Abstract
Purpose Traffic density is one of the most important parameters to consider in the traffic operation field. Owing to limited data sources, traditional methods cannot extract traffic density directly. In the vehicular ad hoc network (VANET) environment, the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interaction technologies create better conditions for collecting the whole time-space and refined traffic data, which provides a new approach to solving this problem. Design/methodology/approach On that basis, a real-time traffic density extraction method has been proposed, including lane density, segment density and network density. Meanwhile, using SUMO and OMNet++ as traffic simulator and network simulator, respectively, the Veins framework as middleware and the two-way coupling VANET simulation platform was constructed. Findings Based on the simulation platform, a simulated intersection in Shanghai was developed to investigate the adaptability of the model. Originality/value Most research studies use separate simulation methods, importing trace data obtained by using from the simulation software to the communication simulation software. In this paper, the tight coupling simulation method is applied. Using real-time data and history data, the research focuses on the establishment and validation of the traffic density extraction model.
Highlights
There is an increasing need for traffic density estimation to improve the management-level transportation system
As an ideal way of collecting traffic information, the traffic parameter estimation method based on the vehicular ad hoc network (VANET) technology has become a hot area of research
4.1 Lane-change maneuver recognition Based on the mature communication simulation and traffic simulation software, the mutually coupled simulation platform was constructed by means of the Veins frame; it can simulate the impact that communication has on road traffic
Summary
There is an increasing need for traffic density estimation to improve the management-level transportation system. The dynamics of a traffic system can be typically expressed using three parameters – density, mean speed and traffic volume, until now, these data have been acquired by means of devices such as loop detectors, radars, magnetometers and television cameras While information from such devices is readily available, it not sufficient to give us a lucid picture, as the coverage of these devices is limited in terms of the area that can be monitored; the full scope and real-time traffic information cannot be obtained directly (Leduc, 2008; Marti et al, 2014). Fogue et al proposed real-time traffic density estimation method under the VANET environment by means of vehicle-to-vehicle (V2V) (Sanguesa et al, 2012), vehicle-to-infrastructure (V2I) (Sanguesa et al, 2012; Barrachina et al, 2015) and V2X (Barrachina et al, 2013) technology, which verifies that the regression models can estimate the traffic density of any given city precisely.
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