Highlight A stepwise explainable deep learning formulation using linear regression (LR) and a recurrent neural network. Facilitate quota-based planning to balance utilization rates between for-hire vehicles (FHVs) and traditional taxis. Data analysis from New York City Taxi & Limousine Commission to observe the correlation between FHV and regular taxis. Real-world data sets examined for coupled LR and long short-term memory (LSTM) framework.

    Abstract As app-based ride-hailing services have been widely adopted within existing traditional taxi markets, researchers have been devoted to understand the important factors that influence the demand of the new mobility. Econometric models (EMs) are mainly utilized to interpret the significant factors of the demand, and deep neural networks (DNNs) have been recently used to improve the forecasting performance by capturing complex patterns in the large datasets. However, to mitigate possible (induced) traffic congestion and balance utilization rates for the current taxi drivers, an effective strategy of proactively managing a quota system for both emerging services and regular taxis is still critically needed. This paper aims to systematically design an explainable deep learning model capable of assessing the quota system balancing the demand volumes between two modes. A two-stage interpretable machine learning modeling framework was developed by a linear regression (LR) model, coupled with a neural network layered by long short-term memory (LSTM). The first stage investigates the correlation between the existing taxis and on-demand ride-hailing services while controlling for other explanatory variables. The second stage fulfills the long short-term memory (LSTM) network structure, capturing the residuals from the first estimation stage in order to enhance the forecasting performance. The proposed stepwise modeling approach (LR-LSTM) forecasts the demand of taxi rides, and it is implemented in the application of pick-up demand prediction using New York City (NYC) taxi data. The experiment result indicates that the integrated model can capture the inter-relationships between existing taxis and ride-hailing services as well as identify the influence of additional factors, namely, the day of the week, weather, and holidays. Overall, this modeling approach can be applied to construct an effective active demand management (ADM) for the short-term period as well as a quota control strategy between on-demand ride-hailing services and traditional taxis.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A stepwise interpretable machine learning framework using linear regression (LR) and long short-term memory (LSTM): City-wide demand-side prediction of yellow taxi and for-hire vehicle (FHV) service


    Contributors:


    Publication date :

    2020-09-02




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Taxi Demand Prediction Using LSTM and Optimized Taxi Geo-distribution

    Patole, Deepti / Mehta, Raj / Mehta, Chintan et al. | Springer Verlag | 2021


    Food insecurity among New York City taxi and for-hire vehicle drivers

    Gany, Francesca M. / Meraji, Nujbat Nasim / Narang, Bharat et al. | IOS Press | 2022


    Multistep Ahead Taxi-Out Time Prediction Using Long Short-Term Memory Networks

    Jeong, Myeongsook / Eun, Yeonju / Jeon, Daekeun | AIAA | 2024


    Taxi Passengers Demand Prediction Using Deep Learning

    KU, Donggyun / KIM, Jooyoung / LEE, Seungjae | TIBKAT | 2017


    Basingstoke Taxi and Private Hire Car Study

    P. A. K. Greening / R. L. Jackson | NTIS | 1982