Sophisticated electronics are within reach of average users. Cooperation between wireless sensor networks and existing consumer electronic infrastructures can assist in the areas of health care and patient monitoring. This will improve the quality of life of patients, provide early detection for certain ailments, and improve doctor-patient efficiency. The goal of our work is to focus on health-related applications of wireless sensor networks. In this paper we detail our experiences building several prototypes and discuss the driving force behind home health monitoring and how current (and future) technologies will enable automated home health monitoring.
Freeway traffic simulations must account for the probabilistic nature of model parameters to capture observed variations in traffic behavior. Fundamental diagrams specify freeway section parameters describing the flow–density relationship in macroscopic simulation models. A triangular fundamental diagram—specified with the free-flow speed, congestion wave speed, and capacity—is commonly adopted in first-order cell transmission models. Capacity (defined as the maximum flow observed in a given freeway section over a particular day) exhibits significant day-today variation, and capacity variations across different sections of the freeway are significantly correlated. Free-flow speeds do not exhibit significant variation, but congestion wave speeds exhibit variation uncorrelated with section capacities or parameters from other sections. A probabilistic graphical approach is presented to model the probabilistic distribution of fundamental diagram parameters of an entire freeway section chosen for simulation. More than 1 year of data from dozens of loop detectors along a 25-mi section of the I-210 freeway westbound in Los Angeles, California, are used for demonstration. The parameters of the distribution are estimated with the expectation–maximization algorithm to account for missing observations. Model selection from among plausible models indicates that a first-order spatial Markov model is appropriate to capture the capacity distribution, which is the joint probability distribution of freeway section capacities. Stochastic simulations with sampled parameters demonstrate that capacity variations can lead to significant variations in congestion patterns and freeway performance.
This paper illustrates the macroscopic modeling and simulation of Interstate 80 Eastbound Freeway in the Bay Area. Traffic flow and occupancy data from loop detectors are used for calibrating the model and specifying the inputs to the simulation. The freeway is calibrated based on the Link-Node Cell Transmission Model and missing ramp flow data are estimated using an iterative learning-based imputation scheme. An adhoc, graphical comparison-based fault detection scheme is used to identify faulty measurements. The simulation results using the calibrated model exhibit good agreement with loop detector measurements with total density error of 3.3% and total flow error of 7.1% over the 23 mile stretch of the freeway under investigation and the particular day for which the ramp flows were imputed.
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