Problem Formulation

Problem Statement : How can the performance of NJ Transit Light Rail be optimized to improve performance and consistency, as well as decrease structural instability?

Kepner-Tregoe (KT) Analysis

Situation Analysis

Timing:

• High Concern: Recent closures of the Hoboken PATH station have increased use and reliance of the light rail system, creating more urgent need for reliability of the light rail system

• Moderate Concern: While AI is rapidly advancing, its implementation to the light rail system will take time

Trend:

• High Concern: Using AI in transportation is a growing trend to improve reliability and performance. Hoboken is also trending towards higher reliance of the light rail system because of the PATH closure

• Moderate Concern: Since long term use of AI in public transport is still in its early stages, the effectiveness of this technology is not yet certain

Impact:

• High Concern: The improved reliability and efficiency will directly benefit residents, while failing to address this issue could result in negatives such as resident dissatisfaction or even safety risks

• Moderate Concern: The costs of integrating this AI technology is significant, therefore the return on investment shall be evaluated and weighed against the benefits

What we Know

1. Dependency on Hoboken light rail system has increased due to PATH station closures

2. AI technology can be integrated to Predict and optimize maintenance related issues

3. Costs to integrate are significant, but potential benefits are as well

Tasks to Perform

1. Install Wireless Sensor Networks to gather data on train maintenance

2. Develop AI algorithms to analyze this data and Predict train failures

3. Integrate and test AI technology in controlled environment to ensure efficiency and safety

4. Deploy final product across all Hoboken light rail trains and stations

Problem Analysis

Identity: 

• The problem is that the Hoboken light rail system is experiencing increased reliability and performance issues

Location: 

• Across all Hoboken light rail stations and trains that are experiencing an increase in usage due to PATH station closures

Timing: 

• the problems have become more apparent since the closures of the PATH stations, leading to more passengers using the light rail instead

Magnitude: 

• Magnitude of problems range from low to severe, such as delays or potential safety risks

Possible Causes

1. Outdated and overused components that are prone to failure

2. Current maintenance schedules could be too lax to properly address train health in regard to increased usage

3. The increased usage and higher passenger loads are a clear reason to point to

4. Existing sensors & algorithm may be inadequate to Predict maintenance efficiently 

Decision Analysis

Corrective Action (in order of importance):

1. Install advanced sensors to collect real time data and develop AI algorithms that use the data to accurately Predict failures 

2. Replace outdated components with more modern and reliable ones

3. Add more trains or increase their frequency to address higher passenger demands

4. Increase maintenance schedules based off Predicted failures

Potential Problem Analysis

Potential Problems:

1. The AI technology could may not be as efficient as hoped 

2. The sensor network could be inefficient or slow in sending real-time data

3. The technology may be more expensive than the budget had planned

4. Technological issues that can arise from the AI integration

Preventive Actions:

1. Test the AI technology and integration rigorously to ensure it functions well under stress

2. Carefully calculate a budget that maximizes costs

3. Find the optimal sensors to design the network and ensure their functionality