In the article “Preventing ‘Bus Bunching’ with Smart Phone Application Implementation”,
Sprogis (n.d.) used AnyLogic’s software simulator to prove his claim that
limiting “Bus Bunching” would boost the experience of public bus transport commuters.
The author mentioned that the long waiting time at the bus stop could be
eradicated and congestion could be reduced by improving minor setbacks to the
bus before a cluster is formed. The author proposed to install the SaaS
(Software as a Service) system knowing that the Massachusetts Bay
Transportation Authority (MBTA) had presented an API (Application Programming Interface)
with real-time bus data that could be retrieved for scrutiny. The author shaped
an actual route using the Geographic Information System software, which allow
him to replicate the situation with ideal results he called “equilibrium”.
Adopting this protocol, transport buses have to maintain distances between each
other as a result, the number of users is spread uniformly, reducing long
waiting time and flooding of buses. The author suggested that MBTA implement
“uber-fication” of their buses using application software to advise drivers
while imposing the protocol. This could further aid decision-makers in
anticipating the issues and would improve the services for the commuters. I
agree with the author that the implementation of the SaaS system in the transport
operation, real-time data could be retrieved to assist bus drivers and operators
in data analysis, headway calculations, and app developments for the commuter’s
benefits.
Firstly, passenger count and headway analysis could be attained to
assist bus operators with the help of different types of sensors such as GPS (Global
Positioning System), APC (Automatic Passenger Count), AVL (Automatic
Vehicle Location), odometers, etc., in the bus operating system. According to
Erath (2013), cities like London and Zurich have adopted an identical approach
where the bus drivers have the information display on their dashboard. Also, as
illustrated by Wang (2018), systems like “AVL and APC”
have been utilized widely in bus operating systems. These could help ensure that
the information is precise regarding the bus location even in tunnels or
downtown areas. These units continue to record real-time data and feed it over to assist bus control
operators in data analysis.
Secondly,
bus drivers could also benefit from the use of AVL, GPS and APC as proposed by
Bartholdi (2011). With the use of these systems, mean headway and alteration
among headways could be achieved, it is also possible to update and correct headways
of buses departing from the control points. Bus drivers will also be alerted to
accommodate their speed in real-time and relies on the estimation of commuter demand
in order to achieve the target speed in which they called “self-equalizing”.
Lastly, commuters could benefit from this system by relying on real-time
data updated on the bus application. According to Moreira-Matias (2016), a
real-time data collection application using historical and real-time AVL data were
proposed to predict and prevent bus bunching from occurring. The outputs
predicted could be correctively adjusted by the system. This system has shown
improvement and a “reduction of 68%” in the statistics of bus bunching incidents,
as demonstrated by cases in a country like Portugal. Which has shown
improvement in bus bunching and commuter’s experience.
In conclusion, the implementation of SaaS system in transport operation is
ideal to prevent bus bunching as real-time data can assist bus drivers and
operators in data analysis, headway calculations and app development for the
benefits of the commuters.
References
Bartholdi,
J., Eisenstein, D. (2011). A self-coordinating bus route to resist bus
bunching. Transportation
Research Part B: Methodological, 46(4), 481-491. https://doi.org/10.1016/j.trb.2011.11.001
Erath, A. (2013). How to
solve the problem of bus bunching. The Straits Times. https://www.straitstimes.com/singapore/how-to-solve-the-problem-of-bus-bunching
Moreira-Matias, L.
(2016). An online learning approach to eliminate bus bunching in
real-time. Applied Soft Computing, 47, 460-482. https://doi.org/10.1016/j.asoc.2016.06.031
Sprogis, D.(n.d.). Preventing
‘Bus Bunching’ with Smart Phone Application Implementation. AnyLogic. https://www.anylogic.com/preventing-bus-bunching-with-smart-phone-application-implementation/
Wang, P., Chen X., Chen W. (2018, October 1). Provision
of bus real-time information: Turning passengers from being contributors of headway
irregularity to controllers. Transportation Research Record: Journal of the
Transportation Research Board, 2672(8). https://doi.org/10.1177/0361198118798722
Thanks, Raymond!
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