scholarly journals Machine Learning Approach to predict Metro Ridership based on Land Use Densities

2021 ◽  
Author(s):  
Aya Hasan Alkhereibi ◽  
Tadesse Wakjira ◽  
Murat kucukvar ◽  
Uvais Qidwai ◽  
Deepti Muley ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Learning Approach ◽  
Transit System ◽  
Metro Station ◽  
Essential Requirement ◽  
Extremely Randomized Trees ◽  
Machine Learning Approach ◽  
Prediction Capability ◽  
Operation And Management

Predicting metro ridership is an essential requirement for efficient metro operation and management. The dependence of metro ridership on the land use densities entails a need for an accurate predictive model. To this end, the current study is aimed to develop a novel machine learning (ML) based model to predict the metro station ridership utilizing the land use densities near metro stations. The ridership data was obtained from Qatar Rail, and the land use data were obtained from the Ministry of Municipality and Environment in Qatar. The land use densities in the catchment area of 800 m around the metro stations have been considered in this study. The non-linear relationship between the metro ridership and land use densities has been captured through different ensemble ML models including random forests, extremely randomized trees, and gradient tree boosting. Results showed that the ML models, once meticulously optimized and trained are capable of producing an accurate prediction for metro ridership. Among the ML models, gradient tree boosting showed the highest prediction capability. The authors concluded that the proposed prediction model can be utilized by both urban and transport planners in their processes to plan the land use around metro stations, predict the transit demand from those plans, and ultimately achieve the optimal use of the transit system i.e., Transit-Oriented Developments.

scholarly journals Outlier detection in WSN by entropy based machine learning approach

2020 ◽  
Vol 20 (3) ◽  
pp. 1435
Author(s):  
Manmohan Singh Yadav ◽  
Shish Ahamad
Keyword(s):  
Machine Learning ◽  
Outlier Detection ◽  
Sensor Data ◽  
Learning Approach ◽  
Random Subspace ◽  
Environmental Disasters ◽  
Machine Learning Approach ◽  
The World ◽  
Prediction Capability

<p>Environmental disasters like flooding, earthquake etc. causes catastrophic effects all over the world. WSN based techniques have become popular in susceptibility modelling of such disaster due to their greater strength and efficiency in the prediction of such threats. This paper demonstrates the machine learning-based approach to predict outlier in sensor data with bagging, boosting, random subspace, SVM and KNN based frameworks for outlier prediction using a WSN data. First of all database is pre processed with 14 sensor motes with presence of outlier due to intrusion. Subsequently segmented database is created from sensor pairs. Finally, the data entropy is calculated and used as a feature to determine the presence of outlier used different approach. Results show that the KNN model has the highest prediction capability for outlier assessment.</p>

scholarly journals A multi-step machine learning approach to assess the impact of COVID-19 lockdown on NO2 attributable deaths in Milan and Rome, Italy

2022 ◽  
Vol 21 (1) ◽  
Author(s):  
Luca Boniardi ◽  
Federica Nobile ◽  
Massimo Stafoggia ◽  
Paola Michelozzi ◽  
Carla Ancona
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Air Pollution ◽  
Random Forest ◽  
Citizen Science ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Forest Models ◽  
Random Forest Models ◽  
The Impact

Abstract Background Air pollution is one of the main concerns for the health of European citizens, and cities are currently striving to accomplish EU air pollution regulation. The 2020 COVID-19 lockdown measures can be seen as an unintended but effective experiment to assess the impact of traffic restriction policies on air pollution. Our objective was to estimate the impact of the lockdown measures on NO2 concentrations and health in the two largest Italian cities. Methods NO2 concentration datasets were built using data deriving from a 1-month citizen science monitoring campaign that took place in Milan and Rome just before the Italian lockdown period. Annual mean NO2 concentrations were estimated for a lockdown scenario (Scenario 1) and a scenario without lockdown (Scenario 2), by applying city-specific annual adjustment factors to the 1-month data. The latter were estimated deriving data from Air Quality Network stations and by applying a machine learning approach. NO2 spatial distribution was estimated at a neighbourhood scale by applying Land Use Random Forest models for the two scenarios. Finally, the impact of lockdown on health was estimated by subtracting attributable deaths for Scenario 1 and those for Scenario 2, both estimated by applying literature-based dose–response function on the counterfactual concentrations of 10 μg/m3. Results The Land Use Random Forest models were able to capture 41–42% of the total NO2 variability. Passing from Scenario 2 (annual NO2 without lockdown) to Scenario 1 (annual NO2 with lockdown), the population-weighted exposure to NO2 for Milan and Rome decreased by 15.1% and 15.3% on an annual basis. Considering the 10 μg/m3 counterfactual, prevented deaths were respectively 213 and 604. Conclusions Our results show that the lockdown had a beneficial impact on air quality and human health. However, compliance with the current EU legal limit is not enough to avoid a high number of NO2 attributable deaths. This contribution reaffirms the potentiality of the citizen science approach and calls for more ambitious traffic calming policies and a re-evaluation of the legal annual limit value for NO2 for the protection of human health.

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