scholarly journals Grapevine Phenology in Four Portuguese Wine Regions: Modeling and Predictions

2021 ◽  
Author(s):  
Samuel Reis ◽  
Helder Fraga ◽  
Cristina Carlos ◽  
José Silvestre ◽  
José Eiras-Dias ◽  
...  
Keyword(s):  
High Performance ◽  
Mean Squared Error ◽  
Cultural Practices ◽  
Meteorological Data ◽  
Efficiency Coefficient ◽  
Short Term ◽  
Phenological Stages ◽  
Predictive Capacity ◽  
Phenological Model ◽  
Wine Region

<p>Phenological models applied to grapevines are valuable tools to assist in the decision of cultural practices related to winegrowers and winemakers. The two-parameter sigmoid phenological model was used to estimate the three main phenological stages of the grapevine development, i.e., budburst, flowering, and veraison. This model was calibrated and validated with phenology data for 51 grapevine varieties distributed in four wine regions in Portugal (Lisboa, Douro, Dão, and Vinhos Verdes). Meteorological data for the selected sites were also used. Hence, 153 model calibrations (51 varieties × 3 phenological stages) and corresponding parameter estimations were carried out based on an unprecedented comprehensive and systematized dataset of phenology in Portugal. For each phenological stage, the centroid of the estimated parameters was subsequently used, and three generalized sigmoid models were constructed (budburst: d =−0.6, e = 8.6; flowering: d = −0.6, e = 13.7; veraison: d = −0.5, e = 13.2). Centroid parameters show high performance for approximately 90% of the varieties and can thereby be used instead of variety-specific parameters. Overall, the RMSE (root-mean-squared-error) is < 7 days, while the EF (efficiency coefficient) is > 0.5. Additionally, according to other studies, the predictive capacity of the models for budburst remains lower than for flowering or veraison. Furthermore, the F-forcing parameter (thermal accumulation) was evaluated for the Lisboa wine region, where the sample size is larger, and for the varieties with model efficiency equal to or greater than 0.5. A ranking and categorization of the varieties in early, intermediate, and late varieties was subsequently undertaken on the basis of F values. In this way, these results of the present study will be incorporated on a web platform, where the sigmoid model must convey valuable information regarding the development/evolution of the vineyard with short-term predictions.</p><p><strong>Keywords: </strong>grapevine; phenology modeling; sigmoid model; wine regions; short-term predictions; Portugal</p>

scholarly journals Grapevine Phenology in Four Portuguese Wine Regions: Modeling and Predictions

2020 ◽  
Vol 10 (11) ◽  
pp. 3708
Author(s):  
Samuel Reis ◽  
Helder Fraga ◽  
Cristina Carlos ◽  
José Silvestre ◽  
José Eiras-Dias ◽  
...  
Keyword(s):  
High Performance ◽  
Mean Squared Error ◽  
Cultural Practices ◽  
Meteorological Data ◽  
Efficiency Coefficient ◽  
Phenological Stages ◽  
Predictive Capacity ◽  
Phenological Model ◽  
Parameter Estimations ◽  
Wine Region

Phenological models applied to grapevines are valuable tools to assist in the decision of cultural practices related to winegrowers and winemakers. The two-parameter sigmoid phenological model was used to estimate the three main phenological stages of the grapevine development, i.e., budburst, flowering, and veraison. This model was calibrated and validated with phenology data for 51 grapevine varieties distributed in four wine regions in Portugal (Lisboa, Douro, Dão, and Vinhos Verdes). Meteorological data for the selected sites were also used. Hence, 153 model calibrations (51 varieties × 3 phenological stages) and corresponding parameter estimations were carried out based on an unprecedented comprehensive and systematized dataset of phenology in Portugal. For each phenological stage, the centroid of the estimated parameters was subsequently used, and three generalized sigmoid models (GSM) were constructed (budburst: d = −0.6, e = 8.6; flowering: d = −0.6, e = 13.7; veraison: d = −0.5, e = 13.2). Centroid parameters show high performance for approximately 90% of the varieties and can thereby be used instead of variety-specific parameters. Overall, the RMSE (root-mean-squared-error) is < 7 days, while the EF (efficiency coefficient) is > 0.5. Additionally, according to other studies, the predictive capacity of the models for budburst remains lower than for flowering or veraison. Furthermore, the F-forcing parameter (thermal accumulation) was evaluated for the Lisboa wine region, where the sample size is larger, and for the varieties with model efficiency equal to or greater than 0.5. A ranking and categorization of the varieties in early, intermediate, and late varieties was subsequently undertaken on the basis of F values. These results can be used to more accurately monitor and predict grapevine phenology during a given season, thus supporting decision making in the Portuguese wine sector.

