Opportunistic vehicular networking: Large-scale bus movement traces as base for network analysis

Abstract

In many road traffic scenarios the ability to communicate among traffic participants is very helpful. Therefore, research and development in academia and industry in that field exists already for many years and is ongoing in several directions. Some examples are Vehicular Ad-hoc Networks (VANETs), e.g., using technologies like IEEE 802.11p, and vehicles communicating with backend systems, e.g., using 2/3/4G cellular networks. In opportunistic vehicular networks, vehicles may not only exchange data for the immediate use such as Cooperative Awareness Messages (CAMs) in the ETSI Intelligent Transport Systems (ITS). Instead, a more general type of network might be set up, also for application scenarios beyond direct road traffic related aspects. For instance, buses of public transportation systems could collect data from the field or distribute data among several buses. Thus, buses could become an important part of smart cities or Internet of Things (IoT) application scenarios. Important questions are then, e.g., how much data could be distributed in such a bus-based opportunistic network or how often is it possible to exchange data between buses. Usually, buses in urban public transport systems follow well planned but nevertheless highly dynamic schedules and trajectories. Thus, traffic conditions have a significant and complex influence on bus mobility, causing very characteristic movement properties that are considerably distinct from other road vehicles. Understanding these special characteristics is essential for the design and evaluation of opportunistic vehicular communication networks. For this purpose we inspect two large-scale bus movement traces and describe the available data and metadata. Moreover, we analyze and compare vehicle density, speed, update intervals, and characteristics that are specific to public transport. Especially for large cities, but even for smaller ones if many devices like vehicles, sensors, and various other IoT things are part of such a network, high-performance computing and simulation approaches are necessary to study, analyse, design, use and maintain such a system.