NEURAL NETWORK MODEL FOR PREDICTING PASSENGER CONGESTION TO OPTIMIZE TRAFFIC MANAGEMENT FOR URBAN PUBLIC TRANSPORT

The development of public transport in cities is an effective way to reduce “congestion” in the road network and, as a result, increase the speed of passenger transportation. Improving the qua¬lity of urban bus services helps attract more passengers. Bus intervals are calculated once for each route line individually, based on the average congestion of passengers at the stops. In turn, the sudden accumulation of a large number of passengers at bus stops causes that not all passengers can move in a timely manner, which causes concern for passengers. This is one of the factors that redu¬ces the quality of passenger transport services. The aim of the study is to develop a model for predicting the congestion of passengers at bus stops to optimize traffic management of urban public transport. Materials and methods. This article presents a neural network model for predicting passenger congestion at bus stops. It takes into account the spatio-temporal characteristics of bus traffic. Results. The developed model for predicting passenger congestion at bus stops was tested on real data from bus route 3 (Dushanbe, Tajikistan). The model made it possible to predict passenger traffic (the number of passengers at bus stops) with an accuracy of 72% to 74.5% of the actual number of passengers at bus stops. Conclusion. The proposed method, in contrast to other methods, allows you to automatically adapt the forecasting model to the changing conditions of the route line. This method is universal and can be used for other route lines (bus stops). It does not require much time to reconfigure.

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Automatic time series forecasting using nonlinear autoregressive neural network model with exogenous input

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v10i5.2862 ◽

2021 ◽

Vol 10 (5) ◽

pp. 2836-2844

Author(s):

Hermansah Hermansah ◽

Dedi Rosadi ◽

Abdurakhman Abdurakhman ◽

Herni Utami

Keyword(s):

Neural Network ◽

Time Series ◽

Network Model ◽

Neural Network Model ◽

Mean Squared Error ◽

Real Data ◽

Hidden Neurons ◽

Nonlinear Autoregressive ◽

Automatic Forecasting ◽

Autoregressive Neural Network

This study aims to determine an automatic forecasting method of univariate time series, using the nonlinear autoregressive neural network model with exogenous input (NARX). In this automatic setting, users only need to supply the input of time series. Then, an automatic forecasting algorithm sets up the appropriate features, estimate the parameters in the model, and calculate forecasts, without the users’ intervention. The algorithm method used include preprocessing, tests for trends, and the application of first differences. The time series were tested for seasonality, and seasonal differences were obtained from a successful analysis. These series were also linearly scaled to [−1, +1]. The autoregressive lags and hidden neurons were further selected through the stepwise and optimization algorithms, respectively. The 20 NARX models were fitted with different random starting weights, and the forecasts were combined using the ensemble operator, in order to obtain the final product. This proposed method was applied to real data, and its performance was compared with several available automatic models in the literature. The forecasting accuracy was also measured by mean squared error (MSE) and mean absolute percent error (MAPE), and the results showed that the proposed method outperformed the other automatic models.

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Deep Neural Networks For Predicting Restricted Mean Survival Times

Bioinformatics ◽

10.1093/bioinformatics/btaa1082 ◽

2021 ◽

Author(s):

Lili Zhao

Keyword(s):

Neural Network ◽

Survival Time ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

Real Data ◽

Supplementary Information ◽

Survival Times ◽

Regression Problem ◽

The Neural Network

Abstract Restricted mean survival time (RMST) is a useful summary measurement of the time-to-event data, and it has attracted great attention for its straightforward clinical interpretation. In this article, I propose a deep neural network model that directly relates the RMST to its baseline covariates for simultaneous prediction of RSMT at multiple times. Each subject’s survival time is transformed into a series of jackknife pseudo observations and then used as quantitative response variables in a deep neural network model. By using the pseudo values, a complex survival analysis is reduced to a standard regression problem, which greatly simplifies the neural network construction. By jointly modelling RMST at multiple times, the neural network model gains prediction accuracy by information sharing across times. The proposed network model was evaluated by extensive simulation studies and was further illustrated on three real datasets. In real data analyses, I also used methods to open the blackbox by identifying subject-specific predictors and their importance in contributing to the risk prediction. Availability and implementation The source code is freely available at http://github.com/lilizhaoUM/DnnRMST Supplementary information Supplementary data are available at Bioinformatics online.

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Neural network model for forecasting the development of road transport enterprises in a non-stationarity economy

E3S Web of Conferences ◽

10.1051/e3sconf/202124407003 ◽

2021 ◽

Vol 244 ◽

pp. 07003

Author(s):

Svetlana Borodulina

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Statistical Processing ◽

Road Transport ◽

Neural Network Modeling ◽

Development Indicators ◽

Transport Services ◽

Effective Performance ◽

Theoretical Foundations

The paper presents the results of the study of the influence of factors on the development indicators of road transport enterprises, describes the development indicators and their dynamics. On the basis of the theoretical foundations of non-stationarity, an interpretation of the concept of non-stationarity of the development of the economy and the industry market is proposed. On the basis of the crisis development scale developed by the author, the results of the cluster analysis of enterprises in the sample and the data of statistical processing of effective performance indicators, the forecast of the crisis development of road transport enterprises of Saint - Petersburg in the transport services market of the region is made using neural network modeling. With the help of the constructed neural network model of the dependence of the degree of crisis on the most significant indicators, it is possible to predict the development of crisis situations, and this indicates the possibility of predicting the non-stationarity development of road transport enterprises and the economy as a whole.

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