This work addresses two essential components in the design of bus-based vehicular networks: a simulation model for bus mobility and a data forwarding algorithm. Mobility simulation models enable researchers to test and evaluate ideas in diverse, complex scenarios that would be impractical to assess in real-world settings due to time and cost constraints. However, developing simulation models that accurately capture the nuances of real-world mobility remains a challenging task that is widely studied in the literature. To this end, we use official data to introduce G2S, a simulation model framework for generating bus mobility on top of the well-known SUMO simulator. We provide three realistic simulation scenarios with varying traffic demands based on General Transit Feed Specification (GTFS) data from Greater Vancouver, Canada. From a data dissemination perspective, we propose BR4C (Bus Routing Protocol based on Contact, Community, and Centrality Characteristics), a historical-based data forwarding strategy designed to improve message delivery between bus lines. Our solution leverages knowledge extracted from past encounters between buses, incorporating metrics such as community structure, centrality, and contact characteristics to enhance message forwarding. BR4C significantly reduces delivery latency while achieving a higher delivery ratio compared to stateof- the-art approaches.

The NetMob Data Challenge releases a comprehensive public transportation dataset from Niterói, addressing the lack of high-quality mobility and passenger demand data. Based on operational records from March 2026, the dataset combines four main sources: GPS telemetry from buses, approximately 7.2 million ticketing transactions, auxiliary transit data (routes, stops, and weather), and urban infrastructure and socio-demographic information. Together, these sources provide a detailed view of both transit supply and passenger demand. The data were preprocessed, cleaned, and anonymized to preserve privacy and improve reliability, including the removal of operational inconsistencies and anonymization of passenger identifiers. Access is restricted to challenge participants who accept the Terms and Conditions and sign an NDA. The paper describes the data collection and preprocessing pipeline, dataset organization, and mobility patterns observed in the system. The dataset supports research on topics such as public transportation efficiency, demand forecasting, accessibility analysis, service reliability, and the influence of external factors like weather on urban mobility.

One of the main issues in the design of vehicular networks is understanding vehicles' mobility, which is determined by their type. In this work, we investigate how the mobility of buses influences the structure of a bus-based vehicular network. In this direction, we present a comprehensive analysis of bus mobility in vehicular networks. We generate bus mobility traces using official data from public transport agencies of four different cities. Our generated traces reveal crucial characteristics of bus-based vehicular networks obtained from them. In particular, we uncover details about the network topology and how spatiotemporal aspects impact it by analyzing four factors: network, component, node, and contact. In addition, with the information gained from our analysis, we perform experiments to assess practical aspects of the design of routing protocols in bus-based vehicular networks. Finally, we make the code and datasets publicly available to the research community as standard benchmark data for validating solutions.

Este artigo apresenta um framework para a criação de conjuntos de dados de referência (ground-truth) destinados à detecção automatizada de pontos de parada. O framework utiliza dados do OpenStreetMap e o SUMO (Simulation of Urban MObility) como fontes de informação essenciais. Além disso, são implementados e comparados métodos amplamente discutidos na literatura para a detecção de pontos de parada, utilizando conjuntos de dados gerados por meio desse framework. Os resultados da análise confirmam a confiabilidade dos métodos estudados. O estudo também introduz novos algoritmos à análise, que demonstram ser promissores na detecção de pontos de parada, além de identificar áreas para melhorias futuras. Destacam-se a necessidade de explorar análises adicionais que considerem métodos alternativos de aquisição de dados e avaliem seus impactos na detecção de pontos de parada.

In this paper, we focus on two fundamental aspects in the design of Bus-based Vehicular Networks: the building of bus mobility scenarios for validating solutions and the dissemination of messages on the network. We present a methodology for generating bus mobility scenarios based on official data and simulation tools. In this regard, we consider the city's road map and additional traffic infrastructure elements. In addition, traffic demand for different days of the week is generated based on GTFS data provided by the city's transport agency. We validate our scenarios with official data, and through a comparative analysis, we show the relevance of this work compared to existing alternatives in the literature. We present a novel routing protocol named BR3C, which aims to forward messages between bus lines for data dissemination. BR3C (Bus Routing protocol based on Community and Centrality Characteristics) considers social metrics extracted from the contacts between bus lines for decision-making. As a result, our protocol significantly reduces delivery latency while keeping delivery ratio values similar to state-of-the-art.

