Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

Fractional variational problems depending on indefinite integrals and with delay

The aim of this paper is to exhibit a necessary and sufficient condition of optimality for functionals depending on fractional integrals and derivatives, on indefinite integrals and on presence of time delay. We exemplify with one example, where we nd analytically the minimizer.

Analysis and control of transient spectra using time-dependent density functional theory.

215 p.

Sounds of the suburb

Sounds of the Suburb was a commissioned public art proposal based upon a brief set by Queensland Rail for the major redevelopment at their Brunswick Street Railway Station, Fortitude Valley, Brisbane. I proposed a large scale, electronic artwork to be distributed across the glass fronted structure of their station’s new concourse building. It was designed as a network of LED based ‘tracking’ - along which would travel electronically animated, ‘trains’ of text synchronised to the actual train timetables. Each message packet moved endlessly through a complex spatial network of ‘tracks’ and ‘stations’ set both inside, outside and via the concourse. The design was underpinned by large scale image of sound waves etched onto the architecture’s glass and was accompanied by two inset monitors each presenting ghosted images of passenger movements within the concourse, time-delay recorded and then cross-combined in realtime to form new composites.----- Each moving, reprogrammable phrase was conceived as a ‘train of thought’ and ostensibly contained an idea or concept about popular cultures surrounding contemporary music – thereby meeting the brief that the work should speak to the diverse musical cultures central to Fortitude Valley’s image as an entertainment hub. These cultural ‘memes’, gathered from both passengers and the music press were situated alongside quotes from philosophies of networking, speed and digital ecologies. These texts would continually propagate, replicate and cross fertlise as they moved throughout the ‘network’, thereby writing a constantly evolving ‘textual soundcape’ of that place. This idea was further cemented through the pace, scale and rhythm of passenger movements continually recorded and re-presented on the smaller screens.

Implementation of an integrated, technology-based, discovery mode assessment item involving an incubation period to enhance learning outcomes for engineering maths students

In this paper we discuss our current efforts to develop and implement an exploratory, discovery mode assessment item into the total learning and assessment profile for a target group of about 100 second level engineering mathematics students. The assessment item under development is composed of 2 parts, namely, a set of "pre-lab" homework problems (which focus on relevant prior mathematical knowledge, concepts and skills), and complementary computing laboratory exercises which are undertaken within a fixed (1 hour) time frame. In particular, the computing exercises exploit the algebraic manipulation and visualisation capabilities of the symbolic algebra package MAPLE, with the aim of promoting understanding of certain mathematical concepts and skills via visual and intuitive reasoning, rather than a formal or rigorous approach. The assessment task we are developing is aimed at providing students with a significant learning experience, in addition to providing feedback on their individual knowledge and skills. To this end, a noteworthy feature of the scheme is that marks awarded for the laboratory work are primarily based on the extent to which reflective, critical thinking is demonstrated, rather than the amount of CBE-style tasks completed by the student within the allowed time. With regard to student learning outcomes, a novel and potentially critical feature of our scheme is that the assessment task is designed to be intimately linked to the overall course content, in that it aims to introduce important concepts and skills (via individual student exploration) which will be revisited somewhat later in the pedagogically more restrictive formal lecture component of the course (typically a large group plenary format). Furthermore, the time delay involved, or "incubation period", is also a deliberate design feature: it is intended to allow students the opportunity to undergo potentially important internal re-adjustments in their understanding, before being exposed to lectures on related course content which are invariably delivered in a more condensed, formal and mathematically rigorous manner. In our presentation, we will discuss in more detail our motivation and rationale for trailing such a scheme for the targeted student group. Some of the advantages and disadvantages of our approach (as we perceived them at the initial stages) will also be enumerated. In a companion paper, the theoretical framework for our approach will be more fully elaborated, and measures of student learning outcomes (as obtained from eg. student provided feedback) will be discussed.

