Artificial Intelligence (AI) and machine learning have been increasingly adopted for travel demand forecasting. The AI-based travel demand forecasting models, though generate accurate predictions, may produce prediction biases and raise fairness issues. Using such biased models for decision-making may lead to transportation policies that exacerbate social inequalities. However, limited studies have been focused on addressing the fairness issues of these models. Therefore, in this study, we propose a novel methodology to develop fairness-aware, highly-accurate travel demand forecasting models. Particularly, the proposed methodology can enhance the fairness of AI models for multiple protected attributes (such as race and income) simultaneously. Specifically, we introduce a new fairness regularization term, which is explicitly designed to measure the correlation between prediction accuracy and multiple protected attributes, into the loss function of the travel demand forecasting model. We conduct two case studies to evaluate the performance of the proposed methodology using real-world ridesourcing-trip data in Chicago, IL and Austin, TX, respectively. Results highlight that our proposed methodology can effectively enhance fairness for multiple protected attributes while preserving prediction accuracy. Additionally, we have compared our methodology with three state-of-the-art methods that adopt the regularization term approach, and the results demonstrate that our approach significantly outperforms them in both preserving prediction accuracy and enhancing fairness. This study can provide transportation professionals with a new tool to achieve fair and accurate travel demand forecasting.


    Access

    Download


    Export, share and cite



    Title :

    Travel Demand Forecasting: A Fair AI Approach


    Contributors:
    Zhang, Xiaojian (author) / Ke, Qian (author) / Zhao, Xilei (author)

    Published in:

    Publication date :

    2024-10-01


    Size :

    3630030 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Passenger travel demand forecasting

    National Research Council, Transportation Research Board, USA | TIBKAT | 1976


    Forecasting Air Travel Demand

    Zheng, Yafei / Lai, Kin / Wang, Shouyang et al. | TIBKAT | 2018


    Travel Demand Forecasting 2009

    Lemp, Jason D | Online Contents | 2009



    Travel demand forecasting 2018

    National Research Council, Transportation Research Board, USA | TIBKAT | 2019