As congestion management strategies begin to put more emphasis on person trips than vehicle trips, the need for vehicle occupancy data has become more critical. The traditional methods of collecting these data include the roadside windshield method and the carousel method. These methods are labor-intensive and expensive. An alternative to these traditional methods is to make use of the vehicle occupancy information in traffic accident records. This method is cost effective and may provide better spatial and temporal coverage than the traditional methods. However, this method is subject to potential biases resulting from under- and over-involvement of certain population sectors and certain types of accidents in traffic accident records. In this dissertation, three such potential biases, i.e., accident severity, driver¡¯s age, and driver¡¯s gender, were investigated and the corresponding bias factors were developed as needed. The results show that although multi-occupant vehicles are involved in higher percentages of severe accidents than are single-occupant vehicles, multi-occupant vehicles in the whole accident vehicle population were not overrepresented in the accident database. On the other hand, a significant difference was found between the distributions of the ages and genders of drivers involved in accidents and those of the general driving population. An information system that incorporates adjustments for the potential biases was developed to estimate the average vehicle occupancies (AVOs) for different types of roadways on the Florida state roadway system. A reasonableness check of the results from the system shows AVO estimates that are highly consistent with expectations. In addition, comparisons of AVOs from accident data with the field estimates show that the two data sources produce relatively consistent results. While accident records can be used to obtain the historical AVO trends and field data can be used to estimate the current AVOs, no known methods have been developed to project future AVOs. Four regression models for the purpose of predicting weekday AVOs on different levels of geographic areas and roadway types were developed as part of this dissertation. The models show that such socioeconomic factors as income, vehicle ownership, and employment have a significant impact on AVOs.

<p> Traffic operations can be described by the relationship that occurs between flow and occupancy. This paper investigates the flow-occupancy relationship of an Ontario system and a Minnesota system to see if the same general relationship occurs in different locations. It is hoped that this investigation will help to further the work being done at McMaster in developing a new incident detection algorithm.</p> <p> In comparing the two data sets, simple analytical procedures were employed to compare the full data set, the uncongested regime, and the calculated fitted lines for the uncongested data. Visual comparison was the basis for much of the analysis.</p> <p> When the comparisons were conducted the relationships were indeed very similar, signifying that the flow-occupancy relationship is the same for different locations.</p> / Thesis / Candidate in Philosophy

No description available.

Large carnivores play many vital biological, economic, and conservation roles, however, their biological traits (low population densities, cryptic behavior) make them difficult to monitor. Pumas have been particularly difficult to study because the lack of distinctive markings on their coats prevents individual identification, precluding mark-recapture and other similar analyses. Further, compared to temperate areas, research on the interspecific interactions of Central American felids is particularly lacking. I used single- and multi-season, single-species occupancy models and two-species co-occurrence models to analyze camera trapping and habitat data collected at eight study sites across Belize. Puma occupancy was positively influenced by jaguar trap success, understory density, canopy cover, and human trap success, and negatively influenced by stream density. Jaguar trap success was the best predictor of where pumas occurred, while prey species were not found to influence puma occupancy. Mean occupancy was 0.740 (0.013) and ranged from 0.587 (0.042) to 0.924 (0.030). Over time, puma occupancy rates were generally high (> 0.90) and stable. Puma occupancy was higher in logged areas, suggesting that current levels of natural resource extraction at those sites were not detrimental to the species. Co-occurrence modeling showed little evidence for interactions between the carnivores, suggesting that jaguars may be acting as an umbrella species and that conservation efforts directed at jaguars are likely to benefit the other carnivores, including pumas. Overall, these findings are positive for puma conservation, but human-induced land use change is expanding and further monitoring will give us insight into how pumas respond to human encroachment. / Master of Science

