results from computational experiments performed at a network test site.Peer ReviewedPostprint (author’s final draft