scholarly journals Relationship Between Phenological and Meteorological Data as an Important Input Into Spring Barley Phenological Model

2019 ◽  
Vol 67 (3) ◽  
pp. 679-688
Author(s):  
Lenka Hájková ◽  
Martin Možný ◽  
Věra Kožnarová ◽  
Lenka Bartošová ◽  
Zdeněk Žalud
Keyword(s):  
Meteorological Data ◽  
Spring Barley ◽  
Meteorological Parameter ◽  
Grain Filling ◽  
Phenological Stage ◽  
Phenological Stages ◽  
Phenological Model ◽  
Average Value ◽  
Precipitation Total ◽  
Grain Filling Period

In this study, phenological and meteorological data have been used to interpret the relationship and influence of weather on current phenological stages of spring barley. The analyses were focused mainly on the stages closely connected with yield and grain filling period – tillering (BBCH 21), heading (BBCH 55) and yellow ripeness (BBCH 85). The aims of this paper were to: (1) calculate the trend in phenological development of spring barley from CHMI phenological stations in period 1991 – 2012 at different climatic zones; (2) evaluate the trend in number of days between phenological stages; (3) evaluate the sums of growing degree days above threshold above 5 °C (GDD) and precipitation totals to phenophase onset calculated since the phenological stage of emergence (BBCH 10); (4) calculate Pearson’s correlation coefficient (PCC) between phenological stage and meteorological parameter. The highest positive PCC was found between GDD and phenological stages of heading and yellow ripeness at Doksany and Strážnice stations situated in lowlands. The average value of GDD to phenological stage heading is within the range from 418.4 to 500.1 °C. The sums of precipitation totals fluctuate from 73.9 mm (Doksany station) to 123.2 mm (Chrastava station). The results of this study suggest that GDD can be a more suitable parameter for phenological model of spring barley development than precipitation total.

scholarly journals Modelling the phenological development of cv. Touriga Nacional and Encruzado in the Dão Wine Region, Portugal

OENO One ◽  
2021 ◽  
Vol 55 (3) ◽  
pp. 337-352
Author(s):  
Pedro Rodrigues ◽  
Vanda Pedroso ◽  
Carla Henriques ◽  
Ana Matos ◽  
Samuel Reis ◽  
...  
Keyword(s):  
Decision Support ◽  
High Performance ◽  
Model Development ◽  
Decision Support Tool ◽  
Weather Conditions ◽  
Sigmoid Function ◽  
Phenological Stages ◽  
Support Tool ◽  
Berry Quality ◽  
Wine Region

The grapevine vegetative cycle, which is morphologically described by its phenological stages, is strongly determined by weather conditions. Phenological models are widely applied in viticulture and are based on the assumption that air temperature is the preponderant environmental factor which determines vine development. In this study, phenological development models (PDMs) were calibrated and validated to simulate several intermediate stages between budbreak and veraison for cv. Touriga Nacional (TN) and cv. Encruzado (EN) winegrape varieties, which are widely grown in the Dao Wine Region, Portugal. These are thermal models, with which the daily sum of the rate of forcing (R) was calculated using a sigmoid function. For this purpose, a high-quality and comprehensive dataset was used which combines phenology data and weather station data in several vineyard sites spread over the region. The model showed an overall high performance (global RMSE of 5.4 days for EN and 5.0 days for TN), although it depended on the phenological stage and variety. The RMSE ranged from 3.2 to 6.2 for TN, and from 3.9 to 6.8 for EN. For both varieties and in all phenological stages, the RMSE was significantly lower than the standard deviation of the phenological observations. For TN, the model efficiency was greater than 0.71 for all phenological stages. In future studies, these models will be combined with specific models that simulate the evolution of winegrape berry quality indicators commonly used for harvest decision support. The relatively low complexity of the selected PDMs enables their use as a crop management and decision support tool. To our knowledge, no previous studies have been carried out on either of these two varieties and their intermediate phenological timings. The present study is an illustration of conceivable model development under diverse environmental conditions, thus allowing similar approaches to be adopted in other wine regions on a worldwide scale.

