Short-Term Metro Ridership Prediction During Unplanned Events

2021 ◽  
pp. 036119812110375
Author(s):  
Yangyang Zhao ◽  
Zhenliang Ma ◽  
Xinguo Jiang ◽  
Haris N. Koutsopoulos
Keyword(s):  
Prediction Accuracy ◽  
Large Scale ◽  
Prediction Models ◽  
Imbalanced Data ◽  
Short Term ◽  
Training Problem ◽  
Term Prediction ◽  
Model Training ◽  
Short Term Prediction ◽  
The Impact

Unplanned events present significant challenges for operations and management in metro systems. Short-term ridership prediction can help agencies to better design contingency strategies under unplanned events. Though many short-term prediction methods have been proposed in the literature, most studies focused on typical situations or planned events. The study develops methods for the short-term metro ridership prediction under unplanned events. It explores event impact representation mechanisms and deals with the imbalanced data training problem in building the prediction model under unplanned events. Typical machine learning and deep learning methods are developed for exploration. A large-scale automatic fare collection (AFC) dataset and event record data for a heavily used metro system are used for empirical studies. The analysis found that the same type of unplanned event shares a similar and consistent demand change pattern (with respect to the demand under typical situations) at the station level. The synthetic minority oversampling technique (SMOTE) can enrich the ridership observations under unplanned events and generate a balanced dataset for model training. Given the occurrence of unplanned events, the results show that a combination of demand change ratio and the SMOTE oversampling technique enables the prediction models to learn the impact of unplanned events and improve the prediction accuracy under unplanned events. However, the oversampling methods (i.e., SMOTE and replication) slightly deteriorate the prediction accuracy for ridership under normal conditions. The findings provide insights into mechanisms for disruption impact representation and oversampling imbalanced data in model training, and guide the development of models for short-term prediction under unplanned events.

scholarly journals Improving the Accuracy of the Martian Ephemeris Short-Term Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Quan Shan ◽  
Peijia Li ◽  
Yong Huang ◽  
Min Fan ◽  
Haitao Li ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Arc Length ◽  
Short Term ◽  
Mars Exploration ◽  
Obvious Effect ◽  
Term Prediction ◽  
Exploration Mission ◽  
Short Term Prediction ◽  
Orbit Insertion ◽  
Better Than

The Chinese Mars exploration mission is planned to be launched in 2020, which includes an orbiter, a lander, and a rover. High precision Martian ephemeris is very important in Mars exploration, especially for the Martian orbit insertion and the Martian lander/rover landing. In this paper, we used simulation data to analyze the short-term prediction accuracy of the Martian ephemeris. The simulation results show that the accuracy of Mars position is expected to be better than 50 m for 180-day prediction, when 90-150 days’ range measurements are used to estimate the orbit of the Mars. Range bias affects the prediction accuracy and the arc length for estimation is limited. The prediction accuracy will improve with higher orbit, and the orbit error of probes has an obvious effect on the prediction accuracy of the Martian ephemeris.

scholarly journals Short-Term Prediction of Energy Consumption in Demand Response for Blocks of Buildings: DR-BoB Approach

Buildings ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 221
Author(s):  
Nashwan Dawood
Keyword(s):  
Demand Response ◽  
Large Scale ◽  
Economic Benefits ◽  
Least Square ◽  
Percentage Error ◽  
Pilot Site ◽  
Short Term ◽  
Rls Algorithm ◽  
Term Prediction ◽  
Short Term Prediction

Load forecasting plays a major role in determining the prices of the energy supplied to end customers. An accurate prediction is vital for the energy companies, especially when it comes to the baseline calculations that are used to predict the energy load. In this paper, an accurate short-term prediction using the Exponentially Weighted Extended Recursive Least Square (EWE-RLS) algorithm based upon a standard Kalman filter is implemented to predict the energy load for blocks of buildings in a large-scale for four different European pilot sites. A new software tool, namely Local Energy Manager (LEM), is developed to implement the RLS algorithm and predict the forecast for energy demand a day ahead with a regular meter frequency of a quarter of an hour. The EWE-RLS algorithm is used to develop the LEM in demand response for blocks of buildings (DR-BOB), this is part of a large-scale H2020 EU project with the aim to generate the energy baselines during and after running demand response (DR) events. This is achieved in order to evaluate and measure the energy reduction as compared with historical data to demonstrate the environmental and economic benefits of DR. The energy baselines are generated based on different market scenarios, different temperature, and energy meter files with three different levels of asset, building, and a whole pilot site level. The prediction results obtained from the Mean Absolute Percentage Error (MAPE) offer a 5.1% high degree of accuracy and stability at a UK pilot site level compared to the asset and whole building scenarios, where it shows a very acceptable prediction accuracy of 10.7% and 19.6% respectively.

scholarly journals Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zhijin Wang ◽  
Yaohui Huang ◽  
Bingyan He ◽  
Ting Luo ◽  
Yongming Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
High Morbidity ◽  
Infectious Diarrhea ◽  
Short Term ◽  
Search Data ◽  
Parsimonious Model ◽  
Using Data ◽  
Short Term Prediction ◽  
And Control

Infectious diarrhea has high morbidity and mortality around the world. For this reason, diarrhea prediction has emerged as an important problem to prevent and control outbreaks. Numerous studies have built disease prediction models using large-scale data. However, these methods perform poorly on diarrhea data. To address this issue, this paper proposes a parsimonious model (PM), which takes historical outpatient visit counts, meteorological factors (MFs) and Baidu search indices (BSIs) as inputs to perform prediction. An experimental evaluation was done to compare the short-term prediction performance of ten algorithms for four groups of inputs, using data collected in Xiamen, China. Results show that the proposed method is effective in improving the prediction accuracy.

scholarly journals The Use of Stochastic Models for Short-Term Prediction of Water Parameters of the Thesaurus Dam, River Nestos, Greece

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 634 ◽  
Author(s):  
Antonis Sentas ◽  
Lina Karamoutsou ◽  
Nikos Charizopoulos ◽  
Thomas Psilovikos ◽  
Aris Psilovikos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stochastic Models ◽  
Prediction Models ◽  
Short Term ◽  
Predictive Capacity ◽  
Water Parameters ◽  
Term Prediction ◽  
Artificial Neural ◽  
Short Term Prediction

The scope of this paper is to evaluate the short-term predictive capacity of the stochastic models ARIMA, Transfer Function (TF) and Artificial Neural Networks for water parameters, specifically for 1, 2 and 3 steps forward (m = 1, 2 and 3). The comparison of statistical parameters indicated that ARIMA models could be proposed as short-term prediction models. In some cases that TF models resulted in better predictions, the difference with ARIMA was minimal and since the latter are simpler in their construction, they are proposed for short-term prediction. Artificial Neural Networks didn’t show a good short-term predictive capacity in comparison with the aforementioned models.

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