Determining Skid Resistance Needs on Horizontal Curves for Different Levels of Precipitation

2020 ◽  
Vol 2674 (9) ◽  
pp. 358-370
Author(s):  
Srinivas R. Geedipally ◽  
Subasish Das ◽  
Michael P. Pratt ◽  
Dominique Lord
Keyword(s):  
Prediction Models ◽  
Weather Conditions ◽  
Annual Precipitation ◽  
Weather Data ◽  
Skid Resistance ◽  
Study Objective ◽  
Horizontal Curves ◽  
Curve Geometry ◽  
Pavement Friction ◽  
Different Levels

Horizontal curves are a major cause of crashes that lead to fatal and serious injuries. Much research has been conducted on the safety implications of geometric and traffic characteristics of curves. Variables describing curve geometry and speed have been incorporated into safety prediction methodologies. However, relatively less research has been conducted on the effects of pavement friction and weather data on safety. The objective of this study is to develop a methodology for determining the pavement friction needs for different levels of precipitation. To accomplish the study objective, rural two-lane, four-lane undivided, and four-lane divided horizontal curve data from Texas were used. Safety prediction models were developed that included traffic and geometric characteristics, skid number, and annual precipitation rate. These models were then used to develop the guidelines for assessing the safety performance of a curve of interest by accounting for curve geometry, pavement skid resistance, and exposure to the wet-weather conditions that are most relevant for considerations of skid resistance. For conducting a planning-level analysis to identify candidate sites for pavement friction treatments, researchers developed thresholds based on the combined effect of skid number and annual precipitation variables. Researchers also provided skid number thresholds for high-priority sites for two example locations that experience significantly different levels of annual precipitation.

scholarly journals An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data

2021 ◽  
Vol 13 (21) ◽  
pp. 11893
Author(s):  
Abdul Rauf Bhatti ◽  
Ahmed Bilal Awan ◽  
Walied Alharbi ◽  
Zainal Salam ◽  
Abdullah S. Bin Humayd ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Real Time ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Weather Conditions ◽  
Energy System ◽  
Weather Data ◽  
Training Performance ◽  
Proposed Model ◽  
Artificial Neural Network Ann

In this work, an improved approach to enhance the training performance of an Artificial Neural Network (ANN) for prediction of the output of renewable energy systems is proposed. Using the proposed approach, a significant reduction of the Mean Squared Error (MSE) in training performance is achieved, specifically from 4.45 × 10−7 to 3.19 × 10−10. Moreover, a simplified application of the already trained ANN is introduced through which photovoltaic (PV) output can be predicted without the availability of real-time current weather data. Moreover, unlike the existing prediction models, which ask the user to apply multiple inputs in order to forecast power, the proposed model requires only the set of dates specifying forecasting period as the input for prediction purposes. Moreover, in the presence of the historical weather data this model is able to predict PV power for different time spans rather than only for a fixed period. The prediction accuracy of the proposed model has been validated by comparing the predicted power values with the actual ones under different weather conditions. To calculate actual power, the data were obtained from the National Renewable Energy Laboratory (NREL), USA and from the Universiti Teknologi Malaysia (UTM), Malaysia. It is envisaged that the proposed model can be easily handled by a non-technical user to assess the feasibility of the photovoltaic solar energy system before its installation.

Pavement Friction Degradation Based on Pennsylvania Field Test Data

2017 ◽  
Vol 2639 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Lingyu Li ◽  
S. Ilgin Guler ◽  
Eric T. Donnell
Keyword(s):  
Skid Resistance ◽  
Pavement Maintenance ◽  
Horizontal Curves ◽  
Pavement Surface ◽  
Lane Departure ◽  
Roadway Design ◽  
Tire Friction ◽  
Pavement Friction ◽  
Lateral Friction ◽  
Over Time

Pavement surface–tire friction is a critical safety element associated with roadway design, construction, and maintenance practices. The skid resistance of pavements generally declines over time and increases the risk of skidding-related crashes. On horizontal curves, lateral friction may be associated with lane-departure incidents, particularly as the pavement ages and drivers demand more lateral friction than the pavement surface–tire interaction can supply. On tangent roadway sections, longitudinal friction affects braking distances. As the skid-resistance properties of a pavement surface decline over time, braking distances increase, and may increase risks to driver safety. A comprehensive understanding of the process of pavement friction degradation could help highway agencies identify roadway segments that need maintenance to reduce the probability of skid-related incidents. This paper presents a survival analysis of friction degradation for asphalt pavement surfaces. Duration models were estimated with data collected annually along an Interstate highway in Pennsylvania to investigate the degradation of friction over time. These models consider traffic volume and roadway features to determine the probability that friction levels will remain above various friction thresholds. The resulting statistical models can help transportation agencies make better decisions about pavement maintenance to reduce safety risk.

scholarly journals Estimating the Impact of High-Fidelity Rainfall Data on Traffic Conditions and Traffic Prediction

