Traffic flow prediction is a fundamental problem in transportation modeling and
management. Many existing approaches fail to provide favorable results due to being: 1) …

This paper proposes a novel neural network (NN) training method that employs the hybrid
exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to …

The prediction of bus arrival time is important for passengers who want to determine their
departure time and reduce anxiety at bus stops that lack timetables. The random forests …

In this paper, we discuss three problems that occur within short-term traffic prediction when
the information from only a single point loop detector is used. First, we analyze the retrieval …

Short-term traffic prediction is of great importance to the management of traffic congestion, a
pervasive and difficult-to-solve problem in many metropolises all over the world. However …

The city's infrastructure is considered the backbone of any country's development process
and there are numerous factors that contribute to its growth. Among these factors, proper …

This paper proposes a new variational approximation for infinite mixtures of Gaussian
processes. As an extension of the single Gaussian process regression model, mixtures of …

Traffic flow prediction is a fundamental component in intelligent transportation systems.
Various computational methods have been applied in this field, among which machine …

Traffic flow forecasting has been a long-standing topic in intelligent transportation systems,
and a renewed interest has been seen in recent years due to the development of artificial …

Providing real-time and accurate travel time information of transit vehicles can be very
helpful as it assists passengers in planning their trips to minimize waiting times. The purpose …