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Research on Traffic Parameter Measurement Methods for Intelligent Transportation Systems

Chunhong He

Chunhong He

School of Urban Construction and Intelligent Manufacturing, Dongguan City UniversityDongguan, China
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He, Chunhong
 und 
Bin Ren

Bin Ren

International School of Microelectronics, Dongguan University of TechnologyDongguan, China
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Ren, Bin
  
24. März 2025
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Online veröffentlicht: 24. März 2025
Eingereicht: 07. Nov. 2024
Akzeptiert: 12. Feb. 2025
DOI: https://doi.org/10.2478/amns-2025-0770
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© 2025 Chunhong He et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

The overall architecture of the intelligent transportation system
The overall architecture of the intelligent transportation system

Figure 2.

Functional module structure of intelligent transportation system
Functional module structure of intelligent transportation system

Figure 3.

The correlation coefficient of the traffic volume of the working day
The correlation coefficient of the traffic volume of the working day

Figure 4.

Correlation between periodic traffic volume
Correlation between periodic traffic volume

Figure 5.

The correlation coefficient of speed between weekdays
The correlation coefficient of speed between weekdays

Figure 6.

The correlation coefficient of the velocity within the period
The correlation coefficient of the velocity within the period

Figure 7.

The correlation coefficient between traffic lanes
The correlation coefficient between traffic lanes

Figure 8.

Correlation coefficient of traffic volume between sections
Correlation coefficient of traffic volume between sections

Figure 9.

Correlation coefficient of speed between lanes
Correlation coefficient of speed between lanes

Figure 10.

Correlation coefficient of speed between sections
Correlation coefficient of speed between sections

Figure 11.

The traffic flow prediction of GCN-BiLSTM model
The traffic flow prediction of GCN-BiLSTM model

Figure 12.

The traffic flow prediction of GCN-LSTM model
The traffic flow prediction of GCN-LSTM model

Figure 13.

The traffic flow prediction of GCN model
The traffic flow prediction of GCN model

Figure 14.

The traffic flow prediction of LSTM model
The traffic flow prediction of LSTM model

Figure 15.

Comparison results of traffic flow prediction error results
Comparison results of traffic flow prediction error results
Sprache:
Englisch
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1 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Biologie, Biologie, andere, Mathematik, Angewandte Mathematik, Mathematik, Allgemeines, Physik, Physik, andere
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