19 citations · 21 across the 3 of their papers we have counts for
6 papers
A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks
Neema Davis, Gaurav Raina, Krishna Jagannathan
We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportation networks. The proposed EVT-LSTM model is derived from the popular LSTM (Lon…
LSTM-Based Anomaly Detection: Detection Rules from Extreme Value Theory
Neema Davis, Gaurav Raina, Krishna Jagannathan
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…
Grids versus Graphs: Partitioning Space for Improved Taxi Demand-Supply Forecasts
Neema Davis, Gaurav Raina, Krishna Jagannathan
Accurate taxi demand-supply forecasting is a challenging application of ITS (Intelligent Transportation Systems), due to the complex spatial and temporal patterns. We investigate t…
Taxi Demand-Supply Forecasting: Impact of Spatial Partitioning on the Performance of Neural Networks
Neema Davis, Gaurav Raina, Krishna Jagannathan
In this paper, we investigate the significance of choosing an appropriate tessellation strategy for a spatio-temporal taxi demand-supply modeling framework. Our study compares (i)…
Taxi demand forecasting: A HEDGE based tessellation strategy for improved accuracy
Neema Davis, Gaurav Raina, Krishna Jagannathan
A key problem in location-based modeling and forecasting lies in identifying suitable spatial and temporal resolutions. In particular, judicious spatial partitioning can play a sig…
Congestion costs incurred on Indian Roads: A case study for New Delhi
Neema Davis, Harry Raymond Joseph, Gaurav Raina +1
We conduct a preliminary investigation into the levels of congestion in New Delhi, motivated by concerns due to rapidly growing vehicular congestion in Indian cities. First, we pro…