Improved Visibility of Single Hazy Images Captured in Inclement Weather Conditions

2013 ◽  
Author(s):  
Bo-Hao Chen ◽  
Shih-Chia Huang
Keyword(s):  
Weather Conditions ◽  
Inclement Weather

scholarly journals Exploring the Spatial-Temporal Relationship between Rainfall and Traffic Flow: A Case Study of Brisbane, Australia

2020 ◽  
Vol 12 (14) ◽  
pp. 5596 ◽  
Author(s):  
Yanmin Qi ◽  
Zuduo Zheng ◽  
Dongyao Jia
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Transportation Network ◽  
Central Business District ◽  
Weather Conditions ◽  
Inclement Weather ◽  
Real Time Traffic ◽  
Policy Analysts ◽  
Business District ◽  
The Impact

The impact of inclement weather on traffic flow has been extensively studied in the literature. However, little research has unveiled how local weather conditions affect real-time traffic flows both spatially and temporally. By analysing the real-time traffic flow data of Traffic Signal Controllers (TSCs) and weather information in Brisbane, Australia, this paper aims to explore weather’s impact on traffic flow, more specifically, rainfall’s impact on traffic flow. A suite of analytic methods has been applied, including the space-time cube, time-series clustering, and regression models at three different levels (i.e., comprehensive, location-specific, and aggregate). Our results reveal that rainfall would induce a change of the traffic flow temporally (on weekdays, Saturday, and Sunday and at various periods on each day) and spatially (in the transportation network). Particularly, our results consistently show that the traffic flow would increase on wet days, especially on weekdays, and that the urban inner space, such as the central business district (CBD), is more likely to be impacted by inclement weather compared with other suburbs. Such results could be used by traffic operators to better manage traffic in response to rainfall. The findings could also help transport planners and policy analysts to identify the key transport corridors that are most susceptible to traffic shifts in different weather conditions and establish more weather-resilient transport infrastructures accordingly.

Wealth, Consumerism, and Culture among the Artisans of Göppingen: Dynamism and Tradition in an Eighteenth-Century Hometown

2013 ◽  
Vol 46 (4) ◽  
pp. 741-778
Author(s):  
Dennis A. Frey
Keyword(s):  
Eighteenth Century ◽  
Early Modern ◽  
Weather Conditions ◽  
Inclement Weather ◽  
Modern Natural ◽  
Scientific Principles ◽  
Natural Philosophers ◽  
Vivid Description ◽  
What Matters

April 21, 1771, brought unusual weather conditions, namely a springtime blizzard, to the Swabian town of Göppingen. We know this because the worsted-wool weaver Ernst Jacob Vayhinger wrote about it in a chronicle that he kept from 1756 to 1784. His exact words were, “The weather is also quite something. I have a barometer, which indicates the clearest weather today, and yet it is snowing so badly. The same thing happened a year ago. As the upper wind brought rain, it [the barometer] was instead showing nice conditions, and the rain was freezing cold.” While the vivid description of inclement weather certainly catches the eye, the presence of a barometer in this weaver's household in 1771 stands out even more. In fact, this weather-based technology was barely a century old in the latter half of the eighteenth century, having been invented by Evangelista Torricelli, an Italian mathematician, in 1643. To be sure, Vayhinger's malfunctioning barometer was almost certainly a water-filled glass instrument rather than the more precise mercury-based instruments of early-modern natural philosophers, but what matters here is that Vayhinger had a relatively new, ornamental wall hanging that indicated an awareness of new scientific principles. And, as this article will show, he was not at all the only one to acquire such novelties in this hometown full ofHandwerker(artisans).

Prediction and Mitigation of Flow Breakdown Occurrence for Weather Affected Networks: Case Study of Chicago, Illinois

2019 ◽  
Vol 2673 (11) ◽  
pp. 628-639
Author(s):  
Monika Filipovska ◽  
Hani S. Mahmassani ◽  
Archak Mittal
Keyword(s):  
Kansas City ◽  
Prediction Method ◽  
Simulated Data ◽  
Weather Conditions ◽  
Mitigation Strategies ◽  
Lower Probability ◽  
Threshold Probability ◽  
Inclement Weather ◽  
Breakdown Probability ◽  
Probability Threshold

