Impact of Weather, Activities, and Service Disruptions on Transportation Demand

2020 ◽  
pp. 036119812096632
Author(s):  
Simon Lepage ◽  
Catherine Morency
Keyword(s):  
Travel Demand ◽  
Moving Average ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Subway Station ◽  
Transportation Demand ◽  
Transportation Service ◽  
Moving Average Models ◽  
Spatial Differences ◽  
Demand Fluctuations

This paper aims to estimate short-term transportation demand fluctuations because of events such as meteorological events, major activities, and subway service disruptions. Four different modes are analyzed and compared, being bikesharing, taxi, subway, and bus. Case study includes 3 years of transactional data on working days collected in Montreal, Canada. Generalized additive models (GAM) are developed for every mode. The dependent variable is the hourly number of trip departures from one subway station neighborhood. Independent variables are data from various events. Different models are calibrated for every subway station neighborhood to better understand spatial differences. Also, performance of GAM and autoregressive integrated moving average models are compared for prediction on different horizons. Results suggest that presence of rain decreases bikesharing, subway, and bus demand, while increasing taxi demand. In fact, after four consecutive hours of rain, bikesharing demand decreases by 28.0%, subway and bus demand decreases by 4.6%, while taxi increases by 13.9%. Wind is only found significant for bikesharing. Temperature is found significant for all four modes but has a larger effect on bikesharing and taxi. Moreover, demand increases significantly during subway service disruptions for the three alternative modes studied, especially for taxi, suggesting an increase in demand of 182% during disruptions of 1 h. Furthermore, activities influence demand for all four modes, but subway seems to be the most affected one. This method allows for a better understanding of travel behaviors and makes it possible to consider a more dynamic adaptation of the transportation service supply to match travel demand based on various events. This could lead to better co-planning of events and transportation service, for example by temporarily increasing subway frequency or changing the position of some bikesharing stations.

scholarly journals Essential habitat for sardine juveniles in Iberian waters

2017 ◽  
Vol 81 (3) ◽  
pp. 351 ◽  
Author(s):  
Sílvia Rodríguez-Climent ◽  
Maria Manuel Angélico ◽  
Vítor Marques ◽  
Paulo Oliveira ◽  
Laura Wise ◽  
...  
Keyword(s):  
Fishery Management ◽  
Generalized Additive Models ◽  
Management Strategies ◽  
Coastal Region ◽  
Additive Models ◽  
Spawning Grounds ◽  
Significant Relationships ◽  
Juvenile Abundance ◽  
Spatial Differences ◽  
Core Areas

In a period when the Iberian sardine stock abundance is at its historical minimum, knowledge of the sardine juvenile’s distribution is crucial for the development of fishery management strategies. Generalized additive models were used to relate juvenile sardine presence with geographical variables and spawning grounds (egg abundance) and to model juvenile abundance with the concurrent environmental conditions. Three core areas of juvenile distribution were identified: the Northern Portuguese shelf (centred off Aveiro), the coastal region in the vicinity of the Tagus estuary, and the eastern Gulf of Cadiz. Spatial differences in the relationship between juvenile presence and egg abundances suggest that essential juvenile habitat might partially differ from the prevailing spawning grounds. Models also depicted significant relationships between juvenile abundance, temperature and geographical variables in combination with salinity in the west and with zooplankton in the south. Results indicate that the sardine juvenile distribution along the Iberian Peninsula waters are an outcome of a combination of dynamic processes occurring early in life, such as egg and larva retention, reduced mortality and favourable feeding grounds for both larvae and juveniles.

scholarly journals The effect of climate variables on the incidence of Crimean Congo Hemorrhagic Fever (CCHF) in Zahedan, Iran

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sairan Nili ◽  
Narges Khanjani ◽  
Yunes Jahani ◽  
Bahram Bakhtiari
Keyword(s):  
Goodness Of Fit ◽  
Moving Average ◽  
Seasonal Pattern ◽  
Generalized Additive Models ◽  
Hemorrhagic Fever ◽  
Additive Models ◽  
Meteorological Variables ◽  
Fatality Rate ◽  
Crimean Congo Hemorrhagic Fever ◽  
Sarima Models

Abstract Background The Crimean-Congo Hemorrhagic fever (CCHF) is endemic in Iran and has a high fatality rate. The aim of this study was to investigate the association between CCHF incidence and meteorological variables in Zahedan district, which has a high incidence of this disease. Methods Data about meteorological variables and CCHF incidence was inquired from 2010 to 2017 for Zahedan district. The analysis was performed using univariate and multivariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models and Generalized Additive Models (GAM) using R software. AIC, BIC and residual tests were used to test the goodness of fit of SARIMA models, and R2 was used to select the best model in GAM/GAMM. Results During the years under study, 190 confirmed cases of CCHF were identified in Zahedan district. The fatality rate of the disease was 8.42%. The disease trend followed a seasonal pattern. The results of multivariate SARIMA showed the (0,1,1) (0,1,1)12 model with maximum monthly temperature lagged 5 months, forecasted the disease better than other models. In the GAM, monthly average temperature lagged 5 months, and the monthly minimum of relative humidity and total monthly rainfall without lag, had a nonlinear relation with the incidence of CCHF. Conclusions Meteorological variables can affect CCHF occurrence.

Changes in the timing and length of the fire season in Spain

2020 ◽  
Author(s):  
Itziar R. Urbieta ◽  
Gonzalo Arellano ◽  
José M. Moreno
Keyword(s):  
Fire Suppression ◽  
Generalized Additive Models ◽  
Summer Season ◽  
Additive Models ◽  
Fire Season ◽  
Burned Area ◽  
Burned Areas ◽  
Spatial Differences ◽  
Mediterranean Regions ◽  
In Fire

<p>Fire activity has decreased in the last decades in Spain, as a whole and in most regions. However, little is known about the changes in the fire season peak, timing, and length. Here we studied the temporal variation in the fire season since the 1970’s for different Spanish regions. We analyzed weekly time series of annually burned area by fitting GAMs (Generalized Additive Models) models in R. Area burned was log transformed and smoothing P-splines were fit to study weekly seasonality. GAMS allowed us to model spring, summer, and autumn fire seasons. Changes in the sign of the smoothing parameter determined the timing (onset/end dates) of each fire season, while the maximum value of the parameter established the peak of the fire season. We applied trend analysis to study inter-annual variation in fire season timing, length, and amplitude. We found temporal and spatial differences in the fire season across regions. In the northern Atlantic regions, models performed better, and captured a bimodal fire season (spring-summer). Nonetheless, the bimodal fire-season structure is no longer distinguishable in recent years, since both are increasing in duration. In the Mediterranean regions, larger peaks of burned areas occur in shorter time spans. The amplitude and duration of the summer season is decreasing, probably due to the increase in fire suppression during the summer. The summer season is starting earlier, while, in general, no trend was found for the end of the season. Furthermore, spring fire peaks in Mediterranean regions are becoming more frequent, suggesting that more attention should be paid to these out-of-season conditions.</p>

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

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