scholarly journals Predictive modeling of hourly probabilities for weather-related road accidents

2020 ◽  
Author(s):  
Nico Becker ◽  
Henning W. Rust ◽  
Uwe Ulbrich
Keyword(s):  
Traffic Management ◽  
Weather Conditions ◽  
Reanalysis Data ◽  
Road Maintenance ◽  
Road Accidents ◽  
Logistic Regression Models ◽  
Weather Forecasts ◽  
Hourly Precipitation ◽  
Adverse Weather ◽  
Accident Probabilities

Abstract. An impact of weather on road accidents has been identified in several studies with a focus mainly on monthly or daily accident counts. We study hourly probabilities of road accidents caused by adverse weather conditions in Germany on the spatial scale of administrative districts. Meteorological predictor variables from radar-based precipitation estimates, high-resolution reanalysis and weather forecasts are used in logistic regression models. Models taking into account temperature and hourly precipitation sums reach the best predictive skill according to different metrics. By introducing meteorological variables, the models hit rate is increased from 0.3 to 0.7, while keeping the false alarm rate constant at 0.2. Accident probability has a non-linear relationship with precipitation. Given an hourly precipitation sum of 1 mm, accident probabilities are about 5 times larger at negative temperatures compared to positive temperatures. Based on ensemble weather forecasts skilful predictions of accident probabilities of up to 21 hours are possible; the loss of skill compared to a model using radar and reanalysis data is negligible. The findings are relevant in the context of impact based warnings for both road users, road maintenance and traffic management authorities, as well as rescue forces.

scholarly journals Predictive modeling of hourly probabilities for weather-related road accidents

2020 ◽  
Vol 20 (10) ◽  
pp. 2857-2871
Author(s):  
Nico Becker ◽  
Henning W. Rust ◽  
Uwe Ulbrich
Keyword(s):  
Traffic Management ◽  
Model Performance ◽  
Weather Conditions ◽  
Reanalysis Data ◽  
Road Maintenance ◽  
Road Accidents ◽  
Logistic Regression Models ◽  
Weather Forecasts ◽  
Accident Probability ◽  
Adverse Weather

Abstract. Impacts of weather on road accidents have been identified in several studies with a focus mainly on monthly or daily accident counts. This study investigates hourly probabilities of road accidents caused by adverse weather conditions in Germany on the spatial scale of administrative districts using logistic regression models. Including meteorological predictor variables from radar-based precipitation estimates, high-resolution reanalysis and weather forecasts improves the prediction of accident probability compared to models without weather information. For example, the percentage of correctly predicted accidents (hit rate) is increased from 30 % to 70 %, while keeping the percentage of wrongly predicted accidents (false-alarm rate) constant at 20 %. When using ensemble weather forecasts up to 21 h instead of radar and reanalysis data, the decline in model performance is negligible. Accident probability has a nonlinear relationship with precipitation. Given an hourly precipitation sum of 1 mm, accident probabilities are approximately 5 times larger at negative temperatures compared to positive temperatures. The findings are relevant in the context of impact-based warnings for road users, road maintenance, traffic management and rescue forces.

Translating weather forecasts to road accident probabilities

2021 ◽  
Author(s):  
Nico Becker ◽  
Henning Rust ◽  
Uwe Ulbrich
Keyword(s):  
Traffic Management ◽  
Model Performance ◽  
Weather Conditions ◽  
Road Accident ◽  
Road Maintenance ◽  
Logistic Regression Models ◽  
Weather Forecasts ◽  
Road Users ◽  
Accident Probability ◽  
Accident Probabilities

<p>In Germany about 1000 severe road accidents are recorded by the police per day. On average, 8 % of these accidents are related to weather conditions, for example due to rain, snow or ice. In this study we compare several versions of a logistic regression models to predict hourly probabilities of such accidents in German administrative districts. We use radar, reanalysis and ensemble forecast data from the regional operational model of the German Meteorological Service DWD as well as police reports to train the model with different combinations of input datasets. By including weather information in the models, the percentage of correctly predicted accidents (hit rate) is increased from 30 % to 70 %, while keeping the percentage of wrongly predicted accidents (false-alarm rate) constant at 20 %. Accident probability increases nonlinearly with increasing precipitation. Given an hourly precipitation sum of 1 mm, accident probabilities are approximately 5 times larger at negative temperatures compared to positive temperatures. When using ensemble weather forecasts to predict accident probabilities for a leadtime of up to 21 h ahead, the decline in model performance is negligible. We suggest to provide impact-based warnings for road users, road maintenance, traffic management and rescue forces.</p>

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.

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

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Xu Wang ◽  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu
Keyword(s):  
Information Systems ◽  
Weather Conditions ◽  
Road Maintenance ◽  
The Road ◽  
Weather Information ◽  
Geostatistical Approach ◽  
Adverse Weather ◽  
Winter Road ◽  
Winter Road Maintenance

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.

