Measuring transit service reliability using data from web-based transit user survey : a case study at Brisbane, Australia

This paper reports an empirical study on measuring transit service reliability using the data from a Web-based passenger survey on a major transit corridor in Brisbane, Australia. After an introduction of transit service reliability measures, the paper presents the results from the case study including study area, data collection, and reliability measures obtained. This includes data exploration of boarding/arrival lateness, in-vehicle time variation, waiting time variation, and headway adherence. Impacts of peak-period effects and separate operation on service reliability are examined. Relationships between transit service characteristics and passenger waiting time are also discussed. A summary of key findings and an agenda of future research are offered in conclusions.

Critical gap estimation by numerical and statistical highest likelihood search

Many traffic situations require drivers to cross or merge into a stream having higher priority. Gap acceptance theory enables us to model such processes to analyse traffic operation. This discussion demonstrated that numerical search fine tuned by statistical analysis can be used to determine the most likely critical gap for a sample of drivers, based on their largest rejected gap and accepted gap. This method shares some common features with the Maximum Likelihood Estimation technique (Troutbeck 1992) but lends itself well to contemporary analysis tools such as spreadsheet and is particularly analytically transparent. This method is considered not to bias estimation of critical gap due to very small rejected gaps or very large rejected gaps. However, it requires a sufficiently large sample that there is reasonable representation of largest rejected gap/accepted gap pairs within a fairly narrow highest likelihood search band.

Raman microscopy study of tyrolite : a multi-anion arsenate mineral

The Raman spectrum of tyrolite, CaCu5(AsO4)2(CO3)(OH) 4.6H2O, from Brixlegg, Tyrol, Austria, is reported. Comparison with copper hydroxy-arsenate and basic carbonates was used to achieve assignments of the observed bands. The AsO43- group is characterized by two υ4 modes around 433 and 480 cm-1 plus a broad band around 840 cm-1 as the υ overlapping with the υ. The υ3 mode is observed as a single band around 355 cm -1. The CO32- υ1 mode is observed around 1035 and 1088 cm-1, although this assignment is difficult because of the in-plane OH bending vibrations at similar frequencies. Two υ4 modes are assigned to the 717 and 755 cm-1 bands. The υ3 mode is present as three bands at 1431, 1463, and 1498 cm-1. A large split caused by bridging carbonates may explain the band at 1370 cm -1. The H2O bending region shows two bands at 1635 and 1667 cm-1 together with stretching modes around 3204 and 3303 cm-1, the first associated with adsorbed H2O, while the second indicates more strongly bonded H2O. Three bands around 3534, 3438, and 3379 cm -1 are assigned to OH stretching modes of the OH groups in the crystal structure. The 202, 262, 301, 524, and 534 cm-1 bands are assigned to Cu-OH bending and stretching modes, whereas the bands around 179, 202, and 217 cm-1 are ascribed to O-(Ca, Cu)-O(H) with the O(H) at much greater distance from the cation. The bands around 503, 570, and 598 cm-1 are ascribed to the Cu-O stretching modes.

Empirical evaluation of an autonomous vehicle in an urban environment

Operation in urban environments creates unique challenges for research in autonomous ground vehicles. Due to the presence of tall trees and buildings in close proximity to traversable areas, GPS outage is likely to be frequent and physical hazards pose real threats to autonomous systems. In this paper, we describe a novel autonomous platform developed by the Sydney-Berkeley Driving Team for entry into the 2007 DARPA Urban Challenge competition. We report empirical results analyzing the performance of the vehicle while navigating a 560-meter test loop multiple times in an actual urban setting with severe GPS outage. We show that our system is robust against failure of global position estimates and can reliably traverse standard two-lane road networks using vision for localization. Finally, we discuss ongoing efforts in fusing vision data with other sensing modalities.

A hardware-based multi-disciplinary design optimization method for aeronautical applications

There are many applications in aeronautics where there exist strong couplings between disciplines. One practical example is within the context of Unmanned Aerial Vehicle(UAV) automation where there exists strong coupling between operation constraints, aerodynamics, vehicle dynamics, mission and path planning. UAV path planning can be done either online or offline. The current state of path planning optimisation online UAVs with high performance computation is not at the same level as its ground-based offline optimizer's counterpart, this is mainly due to the volume, power and weight limitations on the UAV; some small UAVs do not have the computational power needed for some optimisation and path planning task. In this paper, we describe an optimisation method which can be applied to Multi-disciplinary Design Optimisation problems and UAV path planning problems. Hardware-based design optimisation techniques are used. The power and physical limitations of UAV, which may not be a problem in PC-based solutions, can be approached by utilizing a Field Programmable Gate Array (FPGA) as an algorithm accelerator. The inevitable latency produced by the iterative process of an Evolutionary Algorithm (EA) is concealed by exploiting the parallelism component within the dataflow paradigm of the EA on an FPGA architecture. Results compare software PC-based solutions and the hardware-based solutions for benchmark mathematical problems as well as a simple real world engineering problem. Results also indicate the practicality of the method which can be used for more complex single and multi objective coupled problems in aeronautical applications.

