scholarly journals Are Fair Weather Fans Affected by Weather? Rainfall, Habit Formation, and Live Game Attendance

2019 ◽  
Vol 21 (3) ◽  
pp. 304-322 ◽  
Author(s):  
Qi Ge ◽  
Brad R. Humphreys ◽  
Kun Zhou
Keyword(s):  
Habit Formation ◽  
Weather Conditions ◽  
Policy Implications ◽  
Weather Data ◽  
Pricing Strategies ◽  
Ticket Pricing ◽  
Weather Shocks ◽  
Adverse Weather ◽  
The Impact ◽  
Game Attendance

We analyze habit formation in sports attendance utilizing rainfall as an unexpected, transitory shock to attendance costs. Using attendance data from Major League Baseball (MLB) and National Oceanic and Atmospheric Administration weather data, we analyze the impact of variation in game day weather conditions on current and future MLB attendance. The empirical strategy permits identification of both the formation and persistence of habit from exogenous weather shocks. Past adverse weather shocks increase future attendance by about 200 fans per game. This contributes to the literature developing empirical evidence of habit formation in the field and provides policy implications for optimal ticket-pricing strategies.

Impact of weather conditions on airport arrival delay and throughput

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Álvaro Rodríguez-Sanz ◽  
Javier Cano ◽  
Beatriz Rubio Fernández
Keyword(s):  
Decision Making ◽  
Weather Conditions ◽  
Weather Data ◽  
Arrival Process ◽  
Data Set ◽  
Content Type ◽  
Weather Events ◽  
Adverse Weather ◽  
Arrival Processes ◽  
The Impact

Purpose Weather events have a significant impact on airport arrival performance and may cause delays in operations and/or constraints in airport capacity. In Europe, almost half of all regulated airport traffic delay is due to adverse weather conditions. Moreover, the closer airports operate to their maximum capacity, the more severe is the impact of a capacity loss due to external events such as weather. Various weather uncertainties occurring during airport operations can significantly delay some arrival processes and cause network-wide effects on the overall air traffic management (ATM) system. Quantifying the impact of weather is, therefore, a key feature to improve the decision-making process that enhances airport performance. It would allow airport operators to identify the relevant weather information needed, and help them decide on the appropriate actions to mitigate the consequences of adverse weather events. Therefore, this research aims to understand and quantify the impact of weather conditions on airport arrival processes, so it can be properly predicted and managed. Design/methodology/approach This study presents a methodology to evaluate the impact of adverse weather events on airport arrival performance (delay and throughput) and to define operational thresholds for significant weather conditions. This study uses a Bayesian Network approach to relate weather data from meteorological reports and airport arrival performance data with scheduled and actual movements, as well as arrival delays. This allows us to understand the relationships between weather phenomena and their impacts on arrival delay and throughput. The proposed model also provides us with the values of the explanatory variables (weather events) that lead to certain operational thresholds in the target variables (arrival delay and throughput). This study then presents a quantification of the airport performance with regard to an aggregated weather-performance metric. Specific weather phenomena are categorized through a synthetic index, which aims to quantify weather conditions at a given airport, based on aviation routine meteorological reports. This helps us to manage uncertainty at airport arrival operations by relating index levels with airport performance results. Findings The results are computed from a data set of over 750,000 flights on a major European hub and from local weather data during the period 2015–2018. This study combines delay and capacity metrics at different airport operational stages for the arrival process (final approach, taxi-in and in-block). Therefore, the spatial boundary of this study is not only the airport but also its surrounding airspace, to take both the arrival sequencing and metering area and potential holding patterns into consideration. Originality/value This study introduces a new approach for modeling causal relationships between airport arrival performance indicators and meteorological events, which can be used to quantify the impact of weather in airport arrival conditions, predict the evolution of airport operational scenarios and support airport decision-making processes.

Modelling of 10 Gbps Free Space Optics Communication Link Using Array of Receivers in Moderate and Harsh Weather Conditions

2017 ◽  
Vol 38 (3) ◽  
Author(s):  
Amit Gupta ◽  
Nagpal Shaina
Keyword(s):  
Free Space ◽  
Weather Conditions ◽  
Quality Data ◽  
Laser Source ◽  
Communication Link ◽  
Free Space Optical ◽  
Space Optics ◽  
Received Power ◽  
Adverse Weather ◽  
The Impact

AbstractIntersymbol interference and attenuation of signal are two major parameters affecting the quality of transmission in Free Space Optical (FSO) Communication link. In this paper, the impact of these parameters on FSO communication link is analysed for delivering high-quality data transmission. The performance of the link is investigated under the influence of amplifier in the link. The performance parameters of the link like minimum bit error rate, received signal power and Quality factor are examined by employing erbium-doped fibre amplifier in the link. The effects of amplifier are visualized with the amount of received power. Further, the link is simulated for moderate weather conditions at various attenuation levels on transmitted signal. Finally, the designed link is analysed in adverse weather conditions by using high-power laser source for optimum performance.

