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Halton Academy For Management and Technology Private Limited,
39/2475-B1 LR Towers, South Janatha Road, Palarivattom, Ernakulam, Kerala - 682025, India.

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M.Tech in Transportation Engineering

Semester-wise syllabus for an M.Tech in Transportation Engineering

 

Semester 1: Core Foundations 

Courses: 

1. Transportation Planning and Economics

   - Travel demand modeling, four-stage modeling, cost-benefit analysis, and land-use integration. 

2. Traffic Engineering 

   - Traffic flow theory, signal design (Webster’s method), capacity analysis, and level of service (LOS). 

3. Pavement Materials and Design 

   - Flexible/rigid pavement design (IRC, AASHTO standards), material characterization (bitumen, aggregates). 

4. Transportation Systems Analysis 

   - Network optimization, public transit systems, and GIS applications in transport. 

5. Research Methodology 

   - Technical writing, statistical tools (Python/R), and data collection techniques. 

Labs:

- Traffic Simulation Lab (PTV Vissim, Synchro) 

- Pavement Testing Lab (Marshall stability, CBR, bitumen tests) 

 

Semester 2: Advanced Topics & Electives 

Core Courses: 

1. Public Transportation Systems 

   - Bus Rapid Transit (BRT), metro rail planning, fare structures, and transit-oriented development (TOD). 

2. Highway Geometric Design 

   - Horizontal/vertical alignment, intersection design, and IRC guidelines. 

 

Electives (Examples): 

- Intelligent Transportation Systems (ITS) 

- Airport Planning and Design 

- Railway Engineering (track design, signaling) 

- Sustainable Transportation (green corridors, non-motorized transport) 

- Freight and Logistics Management 

Labs: 

- GIS Lab (ArcGIS, QGIS for transportation networks) 

- Highway Design Lab (AutoCAD Civil 3D, MX Road) 

 

Semester 3: Specialization & Project Work 

Electives (Examples): 

- Urban Transportation Planning (smart cities, mobility-as-a-service) 

- Pavement Maintenance and Rehabilitation 

- Transportation Safety and Accident Analysis 

- Port and Harbor Engineering 

- AI/ML in Transportation (traffic prediction, autonomous vehicles) 

Project/Dissertation: 

- Phase 1: Topic selection (e.g., congestion pricing models, EV charging infrastructure, pedestrian safety audits), literature review, and proposal. 

- Seminars: Presentations on trends like connected/autonomous vehicles, shared mobility, or climate-resilient infrastructure. 

 

Semester 4: Thesis/Project Completion 

Thesis/Project: 

- Full-time focus on simulations (e.g., traffic microsimulation), field studies (e.g., traffic surveys), or policy analysis. 

- Final documentation, viva voce defense, and collaboration with agencies (e.g., NHAI, urban planning departments). 

Additional Components:

- Industrial Internship (optional, with firms like AECOM, L&T, or government bodies like MORTH). 

- Workshops: Training in *BIM for Infrastructure, **SUMO (Simulation of Urban Mobility), or **road safety audits*. 

Elective Tracks (Specializations): 

1. Traffic and Urban Mobility 

   - Smart traffic management, shared mobility, and pedestrian-friendly design. 

2. Pavement Engineering 

   - Advanced materials (warm mix asphalt, geosynthetics), lifecycle assessment. 

3. Transportation Policy and Economics 

   - Sustainable funding models, EV policies, and global best practices. 

4. Logistics and Supply Chain 

   - Freight corridor optimization, warehouse location analysis. 

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