Spatial-temporal mining of archived automatic vehicle location (AVL) data for transit service planning applications

An Automatic Vehicle Location (AVL) system is a computer-based vehicle tracking system that is capable of determining a vehicle's location in real time. As a major technology of the Advanced Public Transportation System (APTS), AVL systems have been widely deployed by transit agencies for purposes such as real-time operation monitoring, computer-aided dispatching, and arrival time prediction. AVL systems make a large amount of transit performance data available that are valuable for transit performance management and planning purposes. However, the difficulties of extracting useful information from the huge spatial-temporal database have hindered off-line applications of the AVL data. ^ In this study, a data mining process, including data integration, cluster analysis, and multiple regression, is proposed. The AVL-generated data are first integrated into a Geographic Information System (GIS) platform. The model-based cluster method is employed to investigate the spatial and temporal patterns of transit travel speeds, which may be easily translated into travel time. The transit speed variations along the route segments are identified. Transit service periods such as morning peak, mid-day, afternoon peak, and evening periods are determined based on analyses of transit travel speed variations for different times of day. The seasonal patterns of transit performance are investigated by using the analysis of variance (ANOVA). Travel speed models based on the clustered time-of-day intervals are developed using important factors identified as having significant effects on speed for different time-of-day periods. ^ It has been found that transit performance varied from different seasons and different time-of-day periods. The geographic location of a transit route segment also plays a role in the variation of the transit performance. The results of this research indicate that advanced data mining techniques have good potential in providing automated techniques of assisting transit agencies in service planning, scheduling, and operations control. ^

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Resources, strategies, location determinants, and host country location choice by emerging market firms

The extant literature had studied the determinants of the firms’ location decisions with help of host country characteristics and distances between home and host countries. Firm resources and its internationalization strategies had found limited attention in this literature. To address this gap, the research question in this dissertation was whether and how firms’ resources and internationalization strategies impacted the international location decisions of emerging market firms. ^ To explore the research question, data were hand-collected from Indian software firms on their location decisions taken between April 2000 and March 2009. To analyze the multi-level longitudinal dataset, hierarchical linear modeling was used. The results showed that the internationalization strategies, namely market-seeking or labor-seeking had direct impact on firms’ location decision. This direct relationship was moderated by firm resource which, in case of Indian software firms, was the appraisal at CMMI level-5. Indian software firms located in developed countries with a market-seeking strategy and in emerging markets with a labor-seeking strategy. However, software firms with resource such as CMMI level-5 appraisal, when in a labor-seeking mode, were more likely to locate in a developed country over emerging market than firms without the appraisal. Software firms with CMMI level-5 appraisal, when in market-seeking mode, were more likely to locate in a developed country over an emerging market than firms without the appraisal. ^ It was concluded that the internationalization strategies and resources of companies predicted their location choices, over and above the variables studied in the theoretical field of location determinants.^

A Markov chain location-allocation meta-model for hurricane relief

An emergency is a deviation from a planned course of events that endangers people, properties, or the environment. It can be described as an unexpected event that causes economic damage, destruction, and human suffering. When a disaster happens, Emergency Managers are expected to have a response plan to most likely disaster scenarios. Unlike earthquakes and terrorist attacks, a hurricane response plan can be activated ahead of time, since a hurricane is predicted at least five days before it makes landfall. This research looked into the logistics aspects of the problem, in an attempt to develop a hurricane relief distribution network model. We addressed the problem of how to efficiently and effectively deliver basic relief goods to victims of a hurricane disaster. Specifically, where to preposition State Staging Areas (SSA), which Points of Distributions (PODs) to activate, and the allocation of commodities to each POD. Previous research has addressed several of these issues, but not with the incorporation of the random behavior of the hurricane's intensity and path. This research presents a stochastic meta-model that deals with the location of SSAs and the allocation of commodities. The novelty of the model is that it treats the strength and path of the hurricane as stochastic processes, and models them as Discrete Markov Chains. The demand is also treated as stochastic parameter because it depends on the stochastic behavior of the hurricane. However, for the meta-model, the demand is an input that is determined using Hazards United States (HAZUS), a software developed by the Federal Emergency Management Agency (FEMA) that estimates losses due to hurricanes and floods. A solution heuristic has been developed based on simulated annealing. Since the meta-model is a multi-objective problem, the heuristic is a multi-objective simulated annealing (MOSA), in which the initial solution and the cooling rate were determined via a Design of Experiments. The experiment showed that the initial temperature (T0) is irrelevant, but temperature reduction (δ) must be very gradual. Assessment of the meta-model indicates that the Markov Chains performed as well or better than forecasts made by the National Hurricane Center (NHC). Tests of the MOSA showed that it provides solutions in an efficient manner. Thus, an illustrative example shows that the meta-model is practical.