scholarly journals Examining the Impact of Adverse Weather on Travel Time Reliability of Urban Corridors in Shanghai

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yajie Zou ◽  
Ting Zhu ◽  
Yifan Xie ◽  
Linbo Li ◽  
Ying Chen
Keyword(s):  
Travel Time ◽  
Weather Condition ◽  
Time Of Day ◽  
Weather Data ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Time Data ◽  
Adverse Weather Condition ◽  
Adverse Weather ◽  
The Impact

Travel time reliability (TTR) is widely used to evaluate transportation system performance. Adverse weather condition is an important factor for affecting TTR, which can cause traffic congestions and crashes. Considering the traffic characteristics under different traffic conditions, it is necessary to explore the impact of adverse weather on TTR under different conditions. This study conducted an empirical travel time analysis using traffic data and weather data collected on Yanan corridor in Shanghai. The travel time distributions were analysed under different roadway types, weather, and time of day. Four typical scenarios (i.e., peak hours and off-peak hours on elevated expressway, peak hours and off-peak hours on arterial road) were considered in the TTR analysis. Four measures were calculated to evaluate the impact of adverse weather on TTR. The results indicated that the lognormal distribution is preferred for describing the travel time data. Compared with off-peak hours, the impact of adverse weather is more significant for peak hours. The travel time variability, buffer time index, misery index, and frequency of congestion increased by an average of 29%, 19%, 22%, and 63%, respectively, under the adverse weather condition. The findings in this study are useful for transportation management agencies to design traffic control strategies when adverse weather occurs.

Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil

2019 ◽  
Vol 99 (1) ◽  
pp. 12-24 ◽  
Author(s):  
Rezvan Taki ◽  
Claudia Wagner-Riddle ◽  
Gary Parkin ◽  
Rob Gordon ◽  
Andrew VanderZaag
Keyword(s):  
Missing Values ◽  
Interpolation Method ◽  
Weather Conditions ◽  
Linear Interpolation ◽  
Bias Error ◽  
Research Station ◽  
Ann Model ◽  
Gap Filling ◽  
Adverse Weather ◽  
The Impact

Micrometeorological methods are ideally suited for continuous measurements of N2O fluxes, but gaps in the time series occur due to low-turbulence conditions, power failures, and adverse weather conditions. Two gap-filling methods including linear interpolation and artificial neural networks (ANN) were utilized to reconstruct missing N2O flux data from a corn–soybean–wheat rotation and evaluate the impact on annual N2O emissions from 2001 to 2006 at the Elora Research Station, ON, Canada. The single-year ANN method is recommended because this method captured flux variability better than the linear interpolation method (average R2 of 0.41 vs. 0.34). Annual N2O emission and annual bias resulting from linear and single-year ANN were compatible with each other when there were few and short gaps (i.e., percentage of missing values <30%). However, with longer gaps (>20 d), the bias error in annual fluxes varied between 0.082 and 0.344 kg N2O-N ha−1 for linear and 0.069 and 0.109 kg N2O-N ha−1 for single-year ANN. Hence, the single-year ANN with lower annual bias and stable approach over various years is recommended, if the appropriate driving inputs (i.e., soil temperature, soil water content, precipitation, N mineral content, and snow depth) needed for the ANN model are available.

scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

scholarly journals Impacts of Microclimate Conditions on the Energy Performance of Buildings in Urban Areas

Buildings ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 189 ◽  
Author(s):  
Javanroodi ◽  
M.Nik
Keyword(s):  
Urban Areas ◽  
Energy Demand ◽  
Weather Conditions ◽  
Energy Performance ◽  
Urban Morphology ◽  
Weather Data ◽  
Architectural Form ◽  
Energy Performance Of Buildings ◽  
Microclimate Conditions ◽  
The Impact

Urbanization trends have changed the morphology of cities in the past decades. Complex urban areas with wide variations in built density, layout typology, and architectural form have resulted in more complicated microclimate conditions. Microclimate conditions affect the energy performance of buildings and bioclimatic design strategies as well as a high number of engineering applications. However, commercial energy simulation engines that utilize widely-available mesoscale weather data tend to underestimate these impacts. These weather files, which represent typical weather conditions at a location, are mostly based on long-term metrological observations and fail to consider extreme conditions in their calculation. This paper aims to evaluate the impacts of hourly microclimate data in typical and extreme climate conditions on the energy performance of an office building in two different urban areas. Results showed that the urban morphology can reduce the wind speed by 27% and amplify air temperature by more than 14%. Using microclimate data, the calculated outside surface temperature, operating temperature and total energy demand of buildings were notably different to those obtained using typical regional climate model (RCM)–climate data or available weather files (Typical Meteorological Year or TMY), i.e., by 61%, 7%, and 21%, respectively. The difference in the hourly peak demand during extreme weather conditions was around 13%. The impact of urban density and the final height of buildings on the results are discussed at the end of the paper.

