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 …

Short-term prediction is one of the essential elements of intelligent transportation systems
(ITS). Although fine prediction methodologies have been reported, most prediction methods …

There are numerous studies on traffic volume prediction, using either non-parametric or
parametric methods. The main shortcoming of parametric methods is low prediction …

Short‐term traffic forecasting is becoming more important in intelligent transportation
systems. The k‐nearest neighbour (kNN) method is widely used for short‐term traffic …

Nowadays, short-term traffic flow forecasting has gained increasing attention from
researchers due to traffic congestion in many large and medium-sized cities that pose a …

In this paper, by splitting a traffic flow series into basis series and deviation series, the
concepts of similarity and repeatability of traffic flow patterns are defined using the statistic …

Traffic flow prediction is a fundamental functionality of intelligent transportation systems.
After presenting the state of the art, we focus on nearest neighbor regression methods …

Reliable travel time prediction enables both users and system controllers to be well informed
of the future conditions on roadways. This in turn helps with informed pre-trip plans and …

Intelligent transportation systems (ITS) are becoming more and more effective, benefiting
from big data. Despite this, missing data is a problem that prevents many prediction …

This study presents a data segmentation method, which was intended to improve the
performance of the k‐nearest neighbours algorithm for making short‐term traffic volume …