Wind waves modeling in the polar law weather conditions

2020 ◽  
Author(s):  
Alexandra Kuznetsova ◽  
Evgeny Poplavsky ◽  
Nikita Rusakov ◽  
Yuliya Troitskaya
Keyword(s):  
Wind Waves ◽  
Rapid Development ◽  
Free Water ◽  
Wave Model ◽  
Weather Conditions ◽  
Reanalysis Data ◽  
The Arctic ◽  
Geophysical Research ◽  
Polar Lows ◽  
Adverse Weather

<p>Arctic storms pose a great danger to developing commercial and passenger shipping, coastal infrastructure, and also for oil production from offshore platforms. This is primarily due to high waves and extreme winds. Such episodes of adverse weather conditions due to their rapid development are poorly predicted by modern models. For this purpose, the representation of the event of polar law is studied in the wave model WAVEWATCH III.</p><p>Wind waves were simulated under conditions of polar depression on ice-free water. To simulate wind waves under conditions of polar depression, the Barents Sea was selected, where, according to the data of [1, 2], a large number of polar hurricanes are observed. Among the identified polar hurricanes, for example, in [3], a hurricane that took place on 05.02.2009, observed at coordinates 69 N 40 E is chosen. The preliminary results in the wave model are obtained without the ice influence consideration. The developed model was configured using the CFSR wind reanalysis data. The resulting distribution of significant wave heights is obtained. Then, to consider the attenuation by sea ice, the reanalysis data of the Arctic System Reanalysis Version 2 (ASRv2), which is based on Polar WRF with a resolution of 15 km for the Arctic region, is used. Modeling the destruction of ice by waves during an intense arctic storm will be implemented using WW3 models with an IS2 module.</p><p>The work is supported by RFBR grant 18-05-60299.</p><ol><li>Smirnova, J. E., Golubkin, P. A., Bobylev, L. P., Zabolotskikh, E. V., & Chapron, B. (2015). Polar low climatology over the Nordic and Barents seas based on satellite passive microwave data. Geophysical Research Letters, 42(13), 5603-5609.</li> <li>Smirnova, J., & Golubkin, P. (2017). Comparing polar lows in atmospheric reanalyses: Arctic System Reanalysis versus ERA-Interim. Monthly Weather Review, 145(6), 2375-2383.</li> <li>Noer, G., & Lien, T. (2010). Dates and Positions of Polar lows over the Nordic Seas between 2000 and 2010. Norwegian Meteorological Institute Rep.</li> </ol>

Effectiveness of Predictive Weather-Related Active Transportation and Demand Management Strategies for Network Management

2017 ◽  
Vol 2667 (1) ◽  
pp. 71-87 ◽  
Author(s):  
Zihan Hong ◽  
Hani S. Mahmassani ◽  
Xiang Xu ◽  
Archak Mittal ◽  
Ying Chen ◽  
...  
Keyword(s):  
Traffic Management ◽  
Demand Management ◽  
Management Strategies ◽  
Weather Conditions ◽  
Active Transportation ◽  
Operational Conditions ◽  
Traffic Conditions ◽  
Adverse Weather ◽  
Traffic Performance ◽  
The Impact

This paper presents the development, implementation, and evaluation of predictive active transportation and demand management (ATDM) and weather-responsive traffic management (WRTM) strategies to support operations for weather-affected traffic conditions with traffic estimation and prediction system models. First, the problem is defined as a dynamic process of traffic system evolution under the impact of operational conditions and management strategies (interventions). A list of research questions to be addressed is provided. Second, a systematic framework for implementing and evaluating predictive weather-related ATDM strategies is illustrated. The framework consists of an offline model that simulates and evaluates the traffic operations and an online model that predicts traffic conditions and transits information to the offline model to generate or adjust traffic management strategies. Next, the detailed description and the logic design of ATDM and WRTM strategies to be evaluated are proposed. To determine effectiveness, the selection of strategy combination and sensitivity of operational features are assessed with a series of experiments implemented with a locally calibrated network in the Chicago, Illinois, area. The analysis results confirm the models’ ability to replicate observed traffic patterns and to evaluate the system performance across operational conditions. The results confirm the effectiveness of the predictive strategies tested in managing and improving traffic performance under adverse weather conditions. The results also verify that, with the appropriate operational settings and synergistic combination of strategies, weather-related ATDM strategies can generate maximal effectiveness to improve traffic performance.

scholarly journals Analysis of temperature inversions during periods of adverse weather conditions in Krasnoyarsk in the winter period of 2019-2020

2020 ◽  
Vol 223 ◽  
pp. 03021
Author(s):  
Alexander Dergunov ◽  
Oleg Yakubailik
Keyword(s):  
Particulate Matter ◽  
Weather Conditions ◽  
Temperature Inversion ◽  
Reanalysis Data ◽  
Winter Period ◽  
Meteorological Model ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Temperature Inversions ◽  
High Degree

The work is devoted to the search for relationships between the pollution of the atmosphere of Krasnoyarsk by particulate matter and temperature inversion – an increase in temperature with height in the surface layer of the atmosphere. The research is based on reanalysis data of the NASA GFS meteorological model for air temperature at different altitudes of the atmosphere and the results of measurements of concentrations of particulate matter in the air monitoring system of the FRC KSC SB RAS, as well as information about officially declared periods of adverse weather conditions. The results obtained allow us to conclude that there is a high degree of correlation between these values, and that it is possible to use the GFS model data to predict the environmental situation.

