• ES Español (Latinoamérica)
• EN English

3.14. Transportation Systems Analysis and Modeling (TSA)

This knowledge area encompasses the systems-engineering foundations of transportation, network modeling, simulation methods, optimization techniques, and data analytics applied to transportation problems.

Knowledge Area (KA)	Core Tier1	Core Tier2
3.14.1 Transportation Systems Fundamentals	Elective
3.14.2 Transportation Network Modeling	Elective
3.14.3 Transportation Simulation Methods	Elective
3.14.4 Optimization for Transportation	Elective
3.14.5 Data Analytics for Transportation	Elective

3.14.1. TSA/Transportation Systems Fundamentals ↑ Back to top

Transportation modes and network characteristics, the systems-engineering approach to transportation problems, stakeholders and institutional actors, and performance measurement.
Topics:
Core

• Transportation modes and network characteristics
• Systems-engineering approach to transportation problems
• Stakeholders and institutional actors in transportation systems
• Performance measures and level-of-service concepts for transportation systems

Learning Outcomes:
Core:

1. Describe transportation modes and their network characteristics [Familiarity]
2. Explain the systems-engineering approach applied to transportation problems [Familiarity]
3. Identify stakeholders and institutional actors relevant to a transportation system [Familiarity]
4. Apply performance measures to evaluate a transportation system [Usage]

3.14.2. TSA/Transportation Network Modeling ↑ Back to top

Graph-based representation of transportation networks, shortest-path and routing algorithms, traffic assignment models, and network connectivity and accessibility measures.
Topics:
Core

• Graph-based representation of transportation networks
• Shortest-path and routing algorithms
• Traffic assignment models (user equilibrium, system optimum)
• Network connectivity and accessibility measures

Learning Outcomes:
Core:

1. Represent a transportation network using graph-based data structures [Usage]
2. Compute shortest paths and routes within a transportation network [Usage]
3. Apply traffic assignment models to estimate link flows [Assessment]
4. Evaluate network connectivity and accessibility [Assessment]

3.14.3. TSA/Transportation Simulation Methods ↑ Back to top

Microscopic, macroscopic, and mesoscopic simulation fundamentals, simulation model calibration and validation, and scenario analysis using simulation outputs.
Topics:
Core

• Microsimulation fundamentals for traffic and transit systems
• Macroscopic and mesoscopic simulation fundamentals
• Simulation model calibration and validation
• Scenario analysis using simulation outputs

Learning Outcomes:
Core:

1. Describe the fundamentals of microscopic traffic and transit simulation [Familiarity]
2. Explain macroscopic and mesoscopic simulation approaches [Familiarity]
3. Calibrate and validate a simulation model against field data [Usage]
4. Conduct scenario analysis using simulation outputs [Usage]

3.14.4. TSA/Optimization for Transportation ↑ Back to top

Vehicle routing and scheduling optimization, transportation network design optimization, resource allocation and facility location, and heuristic solution methods.
Topics:
Core

• Vehicle routing and scheduling optimization
• Transportation network design optimization
• Resource allocation and facility location optimization
• Heuristic and metaheuristic solution methods

Learning Outcomes:
Core:

1. Formulate a vehicle routing or scheduling problem as an optimization model [Usage]
2. Solve a transportation network design optimization problem [Usage]
3. Apply resource allocation and facility location methods to a transportation problem [Assessment]
4. Describe heuristic and metaheuristic methods for large-scale transportation optimization [Familiarity]

3.14.5. TSA/Data Analytics for Transportation ↑ Back to top

Big data sources for transportation, GIS applications, machine learning applications, and data visualization for transportation performance monitoring.
Topics:
Core

• Big data sources for transportation (GPS, sensors, mobile, probe vehicles)
• GIS applications in transportation analysis
• Machine learning applications in transportation
• Data visualization and dashboards for transportation performance

Learning Outcomes:
Core:

1. Identify big data sources available for transportation analysis [Familiarity]
2. Apply GIS tools to analyze transportation data [Usage]
3. Describe machine learning applications in transportation [Familiarity]
4. Develop data visualizations and dashboards for transportation performance monitoring [Usage]