With roughly about half of the energy used in buildings attributed to Heating, Ventilation, and Air conditioning (HVAC) systems, there is clearly great potential for energy saving through improved building operations. Accurate knowledge of localised and real-time occupancy numbers can have compelling control applications for HVAC systems. However, existing technologies applied for building occupancy measurements are limited, such that a precise and reliable occupant count is difficult to obtain. For example, passive infrared (PIR) sensors commonly used for occupancy sensing in lighting control applications cannot differentiate between occupants grouped together, video sensing is often limited by privacy concerns, atmospheric gas sensors (such as CO2 sensors) may be affected by the presence of electromagnetic (EMI) interference, and may not show clear links between occupancy and sensor values. Past studies have indicated the need for a heterogeneous multi-sensory fusion approach for occupancy detection to address the short-comings of existing occupancy detection systems. The aim of this research is to develop an advanced instrumentation strategy to monitor occupancy levels in non-domestic buildings, whilst facilitating the lowering of energy use and also maintaining an acceptable indoor climate. Accordingly, a novel multi-sensor based approach for occupancy detection in open-plan office spaces is proposed. The approach combined information from various low-cost and non-intrusive indoor environmental sensors, with the aim to merge advantages of various sensors, whilst minimising their weaknesses. The proposed approach offered the potential for explicit information indicating occupancy levels to be captured. The proposed occupancy monitoring strategy has two main components; hardware system implementation and data processing. The hardware system implementation included a custom made sound sensor and refinement of CO2 sensors for EMI mitigation. Two test beds were designed and implemented for supporting the research studies, including proof-of-concept, and experimental studies. Data processing was carried out in several stages with the ultimate goal being to detect occupancy levels. Firstly, interested features were extracted from all sensory data collected, and then a symmetrical uncertainty analysis was applied to determine the predictive strength of individual sensor features. Thirdly, a candidate features subset was determined using a genetic based search. Finally, a back-propagation neural network model was adopted to fuse candidate multi-sensory features for estimation of occupancy levels. Several test cases were implemented to demonstrate and evaluate the effectiveness and feasibility of the proposed occupancy detection approach. Results have shown the potential of the proposed heterogeneous multi-sensor fusion based approach as an advanced strategy for the development of reliable occupancy detection systems in open-plan office buildings, which can be capable of facilitating improved control of building services. In summary, the proposed approach has the potential to: (1) Detect occupancy levels with an accuracy reaching 84.59% during occupied instances (2) capable of maintaining average occupancy detection accuracy of 61.01%, in the event of sensor failure or drop-off (such as CO2 sensors drop-off), (3) capable of utilising just sound and motion sensors for occupancy levels monitoring in a naturally ventilated space, (4) capable of facilitating potential daily energy savings reaching 53%, if implemented for occupancy-driven ventilation control.

International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / This paper will present and compare several definitions of telemetry radio frequency (RF) signal bandwidth. Measured spectra for different signals will be presented. The bandwidths of these signals will then be determined and measurement methods will be discussed. This discussion will include the effects of spectrum analyzer resolution bandwidth, video bandwidth and detector type. Finally, a proposed spectral mask will be discussed. This spectral mask can be used to calculate the required attenuation for a given frequency offset from the center frequency. The required attenuation is a function of the the bit rate or maximum frequency of interest and the transmitter power. This spectral mask is proposed to be part of the next edition of the Telemetry Standards, Inter-Range Instrumentation Group (IRIG) Standard 106.

No description available.

International Telemetering Conference Proceedings / October 17-20, 1994 / Town & Country Hotel and Conference Center, San Diego, California / The bit rates of telemetry systems are increasing rapidly. Higher bit rates occupy more spectra and result in decreased link margin. The major signal parameters that affect the spectral occupancy and bit error probability (BEP) of binary pulse code modulation (PCM)/frequency modulation (FM) signals are the bit rate, code, premodulation filter, and peak deviation. The measured spectral occupancy is also affected by the spectrum analyzer (or other measurement equipment) settings. Additional parameters that affect the BEP include the receiver intermediate frequency (IF) filter, the FM demodulator, and the bit detector. This paper will present the effects of these parameters on the measured 99% and -60 dBc bandwidths and the BEP of binary PCM/FM telemetry signals. Normalized BEP and bandwidth curves will be presented.