scholarly journals Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 95
Author(s):  
Yuan Gong ◽  
Christina L. Staudhammer ◽  
Susanne Wiesner ◽  
Gregory Starr ◽  
Yinlong Zhang
Keyword(s):  
Climate Change ◽  
Soil Water ◽  
Prescribed Fire ◽  
Water Availability ◽  
Longleaf Pine ◽  
Growing Season ◽  
Soil Water Availability ◽  
Future Climate Change ◽  
Short Term ◽  
Phenological Model

Understanding plant phenological change is of great concern in the context of global climate change. Phenological models can aid in understanding and predicting growing season changes and can be parameterized with gross primary production (GPP) estimated using the eddy covariance (EC) technique. This study used nine years of EC-derived GPP data from three mature subtropical longleaf pine forests in the southeastern United States with differing soil water holding capacity in combination with site-specific micrometeorological data to parameterize a photosynthesis-based phenological model. We evaluated how weather conditions and prescribed fire led to variation in the ecosystem phenological processes. The results suggest that soil water availability had an effect on phenology, and greater soil water availability was associated with a longer growing season (LOS). We also observed that prescribed fire, a common forest management activity in the region, had a limited impact on phenological processes. Dormant season fire had no significant effect on phenological processes by site, but we observed differences in the start of the growing season (SOS) between fire and non-fire years. Fire delayed SOS by 10 d ± 5 d (SE), and this effect was greater with higher soil water availability, extending SOS by 18 d on average. Fire was also associated with increased sensitivity of spring phenology to radiation and air temperature. We found that interannual climate change and periodic weather anomalies (flood, short-term drought, and long-term drought), controlled annual ecosystem phenological processes more than prescribed fire. When water availability increased following short-term summer drought, the growing season was extended. With future climate change, subtropical areas of the Southeastern US are expected to experience more frequent short-term droughts, which could shorten the region’s growing season and lead to a reduction in the longleaf pine ecosystem’s carbon sequestration capacity.

scholarly journals AB1082 INFLUENCE OF THE VARIATION OF THE OPERATOR, PATIENT POSITION AND DEVICE ON THE MEASUREMENT PERFORMANCE OF RADIOFREQUENCY ECHOGRAPHIC MULTI SPECTROMETRY (REMS)

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1830.1-1830
Author(s):  
C. Caffarelli ◽  
G. Adami ◽  
G. Arioli ◽  
G. Bianchi ◽  
M. L. Brandi ◽  
...  
Keyword(s):  
Lumbar Spine ◽  
Femoral Neck ◽  
High Performance ◽  
Mineral Density ◽  
Research Support ◽  
Short Term ◽  
Alphabetical Order ◽  
Patient Position ◽  
Term Monitoring