Vegetation:environment relationships and water management in Shark Slough, Everglades National Park

The hydrologic regime of Shark Slough, the most extensive long hydroperiod marsh in Everglades National Park, is largely controlled by the location, volume, and timing of water delivered to it through several control structures from Water Conservation Areas north of the Park. Where natural or anthropogenic barriers to water flow are present, water management practices in this highly regulated system may result in an uneven distribution of water in the marsh, which may impact regional vegetation patterns. In this paper, we use data from 569 sampling locations along five cross-Slough transects to examine regional vegetation distribution, and to test and describe the association of marsh vegetation with several hydrologic and edaphic parameters. Analysis of vegetation:environment relationships yielded estimates of both mean and variance in soil depth, as well as annual hydroperiod, mean water depth, and 30-day maximum water depth within each cover type during the 1990’s. We found that rank abundances of the three major marsh cover types (Tall Sawgrass, Sparse Sawgrass, and Spikerush Marsh) were identical in all portions of Shark Slough, but regional trends in the relative abundance of individual communities were present. Analysis also indicated clear and consistent differences in the hydrologic regime of three marsh cover types, with hydroperiod and water depths increasing in the order Tall Sawgrass , Sparse Sawgrass , Spikerush Marsh. In contrast, soil depth decreased in the same order. Locally, these differences were quite subtle; within a management unit of Shark Slough, mean annual values for the two water depth parameters varied less than 15 cm among types, and hydroperiods varied by 65 days or less. More significantly, regional variation in hydrology equaled or exceeded the variation attributable to cover type within a small area. For instance, estimated hydroperiods for Tall Sawgrass in Northern Shark Slough were longer than for Spikerush Marsh in any of the other regions. Although some of this regional variation may reflect a natural gradient within the Slough, a large proportion is the result of compartmentalization due to current water management practices within the marsh.We conclude that hydroperiod or water depth are the most important influences on vegetation within management units, and attribute larger scale differences in vegetation pattern to the interactions among soil development, hydrology and fire regime in this pivotal portion of Everglades.

Location, Location, Location: Ensuring a Franchisee's Success

This article examines effective site selection methodologies and determines if good site selection is a science or something best left to luck. The article provides an overview of the current available literature on site selection and then explores three issues: the wrong way to select a site, sample cases of poor site selection, and effective site selection

Predicting delay reductions from freeway traffic diversion for incident management

Traffic incidents are a major source of traffic congestion on freeways. Freeway traffic diversion using pre-planned alternate routes has been used as a strategy to reduce traffic delays due to major traffic incidents. However, it is not always beneficial to divert traffic when an incident occurs. Route diversion may adversely impact traffic on the alternate routes and may not result in an overall benefit. This dissertation research attempts to apply Artificial Neural Network (ANN) and Support Vector Regression (SVR) techniques to predict the percent of delay reduction from route diversion to help determine whether traffic should be diverted under given conditions. The DYNASMART-P mesoscopic traffic simulation model was applied to generate simulated data that were used to develop the ANN and SVR models. A sample network that comes with the DYNASMART-P package was used as the base simulation network. A combination of different levels of incident duration, capacity lost, percent of drivers diverted, VMS (variable message sign) messaging duration, and network congestion was simulated to represent different incident scenarios. The resulting percent of delay reduction, average speed, and queue length from each scenario were extracted from the simulation output. The ANN and SVR models were then calibrated for percent of delay reduction as a function of all of the simulated input and output variables. The results show that both the calibrated ANN and SVR models, when applied to the same location used to generate the calibration data, were able to predict delay reduction with a relatively high accuracy in terms of mean square error (MSE) and regression correlation. It was also found that the performance of the ANN model was superior to that of the SVR model. Likewise, when the models were applied to a new location, only the ANN model could produce comparatively good delay reduction predictions under high network congestion level.

