In the past two decades, most research on anomaly detection has focused on improving the
accuracy of the detection, while largely ignoring the explainability of the corresponding …

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 …

Disruptions due to special events are a well-known challenge in transport operations, since
the transport system is typically designed for habitual demand. Part of the problem relates to …

In transportation, nature, economy, environment, and many other settings, there are multiple
simultaneous phenomena happening that are of interest to model and predict. Over the last …

Discovering metro passenger flow recovery patterns from historical unplanned disruptions
enables operators to better prepare for a new disruption. The task is challenging as …

Numerous researchers have utilized GPS-enabled vehicle data and SafeGraph mobility
data to analyze human movements. However, the comparison of their ability to capture …

In transportation, nature, economy, environment, and many other settings, there are multiple
simultaneous phenomena happening that are of interest to model and predict. Over the last …

Abstract New York City (NYC) is known for its high population density, frequent traffic
congestion, and consequently its relatively expensive travel costs. To save money, time, and …

Der steigende Mobilitätsbedarf in urbanen Räumen erfordert effiziente Mobilitäslösungen.
Diese Arbeit untersucht Optimierungspotentiale für den Betrieb von Ridesourcing-Diensten …

Mobile devices and Internet-based applications are producing a significant volume of data
that may be used to, at least partially, replace some of the hardware necessary to sense …