MasterFaculty of Engineering & Technology

Smart Urban Transportation Networks and Autonomous Vehicle Integration

Welcome to the advanced study of urban mobility! I am Prof. Dr. Lyudmila Larin. As a professor and a pioneering force in the field of Smart Urban Transportation Networks and Autonomous Vehicle Integration, I bring a unique blend of engineering expertise and AI insight to the study of urban mobility. I am honored to lead the Smart Urban Transportation Networks and Autonomous Vehicle Integration (M.Sc.) program at Nexier University. My motto is: "Shaping Cities, Moving Futures".

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Level: Master
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

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The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Mastering the engineering and planning of integrated urban transportation systems. Specializes in intelligent traffic control, autonomous vehicle fleet management, and mobility-as-a-service (MaaS) platforms.

Practical focus

Transportation engineering, urban planning, data science for urban data, fleet management optimization, leadership in smart city solutions.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in technology companies or urban planning firms
Roles as transportation engineers or data scientists
Consultancy in advanced smart urban transportation networks and autonomous vehicle integration
Support roles in academic research projects on smart urban transportation

Career opportunities

Head of Autonomous Mobility Solutions for technology companies or automotive firms
MaaS Platform Architect for urban mobility providers
Transportation Engineer specializing in intelligent traffic control
Researcher in Smart Urban Transportation Networks and Autonomous Vehicle Integration

Jobs and projects

Cultivating an interdisciplinary approach, integrating civil engineering, computer science, and urban planning
Developing strategic thinking for smart urban transportation networks and autonomous vehicle integration
Enhancing problem-solving through the analysis of complex urban mobility challenges
Critical thinking for a comprehensive and nuanced understanding of Smart Urban Transportation Networks and Autonomous Vehicle Integration

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering advanced practical skills in Transportation engineering and urban planning.
- Gaining expertise in data science for urban data and fleet management optimization.
- Developing problem-solving abilities for complex Leadership in smart city solutions.
- Cultivating an interdisciplinary approach, integrating civil engineering, computer science, and urban planning at an advanced level.
Skills you build
- Mastering AI-powered techniques for MaaS ecosystem architecture.
- Applying advanced engineering principles to smart urban transportation networks and autonomous vehicle integration.
- Interpreting and analyzing complex urban transportation systems and their implications for MaaS platforms.
- Identifying optimal multimodal passenger journeys and predicting demand-supply dynamics.

Listed courses

Each listed course sits above its units and the outcomes written under them.

