Modeling bus rapid transit station bus queuing for operational analysis

This paper presents mathematical models for BRT station operation, calibrated using microscopic simulation modelling. Models are presented for station capacity and bus queue length. No reliable model presently exists to estimate bus queue length. The proposed bus queue model is analogous to an unsignalized intersection queuing model.

Modeling Bus Rapid Transit station bus queuing for Bus Rapid Transit station bus operation analysis

Stations on Bus Rapid Transit (BRT) lines ordinarily control line capacity because they act as bottlenecks. At stations with passing lanes, congestion may occur when buses maneuvering into and out of the platform stopping lane interfere with bus flow, or when a queue of buses forms upstream of the station blocking inflow. We contend that, as bus inflow to the station area approaches capacity, queuing will become excessive in a manner similar to operation of a minor movement on an unsignalized intersection. This analogy is used to treat BRT station operation and to analyze the relationship between station queuing and capacity. In the first of three stages, we conducted microscopic simulation modeling to study and analyze operating characteristics of the station under near steady state conditions through output variables of capacity, degree of saturation and queuing. A mathematical model was then developed to estimate the relationship between average queue and degree of saturation and calibrated for a specified range of controlled scenarios of mean and coefficient of variation of dwell time. Finally, simulation results were calibrated and validated.

Establishing definitions and modeling public transport travel time variability

Public transport travel time variability (PTTV) is essential for understanding deteriorations in the reliability of travel time, optimizing transit schedules and route choices. This paper establishes key definitions of PTTV in which firstly include all buses, and secondly include only a single service from a bus route. The paper then analyses the day-to-day distribution of public transport travel time by using Transit Signal Priority data. A comprehensive approach using both parametric bootstrapping Kolmogorov-Smirnov test and Bayesian Information Creation technique is developed, recommends Lognormal distribution as the best descriptor of bus travel time on urban corridors. The probability density function of Lognormal distribution is finally used for calculating probability indicators of PTTV. The findings of this study are useful for both traffic managers and statisticians for planning and researching the transit systems.

A stochastic thermostat algorithm for coarse-grained thermomechanical modeling of large-scale soft matters : theory and application to microfilaments

As all-atom molecular dynamics method is limited by its enormous computational cost, various coarse-grained strategies have been developed to extend the length scale of soft matters in the modeling of mechanical behaviors. However, the classical thermostat algorithm in highly coarse-grained molecular dynamics method would underestimate the thermodynamic behaviors of soft matters (e.g. microfilaments in cells), which can weaken the ability of materials to overcome local energy traps in granular modeling. Based on all-atom molecular dynamics modeling of microfilament fragments (G-actin clusters), a new stochastic thermostat algorithm is developed to retain the representation of thermodynamic properties of microfilaments at extra coarse-grained level. The accuracy of this stochastic thermostat algorithm is validated by all-atom MD simulation. This new stochastic thermostat algorithm provides an efficient way to investigate the thermomechanical properties of large-scale soft matters.

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots

Hot spot identification (HSID) aims to identify potential sites—roadway segments, intersections, crosswalks, interchanges, ramps, etc.—with disproportionately high crash risk relative to similar sites. An inefficient HSID methodology might result in either identifying a safe site as high risk (false positive) or a high risk site as safe (false negative), and consequently lead to the misuse the available public funds, to poor investment decisions, and to inefficient risk management practice. Current HSID methods suffer from issues like underreporting of minor injury and property damage only (PDO) crashes, challenges of accounting for crash severity into the methodology, and selection of a proper safety performance function to model crash data that is often heavily skewed by a preponderance of zeros. Addressing these challenges, this paper proposes a combination of a PDO equivalency calculation and quantile regression technique to identify hot spots in a transportation network. In particular, issues related to underreporting and crash severity are tackled by incorporating equivalent PDO crashes, whilst the concerns related to the non-count nature of equivalent PDO crashes and the skewness of crash data are addressed by the non-parametric quantile regression technique. The proposed method identifies covariate effects on various quantiles of a population, rather than the population mean like most methods in practice, which more closely corresponds with how black spots are identified in practice. The proposed methodology is illustrated using rural road segment data from Korea and compared against the traditional EB method with negative binomial regression. Application of a quantile regression model on equivalent PDO crashes enables identification of a set of high-risk sites that reflect the true safety costs to the society, simultaneously reduces the influence of under-reported PDO and minor injury crashes, and overcomes the limitation of traditional NB model in dealing with preponderance of zeros problem or right skewed dataset.

