2 research outputs found
Public Transit Arrival Prediction: a Seq2Seq RNN Approach
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
Generalized Simultaneous Perturbation-based Gradient Search with Reduced Estimator Bias
We present in this paper a family of generalized simultaneous
perturbation-based gradient search (GSPGS) estimators that use noisy function
measurements. The number of function measurements required by each estimator is
guided by the desired level of accuracy. We first present in detail unbalanced
generalized simultaneous perturbation stochastic approximation (GSPSA)
estimators and later present the balanced versions (B-GSPSA) of these. We
extend this idea further and present the generalized smoothed functional (GSF)
and generalized random directions stochastic approximation (GRDSA) estimators,
respectively, as well as their balanced variants. We show that estimators
within any specified class requiring more number of function measurements
result in lower estimator bias. We present a detailed analysis of both the
asymptotic and non-asymptotic convergence of the resulting stochastic
approximation schemes. We further present a series of experimental results with
the various GSPGS estimators on the Rastrigin and quadratic function
objectives. Our experiments are seen to validate our theoretical findings.Comment: The material in this paper was presented in part at the Conference on
Information Sciences and Systems (CISS) in March 202