Smart Urban Transportation Networks and Autonomous Vehicle Integration

01Advanced Transportation Engineering and Urban Planning
1. FoundationsFoundations of Advanced Transportation Engineering and Urban Planning
  The learner can master advanced practical skills in Transportation engineering and urban planning, as applied to Advanced Transportation Engineering and Urban Planning.
  The learner can gain expertise in data science for urban data and fleet management optimization, as applied to Advanced Transportation Engineering and Urban Planning.
2. MethodsMethods in Advanced Transportation Engineering and Urban Planning
  The learner can develop problem-solving abilities for complex Leadership in smart city solutions, as applied to Advanced Transportation Engineering and Urban Planning.
  The learner can cultivating an interdisciplinary approach, integrating civil engineering, computer science, and urban planning at an advanced level, as applied to Advanced Transportation Engineering and Urban Planning.
3. ApplicationApplication of Advanced Transportation Engineering and Urban Planning
  The learner can master AI-powered techniques for MaaS ecosystem architecture, as applied to Advanced Transportation Engineering and Urban Planning.
  The learner can apply advanced engineering principles to smart urban transportation networks and autonomous vehicle integration, as applied to Advanced Transportation Engineering and Urban Planning.
02Data Science for Urban Mobility
1. FoundationsFoundations of Data Science for Urban Mobility
  The learner can interpreting and analyze complex urban transportation systems and their implications for MaaS platforms, as applied to Data Science for Urban Mobility.
  The learner can identify optimal multimodal passenger journeys and predicting demand-supply dynamics, as applied to Data Science for Urban Mobility.
2. MethodsMethods in Data Science for Urban Mobility
  The learner can apply a method from Data Science for Urban Mobility to a documented case.
  The learner can select an appropriate method from Data Science for Urban Mobility for a stated problem.
3. ApplicationApplication of Data Science for Urban Mobility
  The learner can evaluate a practice of Data Science for Urban Mobility against a stated criterion.
  The learner can transfer Data Science for Urban Mobility to a new documented context.
03Autonomous Vehicle Fleet Management
1. FoundationsFoundations of Autonomous Vehicle Fleet Management
  The learner can explain the core terms of Autonomous Vehicle Fleet Management.
  The learner can distinguish related ideas inside Autonomous Vehicle Fleet Management.
2. MethodsMethods in Autonomous Vehicle Fleet Management
  The learner can apply a method from Autonomous Vehicle Fleet Management to a documented case.
  The learner can select an appropriate method from Autonomous Vehicle Fleet Management for a stated problem.
3. ApplicationApplication of Autonomous Vehicle Fleet Management
  The learner can evaluate a practice of Autonomous Vehicle Fleet Management against a stated criterion.
  The learner can transfer Autonomous Vehicle Fleet Management to a new documented context.
04Case Studies in Smart Urban Transportation Networks and Autonomous Vehicle Integration
1. FoundationsFoundations of Case Studies in Smart Urban Transportation Networks and Autonomous Vehicle Integration
  The learner can explain the core terms of Case Studies in Smart Urban Transportation Networks and Autonomous Vehicle Integration.
  The learner can distinguish related ideas inside Case Studies in Smart Urban Transportation Networks and Autonomous Vehicle Integration.
2. MethodsMethods in Case Studies in Smart Urban Transportation Networks and Autonomous Vehicle Integration
  The learner can apply a method from Case Studies in Smart Urban Transportation Networks and Autonomous Vehicle Integration to a documented case.
  The learner can select an appropriate method from Case Studies in Smart Urban Transportation Networks and Autonomous Vehicle Integration for a stated problem.
3. ApplicationApplication of Case Studies in Smart Urban Transportation Networks and Autonomous Vehicle Integration
  The learner can evaluate a practice of Case Studies in Smart Urban Transportation Networks and Autonomous Vehicle Integration against a stated criterion.
  The learner can transfer Case Studies in Smart Urban Transportation Networks and Autonomous Vehicle Integration to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of Mastering the engineering and planning of integrated urban transportation systems. I specialize in intelligent traffic control, autonomous vehicle fleet management, and mobility-as-a-service (MaaS) platforms. My work seamlessly integrates civil engineering, computer science, and urban planning. I am widely recognized for my contributions, with publications like "Multi-Agent Reinforcement Learning for Autonomous Fleet Coordination" and "Blockchain for Secure MaaS Data Exchange" listed on these platforms. I hold prestigious memberships as a "Head of Autonomous Mobility Solutions" at Waymo (or a equivalent) and a "Keynote Speaker" at the TRB Annual Meeting (Transportation Research Board). My thought leadership is evident through my advanced research on urban air mobility integration, last-mile delivery optimization, and the future of multimodal transportation ecosystems, frequently featured in publications like Transportation Science or IEEE Transactions on Intelligent Transportation Systems.

Applied mentorship

My expertise lies in understanding and navigating the advanced technical challenges of smart urban transportation, focusing on Transportation engineering, urban planning, data science for urban data, fleet management optimization, and Leadership in smart city solutions. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on smart urban transportation networks and autonomous vehicle integration:

Blog Post (Current Academic Topic): "The Rise of On-Demand Autonomous Shuttles: Revolutionizing Public Transit." This blog post academically explores the transformative potential of on-demand autonomous shuttles in modern urban public transit systems. It discusses how these AI-driven vehicles, operating on flexible routes and schedules, can enhance accessibility, reduce operational costs, and complement existing public transport networks, particularly for first-mile/last-mile connectivity in smart cities.