This study investigates the prediction and mitigation of the phenomenon of traffic flow breakdown when affected by varying weather conditions. First, the probability of breakdown occurrence is examined using a survival analysis approach to obtain distributions of pre-breakdown flow rates under different weather conditions. Second, pre-breakdown flow rate distributions were applied in breakdown prediction for the implementation of breakdown mitigation strategies. In the first part, a set of data from the network of Kansas City was used to demonstrate the applicability of the Kaplan–Meier Product Limit method to estimating the breakdown probability under various weather conditions. Then, using simulated data on the network of Chicago, the K-M approach was used again to obtain survival likelihood distributions, which in turn yield breakdown probability, for 13 different weather cases as combinations of weather categories for different levels of visibility, rain, and snow precipitation. In the second part, continuing with the simulated data, dynamic speed limits (DSL) were applied to demonstrate the effectiveness of the prediction method presented. A sensitivity analysis of the threshold probability and upstream distance at which DSL should be implemented was performed for clear and inclement weather conditions. In clear weather the performance of the strategy is better at a lower probability threshold and farther upstream location, whereas in inclement weather the performance is better at a lower probability threshold and closer upstream location. The paper demonstrates the effect of changing weather conditions on the likelihood of breakdown occurrence and the implementation of breakdown mitigation strategies.

Weather, Risk, and Voting: An Experimental Analysis of the Effect of Weather on Vote Choice

2018 ◽  
Vol 6 (1) ◽  
pp. 17-32 ◽  
Author(s):  
Anna Bassi
Keyword(s):  
Voter Turnout ◽  
Empirical Studies ◽  
Weather Conditions ◽  
Risk Tolerance ◽  
Vote Share ◽  
Vote Choice ◽  
Turn On ◽  
Inclement Weather ◽  
Weather Risk ◽  
Bad Weather

AbstractA number of theoretical and empirical studies analyze the effect of inclement weather on voter turnout and in turn on parties’ vote share. However, empirical findings suggest that the effect of weather on parties’ vote share is greater than can be explained by its influence on voter turnout alone. This article provides experimental evidence of the effect of weather on vote choice between more- versus less-risky candidates. Findings show that bad weather significantly and sizeably depresses risk tolerance making voters less likely to vote for risky candidates. This article also provides evidence of a possible mechanism: unpleasant weather conditions depress agents’ mood, making agents less inclined to vote for candidates who are perceived as more risky.

scholarly journals Appraisal of Temporal Transferability of Cold Region Winter Weather Traffic Models for Major Highway Segments in Alberta Canada

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 137
Author(s):  
Hyuk-Jae Roh ◽  
Furqan Bhat ◽  
Prasanta Sahu ◽  
Satish Sharma ◽  
Babak Mehran ◽  
...  
Keyword(s):  
Travel Demand ◽  
Weather Conditions ◽  
Winter Weather ◽  
Truck Traffic ◽  
Passenger Car ◽  
Dummy Variable ◽  
Inclement Weather ◽  
Car Traffic ◽  
Weather Model ◽  
Adverse Weather

This paper evaluates the effect of inclement weather conditions on the travel demand for three classes of vehicles for a primary highway in the province of Alberta, Canada. The demand variables are passenger cars, trucks, and total traffic. It is well known from previous studies that adverse weather conditions such as low temperatures and heavy snowfall cause variation in traffic flow patterns. A winter weather model, based on the dummy variable regression model, was developed to quantify the variations in traffic volume due to snowfall and temperature changes. To establish the relationships, vehicular data was collected from six weigh-in-motion (WIM) sites, and the weather data associated with the WIM sites was collected from nearby weather stations. The study revealed that the variation in truck traffic, due to inclement weather conditions, was insignificant compared to variation in passenger car traffic. This study also investigated the temporal transferability of the developed winter weather model to test if a model can be applied irrespective of the time when it was developed. In addition, an attempt was made to check if the model coefficients could be optimized differently for different classes of traffic for estimating correct traffic variations. To evaluate transferability, the performance of both dummy variable regression and naive (without dummy variables) models was investigated. The results revealed that the dummy variable regression models show better performance for passenger car traffic and total traffic and naive winter weather models give better results for truck traffic.

