861 research outputs found
Statistical mechanics of non-hamiltonian systems: Traffic flow
Statistical mechanics of a small system of cars on a single-lane road is
developed. The system is not characterized by a Hamiltonian but by a
conditional probability of a velocity of a car for the given velocity and
distance of the car ahead. Distribution of car velocities for various densities
of a group of cars are derived as well as probabilities of density fluctuations
of the group for different velocities. For high braking abilities of cars
free-flow and congested phases are found. Platoons of cars are formed for
system of cars with inefficient brakes. A first order phase transition between
free-flow and congested phase is suggested.Comment: 12 pages, 6 figures, presented at TGF, Paris, 200
Calibrating Car-Following Models using Trajectory Data: Methodological Study
The car-following behavior of individual drivers in real city traffic is
studied on the basis of (publicly available) trajectory datasets recorded by a
vehicle equipped with an radar sensor. By means of a nonlinear optimization
procedure based on a genetic algorithm, we calibrate the Intelligent Driver
Model and the Velocity Difference Model by minimizing the deviations between
the observed driving dynamics and the simulated trajectory when following the
same leading vehicle. The reliability and robustness of the nonlinear fits are
assessed by applying different optimization criteria, i.e., different measures
for the deviations between two trajectories. The obtained errors are in the
range between~11% and~29% which is consistent with typical error ranges
obtained in previous studies. In addition, we found that the calibrated
parameter values of the Velocity Difference Model strongly depend on the
optimization criterion, while the Intelligent Driver Model is more robust in
this respect. By applying an explicit delay to the model input, we investigated
the influence of a reaction time. Remarkably, we found a negligible influence
of the reaction time indicating that drivers compensate for their reaction time
by anticipation. Furthermore, the parameter sets calibrated to a certain
trajectory are applied to the other trajectories allowing for model validation.
The results indicate that ``intra-driver variability'' rather than
``inter-driver variability'' accounts for a large part of the calibration
errors. The results are used to suggest some criteria towards a benchmarking of
car-following models
Micro-costing study of rituximab subcutaneous injection versus intravenous infusion in dutch setting
A framework for applying natural language processing in digital health interventions
BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making.
OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes.
METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model.
RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms.
CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts
- …