Queueing Systems in Selected Bus Terminals along EDSA and Regional Bus Stations in NCR during Community Quarantine Brought about by COVID-19
Most countries across the globe were affected by the 2019 Novel Coronavirus Disease (COVID-19), an infectious disease caused by the most recently discovered coronavirus. In the Philippines, data from the Department of Health (DOH) showed that it was not until March 6, 2020, that the number of reported cases rose to 5. On March 7, DOH announced that the 5th case of COVID-19 is the first case of local transmission in the country. On March 8, 2020, President Rodrigo Duterte signed Proclamation No. 922 declaring the Philippines under a “State of Public Health Emergency” following the recommendation of the Department of Health (DOH) to place the Philippines in Code Red Sublevel-1 due to COVID-19. On March 12, he announced the entire Metro Manila will be placed into community quarantine effective midnight of March 15 to address the increasing number of community transmission of COVID-19. Pursuant to this pronouncement, a series of transport-related policies were put in place. The Department of Transportation (DOTr), together with the Inter-Agency Task Force for the Management of Emerging Infectious Disease (IATF-EID), issued on 03 May 2020 the Omnibus Public Transport Protocols/Guidelines which mandates, among others, the reduction of passenger capacity of all public and private transportation consistent with the social distancing standards and other health measures. When President Duterte placed the entire island of Luzon under an Enhanced Community Quarantine (ECQ) on March 16, all forms of public transport including city and regional buses were prohibited from plying the streets of Metro Manila. After 4 months of placing the National Capital Region (NCR) under extended community quarantine (ECQ), the number of daily COVID-19 cases has decreased. This has led the IATF-EID to downgrade the status of place NCR to the more relaxed general community quarantine (GCQ) starting June 1. Under this declaration, public transportation in Metro Manila has gradually reopened while regional buses have been allowed to run since June 22. This policy pivot was done to gradually open the economy.
However, due to the need to maintain minimum health standards particularly social distancing and no-contact payment in, these buses have been running under reduced capacities. This highlighted the insufficiency of buses to match the increased demand for public transport. This is further exacerbated by the ever-changing traffic rules in EDSA as well as the different requirements for regional travels.
This study seeks to assess the current queuing systems in place at selected city bus terminals along EDSA and regional bus stations around Metro Manila. From the described queueing systems, we hope to propose policy measures that will help shorten the queue length of passengers, service, and waiting times in these stations and terminals.
This study is designed to assess the efficiency of the current queuing system being implemented at selected city bus terminals along EDSA and major regional bus stations surrounding Metro Manila. Specifically, the study aims to:
Specifically, we aim to:
- Describe the transportation policy environment at the local, sub-regional (MMDA) and regional levels.
- Illustrate the queuing system in place in at least four (4) major city bus terminals along EDSA and at least (2) major regional bus stations in Metro Manila, including the
- Customer and inter-arrival time description
- Server and service time description
- Average waiting time of customers (i.e., waiting time in queue and in the system)
- Average length of queue
- Analyze the current queuing systems in at least four (4) major city bus terminals along EDSA and at least (2) major regional bus stations in Metro Manila
- Predict the optimal number of buses at the regional bus stations available for passengers;
- Recommend the type of queue (queuing system) to be implemented in the study areas.
Public transportation has been a challenge throughout the country, especially in the National Capital Region, even before the COVID-19 pandemic devastated the economies of the world. Due to the restrictions in place to reduce the COVID-19 transmission among commuters, there is a need to come up with a scientific and data-driven approach to come up with a responsive policy solution that ensures the protection of commuters.
The Philippines confirmed its first case of COVID-19 (2019 nCoV) on January 30, 2020 . The first case of local transmission was confirmed on March 7, 2020 . On March 16, 2020, Luzon was placed under “enhanced community quarantine” (ECQ). In connection to this, all kinds of public transportation were suspended .
To slowly open the country’s economy, community lockdown regulations were relaxed and some establishments were allowed to operate. As a result, some modes of public transportation were permitted to function. However, minimum health and safety protocols were still imposed to prevent the transmission of the disease. One of these protocols is physical distancing and thus passenger capacity in each vehicle was reduced. While imposing these guidelines inside the vehicles, safety protocols must also be employed in the queues of commuters and overcrowding must be avoided. Thus, current queueing systems must be studied and efficient scheduling of public transportation and queueing schemes must be established.
