This paper systematically reviews studies that forecast short-term traffic conditions using
spatial dependence between links. We extract and synthesise 130 research papers …

A spatiotemporal approach that simultaneously utilises both spatial and temporal
relationships is gaining scientific interest in the field of traffic flow forecasting. Accurate …

Accurate short-term traffic forecasts help people choose transportation and travel time.
Through the query data, many models for traffic flow prediction have neglected the temporal …

This paper proposes an optimized prediction algorithm of radial basis function neural
network based on an improved artificial bee colony (ABC) algorithm in the big data …

Obtaining accurate information about future traffic flows of all links in a traffic network is of
great importance for traffic management and control applications. This research studies two …

Nowadays traffic congestion has become significantly worse. Not only has it led to economic
losses, but also to environmental damages, wastage of time and energy, human stress and …

This study examines the spatiotemporal dependency between traffic links. We model the
traffic flow of 140 traffic links in a sub-network of the Minneapolis-St. Paul highway system for …

In order to forecast the traffic flow more precisely, a novel hybrid model is proposed with
multiple sources of traffic data in the spatiotemporal dimension. In the practical application of …

Traffic counts (or link counts) are defined as cumulative traffic in the lanes between two
consecutive intersections on a road network. Established methods of link count estimation …

Traffic volume prediction plays a critical role in transportation system and infrastructure
management. This paper develops the first application of an unobserved component model …