Background:The monitoring of bone mineral density (BMD) is a key aspect for patients undergoing pharmacological treatments that might cause BMD changes at non-physiological rates. At present, the short-term follow-up of patients under treatment in terms of BMD change with time remains an unmet clinical need, since the current techniques, including the gold standard dual X-ray absorptiometry (DXA), require at least 1 year between two consecutive measurements [1]. Therefore, an effective strategy for the assessment of BMD should guarantee high accuracy, precision and repeatability of the measurements.Objectives:The aim is to assess the influence of the variation 1) in patient position, 2) operator (both intra- and inter-) and 3) device on the REMS performance at lumbar spine and femoral neck.Methods:210 women were enrolled, divided in 7 groups of 30-patient each for the assessment of the parameters of interest, i.e. inter-device, intra- and inter-operator repeatability for lumbar spine scans and inter-patient position, inter-device, intra- and inter-operator repeatability for femoral neck scans.All patients underwent 2 REMS scans at lumbar spine or femoral neck, performed by the same operator or by 2 different operators or by the same operator using 2 different devices or in different patient position (i.e. supine without constraints or with a constrained 25°-rotation of the leg). The percentage coefficient of variation (CV%) with 95% confidence interval and least significant change for a 95% confidence level (LSC) have been calculated.Results:For lumbar spine, intra-operator repeatability resulted in CV%=0.37% (95%CI: 0.26%-0.48%), with LSC=1.02%, inter-operator repeatability resulted in CV%=0.55% (95% CI: 0.42%-0.68%), with LSC=1.52%, inter-device repeatability resulted in CV%=0.53% (95% CI: 0.40%-0.66%), with LSC=1.47%.For femoral neck, intra-operator repeatability resulted in CV%=0.33% (95%CI: 0.23%-0.43%), with LSC=0.91%, inter-operator repeatability resulted in CV%=0.47% (95% CI: 0.35%-0.59%), with LSC=1.30%, inter-device repeatability resulted in CV%=0.42% (95% CI: 0.30%-0.51%), with LSC=1.16%, inter-patient position repeatability resulted in CV%=0.24% (95% CI: 0.18%-0.30%), with LSC=0.66%.Conclusion:REMS densitometry is highly precise for both anatomical sites, showing high performance in repeatability. These results suggest that REMS might be a suitable technology for short-term monitoring. Moreover, thanks to its ionizing radiation-free approach, it might be applied for population mass investigations and prevention programs also in paediatric patients and pregnant women.References:Note:Carla Caffarelli, Giovanni Adami§, Giovanni Arioli§, Gerolamo Bianchi§, Maria Luisa Brandi§, Sergio Casciaro§, Luisella Cianferotti§, Delia Ciardo§, Francesco Conversano§, Davide Gatti§, Giuseppe Girasole§, Monica Manfredini§, Maurizio Muratore§, Paola Pisani§, Eugenio Quarta§, Laura Quarta§, Stefano Gonnelli§Equal contributors listed in alphabetical orderDisclosure of Interests:Carla Caffarelli: None declared, Giovanni Adami: None declared, Giovanni Arioli *: None declared, Gerolamo Bianchi Grant/research support from: Celgene, Consultant of: Amgen, Janssen, Merck Sharp & Dohme, Novartis, UCB, Speakers bureau: Abbvie, Abiogen, Alfa-Sigma, Amgen, BMS, Celgene, Chiesi, Eli Lilly, GSK, Janssen, Medac, Merck Sharp & Dohme, Novartis, Pfizer, Roche, Sanofi Genzyme, Servier, UCB, Maria Luisa Brandi: None declared, Sergio Casciaro: None declared, Luisella Cianferotti: None declared, Delia Ciardo: None declared, Francesco Conversano: None declared, Davide Gatti Speakers bureau: Davide Gatti reports personal fees from Abiogen, Amgen, Janssen-Cilag, Mundipharma, outside the submitted work., Giuseppe Girasole: None declared, Monica Manfedini: None declared, Maurizio Muratore: None declared, Paola Pisani: None declared, Eugenio Quarta: None declared, Laura Quarta: None declared, Stefano Gonnelli: None declared

scholarly journals “Is It Overtraining or Just Work Ethic?”: Coaches’ Perceptions of Overtraining in High-Performance Strength Sports

Sports ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 85
Author(s):  
Lee Bell ◽  
Alan Ruddock ◽  
Tom Maden-Wilkinson ◽  
Dave Hembrough ◽  
David Rogerson
Keyword(s):  
Physical Performance ◽  
Thematic Analysis ◽  
High Performance ◽  
Higher Order ◽  
Short Term ◽  
Semistructured Interviews ◽  
Diagnostic Symptoms ◽  
Careful Manipulation ◽  
Sport Coaches

Optimal physical performance is achieved through the careful manipulation of training and recovery. Short-term increases in training demand can induce functional overreaching (FOR) that can lead to improved physical capabilities, whereas nonfunctional overreaching (NFOR) or the overtraining syndrome (OTS) occur when high training-demand is applied for extensive periods with limited recovery. To date, little is known about the OTS in strength sports, particularly from the perspective of the strength sport coach. Fourteen high-performance strength sport coaches from a range of strength sports (weightlifting; n = 5, powerlifting; n = 4, sprinting; n = 2, throws; n = 2, jumps; n = 1) participated in semistructured interviews (mean duration 57; SD = 10 min) to discuss their experiences of the OTS. Reflexive thematic analysis resulted in the identification of four higher order themes: definitions, symptoms, recovery and experiences and observations. Additional subthemes were created to facilitate organisation and presentation of data, and to aid both cohesiveness of reporting and publicising of results. Participants provided varied and sometimes dichotomous perceptions of the OTS and proposed a multifactorial profile of diagnostic symptoms. Prevalence of OTS within strength sports was considered low, with the majority of participants not observing or experiencing long-term reductions in performance with their athletes.

Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network 

2021 ◽  
Author(s):  
Sascha Flaig ◽  
Timothy Praditia ◽  
Alexander Kissinger ◽  
Ulrich Lang ◽  
Sergey Oladyshkin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Meteorological Data ◽  
Water Levels ◽  
Typical Structure ◽  
Water Abstraction ◽  
Physical Knowledge ◽  
Artificial Neural ◽  
The Impact

&lt;p&gt;In order to prevent possible negative impacts of water abstraction in an ecologically sensitive moor south of Munich (Germany), a &amp;#8220;predictive control&amp;#8221; scheme is in place. We design an artificial neural network (ANN) to provide predictions of moor water levels and to separate hydrological from anthropogenic effects. As the moor is a dynamic system, we adopt the &amp;#8222;Long short-term memory&amp;#8220; architecture.&lt;/p&gt;&lt;p&gt;To find the best LSTM setup, we train, test and compare LSTMs with two different structures: (1) the non-recurrent one-to-one structure, where the series of inputs are accumulated and fed into the LSTM; and (2) the recurrent many-to-many structure, where inputs gradually enter the LSTM (including LSTM forecasts from previous forecast time steps). The outputs of our LSTMs then feed into a readout layer that converts the hidden states into water level predictions. We hypothesize that the recurrent structure is the better structure because it better resembles the typical structure of differential equations for dynamic systems, as they would usually be used for hydro(geo)logical systems. We evaluate the comparison with the mean squared error as test metric, and conclude that the recurrent many-to-many LSTM performs better for the analyzed complex situations. It also produces plausible predictions with reasonable accuracy for seven days prediction horizon.&lt;/p&gt;&lt;p&gt;Furthermore, we analyze the impact of preprocessing meteorological data to evapotranspiration data using typical ETA models. Inserting knowledge into the LSTM in the form of ETA models (rather than implicitly having the LSTM learn the ETA relations) leads to superior prediction results. This finding aligns well with current ideas on physically-inspired machine learning.&lt;/p&gt;&lt;p&gt;As an additional validation step, we investigate whether our ANN is able to correctly identify both anthropogenic and natural influences and their interaction. To this end, we investigate two comparable pumping events under different meteorological conditions. Results indicate that all individual and combined influences of input parameters on water levels can be represented well. The neural networks recognize correctly that the predominant precipitation and lower evapotranspiration during one pumping event leads to a lower decrease of the hydrograph.&lt;/p&gt;&lt;p&gt;To further demonstrate the capability of the trained neural network, scenarios of pumping events are created and simulated.&lt;/p&gt;&lt;p&gt;In conclusion, we show that more robust and accurate predictions of moor water levels can be obtained if available physical knowledge of the modeled system is used to design and train the neural network. The artificial neural network can be a useful instrument to assess the impact of water abstraction by quantifying the anthropogenic influence.&lt;/p&gt;

scholarly journals Particulated Juvenile Articular Cartilage (PJAC) Allograft for Treatment of Lateral Femoral Condyle defect in High-Performance Athletes

2018 ◽  
Vol 6 (6_suppl3) ◽  
pp. 2325967118S0005 ◽  
Author(s):  
Gabriella Bucci ◽  
Michael Begg ◽  
Kevin Pillifant ◽  
Steven B Singleton
Keyword(s):  
Articular Cartilage ◽  
Cartilage Defect ◽  
High Performance ◽  
Femoral Condyle ◽  
Cartilage Lesion ◽  
Lateral Femoral Condyle ◽  
High Grade ◽  
Chondral Defect ◽  
Short Term ◽  
High Level