Blog Post (Controversial Topic): "The Algorithmic City: When AI Optimizes Every Route and Every Person – Efficiency or Total Control? The Ethical Crossroads of Hyper-Intelligent Urban Planning." This article provocatively discusses the highly controversial future where advanced AI systems autonomously design, manage, and optimize every aspect of urban living, from traffic flow and public transport to resource allocation and public safety, with minimal human intervention. It questions whether AI, despite its potential for unprecedented efficiency and sustainability, could inadvertently lead to a loss of individual freedom, algorithmic discrimination in resource distribution, or an opaque, unchallengeable centralized control over urban life. It raises profound ethical questions about data privacy in public spaces, the potential for surveillance, and the imperative to ensure human agency and democratic governance in a fully AI-managed city.

Article: "AI-Driven Fleet Management for Autonomous Ride-Sharing Services." This article details the application of AI algorithms for optimizing fleet management in autonomous ride-sharing services. It explores how machine learning can dynamically match passengers with vehicles, optimize routing for multiple pickups/drop-offs, and predict demand fluctuations, ensuring efficient and scalable autonomous mobility services.

Peer-Reviewed Journal Article: "Integrated Mobility-as-a-Service Platforms for Smart Cities." Published in the

International Journal of Urban Mobility ( Peer-Reviewed Journal) , this article presents groundbreaking research on mastering the engineering and planning of integrated urban transportation systems. It specializes in intelligent traffic control, autonomous vehicle fleet management, and mobility-as-a-service (MaaS) platforms, showcasing novel approaches for seamless urban mobility.

Book: "Seamless Cities: Engineering Smart Urban Transportation Networks." This book provides advanced insights into mastering the engineering and planning of integrated urban transportation systems. It covers intelligent traffic control, autonomous vehicle fleet management, and mobility-as-a-service (MaaS) platforms.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the advanced technical challenges of smart urban transportation:

"Autonomous Vehicle Routing and Scheduling Algorithms" (Technical Manual).

"Big Data Analytics for Urban Mobility Patterns" (Research Paper).

"Implementing Mobility-as-a-Service Platforms: Case Studies" (Practical Guide).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": MaaS Ecosystem Architect. When a student proposes an integrated urban mobility system, I can instantly use the GAF engine to synthesize its optimal Mobility-as-a-Service (MaaS) ecosystem. This includes simulating multimodal passenger journeys, predicting demand-supply dynamics for various transport modes, and highlighting integration challenges, allowing for rapid iteration and optimization of seamless urban transportation networks.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": Fleet Optimization Planner. When students are designing autonomous vehicle fleets, I can instantly activate a GAF-powered "Fleet Optimization Planner." This tool models vehicle dispatch, routing, and recharging strategies, predicts service efficiency and operational costs, and suggests optimal fleet sizing and deployment for various demand scenarios in an urban environment.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Lyudmila Larin

Meet your professor Open the classroom

AI Super Mentor

Dr. David Rivera

Transportation engineering, urban planning, data science for urban data, fleet management optimization, leadership in smart city solutions.

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Same faculty and level

Related programs

Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

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Smart Urban Transportation Networks and Autonomous Vehicle Integration

Academic focus

Practical focus

Internships in technology companies or urban planning firms

Roles as transportation engineers or data scientists

Consultancy in advanced smart urban transportation networks and autonomous vehicle integration

Support roles in academic research projects on smart urban transportation

Head of Autonomous Mobility Solutions for technology companies or automotive firms

MaaS Platform Architect for urban mobility providers

Transportation Engineer specializing in intelligent traffic control

Researcher in Smart Urban Transportation Networks and Autonomous Vehicle Integration

Cultivating an interdisciplinary approach, integrating civil engineering, computer science, and urban planning

Developing strategic thinking for smart urban transportation networks and autonomous vehicle integration

Enhancing problem-solving through the analysis of complex urban mobility challenges

Critical thinking for a comprehensive and nuanced understanding of Smart Urban Transportation Networks and Autonomous Vehicle Integration

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Lyudmila Larin

Dr. David Rivera

Find the program that expands your universe.