scholarly journals Walking against or with traffic? Evaluating pedestrian fatalities and head injuries in Taiwan

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Chih-Wei Pai ◽  
Ping-Ling Chen ◽  
Shiao-Tzu Ma ◽  
Shan-Hong Wu ◽  
Václav Linkov ◽  
...  
Keyword(s):  
Head Injuries ◽  
Weather Conditions ◽  
Influential Factors ◽  
Traffic Crashes ◽  
Logistic Regression Models ◽  
Crash Data ◽  
The United Kingdom ◽  
Sight Distance ◽  
Adverse Weather ◽  
Negative Effect

Abstract Background Allowing contraflow cycling on one-way streets has been reported to reduce crash risks in Belgium and the United Kingdom. Similarly, walking against traffic on roadways without sidewalks substantially improves pedestrian safety. This study examined fatalities and head injuries sustained by pedestrians in against-traffic and with-traffic crashes. Methods Using police-reported crash data in Taiwan between 2011 and 2016, fatalities and head injuries were compared for pedestrians involved in against-traffic and with-traffic crashes. Results Of the 14,382 pedestrians involved in crashes, 10,749 and 3633 pedestrians in with-traffic and against-traffic crashes, respectively, were reported. Compared with pedestrians involved in against-traffic crashes, those in with-traffic crashes were more likely to sustain fatalities and head injuries. Results of logistic regression models revealed several influential factors on pedestrian fatalities and head injuries, including elderly pedestrians, male drivers, intoxicated drivers, rural roadways, unlit streets in darkness, limited sight distance, adverse weather conditions, midnight hours, and a heavy vehicle as the crash partner. Conclusions Pedestrians in with-traffic crashes were more likely to sustain fatalities and head injuries compared with those in against-traffic crashes. Furthermore, the negative effect of walking with traffic on injuries was more pronounced in reduced-visibility conditions.

scholarly journals Prediction of Loss of Position during Dynamic Positioning Drilling Operations Using Binary Logistic Regression Modeling

2021 ◽  
Vol 9 (2) ◽  
pp. 139
Author(s):  
Zaloa Sanchez-Varela ◽  
David Boullosa-Falces ◽  
Juan Luis Larrabe Barrena ◽  
Miguel A. Gomez-Solaeche
Keyword(s):  
Logistic Regression ◽  
Human Error ◽  
Binary Logistic Regression ◽  
Weather Conditions ◽  
Dynamic Positioning ◽  
Dynamic Positioning System ◽  
Logistic Regression Models ◽  
Adverse Weather ◽  
Drilling Operations ◽  
Offshore Industry

The prediction of loss of position in the offshore industry would allow optimization of dynamic positioning drilling operations, reducing the number and severity of potential accidents. In this paper, the probability of an excursion is determined by developing binary logistic regression models based on a database of 42 incidents which took place between 2011 and 2015. For each case, variables describing the configuration of the dynamic positioning system, weather conditions, and water depth are considered. We demonstrate that loss of position is significantly more likely to occur when there is a higher usage of generators, and the drilling takes place in shallower waters along with adverse weather conditions; this model has very good results when applied to the sample. The same method is then applied for obtaining a binary regression model for incidents not attributable to human error, showing that it is a function of the percentage of generators in use, wind force, and wave height. Applying these results to the risk management of drilling operations may help focus our attention on the factors that most strongly affect loss of position, thereby improving safety during these operations.

scholarly journals Design, Modelling, and Implementation of a Fuzzy Controller for an Intelligent Road Signaling System

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
José Manuel Lozano Domínguez ◽  
Tomás de J. Mateo Sanguino
Keyword(s):  
Traffic Accidents ◽  
Fuzzy Controller ◽  
Low Cost ◽  
Weather Conditions ◽  
Prototype System ◽  
Road Maintenance ◽  
Good Test ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
High Level

Crossing points are not always 100% visible for drivers due to different factors (e.g., poor road maintenance, occlusion of vertical signs, and adverse weather conditions). USA estimated in 2015 the number of traffic accidents involving pedestrians and vehicles in 70,000 of whom 5,376 resulted in deceased people. To contribute in this field, this paper presents the design, implementation, and testing of a smart prototype system applied to pedestrian crossings—not regulated by semaphores—which try to reduce the accident rate on roads. The hardware and software system consists of a set of autonomous, intelligent, and wireless low-cost devices that generate a visual warning barrier perceived by drivers from a suitable distance when pedestrians traverse a crosswalk. In this way, drivers can reduce the speed of their vehicles and stop safely. The system’s intelligence is carried out by a fuzzy controller that performs sensory fusion at both low level and high level with various types of sensors from local and neighboring devices. The tests conducted have determined an average success of 94.64% and a precision of 100%, thus corresponding with a very good test according to a ROC analysis. As a result, the system proposed has been patented and extended to international PCT.

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