Road/Weather Information System Architecture for Integration and Expansion

Kwon, Taek Mu; Fleege, E J

doi:10.3141/1700-01

Cited by 4 publications

(2 citation statements)

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“…Meteorological conditions are observed along highways through various Road Weather Information Systems (RWIS) (Kwon and Fleege ). With the ability to monitor pavement temperature (i.e.…”

Section: Meteorological Observing Networkmentioning

confidence: 99%

On the Availability of High‐Resolution Data for Near‐Surface Climate Analysis in the Continental U.S.

Oswald

Dupigny‐Giroux

2015

Geography Compass

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Increased recognition of potential future climate changes has sparked an urgency in understanding the various linkages among weather, climate and sectors of society. Furthermore, the need for improving climate models is also paramount. Meteorological observations are critical for accomplishing both. A deeper knowledge of the origin, nuances and availability of observation-based climate data can lead to better analyses and validations. In this article, we review the suite of current meteorological observing networks across the continental U.S. In addition, we discuss both the statistical procedures that are used to construct climate data products from observing records, and the climate data products themselves. The focus here is on datasets with daily to hourly time steps, with an emphasis on gridded climate data products of spatial resolutions under 15 km. Ultimately, this paper is geared towards climatologists as well as those outside of the climate field, who need data to empower near-surface atmospheric science research at the local scale. Presently, new observing networks supported by massive volunteer communities are potential game changers, yet the public still lacks both daily precipitation data suitable for high-resolution and/or long-term analyses and rigorous validation of high-resolution gridded climate data products.

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“…Meteorological conditions are observed along highways through various Road Weather Information Systems (RWIS) (Kwon and Fleege ). With the ability to monitor pavement temperature (i.e.…”

Section: Meteorological Observing Networkmentioning

confidence: 99%

On the Availability of High‐Resolution Data for Near‐Surface Climate Analysis in the Continental U.S.

Oswald

Dupigny‐Giroux

2015

Geography Compass

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“…Drivers are very interested in the degree of road surface slipperiness. Advanced traffic and weather information systems indicate current road weather and traffic conditions (1)(2)(3). Air and road surface temperatures are the most important factors in such an information system.…”

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confidence: 99%

Extracting Slipperiness Component from Weather and Traffic Data for Winter Maintenance Operations

Nakatsuji

Hamada

Kawamura

2002

Transportation Research Record

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Although traffic and weather information systems indicate existing air and road surface temperatures, many drivers want to know not the temperature but the degree of road slipperiness. The friction coefficient is the best index for snow and ice conditions, but it is difficult to calculate. Some weather condition data are closely correlated with the friction coefficient. In this study, the use of weather and traffic data to determine the degree of road slipperiness was examined quantitatively by analyzing field data obtained at an intersection on a trunk line. Two kinds of filters were effective in extracting the slipperiness component from the observed data—the Kohonen Feature Map (KFM) and principal component analysis (PCA). KFM kept the distribution of the observed data uniform by eliminating excessive data, and PCA eliminated multicollinearity among the observed variables. The first two principal components successfully represented the original observed data with an accumulated proportion of more than 0.85. A linear multiple regression model, in which the PCA score values were adopted as the explanatory variable, was also justified. The multiple correlation coefficient was satisfactory for many patterns, and the radiation-related data were effective in improving estimate precision.

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On-Street and Off-Street Parking Availability Prediction Using Multivariate Spatiotemporal Models

Rajabioun

Ioannou

2015

IEEE Trans. Intell. Transport. Syst.

157

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Parking guidance and information (PGI) systems are becoming important parts of intelligent transportation systems due to the fact that cars and infrastructure are becoming more and more connected. One major challenge in developing efficient PGI systems is the uncertain nature of parking availability in parking facilities (both on-street and off-street). A reliable PGI system should have the capability of predicting the availability of parking at the arrival time with reliable accuracy. In this paper, we study the nature of the parking availability data in a big city and propose a multivariate autoregressive model that takes into account both temporal and spatial correlations of parking availability. The model is used to predict parking availability with high accuracy. The prediction errors are used to recommend the parking location with the highest probability of having at least one parking spot available at the estimated arrival time. The results are demonstrated using real-time parking data in the areas of San Francisco and Los Angeles.

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Road/Weather Information System Architecture for Integration and Expansion

Cited by 4 publications

References 0 publications

On the Availability of High‐Resolution Data for Near‐Surface Climate Analysis in the Continental U.S.

On the Availability of High‐Resolution Data for Near‐Surface Climate Analysis in the Continental U.S.

Extracting Slipperiness Component from Weather and Traffic Data for Winter Maintenance Operations

On-Street and Off-Street Parking Availability Prediction Using Multivariate Spatiotemporal Models

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