Occupant location prediction in smart buildings using association rule mining

Heating, ventilation, air conditioning (HVAC) systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. We present an approach to occupant location prediction based on association rule mining, allowing prediction based on historical occupant locations. Association rule mining is a machine learning technique designed to find any correlations which exist in a given dataset. Occupant location datasets have a number of properties which differentiate them from the market basket datasets that association rule mining was originally designed for. This thesis adapts the approach to suit such datasets, focusing the rule mining process on patterns which are useful for location prediction. This approach, named OccApriori, allows for the prediction of occupants’ next locations as well as their locations further in the future, and can take into account any available data, for example the day of the week, the recent movements of the occupant, and timetable data. By integrating an existing extension of association rule mining into the approach, it is able to make predictions based on general classes of locations as well as specific locations.

Optimal location of solid waste containers in Alcalá de Henares

The waste’s rise is a problem that affects the environment as a whole and we cannot forget about it. A good waste’s management is the key to improve the future prospect, and the waste collection is key within the management activities. To find out the better way to collect wastes leads to a reduction of the social, economic and environmental cost. With the use of the Geographic Information Systems it has been intended to elaborate a methodology which allowed us to identify the most suitable places for the location of the collection containers of the different sorts of the solid urban wastes. Taking into account that different types of wastes exist, not all of them should be managed in the same way. Therefore we have to differentiate between models where we apply efficiency and models where we apply equity for the collection of wastes, bearing in mind the necessities of each waste.

Highway Location Reference Procedure Project, 1990

The purpose of the proposed Highway Location Reference Procedure is stated in the contract as follows: "Establishment of a highway network locational reference process that will primarily allow for the proper correlation of pavement management data, and secondarily provide the basis for other existing and future data base integration and for the planned Iowa DOT Geographic Information System. In addition, the locational reference process will be able to correlate network applications with a statewide spatial location method to facilitate the relationship of Iowa DOT data to that of other agencies and to allow for the graphic display of the network in map form." The Design Specifications and Implementation Plan, included in this Final Report, are intended to provide the basis for proceeding with immediate development and implementation of the pavement management system. These specifications will also support the future Iowa DOT implementation of other integrated data bases and/or the planned Geographic Information System.

Dermoscopic variability of basal cell carcinoma according to clinical type and anatomic location

Background Correctly diagnosing basal cell carcinoma (BCC) clinical type is crucial for the therapeutic management. A systematic description of the variability of all reported BCC dermoscopic features according to clinical type and anatomic location is lacking. Objectives To describe the dermoscopic variability of BCC according to clinical type and anatomic location and to test the hypothesis of a clinical/dermoscopic continuum across superficial BCCs (sBCCs) with increasing palpability. Methods Clinical/dermoscopic images of nodular BCCs (nBCCs) and sBCCs with different degrees of palpability were retrospectively evaluated for the presence of dermoscopic criteria including degree of pigmentation, BCC-associated patterns, diverse vascular patterns, melanocytic patterns and polarized light patterns. Results We examined 501 histopathologically proven BCCs (66.9% sBCCs; 33.1% nBCCs), mainly located on trunk (46.7%; mostly sBCCs) and face (30.5%; mostly nBCCs). Short fine telangiectasias, leaf-like areas, spoke-wheel areas, small erosions and concentric structures were significantly associated with sBCC, whereas arborizing telangiectasias, blue-white veil-like structures, white shiny areas and rainbow pattern with nBCCs. Short fine telangiectasia, spoke-wheel areas and small erosions were independently associated with trunk location, whereas arborizing telangiectasias with facial location. Scalp BCCs had significantly more pigmentation and melanocytic criteria than BCCs located elsewhere. Multiple clinical/dermoscopic parameters displayed a significant linear trend across increasingly palpable sBCCs. Conclusions Particular dermoscopic criteria are independently associated with clinical type and anatomic location of BCC. Heavily pigmented, scalp BCCs are the most challenging to diagnose. A clinical/dermoscopic continuum across increasingly palpable sBCCs was detected and could be potentially important for the non-surgical management of the disease.

