Modified State-Dependent Queuing Model for the Capacity Analysis of Metro Rail Transit Station Corridor during COVID-19
Abstract
:1. Introduction
2. Method
2.1. Developing Queuing Model
Uncongested Condition and Constant Arrivals and Plugging , we get; Putting gets us | Congested Condition and Discouraged Arrivals and |
- The expected number of commuters in the corridor facility:
- The average area occupied per commuter:
2.2. Developing Discrete Event Simulation Model
3. Results
3.1. Data
3.2. Verification of Proposed Model
3.3. Sensitivity Analysis
3.3.1. Effect of Average Commuter Arrival Demand on EN and EA
3.3.2. Effect of Commuter Arrival Demand on the Throughput
3.3.3. Effect of Corridor Width (W) on EN and EA
3.3.4. Effect of Commuter Arrival Demand on the Steady-State Probability Distribution
4. Conclusions
- The transient solution of our proposed queuing models derived using the BD process appears to agree quite well with the values determined using the DES framework
- For the data considered herein, the computational experiments indicate that the expected number of commuters (EN) and blocking probability for the model are smaller than for the model. The values of EN for the model are much smaller due to a state-dependent arrival rate effect, depicting the social distancing protocol of COVID-19.
- The model for the service facility capacity analysis in the COVID-19 situation overestimates the results and shows a larger value of EN, even at a smaller arrival demand . Therefore, the model cannot be used for the analysis when pandemic conditions are considered.
- The sensitivity analysis also revealed that the throughput value drops rapidly for the model, even at a lower arrival demand compared to . However, for the model, the values of throughput increase linearly with arrival rates and no blocking is observed.
- The average area occupied per commuter (EA) is higher in the case of the model, while it is smaller in the case of the model. The average area occupied per commuter (EA) is highest in the case of . This means that at a certain density, when arrival rates are discouraged, social distancing is ensured, allowing commuters to travel at a free-flowing speed despite a small corridor area.
- For model, the blocking remains at zero for all arrival rates to ensure social distancing in the corridor.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Fluctuated Arrival Rate | Fluctuated Service Rate |
---|---|
Corridor Dimensions (Area: 8 × 2.5 m2) | Corridor Dimensions (Area: 7 × 2 m2) | |||
---|---|---|---|---|
Model | State-Dependent DES Model (95% Confidence Interval) | Model | State-Dependent DES Model (95% Confidence Interval) | |
ped/s | ped/s | |||
EN (ped) | 9.33 | 9.13 (9.05, 9.21) | 26.81 | 25.38 (26.46, 24.77) |
0.00 | 0.00 (0.00, 0.00) | 0.15 | 0.141 (0.135, 0.146) | |
EA (m2/ped) | 2.14 | 2.09 (1.99, 2.19) | 0.52 | 0.51 (0.443, 0.541) |
1.75 | 1.75 (1.73–1.84 | 3.69 | 3.72 (3.63, 3.78) |
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Khattak, A.; Almujibah, H.; Chen, F.; Alyami, H.S.
Modified State-Dependent
Khattak A, Almujibah H, Chen F, Alyami HS.
Modified State-Dependent
Khattak, Afaq, Hamad Almujibah, Feng Chen, and Hussain S. Alyami.
2022. "Modified State-Dependent