The promise of computational journalism

Computational journalism involves the application of software and technologies to the activities of journalism, and it draws from the fields of computer science, the social sciences, and media and communications. New technologies may enhance the traditional aims of journalism, or may initiate greater interaction between journalists and information and communication technology (ICT) specialists. The enhanced use of computing in news production is related in particular to three factors: larger government data sets becoming more widely available; the increasingly sophisticated and ubiquitous nature of software; and the developing digital economy. Drawing upon international examples, this paper argues that computational journalism techniques may provide new foundations for original investigative journalism and increase the scope for new forms of interaction with readers. Computer journalism provides a major opportunity to enhance the delivery of original investigative journalism, and to attract and retain readers online.

Data preprocessing for anomaly based network intrusion detection : a review

Data preprocessing is widely recognized as an important stage in anomaly detection. This paper reviews the data preprocessing techniques used by anomaly-based network intrusion detection systems (NIDS), concentrating on which aspects of the network traffic are analyzed, and what feature construction and selection methods have been used. Motivation for the paper comes from the large impact data preprocessing has on the accuracy and capability of anomaly-based NIDS. The review finds that many NIDS limit their view of network traffic to the TCP/IP packet headers. Time-based statistics can be derived from these headers to detect network scans, network worm behavior, and denial of service attacks. A number of other NIDS perform deeper inspection of request packets to detect attacks against network services and network applications. More recent approaches analyze full service responses to detect attacks targeting clients. The review covers a wide range of NIDS, highlighting which classes of attack are detectable by each of these approaches. Data preprocessing is found to predominantly rely on expert domain knowledge for identifying the most relevant parts of network traffic and for constructing the initial candidate set of traffic features. On the other hand, automated methods have been widely used for feature extraction to reduce data dimensionality, and feature selection to find the most relevant subset of features from this candidate set. The review shows a trend toward deeper packet inspection to construct more relevant features through targeted content parsing. These context sensitive features are required to detect current attacks.

Systemic inflammatory responses to maximal versus submaximal lengthening contractions of the elbow flexors

We compared changes in markers of muscle damage and systemic inflammation after submaximal and maximal lengthening muscle contractions of the elbow flexors. Using a cross-over design, 10 healthy young men not involved in resistance training completed a submaximal trial (10 sets of 60 lengthening contractions at 10% maximum isometric strength, 1 min rest between sets), followed by a maximal trial (10 sets of three lengthening contractions at 100% maximum isometric strength, 3 min rest between sets). Lengthening contractions were performed on an isokinetic dynamometer. Opposite arms were used for the submaximal and maximal trials, and the trials were separated by a minimum of two weeks. Blood was sampled before, immediately after, 1 h, 3 h, and 1-4 d after each trial. Total leukocyte and neutrophil numbers, and the serum concentration of soluble tumor necrosis factor-alpha receptor 1 were elevated after both trials (P < 0.01), but there were no differences between the trials. Serum IL-6 concentration was elevated 3 h after the submaximal contractions (P < 0.01). The concentrations of serum tumor necrosis factor-alpha, IL-1 receptor antagonist, IL-10, granulocyte-colony stimulating factor and plasma C-reactive protein remained unchanged following both trials. Maximum isometric strength and range of motion decreased significantly (P < 0.001) after both trials, and were lower from 1-4 days after the maximal contractions compared to the submaximal contractions. Plasma myoglobin concentration and creatine kinase activity, muscle soreness and upper arm circumference all increased after both trials (P < 0.01), but were not significantly different between the trials. Therefore, there were no differences in markers of systemic inflammation, despite evidence of greater muscle damage following maximal versus submaximal lengthening contractions of the elbow flexors.

An FPGA-based approach to multi-objective evolutionary algorithm for multi-disciplinary design optimisation

This paper investigates the field programmable gate array (FPGA) approach for multi-objective and multi-disciplinary design optimisation (MDO) problems. One class of optimisation method that has been well-studied and established for large and complex problems, such as those inherited in MDO, is multi-objective evolutionary algorithms (MOEAs). The MOEA, nondominated sorting genetic algorithm II (NSGA-II), is hardware implemented on an FPGA chip. The NSGA-II on FPGA application to multi-objective test problem suites has verified the designed implementation effectiveness. Results show that NSGA-II on FPGA is three orders of magnitude better than the PC based counterpart.