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.

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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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This paper presents a model based fault detection and exclusion scheme that implements a decision logic to automatically identify faulty or mislocated freeway traffic sensors in the presence of unknown on-ramp and off-ramp flows. The algorithm is deployed within the framework of a suite of software tools, named TOPl, which models traffic flow via a macroscopic model, calibrates the model based on available data and runs simulations to evaluate various operational strategies such as ramp metering, demand management, incident management, etc. TOPl has been used to model various freeways in California, such as Interstate 80, Interstate 210, Interstate 880 and Interstate 680. Two main difficulties with data collection on California freeways were found to be missing ramp flow and faulty mainline data, which decrease the accuracy of the model and increase the time and effort invested in model calibration. The former of these difficulties has been previously addressed by an iterative learning algorithm that estimates the missing ramp flows and the latter is tackled by the method presented in this work.