· What were the traditional methods of data collection in the transit system
· Why are the traditional methods insufficient in satisfying the requirement of data collection
· Give a synopsis of the case study and your thoughts regarding the requirements of the optimization and performance measurement requirements and the impact to expensive and labor-intensive nature.

What were the traditional methods of data collection in the transit system?

Traditional data collection in the transit system for performance evaluation includes:

 Farebox collections.

 Cash receipts.

 Credit card collections.

 Passenger surveys.

 Transit ridership counts.

 Fare card use.

 Customer complaints.

The farebox is the traditional method used for collecting revenue.

The traditional methods in the transit system, and more recent ways of conducting surveys in

the transit system, are described below. One way to gather data about transit users is to survey

their perceptions of the transit system and the services they use. This approach can provide

helpful information on problems encountered by transit users and can lead to improvements

in the system as perceived problems are addressed.

The traditional ways of conducting surveys of transit users have changed over time,

particularly in the United States. The most commonly used methods today are questionnaires

that are administered over the telephone or via the Internet.1 In addition to this traditional

survey method, more recently, surveys of users have also been conducted in transit stations

using specially designed paper surveys.

Of course, the traditional methods of collecting data were the ones that our predecessors used

for nearly a hundred years—cards and pencils, paper surveys, and questionnaires. We

continue to use these traditional data collection methods, but now we have computer systems

capable of receiving the data. Data collection now takes place using data collection systems

on bus stops, streetcars, and train platforms, on pay stations at the downtown subway stations,

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and in faregates. Dataran processes the data collected from these sources, which we operate

to provide real-time information for planning and service delivery. Obstacle detection of rail

transit based on deep learning. Measurement, 176, 109241. (He et al.,2021).

Why are the traditional methods insufficient in satisfying the requirement of data

collection?

The traditional way of data collection is a lengthy process, requires high costs, and consumes

a lot of time. The process begins with generating a list of keywords and keywords’ synonyms

representing all the possible keywords, then gathering related data from the web, and then,

finally, compiling all the gathered data into a single database for further analysis.

Recently, a new era of data collection has dawned. A researcher’s job is to have as many ideas

and thoughts as possible, have a plethora of information, and develop novel solutions to a

broad range of problems. In such a time, data collection should be a quick and easy process to

collect the data.

The traditional approaches based on sampling or the use of historical data or expert opinion

are all valid methods of data collection. However, the traditional methods are too laborious,

too costly, and may not be acceptable for various reasons. The above analysis can help us

understand the limitation of traditional methods, mainly based on the static relationship

between the observed variables and the response variables. However, with the development

of the Internet, there are some changes to the relationship between the observed variables and

the response variables. On the one hand, the observed variables now include more variables,

which is beneficial for discovering the association between two variables in the observed

variables.

However, with the increase of the number of variables, the computation amount increases

enormously. On the other hand, more observed variables can bring more information to

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predict the response variable, and the observed variables that contain too many features will

have less informative power. So, we need to find a method to filter some irrelevant features

from the observed variables, but the traditional methods are incapable of doing this. It is

possible to use the information from a few observed variables and their features to learn the

relationship between the response variables and the observed variables. From the prediction

perspective, the above methods are still based on the static relationship between the response

variables and the observed variables, so they cannot reveal the relationship in dynamic

environments. Assuring the safety of machine learning for pedestrian detection at crossings.

In International Conference on Computer Safety, Reliability, and Security (pp. 197-212).

Springer, Cham. (Gauerhof et al.,2020, September).

Give a synopsis of the case study and your thoughts regarding the optimization and

performance measurement requirements and the impact on the expensive and labor-

intensive nature.

The use of optimization in general practice has been an uphill battle for quite some time. The

optimization problem itself is a non-linear, complex system. Furthermore, the solution

requires many hours of computations. These days, we are looking for the best possible

solution, but there are no easy recipes to follow for this kind of problem.

A common approach to this optimization problem is to use an extensive grid search in multi-

dimensional parameter space. It is not the most efficient way to deal with this kind of

problem. In general, the space of all possible parameters to the problem is enormous. A vital

optimization problem is using one or multiple kernels and how many and where to place the

grid points. In addition to the number and placement of grid points, regularization parameters

are an essential tuning parameter to be optimized. The result from the algorithm used to solve

the problem is a set of possible solutions and which one of these is the most efficient.

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These optimization problems are typically solved iteratively. The best possible solution is

determined during the iterations using some optimization method (i.e., simplex, interior point,

Lagrange, etc.). A grid point is now defined. The optimization method is then executed using

the grid point, and a new solution is calculated. In this manner, the set of possible solutions is

built up. However, an essential aspect of the optimization process is that the best possible

solution is always determined initially. It means that the algorithm cannot determine any

better solution during the process than the best one it has found. It is mainly an issue if the

algorithm cannot adjust to the grid point during the optimization process.

The business case for the Optimization and Performance Measurement module is to reduce

the time and cost of the business by improving its processes. The business will benefit in this

way by reducing downtime for production or having a quicker recovery of production when

systems fail. Since this will result in better product output, they will receive a return on

investment from the investment. Since the business case for the Optimization and

Performance Measurement module is to reduce the time and cost of the business by

improving their processes, the focus will be placed on improving the efficiency of the

processes. The business will benefit in this way by reducing downtime for production or

having a quicker recovery of production when systems fail.

Since this will result in better product output, they will receive a return on investment from

the investment. The business requires improved measurement of performance to allow for

decision-making. It will allow setting the right goals and targets. It will also allow them to

track and monitor their performance. Effectiveness analysis of resources consumption,

environmental impact and production efficiency in traditional manufacturing using new

technologies: Case from sand casting. Energy Conversion and Management, 209, 112671.

(Zheng et al.,2020).

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References

He, D., Zou, Z., Chen, Y., Liu, B., Yao, X., & Shan, S. (2021). Obstacle detection of rail

transit based on deep learning. Measurement, 176, 109241.

Gauerhof, L., Hawkins, R., Picardi, C., Paterson, C., Hagiwara, Y., & Habli, I. (2020,

September). Assuring the safety of machine learning for pedestrian detection at

crossings. In International Conference on Computer Safety, Reliability, and

Security (pp. 197-212). Springer, Cham.

Zheng, J., Chen, A., Zheng, W., Zhou, X., Bai, B., Wu, J., … & Wang, W. (2020).

Effectiveness analysis of resources consumption, environmental impact and

production efficiency in traditional manufacturing using new technologies: Case from

sand casting. Energy Conversion and Management, 209, 112671.

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