ITSC 2024 Paper Abstract

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Paper ThAT16.4

Azcarate Urrutia, Inigo (MIT), Coretti Sanchez, Naroa (MIT), Antonelli, Diego (NTT Data), Alonso, Luis (MIT), Larson, Kent (MIT)

Mode Choice Modeling for Sustainable Regional Commuting Using Machine Learning: A Case Study in Gipuzkoa, Spain

Scheduled for presentation during the Poster Session "Travel Information, Travel Guidance, and Travel Demand Management" (ThAT16), Thursday, September 26, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Keywords Simulation and Modeling, Travel Information, Travel Guidance, and Travel Demand Management, Emission and Noise Mitigation

Abstract

Addressing mobility is essential for achieving mid-century emissions reduction targets. The role of local governments is pivotal in shaping this future, yet they often lack adequate tools for informed decision-making regarding appropriate interventions within their unique contexts. To bridge this gap, we developed a machine learning (ML) model based on a random forest algorithm that predicts the impacts of different interventions on mobility mode choices and CO2 emissions. Moreover, we address the model's interpretability by offering a quantitative analysis of feature importance. Through a case study focused on commuting trips in Gipuzkoa, Spain, we explore a range of intervention scenarios, illustrating potential emissions reductions between 2% to 58%. Additionally, we integrated this model into a user-friendly web-based interface, which could support local governments in strategic mobility planning, thereby facilitating more informed and effective policy decisions.

 

 

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