Arrival/Travel times for public transit exhibit variability on account of factors like seasonality, dwell times at bus stops, traffic signals, travel demand fluctuation, spatial and temporal correlations, etc. The developing world in particular is plagued by additional factors like lack of lane discipline, excess vehicles, diverse modes of transport and so on. This renders the bus arrival time prediction (BATP) to be a challenging problem especially in the developing world. Present disclosure provides system and method that implement recurrent neural networks (RNNs) for BATP (in real-time), wherein the system incorporates information pertaining to spatial and temporal correlations and seasonal correlations. More specifically, a Gated Recurrent Unit (GRU) based Encoder-Decoder (ED) model with one or more bi-directional layers at the decoder is implemented for BATP based on relevant additional synchronized inputs (from previous trips) at each step of the decoder. The system further captures congestion influences on travel time prediction.


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    Title :

    Systems and methods for public transit arrival time prediction


    Contributors:

    Publication date :

    2025-02-18


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G01C Messen von Entfernungen, Höhen, Neigungen oder Richtungen , MEASURING DISTANCES, LEVELS OR BEARINGS



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