Tuesday 10 November 2020

Annotated summary

Hwang. H.T, Varma.A. (2014). Hydrogen storage for fuel cell vehicles. Current Opinion in Chemical Engineering, Volume 5, August 2014, Pages 42-48. https://doi.org/10.1016/j.coche.2014.04.004

The article focuses on the different types of storage tanks and materials used and how different temperatures can affect hydrogen gas volumetric energy density. Hydrogen gas has high energy density on a mass basis as compared to petroleum (120 MJ/kg for hydrogen vs. 44 MJ/kg for petroleum). Sadly, it has low volumetric energy density (0.01 MJ/L for hydrogen at STP vs. 32 MJ/L for petroleum), yet it can be increased by liquefying it. It is also mentioned in the article that pressurizing hydrogen to -253.15 degrees celsius helps improve the volumetric density but due to this, it prompts a challenge to store hydrogen in large quantities for vehicle applications. The author also mentioned the constraints like safety, weight, volume, cost, and efficiency on hydrogen storage for a single trip of more than 500km.

The article also listed possible ways of hydrogen storage, some advantages and disadvantages that are useful for our project research. On the aspect of storage, compressed hydrogen storage offers a promising option but falls short on the cost due to the material used. Better production is needed to advance gravimetric and volumetric capacities, kinetics within suitable temperature/pressure ranges, along with the overall cost. A deeper understanding is required between cost, energy efficiency and environmental impact are essential in the system cycle. From the article, we can take into consideration for hydrogen to be implemented in-vehicle applications, together with material exploration overall system advancement are required to overcome impediment related to hydrogen storage. The article provides adequate analysis that our team can consider upon for our research project.

Revised on 7/12/2020

Reviewed: Joshua, Sebastian, and Dorothy's summary.

Tuesday 3 November 2020

Summary reader response

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


Revised on 3/11/2020

Critical Reflection

It was a fruitful 14 weeks for me. Before attending this module, I am not used to formal writing and an introvert myself. I am not confident...