Aquatic weeds hotspots in the Lake Victoria basin Uganda and on Lake Kyoga: implications for their management

Proliferation of invasive aquatic weeds has developed into a major ecological and socio economic issue for many regions of the world. As a consequence, inference on where to target control and other management efforts is critical in the management of aquatic weeds (Ibáñez et al., 2009). Notwithstanding, aquatic systems in Uganda in general and in the basins of Lakes Victoria and Kyoga in particular, have fallen victims to aquatic weeds invasion and subsequent infestation. If these aquatic weeds infestations are to be minimized and their impacts mitigated, management decisions ought to be based on up-to-date data and information in relation to location of infestation hotspots. Aquatic systems in the basins of the two production systems are important sources of livelihoods especially from fish production and trade yet they are prone to infestation by aquatic weeds. Thus, the invasion and subsequent infestation of aquatic ecosystems by aquatic weeds pose a major conservation threat to various aquatic resources (Catford et al., 2011; Kayanja, 2002). This paper examines the extent to which aquatic weeds have infested aquatic ecosystems in the basins of Lakes Victoria and Kyoga. The information is expected to guide management of major aquatic weeds through rational allocation of the scarce resources by targeting hotspots.

Impact of Routine Fractional Flow Reserve Evaluation During Coronary Angiography on Management Strategy and Clinical Outcome: One-Year Results of the POST-IT.

Penetration of fractional flow reserve (FFR) in clinical practice varies extensively, and the applicability of results from randomized trials is understudied. We describe the extent to which the information gained from routine FFR affects patient management strategy and clinical outcome. METHODS AND RESULTS: Nonselected patients undergoing coronary angiography, in which at least 1 lesion was interrogated by FFR, were prospectively enrolled in a multicenter registry. FFR-driven change in management strategy (medical therapy, revascularization, or additional stress imaging) was assessed per-lesion and per-patient, and the agreement between final and initial strategies was recorded. Cardiovascular death, myocardial infarction, or unplanned revascularization (MACE) at 1 year was recorded. A total of 1293 lesions were evaluated in 918 patients (mean FFR, 0.81±0.1). Management plan changed in 406 patients (44.2%) and 584 lesions (45.2%). One-year MACE was 6.9%; patients in whom all lesions were deferred had a lower MACE rate (5.3%) than those with at least 1 lesion revascularized (7.3%) or left untreated despite FFR≤0.80 (13.6%; log-rank P=0.014). At the lesion level, deferral of those with an FFR≤0.80 was associated with a 3.1-fold increase in the hazard of cardiovascular death/myocardial infarction/target lesion revascularization (P=0.012). Independent predictors of target lesion revascularization in the deferred lesions were proximal location of the lesion, B2/C type and FFR. CONCLUSIONS: Routine FFR assessment of coronary lesions safely changes management strategy in almost half of the cases. Also, it accurately identifies patients and lesions with a low likelihood of events, in which revascularization can be safely deferred, as opposed to those at high risk when ischemic lesions are left untreated, thus confirming results from randomized trials.

Errors in preparation and medication management in nursing professionals

The preparation and administration of medications is one of the most common and relevant functions of nurses, demanding great responsibility. Incorrect administration of medication, currently constitutes a serious problem in health services, and is considered one of the main adverse effects suffered by hospitalized patients. Objectives: Identify the major errors in the preparation and administration of medication by nurses in hospitals and know what factors lead to the error occurred in the preparation and administration of medication. Methods: A systematic review of the literature. Deined as inclusion criteria: original scientiic papers, complete, published in the period 2011 to May 2016, the SciELO and LILACS databases, performed in a hospital environment, addressing errors in preparation and administration of medication by nurses and in Portuguese language. After application of the inclusion criteria obtained a sample of 7 articles. Results: The main errors identiied in the pr eparation and administration of medication were wrong dose 71.4%, wrong time 71.4%, 57.2% dilution inadequate, incorrect selection of the patient 42.8% and 42.8% via inadequate. The factors that were most commonly reported by the nursing staff, as the cause of the error was the lack of human appeal 57.2%, inappropriate locations for the preparation of medication 57.2%, the presence of noise and low brightness in preparation location 57, 2%, professionals untrained 42.8%, fatigue and stress 42.8% and inattention 42.8%. Conclusions: The literature shows a high error rate in the preparation and administration of medication for various reasons, making it important that preventive measures of this occurrence are implemented.

Addressing wildfire risk in forest management with a wildfire resistance indicator

Mestrado Mediterranean Forestry and Natural Resources Management - Instituto Superior de Agronomia - UL