Error modeling and calibration of exteroceptive sensors for accurate mapping applications

Reliable robotic perception and planning are critical to performing autonomous actions in uncertain, unstructured environments. In field robotic systems, automation is achieved by interpreting exteroceptive sensor information to infer something about the world. This is then mapped to provide a consistent spatial context, so that actions can be planned around the predicted future interaction of the robot and the world. The whole system is as reliable as the weakest link in this chain. In this paper, the term mapping is used broadly to describe the transformation of range-based exteroceptive sensor data (such as LIDAR or stereo vision) to a fixed navigation frame, so that it can be used to form an internal representation of the environment. The coordinate transformation from the sensor frame to the navigation frame is analyzed to produce a spatial error model that captures the dominant geometric and temporal sources of mapping error. This allows the mapping accuracy to be calculated at run time. A generic extrinsic calibration method for exteroceptive range-based sensors is then presented to determine the sensor location and orientation. This allows systematic errors in individual sensors to be minimized, and when multiple sensors are used, it minimizes the systematic contradiction between them to enable reliable multisensor data fusion. The mathematical derivations at the core of this model are not particularly novel or complicated, but the rigorous analysis and application to field robotics seems to be largely absent from the literature to date. The techniques in this paper are simple to implement, and they offer a significant improvement to the accuracy, precision, and integrity of mapped information. Consequently, they should be employed whenever maps are formed from range-based exteroceptive sensor data. © 2009 Wiley Periodicals, Inc.

The enemy of music : modeling the behavior of a cultural industry in crisis

The purpose of this explorative study is to contribute to the understanding of current music industry dynamics. The situation is undeniably quite dramatic: Since the turn of the millennium, the global music industry has declined by $ US 6.2 billion in value—a fall of 16.3% in constant dollar terms. IFPI, the trade organization representing the international recording industry, identifies a number of exogenous factors as the main drivers of the downturn. This article suggests that other factors, in addition to those identified by IFPI, may have contributed to the current difficulties. A model is presented which indicates that business strategies which were designed to cope with the challenging business environment have reduced product diversity, damaged profitability, and contributed to the problem they were intended to solve.

Modeling perceived influences on journalism : evidence from a cross-national survey of journalists

Surveying 1,700 journalists from seventeen countries, this study investigates perceived influences on news work. Analysis reveals a dimensional structure of six distinct domains—political, economic, organizational, professional, and procedural influences, as well as reference groups. Across countries, these six dimensions build up a hierarchical structure where organizational, professional, and procedural influences are perceived as more powerful limits to journalists' work than political and economic influences.

Control, operation and power sharing among parallel converter-interfaced DERs in a microgrid in the presence of unbalanced and harmonic loads

This paper demonstrates power management and control of DERs in an autonomous MG. The paper focuses on the control and performance of converter-interfaced DERs in voltage controlled mode. Several case studies are considered for a MG based on the different types of loads supplied by the MG (i.e. balanced three-phase, unbalanced, single-phase and harmonic loads). DERs are controlled by adjusting the voltage magnitude and angle in their converter output through droop control, in a decentralized concept. Based on this control method, DERs can successfully share the total demand of the MG in the presence of any type of loads. This includes proper total power sharing, unbalanced power sharing as well as harmonic power sharing, depending on the load types. The efficacy of the proposed power control, sharing and management among DERs in a microgrid is validated through extensive simulation studies using PSCAD/EMTDC.

Primary control level of parallel distributed energy resources converters in system of multiple interconnected autonomous microgrids within self-healing networks

To minimise the number of load sheddings in a microgrid (MG) during autonomous operation, islanded neighbour MGs can be interconnected if they are on a self-healing network and an extra generation capacity is available in the distributed energy resources (DER) of one of the MGs. In this way, the total load in the system of interconnected MGs can be shared by all the DERs within those MGs. However, for this purpose, carefully designed self-healing and supply restoration control algorithm, protection systems and communication infrastructure are required at the network and MG levels. In this study, first, a hierarchical control structure is discussed for interconnecting the neighbour autonomous MGs where the introduced primary control level is the main focus of this study. Through the developed primary control level, this study demonstrates how the parallel DERs in the system of multiple interconnected autonomous MGs can properly share the load of the system. This controller is designed such that the converter-interfaced DERs operate in a voltage-controlled mode following a decentralised power sharing algorithm based on droop control. DER converters are controlled based on a per-phase technique instead of a conventional direct-quadratic transformation technique. In addition, linear quadratic regulator-based state feedback controllers, which are more stable than conventional proportional integrator controllers, are utilised to prevent instability and weak dynamic performances of the DERs when autonomous MGs are interconnected. The efficacy of the primary control level of the DERs in the system of multiple interconnected autonomous MGs is validated through the PSCAD/EMTDC simulations considering detailed dynamic models of DERs and converters.

