This paper introduces DataFITS (Data Fusion on Intelligent Transportation System), an open-source framework that collects and fuses traffic-related data from various sources, creating a comprehensive dataset. We hypothesize that a heterogeneous data fusion framework can enhance information coverage and quality for traffic models, increasing the efficiency and reliability of Intelligent Transportation System (ITS) applications. Our hypothesis was verified through two applications that utilized traffic estimation and incident classification models. DataFITS collected four data types from seven sources over nine months and fused them in a spatiotemporal domain. Traffic estimation models used descriptive statistics and polynomial regression, while incident classification employed the k-nearest neighbors (k-NN) algorithm with Dynamic Time Warping (DTW) and Wasserstein metric as distance measures. Results indicate that DataFITS significantly increased road coverage by 137% and improved information quality for up to 40% of all roads through data fusion. Traffic estimation achieved an$\text{R}^2$score of 0.91 using a polynomial regression model, while incident classification achieved 90% accuracy on binary tasks (incident or non-incident) and around 80% on classifying three different types of incidents (accident, congestion, and non-incident).

Due to the increasing location-aware devices, mobility traces datasets have become an essential source for smart cities planning. Given this scenario, we propose MobVis, a framework to characterize mobility traces through different metrics, allowing comparisons between different mobility traces in a simplified way. Furthermore, MobVis can extract and visualize spatial, temporal, and social aspects of mobility data through a Web interface. MobVis architecture has five main components: input data; data preparation; data processing and analysis to extract mobility metrics; visualization; and a web interface. To demonstrate the framework's process, we created a use case analyzing the characteristics of two distinct traces (Taxi and IoT-Objects). Then, through different metrics, we evaluated the data in two aspects: i) descriptive, through a set of graphics and quantitative data that enables characterizing each trace; and ii) comparative, presenting the main differences between the traces.

This investigation starts with the hypothesis that fusing heterogeneous data sources can increase the data coverage and improve the accuracy of traffic-related applications in Intel-ligent Transportation Systems (ITS). Therefore, we designed (i) a Data Fusion on Intelligent Transportation Systems (DataFITS) framework that allows collecting data from numerous sources and fusing them according to spatial and temporal criteria; (ii) a traffic estimation method that groups road segments into regions, identify correlations between them, and measure the traffic distribution to estimate traffic. As a result, DataFITS increased by 130% the number of road segments coverage and enhanced, by fusion process, around 35% of road overlapping data sources. We evaluate the traffic estimation of the 15 most correlated regions, where the fused data together with correlated areas resulted in the best traffic estimation accuracy by reaching up to 40% in some cases and 9% on average.

This paper introduces a hierarchy of queues complementing each other to handle ever-changing communication scenarios in tactical networks. The first queue stores the QoS-constrained messages from command and control systems. These messages are fragmented into IP packets, which are stored in a queue of packets (second) to be sent to the radio buffer (third), which is a queue with limited space therefore, open to overflow. We start with the hypothesis that these three queues can handle ever-changing user(s) data flows (problem$A$) through ever-changing network conditions (problem$B$) using cross-layer information exchange, such as buffer occupancy, data rate, queue size and latency (problem$A|B$). We introduce two stochastic models to create sequences of QoS-constrained messages ($A$) and to create ever-changing network conditions ($B$). In sequence, we sketch a control loop to shape$A$to$B\;$to test our hypothesis using model$A|B$, which defines enforcement points at the incoming/outgoing chains of the system together with a control plane. Then, we discuss experimental results in a network with VHF radios using data flows that overflows the radio buffer over ever-changing data rate patterns. We discuss quantitative results showing the performance and limitations of our solutions for problems$A$,$B$, and$A|B$.

In this work, we propose the Road Data Enrichment (RoDE), a framework that fuses data from heterogeneous data sources to enhance Intelligent Transportation System (ITS) services, such as vehicle routing and traffic event detection. We describe RoDE through two services: (i) Route service, and (ii) Event service. For the first service, we present the Twitter MAPS (T-MAPS), a low-cost spatiotemporal model to improve the description of traffic conditions through Location-Based Social Media (LBSM) data. As a case study, we explain how T-MAPS is able to enhance routing and trajectory descriptions by using tweets. Our experiments compare T-MAPS' routes against Google Maps' routes, showing up to 62% of route similarity, even though T-MAPS uses fewer and coarse-grained data. We then propose three applications, Route Sentiment (RS), Route Information (RI), and Area Tags (AT), to enrich T-MAPS' suggested routes. For the second service, we present the Twitter Incident (T-Incident), a low-cost learning-based road incident detection and enrichment approach built using heterogeneous data fusion. Our approach uses a learning-based model to identify patterns on social media data which is then used to describe a class of events, aiming to detect different types of events. Our model to detect events achieved scores above 90%, thus allowing incident detection and description as a RoDE application. As a result, the enriched event description allows ITS to better understand the LBSM user's viewpoint about traffic events (e.g., jams) and points of interest (e.g., restaurants, theaters, stadiums).

