Prediction of Traffic Flow via Connected Vehicles

Influential Subpaces of Connected Vehicles in Highway Traffic

This work introduces the novel concept of an influential subspace, with focus on its application to highway traffic containing connected vehicles. In this context, an influential subspace of a connected vehicle is defined as the region of a highway where the connected vehicle has the ability to positively influence the macrostate, i.e. the traffic jam, so as to dissipate it within a specified t...

Adaptive Online Traffic Flow Prediction Using Aggregated Neuro Fuzzy Approach

Short term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. Although various methodologies have been applied to forecast traffic parameters, several researchers have showed that compared with the individual methods, hybrid methods provide more accurate results . These results made the hybrid tools and approaches a more common method for ...

Traffic Congestion Detection System through Connected Vehicles and Big Data

This article discusses the simulation and evaluation of a traffic congestion detection system which combines inter-vehicular communications, fixed roadside infrastructure and infrastructure-to-infrastructure connectivity and big data. The system discussed in this article permits drivers to identify traffic congestion and change their routes accordingly, thus reducing the total emissions of CO₂ ...

Improving Travel times for Emergency Response Vehicles: Traffic Control Strategies Based on Connected Vehicles Technologies

Mobile Crowd Sensing for Traffic Prediction in Internet of Vehicles

The advances in wireless communication techniques, mobile cloud computing, automotive and intelligent terminal technology are driving the evolution of vehicle ad hoc networks into the Internet of Vehicles (IoV) paradigm. This leads to a change in the vehicle routing problem from a calculation based on static data towards real-time traffic prediction. In this paper, we first address the taxonomy...