In the article “Preventing ‘Bus Bunching’ with Smart Phone Application Implementation”, Dave
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.
Sprogis surveyed 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. Sprogis proposed to install the SaaS system knowing that the
Massachusetts Bay Transportation Authority (MBTA) had presented an API with real-time
bus data that could be retrieved for scrutiny. Sprogis shaped an actual route
using Geographic Information System software which allows him to replicate the
situation with his 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
inundate of buses. Sprogis 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 Sprogis in 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, APC,
odometers, etc. in the bus operating system. According to Erath (2013), cities
like London and Zurich have adopted an identical approach. Also illustrated by Wang (2018), systems like “AVL (Automatic Vehicle Location) and APC (Automatic
Passenger Count)” have been utilized widely in bus operating systems. This
could help establish the precision of the bus location even in tunnels or
downtown areas. These units are on board buses that 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 this system as proposed by Bartholdi
(2011), with the use of AVL and GPS. With these two data, we could achieve mean
headway and alteration among headways. With the help of these data it is
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 country like Portugal. As a result, has shown improvement in
bus bunching and commuter’s experience.
In conclusion, the proposal made by Sprogis (n.d.) is feasible and with
the advancement in technologies and resources. Drivers and bus control
operators could benefit from data analysis. Also, data could be easily attained
for the benefits of commuters in terms of mobile applications.
References
Bartholdi,
J., Eisenstein, D. (2011). A self-coördinating 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. https://www.anylogic.com/preventing-bus-bunching-with-smart-phone-application-implementation/
Wang, P., Chen X., Chen W. (2018). Provision of Bus Real-Time
Information: Turning Passengers from Being Contributors of Headway Irregularity
to Controllers. Sage Journals. https://doi.org/10.1177/0361198118798722