Electromyographic activity in lower limb muscles is temporally associated with the slow phase of oxygen uptake during cycling

Although the "slow" phase of pulmonary oxygen uptake (Vo2) appears to represent energetic processes in contracting muscle, electromyographic evidence tends not to support this. The present study assessed normalized integrated electromyographic (NIEMG) activity in eight muscles that act about the hip, knee and ankle during 8 min of moderate (ventilatory threshold) cycling in six male cyclists. (Vo2) was measured breath by breath during four repeated trials at each of the two intensities. Moderate and very heavy exercise followed a 4-min period of light exercise (50 W). During moderate exercise the slow (Vo2) phase was absent and NIEMG in all muscles did not increase after the first minute of exercise. During very heavy exercise, the slow phase emerged (time delay=58 ± 16 s) and increased progressively (time constant=120 ± 35 s) to an amplitude (0.83 ± 0.16 L/min) that was approximately 21% of the total (Vo2) response. This slow (Vo2) phase coincided with a significant increase in NIEMG in most muscles, and differences in NIEMG activities between the two intensities revealed "slow" muscle activation profiles that differed between muscles in terms of the onset, amplitude and shape of these profiles. This supports the hypothesis that the slow (Vo2) phase is a function of these different slow muscle activation profiles.

Adaptive instantaneous frequency estimation: Techniques and algorithms

This thesis deals with the problem of the instantaneous frequency (IF) estimation of sinusoidal signals. This topic plays significant role in signal processing and communications. Depending on the type of the signal, two major approaches are considered. For IF estimation of single-tone or digitally-modulated sinusoidal signals (like frequency shift keying signals) the approach of digital phase-locked loops (DPLLs) is considered, and this is Part-I of this thesis. For FM signals the approach of time-frequency analysis is considered, and this is Part-II of the thesis. In part-I we have utilized sinusoidal DPLLs with non-uniform sampling scheme as this type is widely used in communication systems. The digital tanlock loop (DTL) has introduced significant advantages over other existing DPLLs. In the last 10 years many efforts have been made to improve DTL performance. However, this loop and all of its modifications utilizes Hilbert transformer (HT) to produce a signal-independent 90-degree phase-shifted version of the input signal. Hilbert transformer can be realized approximately using a finite impulse response (FIR) digital filter. This realization introduces further complexity in the loop in addition to approximations and frequency limitations on the input signal. We have tried to avoid practical difficulties associated with the conventional tanlock scheme while keeping its advantages. A time-delay is utilized in the tanlock scheme of DTL to produce a signal-dependent phase shift. This gave rise to the time-delay digital tanlock loop (TDTL). Fixed point theorems are used to analyze the behavior of the new loop. As such TDTL combines the two major approaches in DPLLs: the non-linear approach of sinusoidal DPLL based on fixed point analysis, and the linear tanlock approach based on the arctan phase detection. TDTL preserves the main advantages of the DTL despite its reduced structure. An application of TDTL in FSK demodulation is also considered. This idea of replacing HT by a time-delay may be of interest in other signal processing systems. Hence we have analyzed and compared the behaviors of the HT and the time-delay in the presence of additive Gaussian noise. Based on the above analysis, the behavior of the first and second-order TDTLs has been analyzed in additive Gaussian noise. Since DPLLs need time for locking, they are normally not efficient in tracking the continuously changing frequencies of non-stationary signals, i.e. signals with time-varying spectra. Nonstationary signals are of importance in synthetic and real life applications. An example is the frequency-modulated (FM) signals widely used in communication systems. Part-II of this thesis is dedicated for the IF estimation of non-stationary signals. For such signals the classical spectral techniques break down, due to the time-varying nature of their spectra, and more advanced techniques should be utilized. For the purpose of instantaneous frequency estimation of non-stationary signals there are two major approaches: parametric and non-parametric. We chose the non-parametric approach which is based on time-frequency analysis. This approach is computationally less expensive and more effective in dealing with multicomponent signals, which are the main aim of this part of the thesis. A time-frequency distribution (TFD) of a signal is a two-dimensional transformation of the signal to the time-frequency domain. Multicomponent signals can be identified by multiple energy peaks in the time-frequency domain. Many real life and synthetic signals are of multicomponent nature and there is little in the literature concerning IF estimation of such signals. This is why we have concentrated on multicomponent signals in Part-H. An adaptive algorithm for IF estimation using the quadratic time-frequency distributions has been analyzed. A class of time-frequency distributions that are more suitable for this purpose has been proposed. The kernels of this class are time-only or one-dimensional, rather than the time-lag (two-dimensional) kernels. Hence this class has been named as the T -class. If the parameters of these TFDs are properly chosen, they are more efficient than the existing fixed-kernel TFDs in terms of resolution (energy concentration around the IF) and artifacts reduction. The T-distributions has been used in the IF adaptive algorithm and proved to be efficient in tracking rapidly changing frequencies. They also enables direct amplitude estimation for the components of a multicomponent