Modeling the effectiveness of counterterrorism interventions

After the terrorist attacks in the United States on 11 September 2001, terrorism and counter-terrorism efforts moved to the front of popular consciousness and became the focus of national security for governments worldwide. With this increased attention came an urgent interest in understanding and identifying what works in fighting terrorism (Belasco 2010). For Australia, understanding the relative effectiveness of counter-terrorism efforts in nearby neighbours of Indonesia, Thailand and the Philippines is highly relevant for our country's national security. Indonesia, Thailand and the Philippines are all countries that are important to Australia not just because of geographic proximity, but also because of a history of economic ties and the role these countries play as Australia’s regional partners...

Wetlands : a perspective for mapping, monitoring, and modeling

Experience gained from numerous projects conducted by the U.S. Environmental Protection Agency's (EPA) Environmental Monitoring Systems Laboratory in Las Vegas, Nevada has provided insight to functional issues of mapping, monitoring, and modeling of wetland habitats. Three case studies in poster form describe these issues pertinent to managing wetland resources as mandated under Federal laws. A multiphase project was initiated by the EPA Alaska operations office to provide detailed wetland mapping of arctic plant communities in an area under petroleum development pressure. Existing classification systems did not meet EPA needs. Therefore a Habitat Classification System (HCS) derived from aerial photography was compiled. In conjunction with this photointerpretive keys were developed. These products enable EPA personnel to map large inaccessible areas of the arctic coastal plain and evaluate the sensitivity of various wetland habitats relative to petroleum development needs.

Parallel Signcryption

The primary motivation for signcryption was the gain in efficiency when both encryption and signing need to be performed. These two cryptographic operations may be done sequentially either by first encrypt and then sign (EtS) or alternatively, by first sign and then encrypt (StE). Further gains in efficiency can be achieved if encryption and signature are carried out in parallel (E&S). More importantly, however, is that these efficiency gains are complemented by gains in security, i.e., we may use relative weak encryption and signature schemes in order to obtain a “stronger” signcryption scheme. The reader is referred to Chaps. 2 and 3 for a discussion of the different “strengths” of security model (e.g., outsider vs. insider adversaries, two-user vs. multi-user setting).

Part 1 MOO methods for multidisciplinary design using parallel evolutionary algorithms, game theory and hierarchical topology. (Part 1.) Theoretical aspects

Two lecture notes describe recent developments of evolutionary multi objective optimization (MO) techniques in detail and their advantages and drawbacks compared to traditional deterministic optimisers. The role of Game Strategies (GS), such as Pareto, Nash or Stackelberg games as companions or pre-conditioners of Multi objective Optimizers is presented and discussed on simple mathematical functions in Part I , as well as their implementations on simple aeronautical model optimisation problems on the computer using a friendly design framework in Part II. Real life (robust) design applications dealing with UAVs systems or Civil Aircraft and using the EAs and Game Strategies combined material of Part I & Part II are solved and discussed in Part III providing the designer new compromised solutions useful to digital aircraft design and manufacturing. Many details related to Lectures notes Part I, Part II and Part III can be found by the reader in [68].

Thermodynamic modeling of ethanol fumigation in a diesel engine

Due to rapidly diminishing international supplies of fossil fuels, such as petroleum and diesel, the cost of fuel is constantly increasing, leading to higher costs of living, as a result of the significant reliance of many industries on motor vehicles. Many technologies have been developed to replace part or all of a fossil fuel with bio-fuels. One of the dual fuel technologies is fumigation of ethanol in diesel engines, which injects ethanol into the intake air stream of the engine. The advantage of this is that it avoids any costly modification of the engine high pressure diesel injection system, while reducing the volume of diesel required and potentially increasing the power output and efficiency. This paper investigates the performance of a diesel engine, converted to implement ethanol fumigation. The project will use both existing experimental data, along with generating computer modeled results using the program AVL Boost. The data from both experiments and the numerical simulation indicate desirable results for the peak pressure and the indicated mean effective pressure (IMEP). Increase in ethanol substitution resulted in elevated combustion pressure and an increase in the IMEP, while the variation of ethanol injection location resulted in negligible change. These increases in cylinder pressure led to a higher work output and total efficiency in the engine as the ethanol substitution was increased. In comparing the numerical and experimental results, the simulation showed a slight elevation, due to the inaccuracies in the heat release models. Future work is required to improve the combustion model and investigate the effect of the variation of the location of ethanol injection.