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Dec 6, 2023
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Uploaded by ProfGorilla13929
Mini Project 4: Tools of Quality, Q6 and E Train NYC Metro Analysis
Department of Industrial & Systems Engineering, University at Buffalo
ISE 508z: Quality Assurance
Dr. Harrison Kelly
Due Date: November 13, 2023
Group #3
Team Members:
Sean Sullivan
Jack Reynolds
Shihan Reza
Shad Chowdury
Henry Abaye-Sylvanus
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Process Analyzed:
Commuter trip from (South Jamaica, Queens) to (World Trade
Center, Manhattan) utilizing Q6 bus and E train during AM peak travel time.
Process Flow Chart:
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Potential Issues:
Travel delays are the most significant issue affecting a commuter on this route.
Data:
All data collected from the official NYC MTA website (seen in our references) for the years
2020-Present.
Q6 Bus Data:
Bus Stop Wait Times:
The data above show a high-moderate correlation between number of customers and increased
bus stop wait times with a correlation coefficient of 0.68.
Bus Travel Times:
The data above show a high-moderate correlation between number of customers and increased
bus travel times with a correlation coefficient of 0.67.
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OMNY Usage:
The data above shows a high-moderate negative correlation between OMNY usage and increased
bus wait times with correlation coefficient of -0.63. OMNY availability was introduce to this bus
route in June 2023 resulting in a smaller data set.
E Train Data:
Train Platform Wait Times:
The data above show a weak correlation between number of customers and train platform wait
times with a correlation coefficient of 0.30.
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Train Travel Times:
The data above show a high-moderate correlation between number of customers and train travel
times with a correlation coefficient of 0.66.
Train Station Escalator/Elevator Availability:
The data above show a very weak correlation between escalator/elevator availability and
customer journey time performance with a correlation coefficient of 0.17. Customer journey time
performance is a measure of passenger delay.
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Major Incident of E Train Line:
The Pareto graphic below illustrates a clear distribution of the main incident types and shows the
frequency and impact of incidents resulting in train delays. The graph highlights the main
reasons for delays and highlights how to nest focus efforts on the significant causes in order to
implement efficient mitigation techniques. The main causes of the observed incidents include
conventional signal problems, track-related incidents, and persons on the track bed/
police/medical staff.
The bar graph below compares events between the E and 7 trains and demonstrates a notable
decrease in signal-related problems for the 7 train. The 7 train saw a reduction of signal
incidents compared to the E train after substituting half of its traditional signals with the new
communication-based train control system.
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Analysis:
Cause and Effect Fishbone Diagram for Travel Delays:
Major Contributors to travel delays:
Bus/Train wait/travels times, escalator/elevator availability and OMNY usage were
identified as potential contributors to travel delays using the above process flow chart. An
analysis of monthly data, from 1/2020-present, shows opportunities for improvement in
travel times.
Bus wait times, bus travel times and train travel times are all shown to have high-
moderate correlations between number of passengers and delay. Train platform wait times
were less significantly correlated to number of passengers but the value is not negligible.
It seems clear that the number of passengers per bus/train negatively impacts travel time.
OMNY usage, being a faster way to pay a fare, also shows a high-moderate correlation
between percentage of passengers utilizing the payment method and a decrease in bus
wait times. It follows that as more passengers utilize the payment method, bus wait times
will continue to decrease.
Escalator/elevator availability, a measure of current passenger capacity compared to
design capacity, was shown to have a weak correlation to travel performance, a passenger
delay metric. While increasing escalator/elevator availability will have a positive impact
on travel delays, other factors are more significant. This is likely due to the generally high
level of escalator/elevator availability, indicating a lack of capacity issues.
Between 2022 and 2023, the train line using conventional signal systems experienced 22
to 30 signal problems on average, which resulted in considerable delays. A significant
improvement is represented by the 7 train's use of an updated communication-based
system in place of antiquated signals. By replacing half of the traditional equipment, the
shift significantly lowers signal-related issues; occurrences decreased from 22–30 to 12
after the upgrade. This 45% reduction highlights the efficacy of the new system and
raises the possibility of increased dependability, safety, and efficiency. Using
contemporary communication-based signaling offers a viable way to reduce problems
and improve overall service reliability.
Greatest Opportunities for Improvement:
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Travel Delays are one of the biggest problems in this process. Below, significant contributors to
travel delays can be seen in the cause-and-effect diagram.
System Capacity:
Adding capacity to the system would significantly improve travel
delays according to the data above. Travel and wait times are both correlated with
number of passengers and reducing the number of passengers waiting or riding at any
given time would improve total travel time more than any other issue identified. Methods
to increase capacity include:
o
Adding more buses or trains to the route
o
Adding additional express service to the route. Express service for the route
would have the effect of reducing passengers per vehicle by eliminating local
passengers with other destinations.
o
Redesigning the buses or trains in order to add more seats. Human factors data
could be used to design optimal seating arrangement that maximize passenger
capacity.
OMNY
:
As the data above indicates, OMNY saves time when boarding the bus and train.
Advertising improvements could be made to inform the public about the availability and
benefits of using OMNY. The website and app could be improved to make it easier for
people to use and sign up.
Signal System: Switching from traditional to communication-based signal systems would
significantly improve travel delays. By allowing for greater headways, this move
optimizes train timetables. Reducing the requirement for maintaining traditional signals
simplifies operations and lowers maintenance expenses. By reducing the reliance on
mechanical components, the shift increases system dependability by reducing possible
sources of failure. This update significantly improves rail efficiency, allowing for more
seamless operations, shorter wait times, and better overall performance.
Conclusion
The data indicate many time-wasting issues that can be improved upon. All of the solutions
above would contribute to reducing travel delays and provide a better overall experience for the
users of this route. Additional train capacity, express service, OMNY payment and improved
train signaling were all identified as potential improvements. Implementing any of these
proposed solutions would reduce travel times, as indicated by the data.
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Appendix
Analysis data located in accompanying “
MiniProject4_Data_Group3.xlsx
” spreadsheet.
References
“Streamlit.”
Metrics.mta.info
, metrics.mta.info/?home/. Accessed 12 Nov. 2023.
CBTC: Upgrading Signal Technology. MTA. (n.d.). https://new.mta.info/project/cbtc-
signal-upgrades
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