Traditional distance and density-based anomaly detection techniques are unable to detect
periodic and seasonality related point anomalies which occur commonly in streaming data …

Since the early 1980s, short-term traffic forecasting has been an integral part of most
Intelligent Transportation Systems (ITS) research and applications; most effort has gone into …

This paper proposes a new method in construction fuzzy neural network to forecast travel
speed for multi-step ahead based on 2-min travel speed data collected from three remote …

Short term traffic flow forecasting has received sustained attention for its ability to provide the
anticipatory traffic condition required for proactive traffic control and management. Recently …

Accurate forecasting of future traffic flow has a wide range of applications, which is a
fundamental component of intelligent transportation systems. However, timely and accurate …

Urban ride-hailing demand prediction is a long-term but challenging task for online car-
hailing system decision, taxi scheduling and intelligent transportation construction. Accurate …

Prediction of short-term traffic flow has become one of the major research fields in intelligent
transportation systems. Accurately estimated traffic flow forecasts are important for operating …

Timely and accurate traffic flow forecasting is open challenging. Canonical long short-term
memory (LSTM) network is considered qualified to capture the long-term temporal …

Short-term traffic prediction plays a critical role in many important applications of intelligent
transportation systems such as traffic congestion control and smart routing, and numerous …

The unpredictable elements involved in a vehicular traffic system, like human interaction and
weather, lead to a very complicated, high-dimensional, nonlinear dynamical system …