The operation of most signalized intersections is governed by predefined timing plans that are applied during specified times of the day. These plans are designed to accommodate average conditions and are unable to respond to large deviations in traffic flow. We propose a control approach that adjusts time-of-day signaling plans based on a prediction of future traffic flow. The prediction algorithm identifies correlated, low rank structure in historical measurement data and predicts future traffic flow from real-time measurements by determining which structural trends are prominent in the measurements. From this prediction, the controller then determines the optimal time of day to apply new timing plans. We demonstrate the potential benefits of this approach using eight months of high resolution data collected at an intersection in Beaufort, South Carolina.
Truck weight data plays an important role in weight enforcement and pavement condition assessment. This data is primarily obtained through weigh stations and Weigh‐In‐Motion (WIM) stations which are currently very expensive to install and maintain. This article presents results of the implementation of an inexpensive wireless sensor‐based vibration WIM system. The proposed wireless sensor network (WSN) consists of acceleration sensors that report pavement vibration; vehicle detection sensors that report a vehicle's arrival and departure times; and an access point (AP) that synchronizes all the sensors and records the sensor data. The article also describes a new method for speed compensation, an energy‐efficient algorithm (adaptive sampling method) to increase battery life, and a new modeling procedure to estimate gross vehicle weights. The system deployed near a conventional WIM system on I‐80W in Pinole, CA passed the accuracy standards for WIM systems and outperformed a nearby commercial WIM station, based on conventional technology.
A high-resolution (HR) data system for an intersection collects the location (lane), speed, and turn movement of every vehicle as it enters an intersection, together with the signal phase. Some systems also provide video monitoring; others measure pedestrian and bicycle movements; and some have vehicle to infrastructure (V2I) communication capability. The data are available in real time and archived. Real time data are used to implement signal control. Archived data are used to evaluate intersection, corridor, and network performance. The system operates 24 × 7. Uses of a HR data system for assessing intersection performance and improving mobility and safety are discussed. Mobility applications include evaluation of intersection performance, and the design of better signal control. Safety applications include estimates of dilemma zones, red-light violations, and pedestrian-vehicle conflicts.
High accuracy text classifiers are used nowadays in organizing large amounts of biomedical information and supporting clinical decision-making processes. In medical informatics, regular expressionbased classifiers have emerged as an alternative to traditional, discriminative classification algorithms due to their ability to model sequential patterns. This article presents CREGEX (Classifier Regular Expression), a biomedical text classifier based on an automatically generated regular-expressions-based feature space. We conceived an algorithm for automatically constructing an informative and discriminative regularexpressions-based feature space, suitable for binary and multiclass discrimination problems. Regular expressions are automatically generated from training texts using a coarse-to-fine text aligning method, which trades off the lexical variants of words, in terms of gender and grammatical number, and the generation of a feature space containing a large number of noisy features. CREGEX carries out feature selection by filtering keywords and also computes a confidence metric to classify test texts. Three de-identified datasets in Spanish, with information on smoking habits, obesity, and obesity types, were used here to assess the performance of CREGEX. For comparison, Support Vector Machine (SVM) and Naïve Bayes (NB) supervised classifiers were also trained with consecutive sequences of tokens (n-grams) as features. Results show that, in all the datasets used for evaluation, CREGEX not only outperformed both the SVM and NB classifiers in terms of accuracy and F-measure (p-value<0.05) but also used a fewer amount of training examples to achieve the same performance. Such a superior performance is attributed to the regular expressions' ability to represent complex text patterns.
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