Investigating the Impact of Rainfall on Travel Speed

2014 ◽  
Vol 71 (3) ◽  
Author(s):  
Nordiana Mashros ◽  
Johnnie Ben-Edigbe ◽  
Hashim Mohammed Alhassan ◽  
Sitti Asmah Hassan
Keyword(s):  
Empirical Studies ◽  
Heavy Rain ◽  
Weather Conditions ◽  
Travel Speed ◽  
Operating Conditions ◽  
Speed Reduction ◽  
Light Rain ◽  
Rainfall Condition ◽  
Adverse Weather ◽  
The Impact

The road network is particularly susceptible to adverse weather with a range of impacts when different weather conditions are experienced. Adverse weather often leads to decreases in traffic speed and subsequently affects the service levels. The paper is aimed at investigating the impact of rainfall on travel speed and quantifying the extent to which travel speed reduction occurs. Empirical studies were conducted on principle road in Terengganu and Johor, respectively for three months. Traffic data were collected by way of automatic traffic counter and rainfall data from the nearest raingauge station were supplied by the Department of Irrigation and Drainage supplemented by local survey data. These data were filtered to obtain traffic flow information for both dry and wet operating conditions and then were analyzed to see the effect of rainfall on percentile speeds. The results indicated that travel speed at 15th, 50th and 85th percentiles decrease with increasing rainfall intensities. It was observed that allpercentile speeds decreased from a minimum of 1% during light rain to a maximum of 14% during heavy rain. Based on the hypothesis that travel speed differ significantly between dry and rainfall condition; the study found substantial changes in percentile speeds and concluded that rainfalls irrespective of their intensities have significant impact on the travel speed.

scholarly journals Weather Impact on Airport Performance

Aerospace ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 109 ◽  
Author(s):  
Michael Schultz ◽  
Sandro Lorenz ◽  
Reinhard Schmitz ◽  
Luis Delgado
Keyword(s):  
Traffic Management ◽  
Probability Distributions ◽  
Weather Conditions ◽  
Air Traffic ◽  
Weather Data ◽  
Data Set ◽  
Airport Capacity ◽  
Weather Events ◽  
The Individual ◽  
The Impact

Weather events have a significant impact on airport performance and cause delayed operations if the airport capacity is constrained. We provide quantification of the individual airport performance with regards to an aggregated weather-performance metric. Specific weather phenomena are categorized by the air traffic management airport performance weather algorithm, which aims to quantify weather conditions at airports based on aviation routine meteorological reports. Our results are computed from a data set of 20.5 million European flights of 2013 and local weather data. A methodology is presented to evaluate the impact of weather events on the airport performance and to select the appropriate threshold for significant weather conditions. To provide an efficient method to capture the impact of weather, we modelled departing and arrival delays with probability distributions, which depend on airport size and meteorological impacts. These derived airport performance scores could be used in comprehensive air traffic network simulations to evaluate the network impact caused by weather induced local performance deterioration.

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 Cardiovascular risk assessment of the population under adverse weather conditions

2020 ◽  
Vol 99 (5) ◽  
pp. 488-492
Author(s):  
R. B. Tsallagova ◽  
O. I. Kopytenkova ◽  
Fatima K. Makoeva ◽  
A. R. Nanieva
Keyword(s):  
Negative Impact ◽  
Adult Population ◽  
Medical Science ◽  
Weather Conditions ◽  
Air Pressure ◽  
Medical Documentation ◽  
Atmospheric Air ◽  
Adverse Weather ◽  
The Impact ◽  
The City

Introduction. Climate change around the world determines the relevance of the study of its effects on the human health. Nowadays, due to the development of modern medical science, the methods of evidence-based analysis of negative impact of the environmental factors on the public health are being widely implemented into preventive medicine. Their use should make it possible to quantify the various risks with high confidence and to manage them effectively. Material and methods. The weather conditions of the territory of Vladikavkaz for a 15-years period (2001-2015) have been studied. On the basis of the data from the primary medical documentation of emergency medical care (EMC) for the same period, the health status of the adult population in the city has been studied. The impact of the meteorological elements under the study on the frequency of seeking EMC was estimated using the relative (RR) and population risks (Rpop) values. Results. For the city of Vladikavkaz (according to the medical classification of weather conditions), high air humidity is typical for 65% of the days in a year, low air velocity (less than 3 m/s) - 77% of the days in a year. Inter-day fluctuations in temperature and atmospheric air pressure, exceeding the optimal levels for the human body, are recorded more than in 30% of the days in a year. Discussion. The city of Vladikavkaz is characterized by windless, wet weather, with frequent inter-day fluctuations in temperature and atmospheric air pressure, which corresponds to the clinically irritating and acute types of weather. Conclusion. The calculations of the relative and the population risks of impact of meteorological changes on the developing of urgent cardiovascular conditions in the population of Vladikavkaz showed RR of combining two unfavourable weather factors to be of 1.081 (p <0.0001), and the Rpop increases by more than 3600 additional EMC calls due to cardiovascular pathology.

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