Search Results for author:
Found 5 papers, 1 papers with code
A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks
We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportation networks.
LSTM-Based Anomaly Detection: Detection Rules from Extreme Value Theory
In this paper, we explore various statistical techniques for anomaly detection in conjunction with the popular Long Short-Term Memory (LSTM) deep learning model for transportation networks.
Grids versus Graphs: Partitioning Space for Improved Taxi Demand-Supply Forecasts
To explore the Voronoi tessellation scheme, we propose the use of GraphLSTM (Graph-based LSTM), by representing the Voronoi spatial partitions as nodes on an arbitrarily structured graph.
Taxi Demand-Supply Forecasting: Impact of Spatial Partitioning on the Performance of Neural Networks
We find that the LSTM model based on input features extracted from a variable-sized polygon tessellation yields superior performance over the LSTM model based on fixed-sized grid tessellation.
Taxi demand forecasting: A HEDGE based tessellation strategy for improved accuracy
We show that the hybrid tessellation strategy performs consistently better than either of the two strategies across the data sets considered, at multiple time scales, and with different performance metrics.
