Acquiring real‐time traffic information is a basic requirement for dynamic vehicular navigation systems. The majority of the current navigation systems are based on static traffic information. Building on mobile crowdsensing technology, the authors propose context‐aware traffic estimation and navigation system (CANS), a context‐aware system that can estimate traffic state without any requirement for expensive infrastructure. Using only available equipment, it can provide dynamic navigation service to drivers. The proposed system consists of three main components: local traffic estimation, global traffic aggregation, and navigation. In this system, vehicles estimate local traffic state using vehicular contextual information including speed and acceleration by relying on fuzzy logic, and transmit the information to the urban server. The server integrates the received local traffic information from different vehicles and estimates the global traffic state, providing the traffic‐aware navigation system to drivers. CANS performance is evaluated for an urban scenario in a traffic flow in Birjand, Iran. The experiment is conducted for evaluating CANS in both traffic congestion estimation and navigation. The results show an accurate estimation of traffic states along urban roads. Compared with previous approaches, CANS overrides them for its reduced travel time.
CANS: context‐aware traffic estimation and navigation system
IET Intelligent Transport Systems ; 11 , 6 ; 326-333
2017-08-01
8 pages
Article (Journal)
Electronic Resource
English
global traffic aggregation , dynamic vehicular navigation systems , local traffic information , real‐time traffic information acquisition , dynamic navigation service , local traffic estimation , fuzzy logic , Birjand , CANS , vehicular contextual information , vehicle speed , urban roads , context‐aware traffic estimation‐and‐navigation system , road traffic control , ubiquitous computing , vehicle acceleration , Iran , mobile crowdsensing technology , local traffic state estimation , traffic congestion estimation , urban server , travel time reduction , traffic information systems