2021 ◽  
pp. 036119812110263
Author(s):  
Anatolii Prokhorchuk ◽  
Nikola Mitrovic ◽  
Usman Muhammad ◽  
Aleksandar Stevanovic ◽  
Muhammad Tayyab Asif ◽  
...  
Keyword(s):  
Prediction Models ◽  
Predictive Accuracy ◽  
Weather Conditions ◽  
Rainfall Data ◽  
Weather Data ◽  
Traffic Prediction ◽  
Percentage Error ◽  
Traffic Network ◽  
Radar Images ◽  
The Impact

Accurate prediction of network-level traffic parameters during inclement weather conditions can greatly help in many transportation applications. Rainfall tends to have a quantifiable impact on driving behavior and traffic network performance. This impact is often studied for low-resolution rainfall data on small road networks, whereas this study investigates it in the context of a large traffic network and high-resolution rainfall radar images. First, the impact of rainfall intensity on traffic performance throughout the day and for different road categories is analyzed. Next, it is investigated whether including rainfall information can improve the predictive accuracy of the state-of-the-art traffic forecasting methods. Numerical results show that the impact of rainfall on traffic varies for different rainfall intensities as well as for different times of the day and days of the week. The results also show that incorporating rainfall data into prediction models improves their overall performance. The average reduction in mean absolute percentage error (MAPE) for models with rainfall data is 4.5%. Experiments with downsampled rainfall data were also performed, and it was concluded that incorporating higher resolution weather data does indeed lead to an increase in performance of traffic prediction models.

scholarly journals Study on the influence of skid resistance on traffic safety of highway with a high ratio of bridges and tunnels

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Yongjie Ding ◽  
Danni Li ◽  
Mingxuan Huang ◽  
Xuejuan Cao ◽  
Boming Tang
Keyword(s):  
Correlation Analysis ◽  
Traffic Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Prediction Models ◽  
Back Propagation ◽  
Weather Conditions ◽  
High Ratio ◽  
Skid Resistance ◽  
The Relationship

ABSTRACT The safety of highways with a high ratio of bridges and tunnels is related to multiple factors, for example, the skid resistance of the pavement surface. In this study, the distribution of accidents under different conditions was calculated to investigate the relationship between the road skid resistance and the incidence of traffic accidents based on the traffic accident data of the Yuxiang highway. Statistical results show that weather conditions and road alignment may affect traffic accidents. The correlation analysis method was used to study the relationship between three factors and traffic accidents. The results show that road alignment, weather conditions and road skid resistance are related to the incidence of traffic accidents. The traffic accident prediction models were established based on back propagation neural network to verify the correlation analysis results. It is confirmed that road alignment, weather conditions and road skid resistance are the factors that affect traffic accidents.

scholarly journals A new potato late blight disease prediction model and its comparison with two previous models

2006 ◽  
Vol 59 ◽  
pp. 150-154 ◽  
Author(s):  
W.R. Henshall ◽  
D. Shtienberg ◽  
R.M. Beresford
Keyword(s):  
Late Blight ◽  
Prediction Models ◽  
Disease Risk ◽  
Disease Process ◽  
Weather Conditions ◽  
Potato Late Blight ◽  
Weather Data ◽  
Research Station ◽  
Disease Prediction ◽  
Risk Area

There are numerous disease prediction models for potato late blight based on recognition of weather conditions suitable for infection The models have the potential to target fungicide application to times of greatest need with a consequent reduction in chemical use The HartillYoung late blight model was developed about 20 years ago from disease and weather data recorded at the Pukekohe Research Station This paper presents the more sophisticated Shtienberg model which was developed recently from the same data but which treats components of the disease process separately The outputs of the HartillYoung and Shtienberg models and the established Fry model were analysed for the same input weather data at Pukekohe (high disease risk area) and Lincoln (low risk) over the last five growing seasons The Shtienberg model gave broadly similar results to the other two models

Freeway Truck Traffic Safety in Wyoming: Crash Characteristics and Prediction Models

2019 ◽  
Vol 2673 (10) ◽  
pp. 333-342 ◽  
Author(s):  
Muhammad Tahmidul Haq ◽  
Milan Zlatkovic ◽  
Khaled Ksaibati
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Prediction Models ◽  
Weather Conditions ◽  
Significant Variable ◽  
Truck Traffic ◽  
Crash Data ◽  
Horizontal Curves ◽  
Rate Analysis ◽  
Road Geometry

The State of Wyoming experiences a high percentage of truck traffic along all its highways, especially Interstate 80 (I-80). The increased interactions between trucks and other vehicles have raised many operational and safety concerns. This paper presents a safety analysis and a development of safety performance functions (SPFs) along I-80, with a focus on truck crashes. Nine years of historical crash data in Wyoming (2008–2016) were used to observe the involvement of light, medium, and heavy trucks in crashes. Analysis of the major contributory factors showed that 54% of the total truck-related crashes occurred during icy road conditions and about 46% during snowy weather conditions, and approximately 45% involved driving too fast and driving in improper lane. The analysis also included segments with horizontal curves and vertical grades and their impacts on truck crashes. The crash rate analysis showed higher truck crash rate compared with total crash rate considering equal vehicle miles traveled as exposure. A zero-inflated negative binomial model was applied to develop Wyoming-specific SPFs for various truck crash types. The effects of traffic, road geometry characteristics, and weather parameters influencing different truck-related crashes were quantified from these models. Downgrades and steep upgrade sections were found to increase truck-related crashes. The number of rainy days per year was found to be a significant variable affecting truck-related crashes. On the other hand, the presence of climbing lanes has significant safety benefits.