The research presented in this thesis focuses on the investigation of whether the compartmental flow models of bed occupancy originally described by Harrison and Millard (1991) for decision-making around geriatric service care in the English National Health Service can be used to describe data from acute care hospitals in Australia and New Zealand. Australia’s total health expenditure for 2004-05 was $87.3 billion. The use of health care services and expenditure pattern is well established and Australia follows the pattern found in most developed countries, with the greatest expenditure occurring on services for the elderly. Australia is experiencing a shift in population structure, with the proportion of older people forecast to increase. It is expected there will be a need for a greater level of expenditure on health care as the number of elderly people increase. There is an emerging gap between the ability to supply health services and the demand for them. Furthermore, acute care hospital treatment is generally considered expensive and governments have been keen to control this expenditure. It is imperative that governments are able to make decisions based upon robust policy advice. There are serious consequences in both economic resource allocation and patient (and population) health outcomes if decisions about future health service structures are incorrect. In particular, there is a need for better decision-making around bed management at the strategic level. Strategic decision-making relates to decisions that will occur in a longer time frame. Decision-making can benefit from the use of modelling. Models represent a simplified version of reality that preserve the essential features of the situation being examined and can be used as a tool to investigate decision-making options, particularly in complex environments such as the health sector. Historically decision-making relating to hospital beds has used either simple “back of the envelope” calculations or adherence to “rule of thumb” approaches. Most of the approaches have relied upon using the average length of stay metric. While the modelling of hospital bed numbers is not new, much of this work has relied upon the average length of stay, which is known to be a poor measure. Harrison and Millard (1991) introduced the application of the compartmental flow model for modelling hospital bed occupancy and noted its potential to be used to influence policy decision-making. The flow model results are plausible and easily interpreted. However, relatively little work has focussed on the ability of these models to be generalized and be used for predictive purposes. The research undertaken for this thesis consisted of a series of modelling experiments that can be grouped into two key stages: whether the models could be successfully applied to the acute care data; and whether the models could be used for novel purposes, such as forecasting, evaluation of service change, and benchmarking. This entailed the further development of the model, and a consideration of basic modelling issues such as the balance between data-fit and model complexity, in order to capture better variation in the data and also to facilitate linkage to changes in population and seasonality. / http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1301698 / Thesis(Ph.D.) -- School of Psychology, 2007

High Occupancy Vehicle (HOV) lanes are employed in many cities as a traffic congestion mitigation technique. These lanes are intended to provide a travel time benefit to carpools and buses, thus providing an incentive to form carpools or take transit. The time and frustration involved in finding and using HOV lane access points may act as a deterrent to HOV lane use. Thus, proper planning of HOV lane access may be able to improve convenience for potential users, increasing HOV lane utilization. By optimizing HOV lane volumes, high levels of service and trip reliability are ensured for those who carpool. Congestion is also reduced on General Purpose Lanes (GPLs) due to the diverted traffic. Public approval is tied to HOV lane utilization, which may be affected by access. Thus, HOV lane success may be determined in part by accessibility. This research investigated the possible relationship between the time required to access the HOV lane and travelers' choice of HOV lane as their mode. Additionally, the distance to HOV lane access points and the type of access point used were examined for their influence on HOV lane use rates. It was concluded that neither the type of HOV lane access point, nor the added time necessary to access the HOV lane were significant factors in HOV lane use rates. Instead, the convenience of carpool formation and the convenience of HOV lane access to traveler's origins and destinations were found to be the most important factors in HOV lane use rates. Specifically, the HOV lane use rate for the area 5 to 7 miles from the nearest HOV lane exit, which includes Downtown Houston, was much higher than the use rates for any other area.