Planning a road weather information system with GIS

Zhao, Liuhui; Chien, Steven; Liu, Xiaobo; Liu, Weisong

doi:10.1007/s40534-015-0076-0

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Variables such as rainfall, temperature, snowfall, roadway geometry, and vehicle type could be analyzed to determine what site characteristics define an optimal RWIS location, which is a potential area for future research. A linear model was developed by Zhao ( 12 ) to determine optimal RWIS sites, and a similar study for BC highways would be beneficial to optimize the safety benefit of RWISs.…”

Section: Discussionmentioning

confidence: 99%

Safety Assessment of the Integration of Road Weather Information Systems and Variable Message Signs in British Columbia

Esawey

Walker

Sowers

et al. 2019

Transportation Research Record: Journal of the Transportation R

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Adverse weather conditions create an environment in which it is difficult for drivers to navigate safely. Reducing weather-related collisions is a target for road safety professionals in British Columbia (BC), Canada. This study reports the safety benefits of installing road weather information systems (RWISs) coupled with variable message signs (VMSs) on provincial rural highways in BC. The RWIS/VMS system comprises road and weather sensors, as well as two VMSs. The road and weather sensors collect data on pavement surfaces and weather conditions. Information on adverse road/weather conditions are conveyed to road users via the VMSs. The system had been installed at six different locations on rural undivided highways in BC between 2011 and 2014. The analysis made use of police-attended serious crashes (i.e., fatal + injury) that took place during winter seasons. Depending on the implementation date, three or four winter seasons were available as a before-implementation period, while three to six winter seasons were available as an after-implementation period. An Empirical Bayes (EB) approach was employed to ensure that the evaluation results were reliable and to account for the regression-to-the-mean artifact. Safety performance functions (SPFs) were developed using data collected at similar sites. The EB evaluation results showed an overall statistically significant reduction of 32.7% in all winter serious collisions (WSC). An economic evaluation showed that the systems led to a benefit-cost ratio of 4.8 and an overall net present value of more than Can$12 million. The results of this study may motivate transportation agencies and stakeholders to pursue similar systems for mitigating weather-related safety problems.

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Section: Discussionmentioning

confidence: 99%

Safety Assessment of the Integration of Road Weather Information Systems and Variable Message Signs in British Columbia

Esawey

Walker

Sowers

et al. 2019

Transportation Research Record: Journal of the Transportation R

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“…At present, different types of technological systems, like fixed automated spray technology (FAST) [1,2], road weather information systems (RWIS) [3,4], environmental sensors [5], sensor networks for smart roads [6,7], pavement surface temperature sensors [8] and modern weather forecast systems [9], have been developed for detecting the asphalt state or for road monitoring under different atmospheric conditions. Great efforts have been made to integrate all these technological systems into a cooperative-intelligent transportation system (C-ITS) [10].…”

Section: Introductionmentioning

confidence: 99%

GeSi Nanocrystals Photo-Sensors for Optical Detection of Slippery Road Conditions Combining Two Classification Algorithms

Palade

Stavarache

Stoïca

et al. 2020

Sensors

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One of the key elements in assessing traffic safety on the roads is the detection of asphalt conditions. In this paper, we propose an optical sensor based on GeSi nanocrystals embedded in SiO2 matrix that discriminates between different slippery road conditions (wet and icy asphalt and asphalt covered with dirty ice) in respect to dry asphalt. The sensor is fabricated by magnetron sputtering deposition followed by rapid thermal annealing. The photodetector has spectral sensitivity in the 360–1350 nm range and the signal-noise ratio is 102–103. The working principle of sensor setup for detection of road conditions is based on the photoresponse (photocurrent) of the sensor under illumination with the light reflected from the asphalt having different reflection coefficients for dry, wet, icy and dirty ice coatings. For this, the asphalt is illuminated sequentially with 980 and 1064 nm laser diodes. A database of these photocurrents is obtained for the different road conditions. We show that the use of both k-nearest neighbor and artificial neural networks classification algorithms enables a more accurate recognition of the class corresponding to a specific road state than in the case of using only one algorithm. This is achieved by comparing the new output sensor data with previously classified data for each algorithm and then by performing an intersection of the algorithms’ results.

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“…For model-based methods, Singh et al [22] assumed that the RWIS spatial coverage is a decreasing function of distance. Zhao et al [23,24] determined the spatial coverage based on spatial variability. They computed the standard deviation of weather severity in microzones.…”

Section: Introductionmentioning

confidence: 99%

A Geostatistical Investigation into the Effective Spatiotemporal Coverage of Road Weather Information Systems in Alberta, Canada

Wang

Kwon

et al. 2018

Journal of Advanced Transportation

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Inclement weather acutely affects road surface and driving conditions and can negatively impact traffic mobility and safety. Highway authorities have long been using road weather information systems (RWISs) to mitigate the risk of adverse weather on traffic. The data gathered, processed, and disseminated by such systems can improve both the safety of the traveling public as well as the effectiveness of winter road maintenance operations. As the road authorities continue to invest in expanding their existing RWIS networks, there is a growing need to determine the optimal deployment strategies for RWISs. To meet such demand, this study presents an innovative geostatistical approach to quantitatively analyze the spatiotemporal variations of the road weather and surface conditions. With help of constructed semivariograms, this study quantifies and examines both the spatial and temporal coverage of RWIS data. A case study of Alberta, which is one of the leaders in Canada in the use of RWISs, was conducted to indicate the reliability and applicability of the method proposed herein. The findings of this research offer insight for constructing a detailed spatiotemporal RWIS database to manage and deploy different types of RWISs, optimize winter road maintenance resources, and provide timely information on inclement road weather conditions for the traveling public.

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Planning a road weather information system with GIS

Cited by 6 publications

References 11 publications

Safety Assessment of the Integration of Road Weather Information Systems and Variable Message Signs in British Columbia

Safety Assessment of the Integration of Road Weather Information Systems and Variable Message Signs in British Columbia

GeSi Nanocrystals Photo-Sensors for Optical Detection of Slippery Road Conditions Combining Two Classification Algorithms

A Geostatistical Investigation into the Effective Spatiotemporal Coverage of Road Weather Information Systems in Alberta, Canada

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