Oxygen consumption and muscle activation patterns during constant load exercise

The collective purpose of these two studies was to determine a link between the V02 slow component and the muscle activation patterns that occur during cycling. Six, male subjects performed an incremental cycle ergometer exercise test to determine asub-TvENT (i.e. 80% of TvENT) and supra-TvENT (TvENT + 0.75*(V02 max - TvENT) work load. These two constant work loads were subsequently performed on either three or four occasions for 8 mins each, with V02 captured on a breath-by-breath basis for every test, and EMO of eight major leg muscles collected on one occasion. EMG was collected for the first 10 s of every 30 s period, except for the very first 10 s period. The V02 data was interpolated, time aligned, averaged and smoothed for both intensities. Three models were then fitted to the V02 data to determine the kinetics responses. One of these models was mono-exponential, while the other two were biexponential. A second time delay parameter was the only difference between the two bi-exponential models. An F-test was used to determine significance between the biexponential models using the residual sum of squares term for each model. EMO was integrated to obtain one value for each 10 s period, per muscle. The EMG data was analysed by a two-way repeated measures ANOV A. A correlation was also used to determine significance between V02 and IEMG. The V02 data during the sub-TvENT intensity was best described by a mono-exponential response. In contrast, during supra-TvENT exercise the two bi-exponential models best described the V02 data. The resultant F-test revealed no significant difference between the two models and therefore demonstrated that the slow component was not delayed relative to the onset of the primary component. Furthermore, only two parameters were deemed to be significantly different based upon the two models. This is in contrast to other findings. The EMG data, for most muscles, appeared to follow the same pattern as V02 during both intensities of exercise. On most occasions, the correlation coefficient demonstrated significance. Although some muscles demonstrated the same relative increase in IEMO based upon increases in intensity and duration, it cannot be assumed that these muscles increase their contribution to V02 in a similar fashion. Larger muscles with a higher percentage of type II muscle fibres would have a larger increase in V02 over the same increase in intensity.

Restarting trains under moving block signalling : an expert system approach

A high peak power demand at substations will result under Moving Block Signalling (MBS) when a dense queue of trains begins to start from a complete stop at the same time in an electrified railway system. This may cause the power supply interruption and in turn affect the train service substantially. In a recent study, measures of Starting Time Delay (STD) and Acceleration Rate Limit (ARL) are the possible approaches to reduce the peak power demand on the supply system under MBS. Nevertheless, there is no well-defined relationship between the two measures and peak power demand reduction (PDR). In order to attain a lower peak demand at substations on different traffic conditions and system requirements, an expert system is one of the possible approaches to procure the appropriate use of peak demand reduction measures. The main objective of this paper is to study the effect of the train re-starting strategies on the power demand at substations and the time delay suffered by the trains with the aid of computer simulation. An expert system is a useful tool to select various adoptions of STD and ARL under different operational conditions and system requirements.

