Modeling of the effective environment in the Republic of Tatarstan using transport data

Minnikhanov R.N., Anikin I.V., Dagaeva M.V., Faizrakhmanov E.M., Bolshakov T.E.
 pdf (809K)

Automated urban traffic monitoring systems are widely used to solve various tasks in intelligent transport systems of different regions. They include video enforcement, video surveillance, traffic management system, etc. Effective traffic management and rapid response to traffic incidents require continuous monitoring and analysis of information from these complexes, as well as time series forecasting for further anomaly detection in traffic flow. To increase the forecasting quality, data fusion from different sources is needed. It will reduce the forecasting error, related to possible incorrect values and data gaps. We implemented the approach for short-term and middle-term forecasting of traffic flow (5, 10, 15 min) based on data fusion from video enforcement and video surveillance systems. We made forecasting using different recurrent neural network architectures: LSTM, GRU, and bidirectional LSTM with one and two layers. We investigated the forecasting quality of bidirectional LSTM with 64 and 128 neurons in hidden layers. The input window size (1, 4, 12, 24, 48) was investigated. The RMSE value was used as a forecasting error. We got minimum RMSE = 0.032405 for basic LSTM with 64 neurons in the hidden layer and window size = 24.

Keywords: transport modeling, video enforcement, traffic flow forecasting
Citation in English: Minnikhanov R.N., Anikin I.V., Dagaeva M.V., Faizrakhmanov E.M., Bolshakov T.E. Modeling of the effective environment in the Republic of Tatarstan using transport data // Computer Research and Modeling, 2021, vol. 13, no. 2, pp. 395-404
Citation in English: Minnikhanov R.N., Anikin I.V., Dagaeva M.V., Faizrakhmanov E.M., Bolshakov T.E. Modeling of the effective environment in the Republic of Tatarstan using transport data // Computer Research and Modeling, 2021, vol. 13, no. 2, pp. 395-404
DOI: 10.20537/2076-7633-2021-13-2-395-404
URL: http://crm-en.ics.org.ru/journal/article/3060/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Indexed in Scopus

Full-text version of the journal is also available on the web site of the scientific electronic library eLIBRARY.RU

The journal is included in the Russian Science Citation Index

The journal is included in the RSCI

International Interdisciplinary Conference "Mathematics. Computing. Education"

Copyright © 2009–2024 Institute of Computer Science