Factors Affecting Benefits of Implementing Special Signal Timing Plans for Inclement Weather Conditions

2005 ◽  
Vol 1925 (1) ◽  
pp. 146-155 ◽  
Author(s):  
Seli J. Agbolosu-Amison ◽  
Adel W. Sadek ◽  
Brandon Henry
Keyword(s):  
Weather Conditions ◽  
Weather Event ◽  
Factors Affecting ◽  
Traffic Demand ◽  
Control Delay ◽  
Signal Coordination ◽  
Inclement Weather ◽  
Flow Parameters ◽  
Adverse Weather ◽  
The Impact

Gridlock along arterial systems in cold climates is often the result of adverse weather conditions, which can render the normal signal coordination plans unsuitable as a result of changes in the traffic flow parameters. The current study aims at understanding the impact of different factors on the magnitude of the operational benefits to be expected from implementing special timing plans for inclement weather. The factors investigated include ( a) the characteristics of the signalized corridor, ( b) the type of the simulation model used in the evaluation, ( c) the level of traffic demand, and ( d) the duration of the inclement weather event. To achieve the study's objective, two signalized arterial corridors are selected as case studies, and several simulation experiments are conducted. The results show that signal retiming during inclement weather can result in significant operational benefits (as high as a 20% reduction in control delay in some cases). The results also show that the benefits appear to be greatest when traffic loads are close to capacity and tend to decrease when the available capacity is exceeded. Finally, a significant increase in the benefits is realized with the increase in the duration of the inclement weather event. In one experiment, retiming the signals resulted in a 36% reduction in control delay for a 2-h snow event as opposed to only 18% for a 1-h event.

scholarly journals Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ying Chen ◽  
Zhongxiang Huang
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Safety Data ◽  
Method Comparison ◽  
Weather Conditions ◽  
Finite Mixture ◽  
Crash Risk ◽  
Crash Data ◽  
Road Crashes ◽  
Inclement Weather

Inclement weather affects traffic safety in various ways. Crashes on rainy days not only cause fatalities and injuries but also significantly increase travel time. Accurately predicting crash risk under inclement weather conditions is helpful and informative to both roadway agencies and roadway users. Safety researchers have proposed various analytic methods to predict crashes. However, most of them require complete roadway inventory, traffic, and crash data. Data incompleteness is a challenge in many developing countries. It is common that safety researchers only have access to data on sites where a crash has occurred (i.e., zero-truncated data). The conventional crash models are not applicable to zero-truncated safety data. This paper proposes a finite-mixture zero-truncated negative binomial (FMZTNB) model structure. The model is applied to three-year wet-road crash data on 395 divided roadway segments (total 586 km), and the parameters are estimated using the Markov chain Monte Carlo (MCMC) method. Comparison indicates that the proposed FMZTNB model has better fitting performance and is more accurate in predicting the number of wet-road crashes. The model is capable of capturing the heterogeneity within the sample crash data. In addition, lane width showed mixed effects in different components on wet-road crashes, which are not observed in conventional modeling approaches. Practitioners are encouraged to consider the finite-mixture zero-truncated modeling approach when complete safety dataset is not available.

scholarly journals Rancang Bangun Sistem Jemuran Pakaian Otomatis Menggunakan Metode Fuzzy Logic

2019 ◽  
Vol 11 (1) ◽  
pp. 90
Author(s):  
Muchammad Husni ◽  
Henning Titi Ciptaningtyas ◽  
Adetiya Bagus Nusantara
Keyword(s):  
Fuzzy Logic ◽  
Mobile Applications ◽  
Rainy Season ◽  
Weather Conditions ◽  
Rain Water ◽  
Testing System ◽  
Inclement Weather ◽  
The People ◽  
Data Reading ◽  
Testing And Evaluation

Clothesline clothing is a necessity that must be owned by almost every one. Rain or inclement weather up to now become a key issue for the people that have a clothesline. At the time of the rainy season, the majority of people feel anxious when they're drying the clothes. The anxiety will increase while at the time of drying clothes but concerned are located outside the House and in the House was no person. The genesis of the people so reluctant to open places pakain drying, fearing jemurannya wet exposed to rain water. This research has been made on a prototype automatic clothes clothesline system using microcontroller, Arduino wifi module, a motor with sensors-sensors installed in it. In the logic of determining the weather conditions used fuzzy logic to produce output that is accurate. The prototype architecture is intended to make it easier for people when drying their clothes, so when the rain struck, clothesline clothing can move automatically toward the shade so that her clothes were not wet exposed to rain water. Of all the testing and evaluation that have been carried out, i.e. testing the response of the sensor against rain, tools testing response tool against the light sensor, testing mobile applications in data reading on the sensor and testing system in controlling Clothesline the clothes manually using mobile applications, obtained that the system created has been functioning properly and produce output as expected.

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