Public Transit Arrival Prediction: a Seq2Seq RNN Approach

TL;DR

This paper introduces a novel Seq2Seq RNN model using GRU units for real-time bus arrival prediction in challenging traffic conditions, effectively capturing spatial-temporal correlations and congestion effects.

Contribution

It presents a unique Encoder-Decoder RNN architecture with synchronized inputs and bidirectional layers, tailored for the dynamic nature of public transit arrival times in developing regions.

Findings

Outperforms existing state-of-the-art methods on real traffic data.

Effectively models congestion influences on travel time.

Demonstrates robustness in challenging traffic scenarios.

Abstract

Arrival/Travel times for public transit exhibit variability on account of factors like seasonality, dwell times at bus stops, traffic signals, travel demand fluctuation 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. A novel data-driven model based on recurrent neural networks (RNNs) is proposed for BATP (in real-time) in the current work. The model intelligently incorporates both spatial and temporal correlations in a unique (non-linear) fashion distinct from existing approaches. In particular, we propose a Gated Recurrent Unit (GRU) based Encoder-Decoder(ED) OR Seq2Seq RNN model (originally introduced for language translation) for BATP. The geometry of the dynamic real time BATP problem enables a nice fit with the Encoder-Decoder based RNN structure. We feed relevant additional synchronized inputs (from previous trips) at each step of the decoder (a feature classically unexplored in machine translation applications). Further motivated from accurately modelling congestion influences on travel time prediction, we additionally propose to use a bidirectional layer at the decoder (something unexplored in other time-series based ED application contexts). The effectiveness of the proposed algorithms is demonstrated on real field data collected from challenging traffic conditions. Our experiments indicate that the proposed method outperforms diverse existing state-of-art data-driven approaches proposed for the same problem.