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  3. Volume 2 · Number 1
  4. TRAINING NEURAL NETWORK FOR TAXI PASSENGER DEMAND FORECASTING USING GRAPHICS PROCESSING UNITS

TRAINING NEURAL NETWORK FOR TAXI PASSENGER DEMAND FORECASTING USING GRAPHICS PROCESSING UNITS

UJIT.
2020;
Volume 2, Number 1
: 29-36
https://doi.org/10.23939/ujit2020.02.029
Received: October 15, 2020
Accepted: October 25, 2020

Цитування за ДСТУ: Згоба М. І., Грицюк Ю. І. Тренування нейронної мережі для прогнозування попиту на пасажирські пере­ве­зення таксі за допомогою графічних процесорів. Український журнал інформаційних технологій. 2020, т. 2, № 1. С. 29–36.

Citation APA: Zghoba, M. I., & Hrytsiuk, Yu. I. (2020). Neural network training for forecasting the demand for passenger transportation by taxi using graphics processors. Ukrainian Journal of Information Technology, 2(1), 29–36. https://doi.org/10.23939/ujit2020.02.029

Authors:
  1. M. I. Zghoba
  2. Yurii Hrytsiuk
1
Lviv Polytechnic National University, Software Department
2
Lviv Polytechnic National University, Lviv, Ukraine

The peculiarities of neural network training for forecasting taxi passenger demand using graphics processing units are considered, which allowed to speed up the training procedure for different sets of input data, hardware configurations, and its power. It has been found that taxi services are becoming more accessible to a wide range of people. The most important task for any transportation company and taxi driver is to minimize the waiting time for new orders and to minimize the distance from drivers to passengers on order receiving. Understanding and assessing the geographical passenger demand that depends on many factors is crucial to achieve this goal. This paper describes an example of neural network training for predicting taxi passenger demand. It shows the importance of a large input dataset for the accuracy of the neural network. Since the training of a neural network is a lengthy process, parallel training was used to speed up the training.

The neural network for forecasting taxi passenger demand was trained using different hardware configurations, such as one CPU, one GPU, and two GPUs. The training times of one epoch were compared along with these configurations. The impact of different hardware configurations on training time was analyzed in this work. The network was trained using a dataset containing 4.5  million trips within one city. The results of this study show that the training with GPU accelerators doesn't necessarily improve the training time. The training time depends on many factors, such as input dataset size, splitting of the entire dataset into smaller subsets, as well as hardware and power characteristics.

 

machine learning
demand forecasting
neural network training
training speedup
training parallelization
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