Passenger flow prediction in bus transportation system using deep learning.

Autor: Nagaraj N; Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka India., Gururaj HL; Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka India., Swathi BH; Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka India., Hu YC; Department of Computer Science and Information Management, Providence University, 200, Sec. 7, Taiwan Boulevard, Shalu Dist., Taichung City, 43301 Taiwan Republic of China.
Jazyk: angličtina
Zdroj: Multimedia tools and applications [Multimed Tools Appl] 2022; Vol. 81 (9), pp. 12519-12542. Date of Electronic Publication: 2022 Feb 19.
DOI: 10.1007/s11042-022-12306-3
Abstrakt: The forecasting of bus passenger flow is important to the bus transit system's operation. Because of the complicated structure of the bus operation system, it's difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.
Competing Interests: Conflict of interestThe authors declare that they do not have any conflict of interests that influence the work reported in this paper.
(© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.)
Databáze: MEDLINE
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