AI-enhanced urban planning and transportation networks.

Decoding Travel Choices: How AI is Revolutionizing Transportation

Kai Mendoza in Tech & Innovation December 2025 • 4 min read.

"Discover how a cutting-edge k-modal nonparametric mixed logit model is redefining our understanding of travel behavior and urban mobility with AI."

Imagine trying to predict something as complex as how millions of people will choose to get around a city each day. Traditional methods often fall short because human behavior is rarely straightforward. Travel choices are influenced by numerous factors, from the cost and convenience of different modes to personal preferences and unpredictable circumstances. Understanding these choices is vital for effective urban planning, transportation policy, and the development of sustainable mobility solutions.

Discrete choice models (DCMs) have long been the workhorse of demand analysis, but they often struggle to capture the full spectrum of human decision-making. These models rely on taste parameters that reflect how people value things like time, cost, and convenience. However, these parameters can vary greatly across different populations and regions, leading to inaccuracies if not properly accounted for.

Now, a new approach is emerging that promises to revolutionize our understanding of travel choices. A sophisticated group-level agent-based mixed logit model (GLAM), enhanced with AI, offers a more nuanced and accurate way to analyze travel behavior. This innovative model not only addresses the limitations of existing methods but also opens up exciting possibilities for urban planning and transportation management.

What is GLAM Logit and How Does It Improve Travel Analysis?

AI-enhanced urban planning and transportation networks.

GLAM logit represents a significant leap forward in travel demand modeling. Unlike traditional models that rely on predefined parametric distributions to represent taste heterogeneity, GLAM logit takes a non-parametric approach. This means it doesn't assume a specific distribution for preferences, allowing it to capture a wider range of individual tastes and behaviors.

The model achieves this by treating each market segment—defined by factors like geographic location, socioeconomic attributes, and travel patterns—as an individual agent. It then solves a multi-agent inverse utility maximization (MIUM) problem to estimate agent-specific parameters, effectively creating a unique profile for each market segment.

Here’s a closer look at the key advantages of the GLAM logit model:

Non-Parametric Representation: Captures a wider range of taste heterogeneity without being constrained by predefined distributions.
Market-Specific Parameters: Estimates unique parameters for each market segment, providing a more granular understanding of travel behavior.
Agent-Based Approach: Treats each market segment as an individual agent, allowing for a more realistic representation of decision-making processes.
Scalability and Efficiency: Designed to handle large datasets and complex scenarios, making it suitable for real-world applications.

To correct for potential biases, GLAM logit incorporates a control function approach, using instrumental variable regression to account for the endogeneity of factors like price. The model then employs a k-means algorithm to categorize agent-specific parameters into 'taste clusters,' revealing underlying patterns in travel preferences. By combining these techniques, GLAM logit provides a comprehensive and adaptable framework for travel demand analysis.

The Future of Transportation is Data-Driven and AI-Powered

The development and application of GLAM logit represent a significant step towards a more data-driven and AI-powered approach to transportation planning. By providing a more accurate and nuanced understanding of travel behavior, this model empowers urban planners, policymakers, and transportation providers to make informed decisions that improve mobility, sustainability, and quality of life for everyone. As cities continue to grow and evolve, innovative tools like GLAM logit will be essential for navigating the complexities of urban transportation and creating a more efficient and equitable future.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: https://doi.org/10.48550/arXiv.2309.13159,

Title: Estimating A K-Modal Nonparametric Mixed Logit Model With Market-Level Data

Subject: econ.em

Authors: Xiyuan Ren, Joseph Y. J. Chow, Prateek Bansal

Published: 22-09-2023

Everything You Need To Know

What is the key benefit of using the group-level agent-based mixed logit model (GLAM logit) compared to traditional discrete choice models (DCMs) in travel demand analysis?

The primary advantage of GLAM logit is its non-parametric approach to representing taste heterogeneity. Unlike traditional DCMs that rely on predefined parametric distributions, GLAM logit doesn't assume a specific distribution for preferences. This allows it to capture a wider range of individual tastes and behaviors, providing a more accurate understanding of travel choices. This is crucial because human behavior in travel is influenced by numerous factors, and predefined distributions may not adequately represent the complexity of these preferences across different populations and regions.

How does the group-level agent-based mixed logit model (GLAM logit) handle the diversity of travel preferences within a city?

GLAM logit addresses the diversity of travel preferences by treating each market segment—defined by factors like geographic location, socioeconomic attributes, and travel patterns—as an individual agent. It then solves a multi-agent inverse utility maximization (MIUM) problem to estimate agent-specific parameters, effectively creating a unique profile for each market segment. Furthermore, it uses a k-means algorithm to categorize these agent-specific parameters into 'taste clusters,' revealing underlying patterns in travel preferences and providing a granular understanding of travel behavior.

Can you elaborate on how the group-level agent-based mixed logit model (GLAM logit) improves urban planning and transportation management?

GLAM logit empowers urban planners, policymakers, and transportation providers to make informed decisions that improve mobility, sustainability, and quality of life. By providing a more accurate and nuanced understanding of travel behavior, GLAM logit allows for better predictions of how people will respond to changes in transportation options, policies, or infrastructure. This can lead to more effective strategies for reducing congestion, promoting sustainable transportation modes, and improving the overall efficiency of urban transportation systems. The agent-based approach enables simulations that reflect real-world decision-making processes more closely.

What does it mean that the group-level agent-based mixed logit model (GLAM logit) incorporates a control function approach, and why is it important?

The inclusion of a control function approach in GLAM logit addresses the potential endogeneity of factors like price. Endogeneity occurs when a variable (like price) is correlated with the error term in a regression model, leading to biased estimates. By using instrumental variable regression within the control function framework, GLAM logit accounts for this endogeneity, providing more reliable estimates of the true impact of price on travel choices. This is crucial for accurate demand analysis and effective policy interventions.

How does the scalability and efficiency of the group-level agent-based mixed logit model (GLAM logit) contribute to its real-world applicability in transportation planning?

The scalability and efficiency of GLAM logit are vital for its application in real-world transportation planning because urban transportation systems generate vast amounts of data. GLAM logit is designed to handle these large datasets and complex scenarios, making it practical for analyzing travel behavior in large cities. Without this scalability, the model would be limited to smaller, less complex applications, reducing its relevance for addressing the major transportation challenges faced by modern urban areas. Its efficiency allows for timely analysis and informed decision-making in dynamic urban environments.