Several studies have been made to understand congestion dynamics in different transportation systems. Samson et al. (2017) developed a crowd dynamics model of commuters to investigate the behavior in crowd formation of Taft Ave station. They proposed several ticket booths and turnstiles for peak and non-peak hours to minimize overcrowding . In 2015, Othman et al. proposed a data-driven agent-based model to study the congestion dynamics on the Singapore rail transit system (RTS). Their results showed that the Singapore RTS infrastructure is close to its critical capacity and suggested that more comprehensive strategies should be implemented . An effective scheduling and dispatch system of vehicles is also important to shorten passenger waiting time. Escolano et al. (2014) used genetic algorithms to optimize the scheduling and dispatching of public utility vehicles in terminals that cover Epifanio Delos Santos Avenue (EDSA) routes .
The design of this study is a prospective analytical study. It will employ both quantitative and qualitative techniques.
The queuing system is a two-tier system. One tier involves two groups: the customers and the servers. Commuters present at the bus terminal and stations are considered as customers. They are the ones that enter the system (i.e. bus terminals and stations), join the queue and will be seated in the available bus. On the other hand, bus dispatchers/ personnel are the server (i.e. bus personnel). Commuters who will be participating in the study are those who are visibly entering the terminal and eventually entering the bus. Bus dispatchers are those who are visibly controlling the flow of commuters going into the buses. The second tier is the system involving buses (as customers) and bus dispatchers/ personnel as servers. This part of the study is important since efficient public transport requires efficient bus dispatching and this will greatly affect the waiting time of commuters.
Lastly, officials of bus companies who are involved in the planning and dispatching of buses along the routes as well as officials from sub-regional, regional and national agencies who are involved in the crafting and monitoring of the policies related to bus operations will be invited to participate in the study.
Inclusion and Exclusion Criteria
In this study, all commuters entering the bus terminals and stations at predetermined time intervals will be considered and counted as participants in the study. Their presence in the system and the queue for buses will signify their participation in the study. Commuters who did not push through with their trip will be excluded from the study. For regional commuters, commuters without appointments will not be included in the study.
All buses who arrive at selected terminals along EDSA during predetermined time intervals will be counted. These terminals are drop-off points for either city or regional buses. The capacity of these buses per trip will also be collected. a. This study will only consider city buses that pick passengers at the selected pickup points along EDSA and regional buses at selected bus terminals/ stations.
Officials of city and regional bus companies which are picking up and dropping off commuters in the selected bus terminals/ stations/ pick-up points will be invited to participate in the study. These officials should be involved in the planning and dispatching of buses along these routes from March 2020 until present (during the COVID-19 pandemic).
Officials from sub-regional, regional and national agencies who are involved in the crafting and monitoring of the policies related to bus operations during the COVID-19 pandemic response will be invited as well. These officials should be involved in the policy-making process for at least 2 months from March 2020 until the present. This will include but are not limited to the Metropolitan Manila Development Authority, Department of Transportation and Land Transportation, Franchising and Regulatory Board.
Sample Size Computation
A complete enumeration of bus passengers and buses present in the bus terminals/ stations/ pick-up points during the predetermined time intervals will be included in the analysis. Likewise, data collection will be done during predetermined time intervals [peak (6AM – 8AM and 4PM – 6PM) and off-peak hours (1PM – 3PM)]. This is to ensure that the queue of passengers at the bus terminals/ stations/ pick-up points have reached steady-states or a “state by which the servers (i.e., bus conductors) manning the buses have warmed up in serving the commuters riding the bus.” Statistically, this means that the service times will follow a certain probability distribution.
The study will be conducted in at least 4 major city bus pick-up points along EDSA and at least 2 regional bus stations around the National Capital Region. These regional bus stations include at least one station that serves commuters from Region IV-A and at least one that serves commuters from REGION III or other parts of Northern Luzon.
Pre-data collection phase
A list of prospective respondents from the local, sub-regional, regional and national agencies as well as those from the bus companies will be generated. Invitation letters addressed will be sent to them inviting them to participate in the study. Once they have agreed to participate, a member of the research team will set an appointment with them for an in-depth interview.
Letters will also be sent to the companies/ agencies operating the selected bus terminals/ stations and pick-up points. These letters will seek the permission of these agencies/ companies to conduct the queue-related data during a two-month period.
