Dynamic Evaluation Method for Mutation Degree of Passenger Flow in Urban Rail Transit
Abstract
:1. Introduction
2. Literature Review
2.1. Studies on Passenger Flow Fluctuations
2.2. Indicators of Catastrophe Passenger Flow
3. Definition of Mutation Degree of Passenger Flow and Construction of Evaluation Indicators
3.1. Definition of Mutation Degree of Passenger Flow
3.2. Construction of Evaluation Indicators
- Indicators of Vertical Mutation Amplitude
- Vertical Deviation of Passenger Flow
- b.
- Vertical Deviation Rate of Passenger Flow
- c.
- Cumulative Vertical Deviation
- d.
- Cumulated Vertical Deviation Rate
- 2.
- Indicators of Horizontal mutation Amplitude
- Horizontal Change Rate of Passenger Flow
- b.
- Horizontal Deviation Rate of Passenger Flow
- c.
- Cumulative Horizontal Deviation Rate
4. Methodology
4.1. Catastrophe Theory
4.2. Evaluation Method for Mutation Degree of Passenger Flow
- Constructing an evaluation indexes system of mutation degree
- 2.
- Standardizing the evaluation indexes
- 3.
- Calculating the membership function values
- Complementary principle: When the influence of the control variables on state variables can be mutually compensated, the final value is the average value of these control variables, i.e., .
- Non-complementary principle: When the influence of the control variables of the system on the state variables cannot be replaced with each other, that is, they cannot compensate for each other’s shortcomings, the final value is determined based on the minimum principle, i.e., .
5. Data Collection and Analysis
5.1. Data Collection
- Normal passenger flow data: To study the common fluctuation rhythm of passenger flow in urban rail transit, data from a time period without the influence of factors, such as new line access and the epidemic, were selected for comparative analysis. The first week of June 2021, from 1 June to 7 June, was the study period. The weeks before and after the study date, 25 May to 31 May 2021, 8 June to 14 June, and 2 June to 8 June 2020, were used as the reference dates. The corresponding periods for each date are presented in Table 4. Among them, the Dragon Boat Festival was from 12 June to 14 June 2021.
- Mutational passenger flow data: To analyze the relationship between mutational passenger flow and normal passenger flow and obtain the mutation degree of mutational passenger flow, the in-and-out passenger flows of SZRT on 6 September 2019 were selected as the analysis data. At 8:25–8:50 on 6 September, the operation of SZRT Line 2 was suddenly interrupted. It was a Friday on 6 September, with the latest Friday being 30 August, and no special events affecting normal operations occurred on that day. Therefore, the passenger flow on 30 August 2019 was selected for comparison.
5.2. Analysis of the Fluctuation Characteristics of Normal Passenger Flow
5.3. Analysis of the Characteristics of Catastrophe Passenger Flow
6. Results and Discussion
7. Conclusions
- (1)
- The CDCT evaluation method can better reflect the dynamic change in the mutation degree in the whole process under the influence of mutational passenger flow.
- (2)
- When an interference occurs at a certain station or line, in addition to the interfered stations, the passenger flow at other stations in the network also experiences mutation due to the interference. When an interference occurs at a certain line, the station with a high mutation degree is the station with a high demand for inflow and outflow on that line. After the interference is resolved, the mutation degree of the stations directly affected by the interference decreases, whereas other stations may experience a temporary rebound in mutation degree. The degree of mutation of the station constantly changes and is related to the line on which it is located.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Catastrophe Model | State Variable Dimension | Control Variable Dimension | Potential Function |
---|---|---|---|
Fold | 1 | 1 | |
Cusp | 1 | 2 | |
Swallowtail | 1 | 3 | |
Butterfly | 1 | 4 | |
Hyperbolic Umbilical Point | 2 | 3 | |
Elliptic Umbilical Point | 2 | 3 | |
Parabolic Umbilical Point | 2 | 4 |
Catastrophe Model | Bifurcation Set | Normalization Formula |
---|---|---|
Cusp | ||
Swallowtail | ||
Butterfly |
Gradation | I | II | III | IV | V |
---|---|---|---|---|---|
Mutation degree Description | Very high | High | Middle | Low | Very Low |
Scale | (0.9, 1.0] | (0.8, 0.9] | (0.7, 0.8] | (0.4, 0.7] | [0, 0.4] |
Attribute | Year | Tuesday | Wednesday | Thursday | Friday | Saturday | Sunday | Monday |
---|---|---|---|---|---|---|---|---|
Study dates | 2021 | 06.01 | 06.02 | 06.03 | 06.04 | 06.05 | 06.06 | 06.07 |
Reference dates | 2021 | 05.25 | 05.26 | 05.27 | 05.28 | 05.29 | 05.30 | 05.31 |
06.08 | 06.09 | 06.10 | 06.11 | 06.12 | 06.13 | 06.14 | ||
2020 | 06.02 | 06.03 | 06.04 | 06.05 | 06.06 | 06.07 | 06.08 |
Target Layer | Principle | Criterion Layer | Membership Function Values | Principle | Indicator Layer | Initial Evaluation Value | |||
---|---|---|---|---|---|---|---|---|---|
0.54 | complementary | 0.54 | 0.30 | complementary | 0.06 | 0.00 | |||
0.20 | 0.01 | ||||||||
0.39 | 0.02 | ||||||||
0.54 | 0.05 | ||||||||
0.53 | 0.15 | non-complementary | 0.15 | 0.02 | |||||
0.20 | 0.01 | ||||||||
0.42 | 0.03 |
Station | Evaluation Value | Line | Original Station | Transfer Station | Station Attribute | Mutation Degree Description |
---|---|---|---|---|---|---|
Mudu | 0.61 | 1 | Yes | No | Residential-dominant | Low |
Guangji South Road | 0.64 | 1, 2 | No | Yes | Employment-commercial mixed | Low |
Donghuan Road | 0.57 | 1 | No | No | Residential-dominant | Low |
Shihu East Road | 0.55 | 2, 4 | No | Yes | Residential-dominant | Low |
Yueliangwan | 0.93 | 2 | No | No | Employment-dominant | Very high |
Songtao Street | 0.82 | 2 | No | No | Employment-dominant | High |
Sangtiandao | 0.52 | 2 | Yes | No | Employment-dominant | Low |
Longdaobang | 0.51 | 4 | No | No | Residential-employment mixed | Low |
Hongzhaung | 0.56 | 4, 4B | Yes | Yes | Residential-employment mixed | Low |
Lize Road | 0.55 | 4B | No | No | Residential-employment mixed | Low |
Beisita | 0.52 | 4 | No | No | Commercial-tourism mixed | Low |
… | … | … | … | … | … |
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Chen, T.; Ma, J.; Li, S.; Zhu, Z.; Guo, X. Dynamic Evaluation Method for Mutation Degree of Passenger Flow in Urban Rail Transit. Sustainability 2023, 15, 15793. https://doi.org/10.3390/su152215793
Chen T, Ma J, Li S, Zhu Z, Guo X. Dynamic Evaluation Method for Mutation Degree of Passenger Flow in Urban Rail Transit. Sustainability. 2023; 15(22):15793. https://doi.org/10.3390/su152215793
Chicago/Turabian StyleChen, Ting, Jianxiao Ma, Shuang Li, Zhenjun Zhu, and Xiucheng Guo. 2023. "Dynamic Evaluation Method for Mutation Degree of Passenger Flow in Urban Rail Transit" Sustainability 15, no. 22: 15793. https://doi.org/10.3390/su152215793