The prediction of travel time is of paramount interest to the planning, design, operations, and management of any transportation facility. While the average travel time provides an idea of how long a trip will take, it does not provide information on its reliability. In contrast, percentiles provide more detailed information on reliability by determining the range of travel times that can be expected for a given trip. Thus, the prediction of travel time percentiles helps in travel time reliability studies. In this study, the use of Quantile Random Forest (QRF) Regressor is used to predict travel time percentiles. QRF is a flexible machine learning algorithm that can capture the complex relationships between predictor variables and the response variable. The study uses Wi-Fi sensors based data collected from Rajiv Gandhi IT Expressway in Chennai. The performance of the QRF model is evaluated using mean absolute percentage error (MAPE). The results show that the QRF model performed well in predicting travel time percentiles, with the best performance observed for the median percentile. Thus, the QRF model can provide accurate and reliable travel time predictions, which can be used by transportation planners and traffic engineers to optimize traffic flow and improve transportation efficiency.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Travel Time Reliability Prediction Using Quantile Random Forest Regression


    Additional title:

    Transp. in Dev. Econ.


    Contributors:


    Publication date :

    2025-04-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Travel Time Reliability Prediction Using Quantile Random Forest Regression

    Anil Kumar, B. / Chandana, Gunda / Vanajakshi, Lelitha | Springer Verlag | 2025


    Travel Time Probability Prediction Based on Constrained LSTM Quantile Regression

    Hao Li / Zijian Wang / Xiantong Li et al. | DOAJ | 2023

    Free access

    Prediction of Travel Time Reliability on Interstates Using Linear Quantile Mixed Models

    Zhang, Xiaoxiao / Zhao, Mo / Appiah, Justice et al. | Transportation Research Record | 2022


    Travel Time Reliability Prediction Using Random Forests

    Zhao, Mo / Zhang, Xiaoxiao / Appiah, Justice et al. | Transportation Research Record | 2023


    Quantile Regression Analysis of Transit Travel Time Reliability with Automatic Vehicle Location and Farecard Data

    Ma, Zhenliang / Zhu, Sicong / Koutsopoulos, Haris N. et al. | Transportation Research Record | 2017