Data collection phase
An extensive review of publicly-available records will be collected from the target offices and agencies. This will include, but are not limited to, policy documents, monitoring reports and reports from other stakeholders (e.g. academe, international NGOs). Electronic copies of these documents will be requested. Should an electronic version not be available, a scanned copy of it will be generated.
An in-depth interview (IDI) guide will be developed. The IDI guide will focus on capturing the perspectives and experiences of the respondents from government agencies and bus operators when it comes to the evolution of policies on buses during the COVID-19 pandemic. It will also probe into their perspectives on the implementation of these policies, particularly activeness of safeguards on minimum public health standards, resources invested in supporting the level of service capacity, as well as the challenges and gaps in policy implementation.
The data needed in a queueing system study will be collected through 2 means, if possible. A copy of the video surveillance of the buses that arrive in the selected bus terminals/ stations/ pick-up points during the predetermined time intervals will be collected. The data of passengers who arrive at the station, join the queue and take the bus during the predetermined time intervals will be collected by the research team using an observation checklist. The same thing process will be conducted for the buses which arrive in the selected bus terminals/ stations/ pick-up points.
For triangulation, the physical observation of the passengers and buses will also be done. This will be through an approach that minimizes the contact when collecting the queue-related data between the passengers, dispatchers and the research team as well as minimizes the exposure of the research team. Examples of this approach are through the use of QR-codes or tapping existing apps (e.g. SafeTravel.ph).
Data Processing and Analysis.
Notes taken during the IDIs will be encoded in a worksheet. A thematic analysis of the responses gathered during the IDIs will be conducted. Common themes across the different sections will be generated and analyzed. This will be triangulated with the records collected by the team. A rough framework that captures the range of bus transport policies implemented during the COVID-19 pandemic will be generated. This will be assessed against existing and widely-accepted public transportation frameworks.
In queueing analysis, three important data must be collected, the inter-arrival times, service times, and the queue discipline. For the two types of queuing systems that will be studied, the following are the plans for collecting data:
- For the queuing system involving commuters as customers and buses as servers:
- To collect the inter-arrival times of commuters, we will assume that there is a single waiting-line per route. One observer will record the time between arrivals of commuters at the designated waiting line of a selected terminal.
- To record the service times, we will define first the extent of service. Service, in this case, starts when a commuter reaches the front of the line even if a bus is not available yet. This will be timed by the observer until a bus is available and the commuter is seated on the bus. That will be the end of service since it will be assumed that once a passenger is seated, he/she will reach her destination. This is to avoid large differences among service times, if we include the travel time to the destination of the commuter since passengers in one route may have different disembarkation points.
- Here the queue discipline will be assumed to be on a first-come-first-served basis.
- For the queuing system involving bus dispatching:
- Inter-arrival times between buses will be recorded for each considered route. A bus is considered an arrival and will be joining the queue if it is ready for dispatch.
- Service time will be measured from the time a bus is ready until the bus is dispatched.
The recorded arrival and departure data will be processed to obtain the average arrival rate and service rate, respectively. The probability distribution of both data will also be determined to obtain the checkpoints measures of performance. These measures are the average waiting time in the system and in the queue, and the average number of waiting vehicles in the system and in the queue. Simulations will also be conducted if an analytical solution is not possible. In most studies of traffic systems, simulation is the go-to method in replicating the system because of its accessibility and accuracy.
The performance of the queue will be assessed for its efficiency, commuters’ compliance to minimal public health standards and the supportiveness of the bus transport system to commuters’ compliance to minimal public health standards. From a public transport operation perspective, the performance of the queue will be evaluated based on effectiveness (measured by passenger waiting times at the stops, travel time and distance travelled by bus units, as well as, headways of bus arrivals), efficiency (measured by the estimated amount of resources poured into supporting the current level of service) and robustness (measured by presence/ visibility of support to compliance with minimum public health standards, active safeguarding measures). As such, an efficient queuing system will be demonstrated by high passenger throughput and high availability of bus services. On the other hand, it is recognized that cost and revenue parameters may have countervailing effects on queue efficiency. These three parameters have trade-offs which must be recognized and supported moving forward.
Once the queue and queue discipline has been described and analyzed, it will be further evaluated using analytical methods/ simulation and thematic analysis. Possible alternatives will be determined to reduce the waiting times of commuters at designated terminals and stations.