## 1 Introduction  
Vehicular _Ad Hoc_ networks (VANETs) are wireless networks that do not require any fixed infrastructure. These networks are considered essential for cooperative driving among cars on the road. The development of VANETs is backed by strong economical interests since vehicle-to-vehicle (V2V) communication allows the sharing of wireless channels for mobile applications, improving route planning, controlling traffic congestion, improving traffic safety, and providing entertainment [1, 2]. Most of these applications depend on services to disseminate warning messages, which are alert messages sent by a vehicle to warn other vehicles of any potential danger. In the coming future, vehicles will not only distribute information about themselves and their environment using warning messages, but also communicate with other vehicles and the infrastructure via multihop wireless communications [3]. Deploying and testing VANETs involves high cost and intensive labor, being prohibitive in most cases. Hence, simulation is a useful alternative prior to actual implementation [4]. Moreover, VANET simulations must account for some specific characteristics found in vehicular environments. For instance, VANET simulations often involve large and heterogeneous scenarios. Traditional mobile systems also present a large number of parameters potentially affecting their performance, thus increasing considerably the simulation time required to correctly evaluate any proposal in a wide variety of scenarios. In recent years, new architectures and technologies have been proposed for VANETs, thanks to the use of simulation. However, the experiments to validate these proposals tend to overlook the most important and representative factors. Moreover, the scenarios simulated tend to be very simplistic (highways or Manhattan-based layouts), and most of them use the 802.11g standard, already implemented in most simulators, instead of using the 802.11p [5] which is going to be used for inter-vehicular communication. Thus, we find that different proposals in the VANET field lack generality, being uncertain whether they will perform adequately in a real VANET environment. In this paper, we present a statistical analysis based on the \(2^{k}\) factorial methodology [6] to determine the most representative factors that govern the warning message dissemination performance in 802.11p-based VANETs. The aim of this methodology is to reduce the simulation time required to analyze the performance of a given VANET system, since it allows researchers to focus on the key factors affecting their proposals. We start our analysis by selecting the following nine factors that have been widely used in the literature: (i) the number of warning mode vehicles; (ii) the density of vehicles; (iii) the channel bandwidth; (iv) the broadcast scheme; (v) the message priority; (vi) the periodicity of messages; as well as (vii) the mobility model used; (viii) the radio propagation model; and (ix) the simulated roadmap. In a factorial design strategy, all factors are varied together (as opposed to one-at-time). So, a key advantage of this methodology is that it allows researchers to find out not only the most representative factors, but also the possible interactions and interdependencies among them. Based on the aforementioned statistical analysis, we present a city profile classification, since the analysis indicates that VANET researchers must carefully evaluate the benefits of their proposals using different vehicle densities and roadmap scenarios, in order to make their conclusions more representative and closer to reality. This paper is organized as follows. Section 2 describes related work on the factors commonly studied in VANETs, and the use of \(2^{k}\) factorial analyses in wireless networks. Section 3 presents the \(2^{k}\) factorial analysis fundamentals. Section 4 describes the main factors of interest in VANET research.