In recent years, we have witnessed the viability of applying cloud computing concepts to the domain of vehicular networks. A basic component of this infrastructure derived from the merge of cloud computing and vehicular networks is a Vehicular Micro Cloud (VMC), also known as vehicular cloudlets. A VMC is a cluster of connected vehicles that share computational resources. Despite being the focus of many studies in recent years, we still do not have a clear understanding of the characteristics of VMCs in large-scale urban scenarios. In this paper, we investigate some fundamental characteristics of stationary and mobile VMCs obtained from a realistic vehicular mobility trace. We characterize the dwell time and the inter-arrival time in stationary VMCs. Also, using statistical modeling, we identify theoretical distributions that best fit these metrics. For mobile VMCs, we reveal how they occur throughout the city along the day, discussing evolution and lifetime aspects.

Intelligent vehicular networks emerge as a promising technology to provide efficient data communication in transportation systems and smart cities. At the same time, the popularization of devices with attached sensors has allowed the obtaining of a large volume of data with spatiotemporal information from different entities. In this sense, we are faced with a large volume of vehicular mobility traces being recorded. Those traces provide unprecedented opportunities to understand the dynamics of vehicular mobility and provide data-driven solutions. In this article, we give an overview of the main publicly available vehicular mobility traces; then, we present the main issues for preprocessing these traces. Also, we present the methods used to characterize and model mobility data. Finally, we review existing proposals that apply the hidden knowledge extracted from the mobility trace for vehicular networks. This article provides a survey on studies that use vehicular mobility traces and provides a guideline for the proposition of data-driven solutions in the domain of vehicular networks. Moreover, we discuss open research problems and give some directions to undertake them.

AbstractIntelligent vehicular networks emerge as a promising technology to provide efficient data communication in transportation systems and smart cities. At the same time, the popularization of devices with attached sensors has allowed the obtaining of a large volume of data with spatiotemporal information from different entities. In this sense, we are faced with a large volume of vehicular mobility traces being recorded. Those traces provide unprecedented opportunities to understand the dynamics of vehicular mobility and provide data-driven solutions. In this article, we give an overview of the main publicly available vehicular mobility traces; then, we present the main issues for preprocessing these traces. Also, we present the methods used to characterize and model mobility data. Finally, we review existing proposals that apply the hidden knowledge extracted from the mobility trace for vehicular networks. This article provides a survey on studies that use vehicular mobility traces and provides a guideline for the proposition of data-driven solutions in the domain of vehicular networks. Moreover, we discuss open research problems and give some directions to undertake them.

In addition to being one of the primary means of transport, with the advent of sensing and communication technologies, buses belonging to the public transport system have gained a new role in urban centers. They have been applied as a powerful vehicular network that covers an entire city, called BUS-VANET. For the design and validation of solutions for this type of network, the nodes' mobility information is essential. For instance, data from the buses' GPS trajectories can be used to understand the dynamics of encounters between them. This knowledge can be applied to design applications and services for different users, besides providing the necessary information to properly manage this important public transport solution. However, real-world trajectories have several imperfections. In particular, GPS trajectories are heterogeneous, asynchronous, and typically contain a low sample rate. These characteristics impose certain limitations on the use of this dataset in the design of solutions for a BUS-VANET. In this work, we propose a hybrid method of calibrating trajectories based on historical information of trajectories and a road network to overcome these problems. We showed that our method surpasses the state-of-the-art techniques in several perspectives through evaluation with realistic data.

Vehicular networks have received much attention in recent years as they have emerged as one of the leading data communication solutions for smart cities. At the same time, the popularization of sensing devices has enabled the acquisition of a vast amount of vehicular mobility data (mobility traces). In this sense, a recent trend is to use mobility traces to extract hidden knowledge and apply it to improve solutions for vehicular networks. In this article, we present and discuss a workflow, through a short survey, related to the process of generating mobility traces, preprocessing these datasets, and obtaining knowledge to create intelligent vehicular networks. We describe the main types of mobility data highlighting their strengths and weaknesses. We classify the primary methods for obtaining knowledge from mobility data. Also, we exemplify how these mobility traces and methods can be applied to vehicular networks by reviewing recent contributions. Furthermore, we illustrate through a case study how to obtain knowledge from a specific type of mobility trace. Finally, we point out new research directions that involve mobility traces and intelligent vehicular networks.

In this paper, we address a fundamental problem in vehicular networks, which consists of sending messages from a source vehicle to a destination vehicle. This problem becomes even more complex in the absence of fixed infrastructure or any other controlling entity. Although there are some solutions in the literature to work around this problem, they can cause significant network overhead and generate an amount of redundant data. In this regard, we develop a routing protocol that considers individual vehicular mobility as a determining factor for routing decisions. Through simulations using realistic vehicular mobility trace, we have observed that our strategy considerably decreases network overhead and the number of hops between source and destination while maintaining similar values for delivery ratio and latency.

