Generation and Analysis of a Large-Scale Urban Vehicular Mobility Dataset

Abstract

Abstract-The surge in vehicular network research has led, over the last few years, to the proposal of countless network solutions specifically designed for vehicular environments. A vast majority of such solutions has been evaluated by means of simulation, since experimental and analytical approaches are often impractical and intractable, respectively. The reliability of the simulative evaluation is thus paramount to the performance analysis of vehicular networks, and the first distinctive feature that has to be properly accounted for is the mobility of vehicles, i.e., network nodes. Notwithstanding the improvements that vehicular mobility modeling has undergone over the last decade, no vehicular mobility dataset is publicly available today that captures both the macroscopic and microscopic dynamics of road traffic over a large urban region. In this paper, we present a realistic synthetic dataset, covering 24 hours of car traffic in a 400-km 2 region around the city of Köln, in Germany. We describe the generation process and outline how the dataset improves the traces currently employed for the simulative evaluation of vehicular networks. We also show the potential impact that such a comprehensive mobility dataset has on the network protocol performance analysis, demonstrating how incomplete representations of vehicular mobility may result in over-optimistic network connectivity and protocol performance.

Vehicular networks on two Madrid highways

“…VI concludes the paper. The impact of realistic mobility models on the simulation of communication protocols for vehicular networks has been emphasized in many works [1], [8], [11], [12]. This fact has pushed the research community to seek for an ever-increasing realism in road traffic traces used to feed network simulators.…”

“…In particular, a number of studies have considered urban mobility scenarios, created by feeding real-world road topologies of different cities to microscopic traffic simulators such as SUMO [5] or VanetMobiSim [6]. In order to characterize the number, origin, destination and time of trips, these works usually made use of macroscopic data (i.e., origin-destination matrices) collected from user surveys [7], [8], [13] or from roadside detectors such as induction loops, cameras and infrared counters [9]. However, the dynamics of traffic in urban regions -characterized by vehicles traveling at low or medium speed, and frequent intersections regulated by traffic lights or roundabouts -are not comparable to those on highways -featuring instead high speeds and frequent overtakings.…”

On the Impact of Building Attenuation Models in VANET Simulations of Urban Scenarios

“…This could be done by using more appropriate metrics, such as jitter, time to cover an area, number of notified vehicles, etc. We are planning to use real mobility traces, such as the ones provided in [25] or [26], to leverage a trustworthy evaluation of this kind of applications, especially in the analysis of how long and how fast an emergency message can be spread over a large area.…”

Worm Epidemics in Vehicular Networks

“…Indeed, no other synthetic or real-world mobility dataset that is publicly available covers today a similarly wide region in a comparable realistic manner. Other synthetic datasets may feature higher microscopic detail [35], [36], however they cover geographical areas that are two orders of magnitude smaller than that included in the Canton of Zurich dataset. Even real-world data on vehicular mobility, logged from taxis [37], [38] or buses [39], cannot compete in terms of spatial coverage; moreover, such data is only representative of a limited subset of road traffic.…”