Background: A relatively new technology for the treatment of high grade articular cartilage lesions is the implantation of particulated articular cartilage obtained from a juvenile allograft donor (PJAC).1-2 Previous studies have reported the ability of juvenile chondrocytes to migrate from cartilage explants after being secured in a cartilage defect.3 There is little in the literature to use as a reference with respect to the use of PJAC for high grade articular cartilage lesion of the lateral femoral condyle after a failure of treatment with a microfracture in the high level athlete. Objective: The aim of this report is to describe the technique of PJAC transplantation for the treatment of chondral lesions of the lateral femoral condyle and to report the short term outcomes in the high performance athlete. Methods: We present a case report of two patients who were treated in our clinic in December 2014. Case 1: 16 year old female Division 1 university soccer player, who one year prior to our index surgery underwent microfractures of a symptomatic lateral femoral condyle articular cartilage lesion without relief. Cae 2: 29 year old male professional tennis player (case 2) with a recurrent, symptomatic chondral defect on the lateral femoral condyle. The player had undergone multiple arthroscopic procedures on the same knee following an injury sustained while playing in the Australian Open, including a surgery 8 months prior to our index operation that had included lateral meniscal tear repair and microfractures. PJAC procedure consists of a minimal debridement and chondroplasty, performed arthroscopically. For these central lateral femoral condyle lesions, a mini-arthrotomy is created along the lateral parapatellar longitudinal axis over a length of about 3 cm. With the chondral defect localized and prepared, a thin fresh layer of fibrin glue is then applied. The PJAC graft is equally distributed in the defect with space in between the fragments so as not over-fill the defect. Then, a new fibrin glue layer is placed to cover the graft. The overall construct remains just below the level of the normal articular surface. The knee is cycled through the range of motion to ensure that the tissue construct is stable. We present images of the cartilage defect after debridement and the allograft implantation procedure. In addition we will submit an instructional video performed on a knee specimen. Results: Outcomes measured were: IKDC, Lysholm, and Tegner knee scores together with arc of motion of the joint. After 28 months follow up, patients had gained complete range of motion and significantly decreased pain. Improvement for each outcome measure used is reported. Conclusions: PJAC transplantation offers pain relief and improved short term outcomes in high level performance athletes. Both of our patients are back to practicing their sport with notable improvement in symptoms. No complications have been noted. Long-term data is not yet available. References: Am J Farr J, Tabet SK, Margerrison E, Cole BJ. Clinical, Radiographic, and Histological Outcomes After Cartilage Repair With Particulated Juvenile Articular Cartilage: A 2-Year Prospective Study. Sports Med. 2014 Jun;42(6):1417-25. Saltzman BM, Lin J, Lee S. Particulated Juvenile Articular Cartilage Allograft Transplantation for Osteochondral Talar Lesions. Cartilage. 2017 Jan;8(1):61-72. Arshi A, Wang D, Jones KJ. Combined Particulated Juvenile Cartilage Allograft Transplantation and Medial Patellofemoral Ligament Reconstruction for Symptomatic Chondral Defects in the Setting of Recurrent Patellar Instability. Arthrosc Tech. 2016 Oct 10;5(5)

scholarly journals Characterization of COVID-19’s Impact on Mobility and Short-Term Prediction of Public Transport Demand in a Mid-Size City in Spain

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6574
Author(s):  
Ana Belén Rodríguez González ◽  
Mark R. Wilby ◽  
Juan José Vinagre Díaz ◽  
Rubén Fernández Pozo
Keyword(s):  
Public Transport ◽  
High Performance ◽  
Monitoring Network ◽  
Traffic Monitoring ◽  
Health Economy ◽  
Accurate Information ◽  
Short Term ◽  
Public And Private ◽  
The Impact

COVID-19 has dramatically struck each section of our society: health, economy, employment, and mobility. This work presents a data-driven characterization of the impact of COVID-19 pandemic on public and private mobility in a mid-size city in Spain (Fuenlabrada). Our analysis used real data collected from the public transport smart card system and a Bluetooth traffic monitoring network, from February to September 2020, thus covering relevant phases of the pandemic. Our results show that, at the peak of the pandemic, public and private mobility dramatically decreased to 95% and 86% of their pre-COVID-19 values, after which the latter experienced a faster recovery. In addition, our analysis of daily patterns evidenced a clear change in the behavior of users towards mobility during the different phases of the pandemic. Based on these findings, we developed short-term predictors of future public transport demand to provide operators and mobility managers with accurate information to optimize their service and avoid crowded areas. Our prediction model achieved a high performance for pre- and post-state-of-alarm phases. Consequently, this work contributes to enlarging the knowledge about the impact of pandemic on mobility, providing a deep analysis about how it affected each transport mode in a mid-size city.

Sign in / Sign up

Export Citation Format

Share Document