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Predicting Traffic Indexes on Urban Roads Based on Public Transportation Vehicle Data in Experimental Environment

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Intelligent Computing (SAI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 508))

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Abstract

Having the ability to accurately predict the traffic situation on urban roads can be useful for creating administrative traffic control systems, navigation software solutions or for general public awareness. In this paper we are describing our methodology for creating a new experimental setup and then analyzing the performance results of different neural network models for predicting Traffic Indexes calculated from public transportation positioning data as time series.

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References

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Acknowledgment

The research reported here was partially supported by “An innovative software platform for big data learning and gaming analytics for a user-centric adaptation of technology enhanced learning (APTITUDE)” - research projects on the societal challenges – 2018 by Bulgarian National Science Fund with contract №: KP-06OPR03/1 from 13.12.2018 and project FNI-SU-80-10-152/05.04.2021, FNI project of Sofia University “St. Kliment Ohridski” (Bulgaria) “Challenges of developing advanced software systems and tools for big data in cloud environment (DB2BD-4)”.

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Correspondence to Georgi Yosifov .

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Yosifov, G., Petrov, M. (2022). Predicting Traffic Indexes on Urban Roads Based on Public Transportation Vehicle Data in Experimental Environment. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 508. Springer, Cham. https://doi.org/10.1007/978-3-031-10467-1_8

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