How generative AI is transforming urban transportation planning

As urban populations rise and transportation patterns become more complex, traditional models have proven inadequate in handling real-time changes, multimodal interactions, and vast data streams. Generative AI is stepping in to fill these gaps, offering adaptable and scalable solutions.

Cities are expanding rapidly, challenging transportation planning with congestion, multimodal integration, sustainability, and real-time adaptability. Unfortunately, traditional transportation planning methods, reliant on historical data and expert models, struggle to keep up with the complexity of modern transport networks.

A groundbreaking study, "Generative AI in Transportation Planning: A Survey," authored by Longchao Da et al. and published in arXiv (2025), presents a systematic framework for integrating Generative Artificial Intelligence (GenAI) into transportation planning. The research explores how large language models (LLMs), retrieval-augmented generation (RAG), and AI-driven simulations can transform traffic forecasting, infrastructure design, and policy evaluation.

This study is the first to provide a comprehensive taxonomy of how AI can be used in transportation, offering key insights for urban planners, policymakers, and industry professionals aiming to build smarter, more efficient, and sustainable transport systems.

AI-powered solutions for modern transportation challenges

For decades, transportation planning has relied on statistical models, historical trends, and simulation techniques to predict demand, optimize infrastructure, and enhance mobility. However, as urban populations rise and transportation patterns become more complex, traditional models have proven inadequate in handling real-time changes, multimodal interactions, and vast data streams. Generative AI is stepping in to fill these gaps, offering adaptable and scalable solutions.

According to the study, AI-driven methodologies are automating five core transportation functions:

1. Descriptive Analytics: AI-powered data fusion enables real-time monitoring and anomaly detection.
2. Predictive Modeling: AI forecasts traffic congestion, demand fluctuations, and infrastructure needs.
3. Generative Tasks: AI-generated synthetic data improves simulations and system optimization.
4. Simulation-Based Decision-Making: AI-powered modeling enhances multimodal transportation networks.
5. Explainability & Trustworthiness: AI systems are becoming more transparent and interpretable for decision-makers.

The study outlines how large language models (LLMs) can analyze transportation policies, predict urban mobility trends, and even generate public engagement reports. Meanwhile, retrieval-augmented generation (RAG) enhances AI's ability to incorporate live data, making forecasts and recommendations more timely and reliable.

One of the most transformative applications of generative AI is in traffic congestion forecasting. By analyzing historical congestion patterns, real-time sensor data, and urban expansion trends, AI-powered models can predict traffic jams before they occur, enabling planners to proactively adjust traffic signals, optimize transit schedules, and manage road networks more effectively.

Technological innovations driving AI-based transportation planning

The research introduces a new taxonomy categorizing AI-driven transportation planning into core applications and methodologies. These advancements allow AI to learn from large-scale datasets, optimize system efficiency, and adapt to evolving conditions.

Key technological innovations include:

• Retrieval-Augmented Generation (RAG): AI retrieves real-time sensor data, GPS logs, public transit schedules, and urban mobility trends to generate dynamic, real-world recommendations.
• Hierarchical Navigable Small World (HNSW) Vector Databases: AI improves high-speed retrieval of multimodal transportation data, significantly enhancing query response times.
• Scenario-Based Simulation: AI can create synthetic traffic flow models to analyze the impact of new infrastructure projects, congestion pricing, or transit expansions.
• LLM-Driven Predictive Analytics: AI models interpret massive datasets to predict demand spikes, congestion patterns, and potential system breakdowns.

One major breakthrough in generative AI for transportation is synthetic data generation. Using GANs (Generative Adversarial Networks) and Diffusion Models, AI can simulate high-quality, realistic urban mobility datasets. This is particularly beneficial in data-scarce environments, allowing planners to test and validate transportation policies without relying solely on real-world observations.

For instance, AI-driven simulations have already helped cities like New York and Singapore optimize subway operations, bus networks, and smart traffic signals. By integrating real-time IoT sensor data with AI-driven analysis, planners can preemptively address system inefficiencies before they escalate into gridlock.

Real-world applications and AI’s impact on future mobility

The research highlights practical applications of generative AI that are already reshaping transportation networks across the globe.

Urban mobility planning

Generative AI is enhancing urban mobility planning by providing real-time analysis of subway ridership, bus frequencies, and multimodal integration. Cities can now use AI to model demand-responsive transit networks, reduce congestion, and optimize pedestrian flow. For instance, Singapore’s Land Transport Authority (LTA) has implemented AI-driven adaptive traffic control systems that adjust signal timing based on live traffic conditions, reducing commuter delays by 12% during peak hours.

Smart traffic management & emergency response

GenAI enables cities to simulate and predict traffic disruptions caused by major events, weather conditions, or accidents. By integrating AI into emergency response networks, authorities can reroute traffic, dispatch first responders more efficiently, and minimize disruption to essential transit corridors.

Sustainability and carbon reduction

AI-powered eco-routing algorithms help reduce fuel consumption by guiding drivers toward the most efficient travel paths. AI is also instrumental in planning electric vehicle (EV) charging stations, optimizing their placement based on demand patterns and available grid infrastructure.

Policy testing and public engagement

Governments can use generative AI to simulate the effects of policy changes before implementation. AI can model the impacts of congestion pricing, transit subsidies, or infrastructure expansions, providing data-driven insights for decision-makers. Additionally, LLMs can summarize policy documents, draft public engagement reports, and translate technical insights into user-friendly formats for broader stakeholder involvement.

Challenges and the road ahead for AI in transportation

While the potential of generative AI in transportation planning is immense, the study identifies several challenges that must be addressed to ensure ethical and effective AI deployment.

Key challenges Include:

• Data Scarcity & Bias: AI models require high-quality, diverse datasets to avoid reinforcing biases in transportation policy.
• Computational Costs: Running large-scale AI simulations demands significant cloud resources and infrastructure.
• Privacy & Security Risks: Transportation data is highly sensitive, necessitating strong encryption, anonymization, and regulatory compliance (e.g., GDPR, CCPA).
• Explainability & Trust: Many AI models operate as black boxes, making it difficult for policymakers to interpret and validate recommendations.

Despite these challenges, the future of AI-driven transportation planning remains promising. By leveraging AI’s predictive capabilities and real-time adaptability, we can build smarter, more sustainable, and more efficient urban mobility ecosystems for the future. The study outlines key future directions:

• Integration with IoT & smart city infrastructure: AI-driven transportation networks will connect seamlessly with 5G, autonomous vehicles, and real-time IoT sensors to create more adaptive mobility solutions.
• AI-powered multimodal transport networks: Future AI models will enhance ride-sharing, biking, public transit, and pedestrian systems for holistic urban mobility.
• Hybrid AI models: Combining quantum computing, generative AI, and domain-specific simulation models will redefine transportation research in the coming decade.