Understanding the mobility of vehicles plays a fundamental role in the design of solutions for intelligent vehicular networks. Considering that different types of vehicles have different characteristics of mobility, we are interested in investigating how bus mobility impacts the formation of these networks. In this sense, we present clear understanding the bus mobility for vehicular networks. Our analysis is based on a real mobility trace of buses from several days of Dublin, Ireland. Particularly, our study reveals key features of a vehicular network obtained from a real bus mobility trace such as the network structure over the days and how the components of the networks are arranged in the space and time. Additionally, we investigate the potential of bus mobility for urban sensing. In summary, due to network fragmentation identified in our analysis, data dissemination mechanisms that use store-carry-and-forward and street-centric routing are more indicated for intelligent vehicular networks based on bus mobility. Moreover, we show how the use of buses as sensors can compose a powerful urban sensing infrastructure.

The increasing availability of tremendous amounts of data generated by people, vehicles, and things have provided unprecedented opportunities for understanding human behavior in the urban environment. At the same time, crowd management systems can benefit city planning, emergency control, and mobile network design. In this work, we exploit urban data as a way of analyzing crowd behavior. We analyze the types of crowd situations, describe the major types of urban data, and highlight their strengths and weaknesses. We then discuss the key research challenges and opportunities in the analysis of urban environments. Moreover, through case studies, we explain how to apply urban data for spatial, temporal, and semantic observations of crowd situations.

Understanding mobility is a fundamental task in the design of mobile networking solutions. The adoption of mobility traces is extremely relevant both to obtain a meaningful understanding of mobility and to create realistic simulation scenarios. However, those traces may have different features that lead to conclusions inconsistent with reality and, consequently, impact the performance of the proposed solutions. In this work, we propose a methodology to evaluate mobility traces considering their spatial and temporal aspects. Furthermore, we review real, publicly available, and widely adopted mobility traces and discuss the application them to vehicular networks. The results show that the use of mobility data for vehicular networks is extremely timely, but that they must undergo a process of quality improvement.

Vehicular networks are seen as the key communication solution for intelligent transportation systems. An essential task for the development of solutions for vehicular networks is to understand aspects related to their communication topology along the time, mainly because it is directly impacted by vehicular mobility. In this sense, a natural question that arises is how can we model the communication topology in order to have a real representation of network connectivity? Particularly, this question becomes even more complex when we consider the dynamic behavior of mobility over time. In the literature, there are some efforts that aim to model the topology of a vehicular network to better understand its dynamics. However, we note that current approaches have limitations in the temporal perspective leading to the loss of important information. In this work, we show the strengths and weaknesses of current approaches in the characterization and analysis of vehicular network topology. In addition, we present how a model derived from the temporal network theory can be applied to capture the dynamics of a large-scale realistic vehicular mobility trace.

The proliferation of devices with positioning capability has allowed new possibilities for studies and applications in the context of urban mobility. However, the process of analyzing raw trajectories poses several challenges. In this work, we investigate one of the main tasks in this process of trajectory analysis: detecting stops from GPS trajectories. Stops can reveal interesting behavior aspects of a moving object such as its daily routine, bottlenecks in traffic jams, or visiting times of touristic places. Although there are some efforts in this direction, most current methods ignore the presence of noise segments, which typically occur many times in trajectories. In this sense, we present a method that exploits gaps in time and space to identify episodes of movement, stop, and periods where some classification is inconclusive, which we define as noise. In addition, our method does not rely on contextual information as opposed to some current solutions, which make our proposal also suitable for trajectories recorded in free space. We compare our method to the state of the art highlighting its advantages in terms of manipulating noise, supporting spatial filtering and being independent of external resources. Moreover, we conduct an experimental evaluation using a large-scale bus dataset to show the effectiveness of our method in a real application scenario.

In the near future, a significant increase of new services is estimated due to the advent of several heterogeneous devices connected to the Internet, with the capacity to exchange information, collect data and interact with the environment. Therefore, robust network infrastructures will be required to make these services available. The adoption of the Software Defined Networking (SDN) paradigm in Wireless Mesh Networks (WMN) fits in this context, opening up space for new features that optimize the network data flow, such as traffic engineering, flow-based packet forwarding, and interoperability. In this work, a performance analysis of OpenFlow is performed in a multi-interface WMN testbed. Among the experiments, we highlight the impact of heterogeneous mesh routers on the OpenFlow performance in single-channel and multi-channel scenarios, as well as the comparison of OpenFlow with three traditional WMN routing protocols: B.A.T.M.A.N., AODV, and HWMP. The results show a better performance of the OpenFlow in scenarios with homogeneous multi-channel routers in terms of throughput, delivery rate, jitter, and number of lost packets. In addition, the paper introduces problems related to the high overhead of OpenFlow in scenarios with multiple flows, as well as some directions to solve them.