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. 2022 Dec 9;19(24):16524.
doi: 10.3390/ijerph192416524.

A Novel Environment Estimation Method of Whole Sample Traffic Flows and Emissions Based on Multifactor MFD

Jinrui Zang  1 , Pengpeng Jiao  1 , Guohua Song  2 , Zhihong Li  1 , Tingyi Peng  1
Affiliations

A Novel Environment Estimation Method of Whole Sample Traffic Flows and Emissions Based on Multifactor MFD

Jinrui Zang et al. Int J Environ Res Public Health. .

Abstract

Vehicle emissions seriously affect the air environment and public health. The dynamic estimation method of vehicle emissions changing over time on the road network has always been the bottleneck of air quality simulation. The dynamic traffic volume is one of the important parameters to estimate vehicle emission, which is difficult to obtain effectively. A novel estimation method of whole sample traffic volumes and emissions on the entire road network based on multifactor Macroscopic Fundamental Diagram (MFD) is proposed in this paper. First, the intelligent clustering and recognition methods of traffic flow patterns are constructed based on neural network and deep-learning algorithms. Then, multifactor MFD models are developed considering different road types, traffic flow patterns and weekday peak hours. Finally, the high spatiotemporal resolution estimation method of whole sample traffic volumes and emissions are constructed based on MFD models. The results show that traffic flow patterns are clustered efficiently by the Self-Organizing Maps (SOM) algorithm combined with the direct time-varying speed index, which describe 91.7% traffic flow states of urban roads. The Deep Belief Network (DBN) algorithm precisely recognizes 92.1% of the traffic patterns based on the speeds of peak hours. Multifactor MFD models estimate the whole sample traffic volumes with a high accuracy of 91.6%. The case study shows that the vehicle emissions are evaluated dynamically based on the novel estimation method proposed in this paper, which is conducive to the coordinated treatment of air pollution.

Keywords: emission estimation; fundamental diagram; pattern clustering; pattern recognition; traffic volume estimation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Methodology flow chart.
Figure 2
Figure 2
Geometric characteristics of traffic flow speed curves.
Figure 3
Figure 3
Evaluation of clustering methods under different indexes with different colors.
Figure 4
Figure 4
The categories of DI based on different algorithms (curves with different colors mean different days).
Figure 5
Figure 5
Clustering of expressway speed (curves with different colors mean different days).
Figure 6
Figure 6
Clustering of expressway volume (curves with different colors mean different days).
Figure 7
Figure 7
The flow chart of each algorithm.
Figure 8
Figure 8
Pattern recognition results of different algorithms based on each index on an expressway.
Figure 9
Figure 9
Average speed-flow model under different large-vehicle proportions.
Figure 10
Figure 10
Average speed-flow models of peak hours on different weekdays on expressways.
Figure 11
Figure 11
Fundamental diagrams of rainy days and normal days.
Figure 12
Figure 12
Fundamental diagram models of different proposals on expressways: (a) Proposal I, (b) Proposal II, (c) Proposal III.
Figure 13
Figure 13
Measured volume and calculated volume curves on different road types.
Figure 14
Figure 14
Emission intensity of the road network in different periods.
See this image and copyright information in PMC

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