In this work, we propose Traffic Data EnrichmentSensor (TraDES), towards a low-cost traffic sensor for Intelligent Transportation System (ITS) based on heterogeneous data fusion. TraDES aims at fusing data from vehicular traces with road traffic data to enrich current spatiotemporal traffic data. In that direction, we propose a robust methodology to group spatially and temporally these different data sources, producing a vehicular trace with its respective traffic conditions, which is given as input to a learning-based model based on Artificial Neural Networks (ANN). Hence, TraDES is an enriched traffic sensor that is able to sense (detect) traffic conditions using a scalable and low-cost approach and to increase the spatiotemporal traffic data coverage.

In this work, we present Dribble a learn-based timer scheme selector to manage topology changes caused by mobility in the Internet of Things (IoT) context. IoT has turned smart devices part of our everyday lives. They are in everywhere with many shapes, sizes, and capabilities. For IoT to become even more ubiquitous, it is necessary to overcome the challenges posed by mobility. One of them is the management of topology changes, especially at the network layer. Currently, routing protocols check the topology through an advertisement timer scheme. Such schemes face a basic trade-off between being fast to find topology problems and concurrently be energy and overhead control saver. Although there are timer schemes designed to mobile context, all devices are governed by the same one, which is a hard assumption since IoT is heterogeneous and naturally, devices have different behaviors. Thus, Dribble learns the devices' mobility pattern and then it assigns a custom-made timer scheme conveniently for each device. Our results show that personalized timer schemes present better performance than single traditional timer schemes such as Trickle Timer (TT) and Reverse Trickle Timer (RevTT).

The research community has been investigating the potential of processing and wireless communication of vehicles in a transportation system. In this case, VANETs aim to exploit the communication and sensing capabilities of vehicles to feed data into applications and services. VANETs also contribute to the improvement of Advanced Driver Assistant Systems and Intelligent Transportation Systems, which aim to provide services to users such as safer and more comfortable trips. Many of these systems need to authenticate their users, but they do so in a way that an attacking driver can use them. This work explores the driver identification as an extra authentication factor to local services and vehicular networks. In this respect, a virtual sensor was developed to determine the driver's identity, with precision above 98% in our experiments, using embedded sensor data. This virtual sensor was also used to identify a suspected driver. Besides, based on the suspect's identification, we discuss the impacts of these drivers in the data dissemination in a vehicular network.

In this work, we argue that Location-Based Social Media (LBSM) feeds may offer a new layer to improve traffic and transit comprehension. Initially, we showed the significant correlation between Twitter's feed and traditional traffic sensors. Then, we presented the Twitter MAPS (T-MAPS) a low-cost spatiotemporal model to improve the description of traffic conditions through tweets. T-MAPS enhance traditional traffic sensors by carrying the human lens into the transportation system. We conducted a case study by running T-MAPS and Google Maps route recommendation, in which, we showed T-MAPS viability, as an additional traffic descriptor. As a result, we noticed the median of route similarity reached 62%, and for a quarter of the evaluated trajectories, the similarity achieved between 75% and 100%. Also, we presented three route description services, based on natural language analyzes, Route Sentiment (RS), Route Information (RI), and Area' Tags (AT) aiming to enhance the route information.

Physical sensors are an important part of control systems, especially vehicular control systems. Sensor readings help drivers to control their vehicles as well as their internal systems while keeping a vehicle stable and running. Currently, a modern luxury car carries hundreds of diverse and precise sensors and not all of them are visible to the driver. However, there are phenomena and aspects for which there are no physical sensors available. Virtual sensors combine readings from multiple sensors in order to develop their own output values based on conditions and models, and, eventually, substitute and monitor failing physical sensors, as well as sense complex variables. Designing a virtual sensor is usually a difficult process due to the complexity of the different processing stages it comprises. This work studies the process of creating and prototyping vehicular virtual sensors, describing the development stages and presenting examples of virtual sensors created with a framework developed to facilitate the design process.

The development of actions to reduce fuel consumption and emissions and increase transportation systems' efficiency have become a huge challenge. Thus, a low-cost solution to improve fuel efficiency and reduce environmental damages is eco-driving, a group of behaviors focused on improving these aspects. Fuel consumption varies according to different factors: two different vehicles are expected to consume more or less fuel according to their engines' sizes or depending on the person who is driving them. In this work we present a gear virtual sensor for manual transmission cars, which adds information to understand drivers' habits, allowing to analyze individually each gear in relation to consumption. Our methodology developed gives the driver recommendations of the best gear considering speed and torque, reaching up to 29% averaged of efficiency in the fuel consumption and 21% averaged in CO2emissions reduction.