Safety at Curves and Road Geometry Standards in Some European Countries

1996 ◽  
Vol 1523 (1) ◽  
pp. 99-106 ◽  
Author(s):  
Thierry Brenac
Keyword(s):  
Urban Areas ◽  
National Standards ◽  
European Countries ◽  
Design Standards ◽  
The Road ◽  
Horizontal Curves ◽  
Road Design ◽  
Road Geometry ◽  
Design Speed ◽  
Curve Geometry

This paper deals with safety at horizontal curves on two-lane roads outside urban areas and the way the road design standards of different European countries account for this safety aspect. After a review of some research results, the main aspects of curve geometry and the curve's place in the horizontal alignment are analyzed. The main conclusions are that the traditional design speed approach is insufficient and that formal complementary rules in road design standards, especially to improve compatibility between successive elements of the alignment, must be introduced. If such complementary rules already exist in some national standards, they are neither frequent nor homogeneous throughout the different countries, and it seems that they are not based on sufficiently developed knowledge.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

scholarly journals Predicting emergency department visits in a large teaching hospital

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Nathan Singh Erkamp ◽  
Dirk Hendrikus van Dalen ◽  
Esther de Vries
Keyword(s):  
Emergency Department ◽  
The Netherlands ◽  
Teaching Hospital ◽  
Time Trend ◽  
Prediction Models ◽  
Emergency Department Visits ◽  
Weather Data ◽  
Negative Consequences ◽  
Weather Variables ◽  
Ed Visits

Abstract Background Emergency department (ED) visits show a high volatility over time. Therefore, EDs are likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Previous studies on the predictive value of weather variables on ED visits show conflicting results. Also, no such studies were performed in the Netherlands. Therefore, we evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors. Methods Data on all ED visits from June 2016 until December 31, 2019, were extracted. The 2016–2018 data were used as training set, the 2019 data as test set. Weather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute. Weather observations in proximity of the hospital were used to predict the weather in the hospital’s catchment area by applying the inverse distance weighting interpolation method. The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit. Results The number of daily ED visits shows a positive time trend and a large impact of calendar events (higher on Mondays and Fridays, lower on Saturdays and Sundays, higher at special times such as carnival, lower in holidays falling on Monday through Saturday, and summer vacation). The weather itself was a better predictor than weather volatility, but only showed a small effect; the calendar-only prediction model had very similar coefficients to the calendar+weather model for the days of the week, time trend, and special time periods (both MAPE’s were 8.7%). Conclusions Because of this similar performance, and the inaccuracy caused by weather forecasts, we decided the calendar-only model would be most useful in our hospital; it can probably be transferred for use in EDs of the same size and in a similar region. However, the variability in ED visits is considerable. Therefore, one should always anticipate potential unforeseen spikes and dips in ED visits that are not shown by the model.

scholarly journals Advanced Flight Planning and the Benefit of In-Flight Aircraft Trajectory Optimization

2021 ◽  
Vol 13 (3) ◽  
pp. 1383
Author(s):  
Judith Rosenow ◽  
Martin Lindner ◽  
Joachim Scheiderer
Keyword(s):  
New York ◽  
Traffic Control ◽  
Traffic Management ◽  
Trajectory Optimization ◽  
Service Providers ◽  
Weather Conditions ◽  
Air Traffic ◽  
Weather Data ◽  
Weather Forecasts ◽  
Aircraft Trajectory

The implementation of Trajectory-Based Operations, invented by the Single European Sky Air Traffic Management Research program SESAR, enables airlines to fly along optimized waypoint-less trajectories and accordingly to significantly increase the sustainability of the air transport system in a business with increasing environmental awareness. However, unsteady weather conditions and uncertain weather forecasts might induce the necessity to re-optimize the trajectory during the flight. By considering a re-optimization of the trajectory during the flight they further support air traffic control towards achieving precise air traffic flow management and, in consequence, an increase in airspace and airport capacity. However, the re-optimization leads to an increase in the operator and controller’s task loads which must be balanced with the benefit of the re-optimization. From this follows that operators need a decision support under which circumstances and how often a trajectory re-optimization should be carried out. Local numerical weather service providers issue hourly weather forecasts for the coming hour. Such weather data sets covering three months were used to re-optimize a daily A320 flight from Seattle to New York every hour and to calculate the effects of this re-optimization on fuel consumption and deviation from the filed path. Therefore, a simulation-based trajectory optimization tool was used. Fuel savings between 0.5% and 7% per flight were achieved despite minor differences in wind speed between two consecutive weather forecasts in the order of 0.5 m s−1. The calculated lateral deviations from the filed path within 1 nautical mile were always very small. Thus, the method could be easily implemented in current flight operations. The developed performance indicators could help operators to evaluate the re-optimization and to initiate its activation as a new flight plan accordingly.

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