Queueing theory in massive transportation

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In summary, the speaker is working on a project analyzing the transportation system in Bogota, Colombia and is interested in using both simulation and analytic tools. They are unsure of how to model the process of people waiting for a bus and are seeking assistance. They suggest using a system of G/G/100/100 but acknowledge that its accuracy will depend on how well it maps to the reality of the situation.
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jfimbett
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Hi

I'm working on a project trying to analize the massive transportation system in Bogota (Colombia). I know that simulation tools are better in complex environments but I want to try some analytic tools.

I was thinking how to model the process in which people wait for a Bus. But I don't know really how to model this process.

Suposse the capacity of the BUS is 100 people.

I was thinking of a system G/G/100/100. Am I correct?

Can anyone help me? Thank you.
 
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  • #2
jfimbett said:
I know that simulation tools are better in complex environments
They are!

but I want to try some analytic tools.

I was thinking how to model the process in which people wait for a Bus. But I don't know really how to model this process.

You'll find that if you think clearly about how to create a simulation, you will be able to state all the information needed to do an analytic solution.

Suposse the capacity of the BUS is 100 people.

I was thinking of a system G/G/100/100. Am I correct?

Whether you are correct or not can only be determined if you use say what you are trying to use this model for. I don't see how you plan to visualize a bus as 100 servers. One cannot say whether a a model fits a situation unless the user explains how the components of the model map to the component parts of reality.
 

FAQ: Queueing theory in massive transportation

1. What is queueing theory in massive transportation?

Queueing theory is a mathematical study of the phenomenon of waiting lines, or queues, in systems where customers arrive and require service from a limited number of servers. In the context of massive transportation, this theory is used to analyze and optimize the flow of passengers through various modes of transportation such as buses, trains, and airports.

2. How is queueing theory applied in massive transportation systems?

Queueing theory can be applied in various ways in massive transportation systems. For example, it can be used to determine the optimal number of service counters or ticket gates at a transportation hub, to minimize waiting times for passengers. It can also be used to analyze the effect of different boarding methods on overall efficiency and to predict the average wait time for a specific mode of transportation.

3. What are some key factors that impact queueing in massive transportation systems?

There are several factors that can affect queueing in massive transportation systems. These include the arrival rate of passengers, the service rate of the transportation mode, the number of available servers, and the variability and unpredictability of passenger behavior. Other factors such as the layout and design of the transportation hub and the efficiency of the transportation staff can also play a role.

4. How does queueing theory help improve the efficiency of massive transportation systems?

By using models and simulations based on queueing theory, transportation planners and operators can identify potential bottlenecks and inefficiencies in their systems. This allows them to make informed decisions about resource allocation and service design to reduce waiting times and improve the overall flow of passengers. Additionally, queueing theory can help identify areas for improvement and optimization, leading to a more efficient and cost-effective transportation system.

5. What are some limitations of queueing theory in massive transportation?

While queueing theory is a useful tool for analyzing and improving the efficiency of massive transportation systems, it does have some limitations. For instance, it assumes that passengers behave in a predictable and consistent manner, which may not always be the case. It also does not account for external factors such as weather or accidents that can disrupt the flow of passengers. Additionally, queueing theory models can become complex and difficult to analyze when applied to large and complex transportation systems.

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