We develop a deep learning model to predict traffic flows. The main contribution is
development of an architecture that combines a linear model that is fitted using ℓ 1 …

The ability to timely and accurately forecast the evolution of traffic is very important in traffic
management and control applications. This paper proposes a non-parametric and data …

In order to accurately predict the short-term traffic flow, this paper presents a k-nearest
neighbor (KNN) model. Short-term urban expressway flow prediction system based on k-NN …

Delft University of Technology oad traffic is the visible result of the complex interplay
between traffic demand (the amount and mix of vehicles arriving at a particular place and …

The efficient control of traffic on motorways or freeways can produce many benefits,
including quicker journey times, fewer pollutant emissions, and reduced driver stress. If it …

Exploring traffic flow characteristics and predicting its variation patterns are the basis of
Intelligent Transportation Systems. The intermittent characteristics and intense fluctuation on …

Short‐term prediction of passenger flow is very important for the operation and management
of a rail transit system. Based on the traditional Kalman filtering method, this paper puts …

The prediction of short-term volatile traffic becomes increasingly critical for efficient traffic
engineering in intelligent transportation systems. Accurate forecast results can assist in …

Short-term prediction of traffic flow is essential for the deployment of intelligent transportation
systems. In this paper we present an efficient method for short-term traffic flow prediction …

Having access to the future traffic state information is crucial in maintaining successful
intelligent transportation systems (ITS). However, predicting the future traffic state is a …