scholarly journals Automatic Accident Detection Techniques using CCTV Surveillance Videos: Methods, Data sets and Learning Strategies

2020 ◽  
Vol 9 (3) ◽  
pp. 4282-4285
Keyword(s):  
Learning Strategies ◽  
Traffic Management ◽  
Human Life ◽  
Weather Conditions ◽  
Road Accident ◽  
Data Sets ◽  
Priority List ◽  
Surveillance Videos ◽  
Detection Techniques ◽  
Accident Detection

Intelligent communities are utilizing different creative ideas to improve the quality of human life. Due to fast growing sizes of our cities, need of travelling is constantly increasing, which in turn has increased count of vehicles on the roads. Increasing number of vehicles on the roads has brought about numerous difficulties for Street Traffic Management Authorities. Amongst different traffic related issues, road accidents are something worth giving attention to and have to be on the priority list. This paper describes various automatic road accident detection techniques, which automatically detect accidents using surveillance videos in real-time. As these methods do not consider various lighting conditions, changing weather conditions and different traffic patterns, none of the methods are robust enough to address all the incidences of the accident. In this paper, authors have described and compared many such methods.

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>

Pentingnya Mendesain Pembelajaran Menuju Pendidikan Berkualitas

2020 ◽  
Vol 3 (2) ◽  
pp. 304-318
Author(s):  
I Made Putra Aryana
Keyword(s):  
Learning Strategies ◽  
Student Development ◽  
Human Life ◽  
Educational Process ◽  
Learning Design ◽  
Literature Study ◽  
Learning Program ◽  
Personal Lives ◽  
Teacher Needs

This article aims to put forward the learning design so that learning runs well, accompanied by anticipatory steps to minimize the gaps that occur so that learning activities achieve the goals set. The writing of this article uses the literature study method taken from various sources about learning. A teacher needs to have the ability to design and implement a variety of learning strategies that are considered suitable with the interests, talents and in accordance with the level of student development, including utilizing various sources and learning media to ensure the effectiveness of learning. The essence of learning design is the determination of optimal learning methods to achieve the stated goals. There is no learning model that can provide the most effective recipe for developing a learning program. The determination of the design model to develop a learning program depends on the designer's consideration of the model to be used or chosen. The educational process is a series of efforts to guide, direct the potential of human life in the form of basic abilities and personal lives as individual and social creatures and in their relationship with the natural surroundings to become responsible individuals.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

Decision Support System for Advanced Traffic Management Through Data Fusion

2002 ◽  
Vol 1804 (1) ◽  
pp. 173-178 ◽  
Author(s):  
Lawrence A. Klein ◽  
Ping Yi ◽  
Hualiang Teng
Keyword(s):  
Data Fusion ◽  
Traffic Management ◽  
Information Sources ◽  
Partial Information ◽  
Weather Conditions ◽  
Field Testing ◽  
Traffic Operations ◽  
Dempster Shafer Theory ◽  
Classification Technique ◽  
Shafer Theory

The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

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 Assessment of Sampling Effects on Various Satellite-Derived Integrated Water Vapor Datasets Using GPS Measurements in Germany as Reference

2020 ◽  
Vol 12 (7) ◽  
pp. 1170 ◽  
Author(s):  
Cintia Carbajal Henken ◽  
Lisa Dirks ◽  
Sandra Steinke ◽  
Hannes Diedrich ◽  
Thomas August ◽  
...  
Keyword(s):  
Water Vapor ◽  
Pairwise Comparison ◽  
Weather Conditions ◽  
Satellite System ◽  
High Temporal Resolution ◽  
Data Sets ◽  
Frequency Distributions ◽  
Temporal Sampling ◽  
Global Navigation Satellite ◽  
Satellite Instruments

Passive imagers on polar-orbiting satellites provide long-term, accurate integrated water vapor (IWV) data sets. However, these climatologies are affected by sampling biases. In Germany, a dense Global Navigation Satellite System network provides accurate IWV measurements not limited by weather conditions and with high temporal resolution. Therefore, they serve as a reference to assess the quality and sampling issues of IWV products from multiple satellite instruments that show different orbital and instrument characteristics. A direct pairwise comparison between one year of IWV data from GPS and satellite instruments reveals overall biases (in kg/m 2 ) of 1.77, 1.36, 1.11, and −0.31 for IASI, MIRS, MODIS, and MODIS-FUB, respectively. Computed monthly means show similar behaviors. No significant impact of averaging time and the low temporal sampling on aggregated satellite IWV data is found, mostly related to the noisy weather conditions in the German domain. In combination with SEVIRI cloud coverage, a change of shape of IWV frequency distributions towards a bi-modal distribution and loss of high IWV values are observed when limiting cases to daytime and clear sky. Overall, sampling affects mean IWV values only marginally, which are rather dominated by the overall retrieval bias, but can lead to significant changes in IWV frequency distributions.

scholarly journals Road Signs Perception Evaluation by Means of a Semi-immersive Tool

2011 ◽  
Vol 10 (2) ◽  
pp. 39-47
Author(s):  
Roberto Vigano ◽  
Edoardo Rovida ◽  
Riccardo Vincenti ◽  
Marco Ramondino
Keyword(s):  
European Commission ◽  
Virtual Environment ◽  
Weather Conditions ◽  
Road Accident ◽  
Visualization Tool ◽  
Traffic Signs ◽  
Road Signs ◽  
Safety Systems ◽  
Direct Use ◽  
And Training

To reduce the number of road accident victims the European Commission has encouraged the European member states to implement a series of actions in this field. These actions include the development of intelligent and integrated safety systems as well as educational and training initiatives. Educational initiatives include the training of the drivers to improve their ability and sense of responsibility. In addition to the direct use of the vehicle, the training includes the recognition of the traffic signs. Since the recognition may be influenced by both the position of the signal and the weather conditions, the authors have studied the possibility of evaluate the drivers' perception of road signs by means of a virtual environment tool able to perform different operative conditions. A series of tests was conducted to evaluate the visualization tool created and its ability to replace other recognition tests. This paper reports first tests results.

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