A discussion of Fashion Victims: Various responses to the report by War on Want, Critical Perspectives on International Business, Vol. 3 Iss: 2, pp.170 - 185

Purpose – The purpose of this paper is to present a selection of responses to the report Fashion Victims, published by War on Want in December 2006. It offers a range of viewpoints presented by members of the Editorial Advisory Board of CPOIB. These are presented in chronological order of submission. There is some cross-reference by contributors to the work of others, but no attempt is made to present a unified argument. Design/methodology/approach – Presents the full contributions of involved participants, without mediation or editorial change. Findings – A number of different perspectives are presented on the central issue that is summarised by the opening heading in War on Want’s report – “How cheap is too cheap?” It is seen that the answer to this question is very much dependent upon the standpoint of the respondent. Originality/value – In presenting this form of commentary, members of the CPOIB Editorial Board seek to stimulate debate about an issue of concern to contemporary society, without resort to the time delay and mediating processes of peer-review normally attached to academic writing. It is hoped that this discussion will provoke further contributions and a widening of the debate. Keywords Corporate social responsibility, Multinational companies, Conditions of employment, Trade unions

A UKF-based estimation strategy for actuator fault detection of UASs

This paper presents a recursive strategy for online detection of actuator faults on a unmanned aerial system (UAS) subjected to accidental actuator faults. The proposed detection algorithm aims to provide a UAS with the capability of identifying and determining characteristics of actuator faults, offering necessary flight information for the design of fault-tolerant mechanism to compensate for the resultant side-effect when faults occur. The proposed fault detection strategy consists of a bank of unscented Kalman filters (UKFs) with each one detecting a specific type of actuator faults and estimating correspond- ing velocity and attitude information. Performance of the proposed method is evaluated using a typical nonlinear UAS model and it is demonstrated in simulations that our method is able to detect representative faults with a sufficient accuracy and acceptable time delay, and can be applied to the design of fault-tolerant flight control systems of UASs.

Nonlinear actuator fault detection for small-scale UAS

This paper presents a recursive strategy for online detection of actuator faults on a unmanned aerial system (UAS) subjected to accidental actuator faults. The proposed detection algorithm aims to provide a UAS with the capability of identifying and determining characteristics of actuator faults, offering necessary flight information for the design of fault-tolerant mechanism to compensate for the resultant side-effect when faults occur. The proposed fault detection strategy consists of a bank of unscented Kalman filters (UKFs) with each one detecting a specific type of actuator faults and estimating corresponding velocity and attitude information. Performance of the proposed method is evaluated using a typical nonlinear UAS model and it is demonstrated in simulations that our method is able to detect representative faults with a sufficient accuracy and acceptable time delay, and can be applied to the design of fault-tolerant flight control systems of UASs.

Utilizing Wide-Area Signals for off-center SVCs to damp interarea oscillations

Wide-Area Measurement Systems (WAMS) provide the opportunity of utilizing remote signals from different locations for the enhancement of power system stability. This paper focuses on the implementation of remote measurements as supplementary signals for off-center Static Var Compensators (SVCs) to damp inter-area oscillations. Combination of participation factor and residue method is used for the selection of most effective stabilizing signal. Speed difference of two generators from separate areas is identified as the best stabilizing signal and used as a supplementary signal for lead-lag controller of SVCs. Time delays of remote measurements and control signals is considered. Wide-Area Damping Controller (WADC) is deployed in Matlab Simulink framework and is tested under different operating conditions. Simulation results reveal that the proposed WADC improve the dynamic characteristic of the system significantly.

Tracking-based moving object detection

We present a novel approach for multi-object detection in aerial videos based on tracking. The proposed method mainly involves three steps. Firstly, the spatial-temporal saliency is employed to detect moving objects. Secondly, the detected objects are tracked by mean shift in the subsequent frames. Finally, the saliency results are fused with the weight map generated by tracking to get refined detection results, and in turn the modified detection results are used to update the tracking models. The proposed algorithm is evaluated on VIVID aerial videos, and the results show that our approach can reliably detect moving objects even in challenging situations. Meanwhile, the proposed method can process videos in real time, without the effect of time delay.