An Infrastructureless Approach to Estimate Vehicular Density in Urban Environments

Abstract

In Vehicular Networks, communication success usually depends on the density of vehicles, since a higher density allows having shorter and more reliable wireless links. Thus, knowing the density of vehicles in a vehicular communications environment is important, as better opportunities for wireless communication can show up. However, vehicle density is highly variable in time and space. This paper deals with the importance of predicting the density of vehicles in vehicular environments to take decisions for enhancing the dissemination of warning messages between vehicles. We propose a novel mechanism to estimate the vehicular density in urban environments. Our mechanism uses as input parameters the number of beacons received per vehicle, and the topological characteristics of the environment where the vehicles are located. Simulation results indicate that, unlike previous proposals solely based on the number of beacons received, our approach is able to accurately estimate the vehicular density, and therefore it could support more efficient dissemination protocols for vehicular environments, as well as improve previously proposed schemes.

RTAD: A real-time adaptive dissemination system for VANETs

“…Our new density estimation approach, based on the number of neighbors, yields more accurate results, presenting a lower sum for the squared absolute error (approximately 5.098E+03, whereas the beacon-based approach resulted in 6.332E+03 (Sanguesa et al, 2013a)). …”

“…Due to space limitations, it is not possible to present the results of all of the cities simulated, so in some cases we only included the results obtained for San Francisco and Valencia since, according to our previous work (Sanguesa et al, 2013a), the simulation results obtained in these roadmaps are closer to the average ones. Table 2 shows the parameters used for the simulations.…”

“…In Sanguesa et al (2013a), we proposed a method to calculate the density according to the number of beacons received by each vehicle. In this work, we have modified it to increase its accuracy.…”

V2X-d: A vehicular density estimation system that combines V2V and V2I communications

“…Table II shows the main features of each map for the cities under study: the number of streets, the number of junctions, the average segment size, and the number of lanes per street. According to the results obtained in previous work [9], we consider that the parameters that better correlate with the complexity of the roadmap are the number of streets and the number of junctions. Hence, we added a column labeled as SJ Ratio, which represents the result of dividing the number of streets between the number of junctions.…”

“…Other authors, such as Stanica et al [8] and Sanguesa et al [9], propose estimating the traffic density by using V2V communications. According to these approaches, each vehicle is able to estimate the number of nearby vehicles by taking into account beacon information.…”