Large Language Models for Urban Mobility

This Advanced Seminar provides a comprehensive overview of the research landscape of employing Large Language Models (LLMs) for Urban Mobility applications. The presented work in this seminar is categorized based on how LLMs are employed to serve various urban mobility applications. This goes from employing LLMs as a black box with a bit of prompt engineering, to fine-tuning LLMs to fit urban mobility applications, to completely retrain a vanilla LLM architecture with urban mobility data, to modifying the internal LLM loss function to fit urban mobility applications. The seminar concludes by presenting a set of benchmarking and evaluation work while pointing out to research gaps, open problems, and future research directions for employing LLMs to urban mobility applications.

Authors

Institutions

Publication Details

Published
2025-06-02
DOI
https://doi.org/10.1109/mdm65600.2025.00041
Primary Topic
Human Mobility and Location-Based Analysis
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Large Language Models for Urban Mobility

Mohamed F. Mokbel, Youssef Hussein, Mohamed Hemdan
Human Mobility and Location-Based Analysis
article

Large Language Models for Urban Mobility

Mohamed F. Mokbel, Youssef Hussein, Mohamed Hemdan
article en

Abstract

This Advanced Seminar provides a comprehensive overview of the research landscape of employing Large Language Models (LLMs) for Urban Mobility applications. The presented work in this seminar is categorized based on how LLMs are employed to serve various urban mobility applications. This goes from employing LLMs as a black box with a bit of prompt engineering, to fine-tuning LLMs to fit urban mobility applications, to completely retrain a vanilla LLM architecture with urban mobility data, to modifying the internal LLM loss function to fit urban mobility applications. The seminar concludes by presenting a set of benchmarking and evaluation work while pointing out to research gaps, open problems, and future research directions for employing LLMs to urban mobility applications.

University of Minnesota (US)
National Science Foundation
Sustainable cities and communities
Openalex Percentile: Top 82%
Human Mobility and Location-Based Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.