Implementation and development of traffic speed and flow prediction through Artificial Neural Networks

In this work we introduce the most recent techniques to predict traffic flow and speed. This work is composed of the following sections: an introduction, a state of the art, and conclusions sections. In the introduction section we see the importance of being able to predict the traffic conditions for speed and flow; we set our hypothesis that is that using the Levenberg-Marquardt training algorithm we’ll be able to find a global minimum for the problem of predicting traffic conditions; we also specify our general objective that is developing efficient algorithms for the traffic prediction for its variables speed and flow; as well as establishing that our scientifical novelty is using the Levenberg-Marquardt as Artificial Neural Network training algorithm. In the state of the art section we present the summarized contents of the most outstanding research papers about traffic prediction. We continue with the presentation of an approach that uses two statistical algorithms for traffic prediction. This information cover spatial impact, and accidents, and construction events. Additionally, it compares the results of outstanding research done with Artificial Neural Networks, and tatistical Methods, to their own statistical method that consists of two statistical methods embedded into only one by using a threshold used to determine which statistical method should be used and when, depending on the road conditions. We also present the results of traffic speed prediction by data mining techniques and a comparative to Artificial Neural Networks. Additionally, we introduce a section that analyze the problem of traffic prediction but only with Artificial Neural Networks done by the company SIEMENS in Germany. We introduce the Los Angeles Department of Transportation (LA DOT) infrastructure used to obtain the speed and flow measurements as well as the set of programs develop by the author of this thesis to retrieve the information from LA DOT, to process this information and calculate the prediction of flow and speed for a specific sensor. We continue with the problem of traffic prediction. In the process of predicting traffic flow, and speed we made our first approach using a Nonlinear Autoregressive Neural Network with External Input in MATLAB and we obtained promising results. However, this Artificial Neural Network does not have the ability to predict multiple outputs, and we transformed it to a Feedforward Neural Network also from MATLAB. The results obtained are impressive because they reduce dramatically the traffic prediction errors. In order to validate our results with the ones in the international research community we use the data from 95 days which is equivalent to 3 months, which is the commonly reported amount of time studied in this kind of problems. We also present an optimization process for the Feedforward Neural Network for both problems speed and flow prediction. Finally, we present a numerical sensibility analysis in order to determine how robust is our Artificial Neural Network. To close this thesis we present our conclusions as a success of using the Levenberg-Marquardt algorithm to train an Artificial Neural Network for the problem of traffic prediction, and we set up the possibilities of exploring the recently found result by using the learning techniques of deep learning.Consejo Nacional de Ciencia y TecnologíaContinental Guadalajara Services S.A. de C.

Modelling of advanced submicron gate InGaAs/InAIAs pHEMTS and RTD devices for very high frequency applications

InP based InAlAs/InGaAs pseudomorphic High Electron Mobility Transistors (pHEMTs) have shown outstanding performances, which makes them prominent in high frequency mm-wave and submillimeter-wave applications. However, conventional InGaAs/InAlAs pHEMTs have major drawbacks, i.e., very low breakdown voltage and high gate leakage current. These disadvantages degrade device performance, especially in Monolithic Microwave Integrated Circuit (MMIC) low noise amplifiers (LNAs). The optimisation of InAlAs/InGaAs epilayer structures through advanced bandgap engineering together with gate length reduction from 1 m into deep sub-μm regime is the key solution to enabled high breakdown and ultra-high speed, low noise pHEMT devices to be fabricated. Concurrently, device modelling plays a vital role in the design and analysis of pHEMT device and circuit performance. Physical modeling becomes essential to fully characterise and understand the underlying physical phenomenon of the device, while empirical modelling is significant in circuit design and predicts device’s characteristic performance. In this research, the main objectives to accurately model the DC and RF characteristics of the two-dimensional (2D) physical modelling for sub-μm gate length for strained channel InAlAs/InGaAs/InP pHEMT has been accomplished and developed in ATLAS Silvaco. All modelled devices were optimised and validated by experimental devices which were fabricated at the University of Manchester; the sub-micrometer devices were developed with T-gate using I-line optical lithography. The underlying device physics insight are gained, i.e, the effects of changes to the device’s physical structure, theoretical concepts and its general operation, hence a reliable pHEMT model is obtained. The kink anomalies in I-V characteristics was reproduced and the 2D simulation results demonstrate an outstanding agreement with measured DC and RF characteristics. The aims to develop linear and nonlinear models for sub-μm transistors and their implementation in MMIC LNA design is achieved with the 0.25 m In0.7Ga0.3As/In0.52Al0.48As/InP pHEMT. An accurate technique for the extraction of empirical models for the fabricated active devices has been developed and optimised using Advance Design System (ADS) software which demonstrate excellent agreement between experimental and modelled DC and RF data. A precise models for MMIC passive devices have also been obtained and incorporated in the proposed design for a single and double stage MMIC LNAs in C- and X-band frequency. The single stage LNA is designed to achieve maximum gain ranging from 9 to 13 dB over the band of operation while the gain is increased between 20 dB and 26 dB for the double stage LNA designs. A noise figure of less than 1.2 dB and 2 dB is expected respectively, for the C- and X-band LNA designed while retaining stability across the entire frequency bands. Although the RF performance of pHEMT is being vigorously pushed towards terahertz region, novel devices such as Resonant Tunnelling Diode (RTD) are needed to support future ultra-high speed, high frequency applications especially when it comes to THz frequencies. Hence, the study of physical modelling is extended to quantum modelling of an advanced In0.8Ga0.2As/AlAs RTD device to effectively model both large size and submicron RTD using Silvaco’s ATLAS software to reproduce the peak current density, peak-to-valley-current ratio (PVCR), and negative differential resistance (NDR) voltage range. The simple one-dimensional physical modelling for the RTD devices is optimised to achieve an excellent match with the fabricated RTD devices with variations in the spacer thickness, barrier thickness, quantum well thickness and doping concentration

First discovery augmented reality for learning solar systems

The development of Augmented Reality (AR) systems in educational settings should be given more attention and recognition on its contribution to the evolution of education. Although this shift of pedagogical method may disrupt the traditional curriculum model, it also offers great opportunity to complement and improve the modern age education model. This paper presents an AR-based mobile application for exploring Space and Science for primary school students called the First Discovery (FD). This application supplements a traditional book that contains 10 target images for solar system and its planets, which can be scanned by the AR camera in FD application. Evaluation was carried out among primary school children, elementary educators as well as parents, which showed a highly favorable response. It is hoped that the proposed FD application is able to improve the ability of children in retaining knowledge after the AR science learning experience, to enhance information accessibility of the science learning content for children as well as to develop creative learning and the ability of children in exploring and problem solvin

An integrated method for short-term prediction of road traffic conditions for intelligent transportation systems applications

The paper deals with the short-term prediction of road traffic conditions within Intelligent Transportation Systems applications. First, the problem of traffic modeling and the potential of different traffic monitoring technologies are discussed. Then, an integrated method for short-term traffic prediction is presented, which integrates an Artificial Neural Network predictor that forecasts future states in standard conditions, an anomaly detection module that exploits floating car data to individuate possible occurrences of anomalous traffic conditions, and a macroscopic traffic model that predicts speeds and queue progressions in case of anomalies. Results of offline applications on a primary Italian motorway are presented