This study demonstrated a relationship between care given according to Watson's Caring model and increased quality of life of the patients with hypertension. Further, in those patients for whom the caring model was practised, there was a relationship between the Caring model and a decrease in patient's blood pressure. The Watson Caring Model is recommended as a guide to nursing patients with hypertension, as one means of decreasing blood pressure and increase in quality of life.
Vehicle classification is one of the most essential aspects of highway performance monitoring as vehicle classes are needed for various applications including freight planning and pavement design. While most of the existing systems use in-pavement sensors to detect vehicle axles and lengths for classification, researchers have also explored traditional approaches for imagebased vehicle classification which tend to be computationally expensive and typically require a large amount of data for model training. As an alternative to these image-based methods, this paper investigates whether it is possible to transfer the learning (or parameters) of a highly accurate pre-trained (deep neural network) model for classifying truck images generated from 3D-point cloud data from a LiDAR sensor. In other words, without changing the parameters of several well-known convolutional neural networks (CNNs), such as AlexNet, VggNet and ResNet, this paper shows how they can be adopted to extract the needed features to classify trucks, in particular trucks with different types of trailers. This paper demonstrates the applicability of these CNNs for solving the vehicle classification problem through an extensive set of experiments conducted on images created based on data from a LIDAR sensor. Results show that using pre-trained CNN models to extract low-level features within images yield significantly accurate results, even with a relatively small size of training data that are needed for the classification step at the end.
Based on a three-month toll transaction data set that includes an anonymized unique identifier for each vehicle, this paper presents an in-depth analysis of traffic volumes and tolls on the I-66 High-Occupancy Toll (HOT) express lanes in Northern Virginia. The unique identifiers allow quantification of how frequently each vehicle travels through the corridor. Vehicles observed in selected time intervals are categorized into frequent and non-frequent groups based on the total number of trips made by each vehicle. For the morning commute, the analyses show that those traveling frequently on the HOT lanes are more sensitive to high tolls and typically travel earlier in the morning to avoid higher tolls. In other words, when tolls are relatively high (e.g., over $20), the fraction of frequent users in the traffic is much smaller as compared with that of non-frequent users (e.g., 25% versus 75%). To estimate how much toll the HOT-lane users are paying per unit of travel time saved, that is, value of travel time saving (VTTS), speeds on alternative routes parallel to the I-66 corridor are computed from probe data and compared with those on I-66 express lanes. The results show that the mean VTTS is $45.37 and $61.78 for frequent and non-frequent users, respectively, during the morning peak period. Whereas for the afternoon peak, the mean VTTS is $38.14 and $37.64 for frequent and non-frequent users. The implications of the difference in these value of time distributions for dynamic tolling are discussed.
As the number of variables and entities included in a simulation model increase, it becomes more difficult to initialize due to (a) the increasing number of input variables that are required and (b) the difficulty in finding, retrieving, and assigning the initial values of the input variables, especially in Human Social Cultural Behavior Modeling. As a result, the initialization process is generally more time consuming and error prone which motivates the need for semi-automated approaches wherever possible. In this paper, we propose a semi-automated approach for initializing input variables that are challenging to quantify and require additional processing to be assigned their initial values. We apply this approach to initialize a healthcare simulation using data from sources such as the US Census Bureau, County Health Rankings, and Twitter. Results show that the approach works well when we can find variables with existing values even for large input data sets (over 50 variables). However, additional work is required to determine whether the values assigned using this approach yield more accurate simulation results.
For the past few years, several studies have focused on identifying a vehicle’s trajectory with smartphone data. However, these studies predominantly used GPS coordinate information for that purpose. Considering the known limitations of GPS, such as connectivity issues at urban canyons and underpasses, low precision of localization, and high power consumption of smartphones while GPS is in use, this paper focuses on developing alternative methods for identifying a vehicle’s trajectory at an intersection with sensor data other than GPS to minimize GPS dependency. In particular, accelerometer and gyroscope data collected with smartphone inertial sensors and speed data collected with an onboard diagnostics device are used to develop algorithms for maneuver (i.e., left and right turns and through) and trip direction identification at an intersection. In addition, techniques for noise removal and orientation correction from raw inertial sensor data are described. The effectiveness of the method for trajectory identification is assessed with collected field data. Results demonstrate that the developed method is effective in identifying a vehicle’s trajectory at an intersection. Overall, this research shows the feasibility of using alternative sensor data for trajectory identification and thus eliminating the need for continuous GPS connectivity.
Drone delivery, once thought of as fictitious, is becoming a reality with the efforts of both forward-looking enterprises and supportive government policies. This emerging mode of e-commerce delivery raises many concerns. One important concern is the energy efficiency of direct delivery drones compared with conventional delivery trucks at a regional systems level. In this study, we develop and apply methods to quantify the regional energy impacts of drone delivery, then we assess these impacts and compare them with the impacts of truck delivery. To study this problem, we develop an optimization model that determines an optimal set of fulfillment centers (FCs) with variable service capacities that allow drones to make direct e-commerce deliveries. We adopt two drone delivery energy estimation models from the literature and use them as inputs to demonstrate the potential range of energy needs. We also develop another optimization model to account for the energy consumption of diesel trucks (DTs) and battery electric vehicles (BEVs). We test the models using validated simulation data for the Chicago metropolitan area in the U.S. to quantify the energy implications of these three delivery modes. For drone delivery, we further extend our analyses by considering the impact of wind speed and flight patterns. Our results show that direct delivery drones require 15.8% more energy than BEVs on an average windy day, and they need 15% more energy than DTs on a very windy day. We provide essential parameter values for reproducibility and list relevant open problems.
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