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
users. Sprogis surveyed that the long awaiting 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 a SaaS system
knowing that the Massachusetts Bay Transportation Authority (MBTA) had
presented an API with real-time bus data 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 are spread
uniformly, reducing long waiting time and inundate of buses. Sprogis suggested
that MBTA implement “uber-fication” of their buses using app 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 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 type of sensors such as GPS, APC,
odometers etc. in the bus operating system. According to Erath (2013), cities
like London and Zurich have adopted 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 precision
of the bus location even in tunnels or downtown areas. These units are onboard
buses which continues to record real-time data and feed it over to the bus
control operators.
Secondly, bus divers could also benefit from this system as proposed
by Bartholdi (2011), with the use of “AVL” and “GPS”. With these two data we
could achieved mean headway and alteration among headways. This control points requires
these data to update and correct headways of buses departing from the control
points. Bus drivers will 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 call “self-equalizing”.
Lastly, commuters could benefit from this system by relying on the real-time
data updated on the bus applications. According to Moreira-Matias (2016), a
real-time data collection application using historical and real-time AVL data
was proposed to predict and prevent bus bunching from occurring. The outputs
predicted could be correctively adjusted by the system operators. This system
has shown an improvement and a “reduction of 68%” in statistic of bus bunching
incident, as demonstrated by country like Portugal. This as a result, has shown
improvement on 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 the data analysis. Also, data could be easily
attained for the benefits of the 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
Revised on 16/10/20
